diff --git a/IMDB/01-One-hot-encoding.ipynb b/IMDB/01-One-hot-encoding.ipynb
index 57add89644c9c42c777633895cb358b8c9ec42d2..4df53aab927e1e38fe97580a0f2befd56a35152c 100644
--- a/IMDB/01-One-hot-encoding.ipynb
+++ b/IMDB/01-One-hot-encoding.ipynb
@@ -428,18 +428,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 6 - Build the model\n",
-    "Few remarks :\n",
-    " - We'll choose a dense vector size for the embedding output with **dense_vector_size**\n",
-    " - **GlobalAveragePooling1D** do a pooling on the last dimension : (None, lx, ly) -> (None, ly)  \n",
-    "   In other words: we average the set of vectors/words of a sentence\n",
-    " - L'embedding de Keras fonctionne de manière supervisée. Il s'agit d'une couche de *vocab_size* neurones vers *n_neurons* permettant de maintenir une table de vecteurs (les poids constituent les vecteurs). Cette couche ne calcule pas de sortie a la façon des couches normales, mais renvois la valeur des vecteurs. n mots => n vecteurs (ensuite empilés par le pooling)  \n",
-    "Voir : [Explication plus détaillée (en)](https://stats.stackexchange.com/questions/324992/how-the-embedding-layer-is-trained-in-keras-embedding-layer)  \n",
-    "ainsi que : [Sentiment detection with Keras](https://www.liip.ch/en/blog/sentiment-detection-with-keras-word-embeddings-and-lstm-deep-learning-networks)  \n",
-    "\n",
-    "More documentation about this model functions :\n",
-    " - [Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)\n",
-    " - [GlobalAveragePooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D)"
+    "## Step 6 - Build the model"
    ]
   },
   {
@@ -610,7 +599,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/IMDB/02-Keras-embedding.ipynb b/IMDB/02-Keras-embedding.ipynb
index 3a0ffb8e24b9f8d53b2595dc9ff741fb05042d65..6f29e859280ab0fdbb6e03f74a7c4e1562983311 100644
--- a/IMDB/02-Keras-embedding.ipynb
+++ b/IMDB/02-Keras-embedding.ipynb
@@ -243,13 +243,6 @@
    "metadata": {},
    "source": [
     "## Step 4 - Build the model\n",
-    "Few remarks :\n",
-    " - We'll choose a dense vector size for the embedding output with **dense_vector_size**\n",
-    " - **GlobalAveragePooling1D** do a pooling on the last dimension : (None, lx, ly) -> (None, ly)  \n",
-    "   In other words: we average the set of vectors/words of a sentence\n",
-    " - L'embedding de Keras fonctionne de manière supervisée. Il s'agit d'une couche de *vocab_size* neurones vers *n_neurons* permettant de maintenir une table de vecteurs (les poids constituent les vecteurs). Cette couche ne calcule pas de sortie a la façon des couches normales, mais renvois la valeur des vecteurs. n mots => n vecteurs (ensuite empilés par le pooling)  \n",
-    "Voir : [Explication plus détaillée (en)](https://stats.stackexchange.com/questions/324992/how-the-embedding-layer-is-trained-in-keras-embedding-layer)  \n",
-    "ainsi que : [Sentiment detection with Keras](https://www.liip.ch/en/blog/sentiment-detection-with-keras-word-embeddings-and-lstm-deep-learning-networks)  \n",
     "\n",
     "More documentation about this model functions :\n",
     " - [Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)\n",
diff --git a/IMDB/05-LSTM-Keras.ipynb b/IMDB/05-LSTM-Keras.ipynb
index 437be8f6e923dadf6763a435d9f8fdf9789704e8..ad26669ae1c3dcb541276fc8b22d8390f5f07e11 100644
--- a/IMDB/05-LSTM-Keras.ipynb
+++ b/IMDB/05-LSTM-Keras.ipynb
@@ -6,8 +6,8 @@
    "source": [
     "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
     "\n",
-    "# <!-- TITLE --> [IMDB5] - Sentiment analysis with a LSTM network\n",
-    "<!-- DESC --> Still the same problem, but with a network combining embedding and LSTM\n",
+    "# <!-- TITLE --> [IMDB5] - Sentiment analysis with a RNN network\n",
+    "<!-- DESC --> Still the same problem, but with a network combining embedding and RNN\n",
     "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
     "\n",
     "## Objectives :\n",
@@ -62,42 +62,65 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 2 - Retrieve data\n",
-    "\n",
-    "IMDb dataset can bet get directly from Keras - see [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)  \n",
-    "Note : Due to their nature, textual data can be somewhat complex.\n",
-    "\n",
-    "### 2.1 - Data structure :  \n",
-    "The dataset is composed of 2 parts: \n",
-    "\n",
-    " - **reviews**, this will be our **x**\n",
-    " - **opinions** (positive/negative), this will be our **y**\n",
-    "\n",
-    "There are also a **dictionary**, because words are indexed in reviews\n",
-    "\n",
-    "```\n",
-    "<dataset> = (<reviews>, <opinions>)\n",
-    "\n",
-    "with :  <reviews>  = [ <review1>, <review2>, ... ]\n",
-    "        <opinions> = [ <rate1>,   <rate2>,   ... ]   where <ratei>   = integer\n",
-    "\n",
-    "where : <reviewi> = [ <w1>, <w2>, ...]    <wi> are the index (int) of the word in the dictionary\n",
-    "        <ratei>   = int                   0 for negative opinion, 1 for positive\n",
+    "## Step 2 - Parameters\n",
+    "The words in the vocabulary are classified from the most frequent to the rarest.  \n",
+    "`vocab_size` is the number of words we will remember in our vocabulary (the other words will be considered as unknown).  \n",
+    "`hide_most_frequently` is the number of ignored words, among the most common ones  \n",
+    "`review_len` is the review length  \n",
+    "`dense_vector_size` is the size of the generated dense vectors  \n",
+    "`output_dir` is where we will go to save our dictionaries. (./data is a good choice)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vocab_size           = 10000\n",
+    "hide_most_frequently = 0\n",
     "\n",
+    "review_len           = 256\n",
+    "dense_vector_size    = 32\n",
     "\n",
-    "<dictionary> = [ <word1>:<w1>, <word2>:<w2>, ... ]\n",
+    "epochs               = 10\n",
+    "batch_size           = 128\n",
     "\n",
-    "with :  <wordi>   = word\n",
-    "        <wi>      = int\n",
+    "output_dir           = './data'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Override parameters (batch mode) - Just forget this cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pwk.override('vocab_size', 'hide_most_frequently', 'review_len', 'dense_vector_size')\n",
+    "pwk.override('batch_size', 'epochs', 'output_dir')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Retrieve data\n",
     "\n",
-    "```"
+    "IMDb dataset can bet get directly from Keras - see [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)  \n",
+    "Note : Due to their nature, textual data can be somewhat complex."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 2.2 - Get dataset\n",
+    "### 3.1 - Get dataset\n",
     "For simplicity, we will use a pre-formatted dataset - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb/load_data)  \n",
     "However, Keras offers some usefull tools for formatting textual data - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text)  \n",
     "\n",
@@ -110,27 +133,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "vocab_size = 10000\n",
+    "(x_train, y_train), (x_test, y_test) = imdb.load_data( num_words=vocab_size, skip_top=hide_most_frequently, seed= 42,)\n",
     "\n",
-    "# ----- Retrieve x,y\n",
+    "y_train = np.asarray(y_train).astype('float32')\n",
+    "y_test  = np.asarray(y_test ).astype('float32')\n",
     "\n",
-    "# Uncomment this if you want to load dataset directly from keras (small size <20M)\n",
+    "# ---- About\n",
     "#\n",
-    "(x_train, y_train), (x_test, y_test) = imdb.load_data( num_words  = vocab_size,\n",
-    "                                                       skip_top   = 0,\n",
-    "                                                       maxlen     = None,\n",
-    "                                                       seed       = 42,\n",
-    "                                                       start_char = 1,\n",
-    "                                                       oov_char   = 2,\n",
-    "                                                       index_from = 3, )\n",
-    "\n",
-    "# To load a h5 version of the dataset :\n",
-    "#\n",
-    "# with  h5py.File(f'{datasets_dir}/IMDB/origine/dataset_imdb.h5','r') as f:\n",
-    "#        x_train = f['x_train'][:]\n",
-    "#        y_train = f['y_train'][:]\n",
-    "#        x_test  = f['x_test'][:]\n",
-    "#        y_test  = f['y_test'][:]"
+    "print(\"Max(x_train,x_test)  : \", pwk.rmax([x_train,x_test]) )\n",
+    "print(\"Min(x_train,x_test)  : \", pwk.rmin([x_train,x_test]) )\n",
+    "print(\"x_train : {}  y_train : {}\".format(x_train.shape, y_train.shape))\n",
+    "print(\"x_test  : {}  y_test  : {}\".format(x_test.shape,  y_test.shape))"
    ]
   },
   {
@@ -157,7 +170,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 2.3 - Have a look for humans (optional)\n",
+    "### 3.2 - Have a look for humans (optional)\n",
     "When we loaded the dataset, we asked for using \\<start\\> as 1, \\<unknown word\\> as 2  \n",
     "So, we shifted the dataset by 3 with the parameter index_from=3\n",
     "\n",
@@ -217,29 +230,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 2.4 - Have a look for NN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sizes=[len(i) for i in x_train]\n",
-    "plt.figure(figsize=(16,6))\n",
-    "plt.hist(sizes, bins=400)\n",
-    "plt.gca().set(title='Distribution of reviews by size - [{:5.2f}, {:5.2f}]'.format(min(sizes),max(sizes)), \n",
-    "              xlabel='Size', ylabel='Density', xlim=[0,1500])\n",
-    "pwk.save_fig('01-stats-sizes')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Preprocess the data (padding)\n",
+    "## Step 4 - Preprocess the data (padding)\n",
     "In order to be processed by an NN, all entries must have the **same length.**  \n",
     "We chose a review length of **review_len**  \n",
     "We will therefore complete them with a padding (of \\<pad\\>\\)  "
@@ -251,8 +242,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "review_len = 256\n",
-    "\n",
     "x_train = keras.preprocessing.sequence.pad_sequences(x_train,\n",
     "                                                     value   = 0,\n",
     "                                                     padding = 'post',\n",
@@ -273,47 +262,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Save dataset and dictionary (For future use but not mandatory)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ---- Write dataset in a h5 file, could be usefull\n",
-    "#\n",
-    "output_dir = './data'\n",
-    "pwk.mkdir(output_dir)\n",
-    "\n",
-    "with h5py.File(f'{output_dir}/dataset_imdb.h5', 'w') as f:\n",
-    "    f.create_dataset(\"x_train\",    data=x_train)\n",
-    "    f.create_dataset(\"y_train\",    data=y_train)\n",
-    "    f.create_dataset(\"x_test\",     data=x_test)\n",
-    "    f.create_dataset(\"y_test\",     data=y_test)\n",
-    "\n",
-    "with open(f'{output_dir}/word_index.json', 'w') as fp:\n",
-    "    json.dump(word_index, fp)\n",
-    "\n",
-    "with open(f'{output_dir}/index_word.json', 'w') as fp:\n",
-    "    json.dump(index_word, fp)\n",
-    "\n",
-    "print('Saved.')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4 - Build the model\n",
-    "Few remarks :\n",
-    " - We'll choose a dense vector size for the embedding output with **dense_vector_size**\n",
-    " - **GlobalAveragePooling1D** do a pooling on the last dimension : (None, lx, ly) -> (None, ly)  \n",
-    "   In other words: we average the set of vectors/words of a sentence\n",
-    " - L'embedding de Keras fonctionne de manière supervisée. Il s'agit d'une couche de *vocab_size* neurones vers *n_neurons* permettant de maintenir une table de vecteurs (les poids constituent les vecteurs). Cette couche ne calcule pas de sortie a la façon des couches normales, mais renvois la valeur des vecteurs. n mots => n vecteurs (ensuite empilés par le pooling)  \n",
-    "Voir : [Explication plus détaillée (en)](https://stats.stackexchange.com/questions/324992/how-the-embedding-layer-is-trained-in-keras-embedding-layer)  \n",
-    "ainsi que : [Sentiment detection with Keras](https://www.liip.ch/en/blog/sentiment-detection-with-keras-word-embeddings-and-lstm-deep-learning-networks)  \n",
+    "## Step 5 - Build the model\n",
     "\n",
     "More documentation about this model functions :\n",
     " - [Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)\n",
@@ -329,13 +278,11 @@
     "def get_model(dense_vector_size=128):\n",
     "    \n",
     "    model = keras.Sequential()\n",
-    "    model.add(keras.layers.Embedding(input_dim    = vocab_size, \n",
-    "                                     output_dim   = dense_vector_size, \n",
-    "                                     input_length = review_len))\n",
-    "    model.add(keras.layers.LSTM(128, dropout=0.2, recurrent_dropout=0.2))\n",
-    "    model.add(keras.layers.Dense(1,                 activation='sigmoid'))\n",
+    "    model.add(keras.layers.Embedding(input_dim = vocab_size, output_dim = dense_vector_size))\n",
+    "    model.add(keras.layers.GRU(50))\n",
+    "    model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
     "\n",
-    "    model.compile(optimizer = 'adam',\n",
+    "    model.compile(optimizer = 'rmsprop',\n",
     "                  loss      = 'binary_crossentropy',\n",
     "                  metrics   = ['accuracy'])\n",
     "    return model"
@@ -345,8 +292,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 5 - Train the model\n",
-    "### 5.1 - Get it"
+    "## Step 6 - Train the model\n",
+    "### 6.1 - Get it"
    ]
   },
   {
@@ -364,7 +311,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 5.2 - Add callback"
+    "### 6.2 - Add callback"
    ]
   },
   {
@@ -382,9 +329,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 5.1 - Train it\n",
-    "GPU : batch_size=512 :  6' 30s  \n",
-    "CPU : batch_size=512 : 12' 57s"
+    "### 6.3 - Train it\n",
+    "CPU : batch_size=128, epochs=10 : Need 9'30 (CPU, laptop)"
    ]
   },
   {
@@ -393,26 +339,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%time\n",
-    "\n",
-    "n_epochs   = 10\n",
-    "batch_size = 512\n",
+    "pwk.chrono_start()\n",
     "\n",
     "history = model.fit(x_train,\n",
     "                    y_train,\n",
-    "                    epochs          = n_epochs,\n",
+    "                    epochs          = epochs,\n",
     "                    batch_size      = batch_size,\n",
     "                    validation_data = (x_test, y_test),\n",
     "                    verbose         = 1,\n",
-    "                    callbacks       = [savemodel_callback])\n"
+    "                    callbacks       = [savemodel_callback])\n",
+    "\n",
+    "pwk.chrono_show()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 6 - Evaluate\n",
-    "### 6.1 - Training history"
+    "### 6.4 - Training history"
    ]
   },
   {
@@ -428,7 +372,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 6.2 - Reload and evaluate best model"
+    "## Step 7 - Evaluation\n",
+    "Reload and evaluate best model"
    ]
   },
   {
diff --git a/README.ipynb b/README.ipynb
index 66b79369e560a8518779ab8370a19ea90eca2dc7..19f80fd06b77ca4da653b5e27b7df622f9150058 100644
--- a/README.ipynb
+++ b/README.ipynb
@@ -3,13 +3,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "dominican-lyric",
+   "id": "foster-denial",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2021-03-01T14:12:43.437936Z",
-     "iopub.status.busy": "2021-03-01T14:12:43.437503Z",
-     "iopub.status.idle": "2021-03-01T14:12:43.446080Z",
-     "shell.execute_reply": "2021-03-01T14:12:43.445486Z"
+     "iopub.execute_input": "2021-03-07T20:23:29.621532Z",
+     "iopub.status.busy": "2021-03-07T20:23:29.621155Z",
+     "iopub.status.idle": "2021-03-07T20:23:29.624623Z",
+     "shell.execute_reply": "2021-03-07T20:23:29.624187Z"
     },
     "jupyter": {
      "source_hidden": true
@@ -39,9 +39,10 @@
        "\n",
        "**- Prochain rendez-vous -**  \n",
        "\n",
-       "|Jeudi 4 mars, 14h, Séquence 3 : <br>Problématique des données creuses de dimensions variables, l'exemple des données textuelles.<br>Stratégies d'évaluation des modèles.|\n",
+       "\n",
+       "|**Jeudi 11 mars, 14h, Séquence 4 : <br>Réseaux de Neurones Récurrents (RNN)<br>Gestion des données séquentielles et/ou temporelles.**|\n",
        "|--|\n",
-       "|Spécificités et traitement des données creuses/textuelles. <br>Principes de l'Embedding (Keras, CBOW, Skip-Gram) <br>Nous reviendrons également sur les stratégies d'évaluation des modèles. <br>Validation simple, croisée (K-fold), itérative, randomisée. <br>Exemple proposé :<br>Analyse de sentiment avec une analyse de critiques de films.|\n",
+       "|Principes et concepts des réseaux de neurones récurrents.<br>Préparation et gestion d'un dataset réel de type séquence. Mise en œuvre et utilisation des RNN.<br>Long short-term memory (LSTM), Gated recurrent unit (GRU), cellules récurrentes, Générateur de séquences.<br>Exemple proposé :<br>Prédiction de trajectoires d'une coccinelle virtuelle ;-)<br>Prévisions météorologique à 3h et 12h, à partir de données réelles, issues des messages internationaux d’observation en surface (SYNOP) de l’Organisation Météorologique Mondiale (OMM).|\n",
        "|Durée : 2h - Les paramètres de diffusion seront [précisés la veille dans le wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20%C3%A0%20distance/En%20bref) |\n",
        "\n",
        "A propos de **[Fidle à distance](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20%C3%A0%20distance/En%20bref)**\\\n",
@@ -66,7 +67,7 @@
        "\n",
        "\n",
        "Current Version : <!-- VERSION_BEGIN -->\n",
-       "**2.0.17**\n",
+       "**2.0.18**\n",
        "<!-- VERSION_END -->\n",
        "\n",
        "\n",
@@ -138,15 +139,17 @@
        "Retrieving a saved model to perform a sentiment analysis (movie review)\n",
        "- **[IMDB4](IMDB/04-Show-vectors.ipynb)** - [Reload embedded vectors](IMDB/04-Show-vectors.ipynb)  \n",
        "Retrieving embedded vectors from our trained model\n",
-       "- **[IMDB5](IMDB/05-LSTM-Keras.ipynb)** - [Sentiment analysis with a LSTM network](IMDB/05-LSTM-Keras.ipynb)  \n",
-       "Still the same problem, but with a network combining embedding and LSTM\n",
+       "- **[IMDB5](IMDB/05-LSTM-Keras.ipynb)** - [Sentiment analysis with a RNN network](IMDB/05-LSTM-Keras.ipynb)  \n",
+       "Still the same problem, but with a network combining embedding and RNN\n",
        "\n",
        "### Time series with Recurrent Neural Network (RNN)\n",
-       "- **[SYNOP1](SYNOP/01-Preparation-of-data.ipynb)** - [Preparation of data](SYNOP/01-Preparation-of-data.ipynb)  \n",
+       "- **[LADYB1](SYNOP/LADYB1-Ladybug.ipynb)** - [Prediction of a 2D trajectory via RNN](SYNOP/LADYB1-Ladybug.ipynb)  \n",
+       "Artificial dataset generation and prediction attempt via a recurrent network\n",
+       "- **[SYNOP1](SYNOP/SYNOP1-Preparation-of-data.ipynb)** - [Preparation of data](SYNOP/SYNOP1-Preparation-of-data.ipynb)  \n",
        "Episode 1 : Data analysis and preparation of a meteorological dataset (SYNOP)\n",
-       "- **[SYNOP2](SYNOP/02-First-predictions.ipynb)** - [First predictions at 3h](SYNOP/02-First-predictions.ipynb)  \n",
+       "- **[SYNOP2](SYNOP/SYNOP2-First-predictions.ipynb)** - [First predictions at 3h](SYNOP/SYNOP2-First-predictions.ipynb)  \n",
        "Episode 2 : Learning session and weather prediction attempt at 3h\n",
-       "- **[SYNOP3](SYNOP/03-12h-predictions.ipynb)** - [12h predictions](SYNOP/03-12h-predictions.ipynb)  \n",
+       "- **[SYNOP3](SYNOP/SYNOP3-12h-predictions.ipynb)** - [12h predictions](SYNOP/SYNOP3-12h-predictions.ipynb)  \n",
        "Episode 3: Attempt to predict in a more longer term \n",
        "\n",
        "### Unsupervised learning with an autoencoder neural network (AE)\n",
diff --git a/README.md b/README.md
index cf2dc271b361d9bdb34862978ab0d8c0cdf0b019..56aa595129ff1c4394ebc7c1de8c2a4ee8723782 100644
--- a/README.md
+++ b/README.md
@@ -18,9 +18,10 @@ https://www.youtube.com/channel/UC4Sukzudhbwr6fs10cXrJsQ
 
 **- Prochain rendez-vous -**  
 
-|Jeudi 4 mars, 14h, Séquence 3 : <br>Problématique des données creuses de dimensions variables, l'exemple des données textuelles.<br>Stratégies d'évaluation des modèles.|
+
+|**Jeudi 11 mars, 14h, Séquence 4 : <br>Réseaux de Neurones Récurrents (RNN)<br>Gestion des données séquentielles et/ou temporelles.**|
 |--|
-|Spécificités et traitement des données creuses/textuelles. <br>Principes de l'Embedding (Keras, CBOW, Skip-Gram) <br>Nous reviendrons également sur les stratégies d'évaluation des modèles. <br>Validation simple, croisée (K-fold), itérative, randomisée. <br>Exemple proposé :<br>Analyse de sentiment avec une analyse de critiques de films.|
+|Principes et concepts des réseaux de neurones récurrents.<br>Préparation et gestion d'un dataset réel de type séquence. Mise en œuvre et utilisation des RNN.<br>Long short-term memory (LSTM), Gated recurrent unit (GRU), cellules récurrentes, Générateur de séquences.<br>Exemple proposé :<br>Prédiction de trajectoires d'une coccinelle virtuelle ;-)<br>Prévisions météorologique à 3h et 12h, à partir de données réelles, issues des messages internationaux d’observation en surface (SYNOP) de l’Organisation Météorologique Mondiale (OMM).|
 |Durée : 2h - Les paramètres de diffusion seront [précisés la veille dans le wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20%C3%A0%20distance/En%20bref) |
 
 A propos de **[Fidle à distance](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20%C3%A0%20distance/En%20bref)**\
@@ -45,7 +46,7 @@ Don't forget to look at the [Wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/
 
 
 Current Version : <!-- VERSION_BEGIN -->
-**2.0.17**
+**2.0.18**
 <!-- VERSION_END -->
 
 
@@ -117,15 +118,17 @@ A very classical example of word embedding with a dataset from Internet Movie Da
 Retrieving a saved model to perform a sentiment analysis (movie review)
 - **[IMDB4](IMDB/04-Show-vectors.ipynb)** - [Reload embedded vectors](IMDB/04-Show-vectors.ipynb)  
 Retrieving embedded vectors from our trained model
-- **[IMDB5](IMDB/05-LSTM-Keras.ipynb)** - [Sentiment analysis with a LSTM network](IMDB/05-LSTM-Keras.ipynb)  
-Still the same problem, but with a network combining embedding and LSTM
+- **[IMDB5](IMDB/05-LSTM-Keras.ipynb)** - [Sentiment analysis with a RNN network](IMDB/05-LSTM-Keras.ipynb)  
+Still the same problem, but with a network combining embedding and RNN
 
 ### Time series with Recurrent Neural Network (RNN)
-- **[SYNOP1](SYNOP/01-Preparation-of-data.ipynb)** - [Preparation of data](SYNOP/01-Preparation-of-data.ipynb)  
+- **[LADYB1](SYNOP/LADYB1-Ladybug.ipynb)** - [Prediction of a 2D trajectory via RNN](SYNOP/LADYB1-Ladybug.ipynb)  
+Artificial dataset generation and prediction attempt via a recurrent network
+- **[SYNOP1](SYNOP/SYNOP1-Preparation-of-data.ipynb)** - [Preparation of data](SYNOP/SYNOP1-Preparation-of-data.ipynb)  
 Episode 1 : Data analysis and preparation of a meteorological dataset (SYNOP)
-- **[SYNOP2](SYNOP/02-First-predictions.ipynb)** - [First predictions at 3h](SYNOP/02-First-predictions.ipynb)  
+- **[SYNOP2](SYNOP/SYNOP2-First-predictions.ipynb)** - [First predictions at 3h](SYNOP/SYNOP2-First-predictions.ipynb)  
 Episode 2 : Learning session and weather prediction attempt at 3h
-- **[SYNOP3](SYNOP/03-12h-predictions.ipynb)** - [12h predictions](SYNOP/03-12h-predictions.ipynb)  
+- **[SYNOP3](SYNOP/SYNOP3-12h-predictions.ipynb)** - [12h predictions](SYNOP/SYNOP3-12h-predictions.ipynb)  
 Episode 3: Attempt to predict in a more longer term 
 
 ### Unsupervised learning with an autoencoder neural network (AE)
diff --git a/SYNOP/00-Beta-series.ipynb b/SYNOP/00-Beta-series.ipynb
deleted file mode 100644
index c78189f1afd42da7645824d75b99f674d416195c..0000000000000000000000000000000000000000
--- a/SYNOP/00-Beta-series.ipynb
+++ /dev/null
@@ -1,795 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
-    "\n",
-    "# <!-- TITLE --> [SYNOP2] - First predictions at 3h\n",
-    "<!-- DESC --> Episode 2 : Learning session and weather prediction attempt at 3h\n",
-    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
-    "\n",
-    "## Objectives :\n",
-    " - Make a simple prediction (3h)\n",
-    " - Understanding the use of a recurrent neural network\n",
-    "\n",
-    "\n",
-    "SYNOP meteorological data, available at: https://public.opendatasoft.com\n",
-    "\n",
-    "## What we're going to do :\n",
-    "\n",
-    " - Read our dataset\n",
-    " - Select our data and normalize it\n",
-    " - Doing our training\n",
-    " - Making simple predictions\n",
-    "\n",
-    "## Step 1 - Import and init\n",
-    "### 1.1 - Python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
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-       "    padding: 0.5em;\n",
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-       "\n",
-       "div.todo:before { 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-       "    font-weight: bold;\n",
-       "    font-size: 1.1em;\n",
-       "    margin-top:40px;\n",
-       "}\n",
-       "div.todo ul{\n",
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-       "}\n",
-       "div.todo li{\n",
-       "    margin-left:60px;\n",
-       "    margin-top:0;\n",
-       "    margin-bottom:0;\n",
-       "}\n",
-       "\n",
-       "div .comment{\n",
-       "    font-size:0.8em;\n",
-       "    color:#696969;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "</style>\n",
-       "\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**FIDLE 2020 - Practical Work Module**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Version              : 2.0.18\n",
-      "Notebook id          : SYNOP2\n",
-      "Run time             : Sunday 07 March 2021, 00:49:45\n",
-      "TensorFlow version   : 2.2.0\n",
-      "Keras version        : 2.3.0-tf\n",
-      "Datasets dir         : /home/pjluc/datasets/fidle\n",
-      "Run dir              : ./run\n",
-      "Update keras cache   : False\n"
-     ]
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from tensorflow import keras\n",
-    "from tensorflow.keras.callbacks import TensorBoard\n",
-    "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
-    "\n",
-    "import numpy as np\n",
-    "import math, random\n",
-    "from math import sin,cos,pi\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import pandas as pd\n",
-    "import h5py, json\n",
-    "import os,time,sys\n",
-    "\n",
-    "from importlib import reload\n",
-    "\n",
-    "sys.path.append('..')\n",
-    "import fidle.pwk as pwk\n",
-    "\n",
-    "datasets_dir = pwk.init('SYNOP2')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1.2 - Parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ---- About dataset\n",
-    "#\n",
-    "max_t      = 1000\n",
-    "delta_t    = 0.02\n",
-    "\n",
-    "\n",
-    "sequence_len = 20\n",
-    "predict_len  = 5\n",
-    "features_len = 2\n",
-    "\n",
-    "# ---- About training\n",
-    "#\n",
-    "scale            = 1        # Percentage of dataset to be used (1=all)\n",
-    "train_prop       = .7       # Percentage for train (the rest being for the test)\n",
-    "batch_size       = 32\n",
-    "epochs           = 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Override parameters (batch mode) - Just forget this cell"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pwk.override('scale', 'train_prop', 'sequence_len', 'batch_size', 'epochs')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2 - Generation of a fun dataset\n",
-    "### 2.1 - Moving our ladybug"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def ladybug_init(s=122):\n",
-    "    \n",
-    "    if s>0 : random.seed(s)\n",
-    "    ladybug_init.params_x = [ random.gauss(0.,1.) for u in range(8)]\n",
-    "    ladybug_init.params_y = [ random.gauss(0.,1.) for u in range(8)]\n",
-    "    \n",
-    "def ladybug_move(t):\n",
-    "    k=0.5\n",
-    "    [ax1, ax2, ax3, ax4, kx1, kx2, kx3, kx4] = ladybug_init.params_x\n",
-    "    [ay1, ay2, ay3, ay4, ky1, ky2, ky3, ky4] = ladybug_init.params_y\n",
-    "    \n",
-    "    x = ax1*sin(t*(kx1+20)) + ax2*cos(t*(kx2+10)) + ax3*sin(t*(kx3+5)) + ax4*cos(t*(kx4+5))\n",
-    "    y = ay1*cos(t*(ky1+20)) + ay2*sin(t*(ky2+10)) + ay3*cos(t*(ky3+5)) + ay4*sin(t*(ky4+5)) \n",
-    "\n",
-    "\n",
-    "    return x,y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.2 - Get some positions, and build dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train shape is :  (35000, 2)\n",
-      "Test  shape is :  (15000, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ---- Get positions\n",
-    "#\n",
-    "ladybug_init(s=16)\n",
-    "x,y = 0,0\n",
-    "positions=[]\n",
-    "for t in np.arange(0., max_t, delta_t):\n",
-    "    positions.append([x,y])\n",
-    "    x,y = ladybug_move(t)\n",
-    "#     (x,y) = (x+dx, y+dy)\n",
-    "\n",
-    "# ---- Build dataset\n",
-    "#\n",
-    "dataset = np.array(positions)\n",
-    "\n",
-    "k = int(len(dataset)*train_prop)\n",
-    "x_train = dataset[:k]\n",
-    "x_test  = dataset[k:]\n",
-    "\n",
-    "# ---- Normalize\n",
-    "#\n",
-    "mean = x_train.mean()\n",
-    "std  = x_train.std()\n",
-    "x_train = (x_train - mean) / std\n",
-    "x_test  = (x_test  - mean) / std\n",
-    "\n",
-    "print(\"Train shape is : \", x_train.shape)\n",
-    "print(\"Test  shape is : \", x_test.shape)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.3 - Have a look"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pwk.plot_2d_serie(x_train[:1000])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "k1,k2 = sequence_len, predict_len\n",
-    "i = random.randint(0,len(x_test)-k1-k2)\n",
-    "j = i+k1\n",
-    "\n",
-    "pwk.plot_2d_segment( x_test[i:j+k2], x_test[j:j+k2],ms=8 )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.2 - Prepare data generator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**About the splitting of our dataset :**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Nombre de train batchs disponibles :  1094\n",
-      "batch x shape :  (32, 20, 2)\n",
-      "batch y shape :  (32, 2)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**What a batch looks like (x) :**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 1.123e-04  1.123e-04]\n",
-      " [ 1.511e+00  6.018e-01]\n",
-      " [ 1.097e+00  5.693e-01]\n",
-      " [ 6.731e-01  4.094e-01]\n",
-      " [ 2.772e-01  1.533e-01]\n",
-      " [-6.045e-02 -1.522e-01]\n",
-      " [-3.215e-01 -4.521e-01]\n",
-      " [-5.015e-01 -6.920e-01]\n",
-      " [-6.097e-01 -8.268e-01]\n",
-      " [-6.664e-01 -8.278e-01]\n",
-      " [-6.992e-01 -6.871e-01]\n",
-      " [-7.383e-01 -4.192e-01]\n",
-      " [-8.109e-01 -5.871e-02]\n",
-      " [-9.366e-01  3.453e-01]\n",
-      " [-1.124e+00  7.366e-01]\n",
-      " [-1.368e+00  1.061e+00]\n",
-      " [-1.652e+00  1.274e+00]\n",
-      " [-1.946e+00  1.349e+00]\n",
-      " [-2.215e+00  1.281e+00]\n",
-      " [-2.420e+00  1.086e+00]]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**What a batch looks like (y) :**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-2.528  0.801]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ---- Train generator\n",
-    "#\n",
-    "train_generator = TimeseriesGenerator(x_train, x_train, length=sequence_len,  batch_size=batch_size)\n",
-    "test_generator  = TimeseriesGenerator(x_test,  x_test,  length=sequence_len,  batch_size=batch_size)\n",
-    "\n",
-    "# ---- About\n",
-    "#\n",
-    "pwk.subtitle('About the splitting of our dataset :')\n",
-    "\n",
-    "x,y=train_generator[0]\n",
-    "print(f'Nombre de train batchs disponibles : ', len(train_generator))\n",
-    "print('batch x shape : ',x.shape)\n",
-    "print('batch y shape : ',y.shape)\n",
-    "\n",
-    "x,y=train_generator[0]\n",
-    "pwk.subtitle('What a batch looks like (x) :')\n",
-    "pwk.np_print(x[0] )\n",
-    "pwk.subtitle('What a batch looks like (y) :')\n",
-    "pwk.np_print(y[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Create a model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "gru (GRU)                    (None, 200)               122400    \n",
-      "_________________________________________________________________\n",
-      "dropout (Dropout)            (None, 200)               0         \n",
-      "_________________________________________________________________\n",
-      "dense (Dense)                (None, 2)                 402       \n",
-      "=================================================================\n",
-      "Total params: 122,802\n",
-      "Trainable params: 122,802\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = keras.models.Sequential()\n",
-    "model.add( keras.layers.InputLayer(input_shape=(sequence_len, features_len)) )\n",
-    "model.add( keras.layers.GRU(200, activation='relu') )\n",
-    "model.add( keras.layers.Dropout(0.5) )\n",
-    "model.add( keras.layers.Dense(features_len) )\n",
-    "\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Step 4 - Compile and run"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.1 - Callback"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pwk.mkdir('./run/models')\n",
-    "save_dir = './run/models/best_model.h5'\n",
-    "bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.2 - Compile"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.compile(optimizer='adam', \n",
-    "              loss='mse', \n",
-    "              metrics   = ['mae'] )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.3 - Fit\n",
-    "3' with a CPU (laptop)  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/5\n",
-      "1094/1094 [==============================] - 27s 25ms/step - loss: 0.0720 - mae: 0.1922 - val_loss: 0.0118 - val_mae: 0.0879\n",
-      "Epoch 2/5\n",
-      "1094/1094 [==============================] - 35s 32ms/step - loss: 0.0340 - mae: 0.1360 - val_loss: 0.0025 - val_mae: 0.0400\n",
-      "Epoch 3/5\n",
-      "1094/1094 [==============================] - 33s 30ms/step - loss: 0.0320 - mae: 0.1310 - val_loss: 0.0061 - val_mae: 0.0551\n",
-      "Epoch 4/5\n",
-      "1094/1094 [==============================] - 37s 34ms/step - loss: 0.0319 - mae: 0.1302 - val_loss: 0.0039 - val_mae: 0.0509\n",
-      "Epoch 5/5\n",
-      "1094/1094 [==============================] - 39s 36ms/step - loss: 0.0309 - mae: 0.1275 - val_loss: 0.0018 - val_mae: 0.0331\n",
-      "\n",
-      "Duration :  00:02:52 056ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "pwk.chrono_start()\n",
-    "\n",
-    "history=model.fit(train_generator, \n",
-    "                  epochs=epochs, \n",
-    "                  verbose=1,\n",
-    "                  validation_data = test_generator,\n",
-    "                  callbacks = [bestmodel_callback])\n",
-    "\n",
-    "pwk.chrono_show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pwk.plot_history(history,plot={'loss':['loss','val_loss'], 'mae':['mae','val_mae']}, save_as='01-history')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 5 - Predict"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 5.1 - Load model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 5.2 Make a prediction\n",
-    "A basic prediction, with normalized values (so humanly not very understandable)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "s=random.randint(0,len(x_test)-sequence_len)\n",
-    "\n",
-    "sequence      = x_test[s:s+sequence_len]\n",
-    "sequence_true = x_test[s:s+sequence_len+1]\n",
-    "\n",
-    "sequence_pred = loaded_model.predict( np.array([sequence]) )\n",
-    "\n",
-    "pwk.plot_2d_segment(sequence_true, sequence_pred)\n",
-    "\n",
-    "# ---- Show result\n",
-    "pwk.plot_multivariate_serie(sequence_true, predictions=sequence_pred, save_as='02-prediction-norm')\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 5.3 Real prediction\n",
-    "We are now going to make a true prediction, with an un-normalized result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def get_prediction(dataset, model, iterations=4):\n",
-    "\n",
-    "    # ---- Initial sequence\n",
-    "    #\n",
-    "    s=random.randint(0,len(dataset)-sequence_len-iterations)\n",
-    "\n",
-    "    sequence_pred = dataset[s:s+sequence_len].copy()\n",
-    "    sequence_true = dataset[s:s+sequence_len+iterations].copy()\n",
-    "\n",
-    "    # ---- Iterate \n",
-    "    #\n",
-    "    sequence_pred = list(sequence_pred)\n",
-    "\n",
-    "    for i in range(iterations):\n",
-    "        sequence   = sequence_pred[-sequence_len:]\n",
-    "        prediction = model.predict( np.array([sequence]) )\n",
-    "        sequence_pred.append(prediction[0])\n",
-    "\n",
-    "    # ---- Extract the predictions    \n",
-    "    #\n",
-    "    prediction = np.array(sequence_pred[-iterations:])\n",
-    "\n",
-    "    return sequence_true,prediction\n",
-    "\n",
-    "\n",
-    "sequence_true, sequence_pred = get_prediction(x_test, model, iterations=5)\n",
-    "pwk.plot_2d_segment(sequence_true, sequence_pred, ms=8)\n",
-    "\n",
-    "pwk.plot_multivariate_serie(sequence_true, predictions=sequence_pred, save_as='02-prediction-norm')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "End time is : Sunday 07 March 2021, 00:53:53\n",
-      "Duration is : 00:04:08 168ms\n",
-      "This notebook ends here\n"
-     ]
-    }
-   ],
-   "source": [
-    "pwk.end()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/SYNOP/01-Preparation-of-data.ipynb b/SYNOP/01-Preparation-of-data.ipynb
deleted file mode 100644
index 825bf11e54b4731ce11d95bbef58db55c5a09f63..0000000000000000000000000000000000000000
--- a/SYNOP/01-Preparation-of-data.ipynb
+++ /dev/null
@@ -1,3103 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
-    "\n",
-    "# <!-- TITLE --> [SYNOP1] - Preparation of data\n",
-    "<!-- DESC --> Episode 1 : Data analysis and preparation of a meteorological dataset (SYNOP)\n",
-    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
-    "\n",
-    "## Objectives :\n",
-    " - Undestand the data\n",
-    " - cleanup a usable dataset\n",
-    "\n",
-    "\n",
-    "SYNOP meteorological data, can be found on :  \n",
-    "https://public.opendatasoft.com  \n",
-    "\n",
-    "About SYNOP datasets :  \n",
-    "https://public.opendatasoft.com/explore/dataset/donnees-synop-essentielles-omm/information/?sort=date\n",
-    "\n",
-    "This dataset contains a set of measurements (temperature, pressure, ...) made every 3 hours at the LYS airport.  \n",
-    "The objective will be to predict the evolution of the weather !\n",
-    "\n",
-    "## What we're going to do :\n",
-    "\n",
-    " - Read the data\n",
-    " - Cleanup and build a usable dataset\n",
-    "\n",
-    "## Step 1 - Import and init"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
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-      "text/markdown": [
-       "<br>**FIDLE 2020 - Practical Work Module**"
-      ],
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-     "name": "stdout",
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-     "text": [
-      "Version              : 2.0.17\n",
-      "Notebook id          : SYNOP1\n",
-      "Run time             : Friday 05 March 2021, 16:12:27\n",
-      "TensorFlow version   : 2.2.0\n",
-      "Keras version        : 2.3.0-tf\n",
-      "Datasets dir         : /home/pjluc/datasets/fidle\n",
-      "Run dir              : ./run\n",
-      "Update keras cache   : False\n"
-     ]
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from tensorflow import keras\n",
-    "from tensorflow.keras.callbacks import TensorBoard\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import pandas as pd\n",
-    "import h5py, json\n",
-    "import os,time,sys\n",
-    "import math, random\n",
-    "\n",
-    "from importlib import reload\n",
-    "\n",
-    "sys.path.append('..')\n",
-    "import fidle.pwk as pwk\n",
-    "\n",
-    "datasets_dir = pwk.init('SYNOP1')\n",
-    "\n",
-    "pd.set_option('display.max_rows',200)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2 - Parameters\n",
-    "`output_dir` : where to write enhanced dataset, could be :\n",
-    " - `./data`, for simplicity and convenience (best choice because enhanced dataset will be small)\n",
-    " - `<datasets_dir>/SYNOP/enhanced` to save enhanced dataset in your datasets dir.  \n",
-    " \n",
-    "Uncomment the right lines according to what you want :"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ---- Our future enhanced dataset\n",
-    "#\n",
-    "dataset_name = 'synop-LYS.csv'\n",
-    "dataset_desc = 'synop.json'\n",
-    "\n",
-    "# ---- For smart tests :\n",
-    "#\n",
-    "output_dir = './data' \n",
-    "\n",
-    "# ---- To save enhanced dataset in the dataset_dir\n",
-    "#\n",
-    "# output_dir = f'{datasets_dir}/SYNOP/enhanced'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Override parameters (batch mode) - Just forget this cell"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pwk.override('output_dir')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Retrieve the dataset\n",
-    "There are two parts to recover:\n",
-    " - The data itself (csv)\n",
-    " - Description of the data (json)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_filename   = 'origine/donnees-synop-essentielles-omm-LYS.csv'\n",
-    "schema_filename = 'origine/schema.json'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3.1 - Read dataset description\n",
-    "We need the list and description of the columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(f'{datasets_dir}/SYNOP/{schema_filename}','r') as json_file:\n",
-    "    schema = json.load(json_file)\n",
-    "\n",
-    "synop_codes=list( schema['definitions']['donnees-synop-essentielles-omm_records']['properties']['fields']['properties'].keys() )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3.2 - Read data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**Raw data :**"
-      ],
-      "text/plain": [
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-       "      <td>150.0</td>\n",
-       "      <td>4.9</td>\n",
-       "      <td>274.75</td>\n",
-       "      <td>271.15</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.077833</td>\n",
-       "      <td>45.7265</td>\n",
-       "      <td>Colombier-Saugnieu</td>\n",
-       "      <td>69299</td>\n",
-       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
-       "      <td>246900575</td>\n",
-       "      <td>Rhône</td>\n",
-       "      <td>69</td>\n",
-       "      <td>Auvergne-Rhône-Alpes</td>\n",
-       "      <td>84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29159</th>\n",
-       "      <td>7481</td>\n",
-       "      <td>2020-02-14T01:00:00+01:00</td>\n",
-       "      <td>102080.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>280.0</td>\n",
-       "      <td>4.5</td>\n",
-       "      <td>283.15</td>\n",
-       "      <td>276.15</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.077833</td>\n",
-       "      <td>45.7265</td>\n",
-       "      <td>Colombier-Saugnieu</td>\n",
-       "      <td>69299</td>\n",
-       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
-       "      <td>246900575</td>\n",
-       "      <td>Rhône</td>\n",
-       "      <td>69</td>\n",
-       "      <td>Auvergne-Rhône-Alpes</td>\n",
-       "      <td>84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29160</th>\n",
-       "      <td>7481</td>\n",
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-       "      <td>210.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>3.4</td>\n",
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-       "      <td>89.0</td>\n",
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-       "      <td>246900575</td>\n",
-       "      <td>Rhône</td>\n",
-       "      <td>69</td>\n",
-       "      <td>Auvergne-Rhône-Alpes</td>\n",
-       "      <td>84</td>\n",
-       "    </tr>\n",
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-       "      <td>7481</td>\n",
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-       "      <td>-160.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>6.9</td>\n",
-       "      <td>290.15</td>\n",
-       "      <td>273.75</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>69299</td>\n",
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-       "      <td>246900575</td>\n",
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-       "      <td>69</td>\n",
-       "      <td>Auvergne-Rhône-Alpes</td>\n",
-       "      <td>84</td>\n",
-       "    </tr>\n",
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-       "      <th>29162</th>\n",
-       "      <td>7481</td>\n",
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-       "      <td>102030.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>4.9</td>\n",
-       "      <td>281.45</td>\n",
-       "      <td>278.55</td>\n",
-       "      <td>82.0</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>69299</td>\n",
-       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
-       "      <td>246900575</td>\n",
-       "      <td>Rhône</td>\n",
-       "      <td>69</td>\n",
-       "      <td>Auvergne-Rhône-Alpes</td>\n",
-       "      <td>84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29163</th>\n",
-       "      <td>7481</td>\n",
-       "      <td>2020-01-26T19:00:00+01:00</td>\n",
-       "      <td>102010.0</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>170.0</td>\n",
-       "      <td>3.7</td>\n",
-       "      <td>282.85</td>\n",
-       "      <td>279.15</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>Auvergne-Rhône-Alpes</td>\n",
-       "      <td>84</td>\n",
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-       "    <tr>\n",
-       "      <th>29164</th>\n",
-       "      <td>7481</td>\n",
-       "      <td>2020-02-08T19:00:00+01:00</td>\n",
-       "      <td>102540.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>283.75</td>\n",
-       "      <td>277.65</td>\n",
-       "      <td>66.0</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>Rhône</td>\n",
-       "      <td>69</td>\n",
-       "      <td>Auvergne-Rhône-Alpes</td>\n",
-       "      <td>84</td>\n",
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-       "  </tbody>\n",
-       "</table>\n",
-       "<p>10 rows × 81 columns</p>\n",
-       "</div>"
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-      "text/plain": [
-       "       ID OMM station Date                        Pression au niveau mer  \\\n",
-       "29155  7481            2019-11-16T01:00:00+01:00  100640.0                 \n",
-       "29156  7481            2019-11-16T19:00:00+01:00  101090.0                 \n",
-       "29157  7481            2020-02-12T16:00:00+01:00  102460.0                 \n",
-       "29158  7481            2020-02-13T04:00:00+01:00  102100.0                 \n",
-       "29159  7481            2020-02-14T01:00:00+01:00  102080.0                 \n",
-       "29160  7481            2020-02-14T07:00:00+01:00  102430.0                 \n",
-       "29161  7481            2020-02-15T16:00:00+01:00  102190.0                 \n",
-       "29162  7481            2020-01-25T22:00:00+01:00  102030.0                 \n",
-       "29163  7481            2020-01-26T19:00:00+01:00  102010.0                 \n",
-       "29164  7481            2020-02-08T19:00:00+01:00  102540.0                 \n",
-       "\n",
-       "       Variation de pression en 3 heures  Type de tendance barométrique  \\\n",
-       "29155  130.0                              1.0                             \n",
-       "29156   90.0                              3.0                             \n",
-       "29157 -180.0                              6.0                             \n",
-       "29158 -240.0                              8.0                             \n",
-       "29159  230.0                              1.0                             \n",
-       "29160  210.0                              2.0                             \n",
-       "29161 -160.0                              6.0                             \n",
-       "29162   20.0                              1.0                             \n",
-       "29163   80.0                              3.0                             \n",
-       "29164  150.0                              2.0                             \n",
-       "\n",
-       "       Direction du vent moyen 10 mn  Vitesse du vent moyen 10 mn  \\\n",
-       "29155  190.0                          1.0                           \n",
-       "29156  130.0                          3.5                           \n",
-       "29157  360.0                          2.3                           \n",
-       "29158  150.0                          4.9                           \n",
-       "29159  280.0                          4.5                           \n",
-       "29160  140.0                          3.4                           \n",
-       "29161  180.0                          6.9                           \n",
-       "29162  140.0                          4.9                           \n",
-       "29163  170.0                          3.7                           \n",
-       "29164  190.0                          6.2                           \n",
-       "\n",
-       "       Température  Point de rosée  Humidité  ...  Longitude  Latitude  \\\n",
-       "29155  272.75       272.75          100.0     ...  5.077833   45.7265    \n",
-       "29156  276.95       274.65           85.0     ...  5.077833   45.7265    \n",
-       "29157  283.45       271.75           44.0     ...  5.077833   45.7265    \n",
-       "29158  274.75       271.15           77.0     ...  5.077833   45.7265    \n",
-       "29159  283.15       276.15           62.0     ...  5.077833   45.7265    \n",
-       "29160  280.15       278.45           89.0     ...  5.077833   45.7265    \n",
-       "29161  290.15       273.75           33.0     ...  5.077833   45.7265    \n",
-       "29162  281.45       278.55           82.0     ...  5.077833   45.7265    \n",
-       "29163  282.85       279.15           78.0     ...  5.077833   45.7265    \n",
-       "29164  283.75       277.65           66.0     ...  5.077833   45.7265    \n",
-       "\n",
-       "      communes (name)      communes (code)  EPCI (name)                  \\\n",
-       "29155  Colombier-Saugnieu  69299            CC de l'Est Lyonnais (CCEL)   \n",
-       "29156  Colombier-Saugnieu  69299            CC de l'Est Lyonnais (CCEL)   \n",
-       "29157  Colombier-Saugnieu  69299            CC de l'Est Lyonnais (CCEL)   \n",
-       "29158  Colombier-Saugnieu  69299            CC de l'Est Lyonnais (CCEL)   \n",
-       "29159  Colombier-Saugnieu  69299            CC de l'Est Lyonnais (CCEL)   \n",
-       "29160  Colombier-Saugnieu  69299            CC de l'Est Lyonnais (CCEL)   \n",
-       "29161  Colombier-Saugnieu  69299            CC de l'Est Lyonnais (CCEL)   \n",
-       "29162  Colombier-Saugnieu  69299            CC de l'Est Lyonnais (CCEL)   \n",
-       "29163  Colombier-Saugnieu  69299            CC de l'Est Lyonnais (CCEL)   \n",
-       "29164  Colombier-Saugnieu  69299            CC de l'Est Lyonnais (CCEL)   \n",
-       "\n",
-       "       EPCI (code)  department (name)  department (code)  \\\n",
-       "29155  246900575    Rhône              69                  \n",
-       "29156  246900575    Rhône              69                  \n",
-       "29157  246900575    Rhône              69                  \n",
-       "29158  246900575    Rhône              69                  \n",
-       "29159  246900575    Rhône              69                  \n",
-       "29160  246900575    Rhône              69                  \n",
-       "29161  246900575    Rhône              69                  \n",
-       "29162  246900575    Rhône              69                  \n",
-       "29163  246900575    Rhône              69                  \n",
-       "29164  246900575    Rhône              69                  \n",
-       "\n",
-       "       region (name)         region (code)  \n",
-       "29155  Auvergne-Rhône-Alpes  84             \n",
-       "29156  Auvergne-Rhône-Alpes  84             \n",
-       "29157  Auvergne-Rhône-Alpes  84             \n",
-       "29158  Auvergne-Rhône-Alpes  84             \n",
-       "29159  Auvergne-Rhône-Alpes  84             \n",
-       "29160  Auvergne-Rhône-Alpes  84             \n",
-       "29161  Auvergne-Rhône-Alpes  84             \n",
-       "29162  Auvergne-Rhône-Alpes  84             \n",
-       "29163  Auvergne-Rhône-Alpes  84             \n",
-       "29164  Auvergne-Rhône-Alpes  84             \n",
-       "\n",
-       "[10 rows x 81 columns]"
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-       "                <tr>\n",
-       "                        <th id=\"T_57323_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "                        <td id=\"T_57323_row0_col0\" class=\"data row0 col0\" >numer_sta</td>\n",
-       "                        <td id=\"T_57323_row0_col1\" class=\"data row0 col1\" >ID OMM station</td>\n",
-       "                        <td id=\"T_57323_row0_col2\" class=\"data row0 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
-       "                        <td id=\"T_57323_row1_col0\" class=\"data row1 col0\" >date</td>\n",
-       "                        <td id=\"T_57323_row1_col1\" class=\"data row1 col1\" >Date</td>\n",
-       "                        <td id=\"T_57323_row1_col2\" class=\"data row1 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "                        <td id=\"T_57323_row2_col0\" class=\"data row2 col0\" >pmer</td>\n",
-       "                        <td id=\"T_57323_row2_col1\" class=\"data row2 col1\" >Pression au niveau mer</td>\n",
-       "                        <td id=\"T_57323_row2_col2\" class=\"data row2 col2\" >17</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
-       "                        <td id=\"T_57323_row3_col0\" class=\"data row3 col0\" >tend</td>\n",
-       "                        <td id=\"T_57323_row3_col1\" class=\"data row3 col1\" >Variation de pression en 3 heures</td>\n",
-       "                        <td id=\"T_57323_row3_col2\" class=\"data row3 col2\" >2</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "                        <td id=\"T_57323_row4_col0\" class=\"data row4 col0\" >cod_tend</td>\n",
-       "                        <td id=\"T_57323_row4_col1\" class=\"data row4 col1\" >Type de tendance barométrique</td>\n",
-       "                        <td id=\"T_57323_row4_col2\" class=\"data row4 col2\" >2</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
-       "                        <td id=\"T_57323_row5_col0\" class=\"data row5 col0\" >dd</td>\n",
-       "                        <td id=\"T_57323_row5_col1\" class=\"data row5 col1\" >Direction du vent moyen 10 mn</td>\n",
-       "                        <td id=\"T_57323_row5_col2\" class=\"data row5 col2\" >3</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "                        <td id=\"T_57323_row6_col0\" class=\"data row6 col0\" >ff</td>\n",
-       "                        <td id=\"T_57323_row6_col1\" class=\"data row6 col1\" >Vitesse du vent moyen 10 mn</td>\n",
-       "                        <td id=\"T_57323_row6_col2\" class=\"data row6 col2\" >2</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "                        <td id=\"T_57323_row7_col0\" class=\"data row7 col0\" >t</td>\n",
-       "                        <td id=\"T_57323_row7_col1\" class=\"data row7 col1\" >Température</td>\n",
-       "                        <td id=\"T_57323_row7_col2\" class=\"data row7 col2\" >14</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
-       "                        <td id=\"T_57323_row8_col0\" class=\"data row8 col0\" >td</td>\n",
-       "                        <td id=\"T_57323_row8_col1\" class=\"data row8 col1\" >Point de rosée</td>\n",
-       "                        <td id=\"T_57323_row8_col2\" class=\"data row8 col2\" >17</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
-       "                        <td id=\"T_57323_row9_col0\" class=\"data row9 col0\" >u</td>\n",
-       "                        <td id=\"T_57323_row9_col1\" class=\"data row9 col1\" >Humidité</td>\n",
-       "                        <td id=\"T_57323_row9_col2\" class=\"data row9 col2\" >17</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
-       "                        <td id=\"T_57323_row10_col0\" class=\"data row10 col0\" >vv</td>\n",
-       "                        <td id=\"T_57323_row10_col1\" class=\"data row10 col1\" >Visibilité horizontale</td>\n",
-       "                        <td id=\"T_57323_row10_col2\" class=\"data row10 col2\" >31</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
-       "                        <td id=\"T_57323_row11_col0\" class=\"data row11 col0\" >ww</td>\n",
-       "                        <td id=\"T_57323_row11_col1\" class=\"data row11 col1\" >Temps présent</td>\n",
-       "                        <td id=\"T_57323_row11_col2\" class=\"data row11 col2\" >1</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
-       "                        <td id=\"T_57323_row12_col0\" class=\"data row12 col0\" >w1</td>\n",
-       "                        <td id=\"T_57323_row12_col1\" class=\"data row12 col1\" >Temps passé 1</td>\n",
-       "                        <td id=\"T_57323_row12_col2\" class=\"data row12 col2\" >542</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
-       "                        <td id=\"T_57323_row13_col0\" class=\"data row13 col0\" >w2</td>\n",
-       "                        <td id=\"T_57323_row13_col1\" class=\"data row13 col1\" >Temps passé 2</td>\n",
-       "                        <td id=\"T_57323_row13_col2\" class=\"data row13 col2\" >552</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
-       "                        <td id=\"T_57323_row14_col0\" class=\"data row14 col0\" >n</td>\n",
-       "                        <td id=\"T_57323_row14_col1\" class=\"data row14 col1\" >Nebulosité totale</td>\n",
-       "                        <td id=\"T_57323_row14_col2\" class=\"data row14 col2\" >801</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
-       "                        <td id=\"T_57323_row15_col0\" class=\"data row15 col0\" >nbas</td>\n",
-       "                        <td id=\"T_57323_row15_col1\" class=\"data row15 col1\" >Nébulosité  des nuages de l' étage inférieur</td>\n",
-       "                        <td id=\"T_57323_row15_col2\" class=\"data row15 col2\" >2381</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
-       "                        <td id=\"T_57323_row16_col0\" class=\"data row16 col0\" >hbas</td>\n",
-       "                        <td id=\"T_57323_row16_col1\" class=\"data row16 col1\" >Hauteur de la base des nuages de l'étage inférieur</td>\n",
-       "                        <td id=\"T_57323_row16_col2\" class=\"data row16 col2\" >8861</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
-       "                        <td id=\"T_57323_row17_col0\" class=\"data row17 col0\" >cl</td>\n",
-       "                        <td id=\"T_57323_row17_col1\" class=\"data row17 col1\" >Type des nuages de l'étage inférieur</td>\n",
-       "                        <td id=\"T_57323_row17_col2\" class=\"data row17 col2\" >3377</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
-       "                        <td id=\"T_57323_row18_col0\" class=\"data row18 col0\" >cm</td>\n",
-       "                        <td id=\"T_57323_row18_col1\" class=\"data row18 col1\" >Type des nuages de l'étage moyen</td>\n",
-       "                        <td id=\"T_57323_row18_col2\" class=\"data row18 col2\" >6912</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
-       "                        <td id=\"T_57323_row19_col0\" class=\"data row19 col0\" >ch</td>\n",
-       "                        <td id=\"T_57323_row19_col1\" class=\"data row19 col1\" >Type des nuages de l'étage supérieur</td>\n",
-       "                        <td id=\"T_57323_row19_col2\" class=\"data row19 col2\" >8494</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
-       "                        <td id=\"T_57323_row20_col0\" class=\"data row20 col0\" >pres</td>\n",
-       "                        <td id=\"T_57323_row20_col1\" class=\"data row20 col1\" >Pression station</td>\n",
-       "                        <td id=\"T_57323_row20_col2\" class=\"data row20 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
-       "                        <td id=\"T_57323_row21_col0\" class=\"data row21 col0\" >niv_bar</td>\n",
-       "                        <td id=\"T_57323_row21_col1\" class=\"data row21 col1\" >Niveau barométrique</td>\n",
-       "                        <td id=\"T_57323_row21_col2\" class=\"data row21 col2\" >29165</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
-       "                        <td id=\"T_57323_row22_col0\" class=\"data row22 col0\" >geop</td>\n",
-       "                        <td id=\"T_57323_row22_col1\" class=\"data row22 col1\" >Géopotentiel</td>\n",
-       "                        <td id=\"T_57323_row22_col2\" class=\"data row22 col2\" >29165</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
-       "                        <td id=\"T_57323_row23_col0\" class=\"data row23 col0\" >tend24</td>\n",
-       "                        <td id=\"T_57323_row23_col1\" class=\"data row23 col1\" >Variation de pression en 24 heures</td>\n",
-       "                        <td id=\"T_57323_row23_col2\" class=\"data row23 col2\" >14443</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
-       "                        <td id=\"T_57323_row24_col0\" class=\"data row24 col0\" >tn12</td>\n",
-       "                        <td id=\"T_57323_row24_col1\" class=\"data row24 col1\" >Température minimale sur 12 heures</td>\n",
-       "                        <td id=\"T_57323_row24_col2\" class=\"data row24 col2\" >21883</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
-       "                        <td id=\"T_57323_row25_col0\" class=\"data row25 col0\" >tn24</td>\n",
-       "                        <td id=\"T_57323_row25_col1\" class=\"data row25 col1\" >Température minimale sur 24 heures</td>\n",
-       "                        <td id=\"T_57323_row25_col2\" class=\"data row25 col2\" >29165</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
-       "                        <td id=\"T_57323_row26_col0\" class=\"data row26 col0\" >tx12</td>\n",
-       "                        <td id=\"T_57323_row26_col1\" class=\"data row26 col1\" >Température maximale sur 12 heures</td>\n",
-       "                        <td id=\"T_57323_row26_col2\" class=\"data row26 col2\" >21883</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
-       "                        <td id=\"T_57323_row27_col0\" class=\"data row27 col0\" >tx24</td>\n",
-       "                        <td id=\"T_57323_row27_col1\" class=\"data row27 col1\" >Température maximale sur 24 heures</td>\n",
-       "                        <td id=\"T_57323_row27_col2\" class=\"data row27 col2\" >29165</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
-       "                        <td id=\"T_57323_row28_col0\" class=\"data row28 col0\" >tminsol</td>\n",
-       "                        <td id=\"T_57323_row28_col1\" class=\"data row28 col1\" >Température minimale du sol sur 12 heures</td>\n",
-       "                        <td id=\"T_57323_row28_col2\" class=\"data row28 col2\" >27364</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
-       "                        <td id=\"T_57323_row29_col0\" class=\"data row29 col0\" >sw</td>\n",
-       "                        <td id=\"T_57323_row29_col1\" class=\"data row29 col1\" >Méthode de mesure Température du thermomètre mouillé</td>\n",
-       "                        <td id=\"T_57323_row29_col2\" class=\"data row29 col2\" >29165</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
-       "                        <td id=\"T_57323_row30_col0\" class=\"data row30 col0\" >tw</td>\n",
-       "                        <td id=\"T_57323_row30_col1\" class=\"data row30 col1\" >Température du thermomètre mouillé</td>\n",
-       "                        <td id=\"T_57323_row30_col2\" class=\"data row30 col2\" >29165</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
-       "                        <td id=\"T_57323_row31_col0\" class=\"data row31 col0\" >raf10</td>\n",
-       "                        <td id=\"T_57323_row31_col1\" class=\"data row31 col1\" >Rafale sur les 10 dernières minutes</td>\n",
-       "                        <td id=\"T_57323_row31_col2\" class=\"data row31 col2\" >14127</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
-       "                        <td id=\"T_57323_row32_col0\" class=\"data row32 col0\" >rafper</td>\n",
-       "                        <td id=\"T_57323_row32_col1\" class=\"data row32 col1\" >Rafales sur une période</td>\n",
-       "                        <td id=\"T_57323_row32_col2\" class=\"data row32 col2\" >9</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
-       "                        <td id=\"T_57323_row33_col0\" class=\"data row33 col0\" >per</td>\n",
-       "                        <td id=\"T_57323_row33_col1\" class=\"data row33 col1\" >Periode de mesure de la rafale</td>\n",
-       "                        <td id=\"T_57323_row33_col2\" class=\"data row33 col2\" >8</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
-       "                        <td id=\"T_57323_row34_col0\" class=\"data row34 col0\" >etat_sol</td>\n",
-       "                        <td id=\"T_57323_row34_col1\" class=\"data row34 col1\" >Etat du sol</td>\n",
-       "                        <td id=\"T_57323_row34_col2\" class=\"data row34 col2\" >12278</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
-       "                        <td id=\"T_57323_row35_col0\" class=\"data row35 col0\" >ht_neige</td>\n",
-       "                        <td id=\"T_57323_row35_col1\" class=\"data row35 col1\" >Hauteur totale de la couche de neige, glace, autre au sol</td>\n",
-       "                        <td id=\"T_57323_row35_col2\" class=\"data row35 col2\" >12083</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
-       "                        <td id=\"T_57323_row36_col0\" class=\"data row36 col0\" >ssfrai</td>\n",
-       "                        <td id=\"T_57323_row36_col1\" class=\"data row36 col1\" >Hauteur de la neige fraîche</td>\n",
-       "                        <td id=\"T_57323_row36_col2\" class=\"data row36 col2\" >2914</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
-       "                        <td id=\"T_57323_row37_col0\" class=\"data row37 col0\" >perssfrai</td>\n",
-       "                        <td id=\"T_57323_row37_col1\" class=\"data row37 col1\" >Periode de mesure de la neige fraiche</td>\n",
-       "                        <td id=\"T_57323_row37_col2\" class=\"data row37 col2\" >4489</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
-       "                        <td id=\"T_57323_row38_col0\" class=\"data row38 col0\" >rr1</td>\n",
-       "                        <td id=\"T_57323_row38_col1\" class=\"data row38 col1\" >Précipitations dans la dernière heure</td>\n",
-       "                        <td id=\"T_57323_row38_col2\" class=\"data row38 col2\" >95</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
-       "                        <td id=\"T_57323_row39_col0\" class=\"data row39 col0\" >rr3</td>\n",
-       "                        <td id=\"T_57323_row39_col1\" class=\"data row39 col1\" >Précipitations dans les 3 dernières heures</td>\n",
-       "                        <td id=\"T_57323_row39_col2\" class=\"data row39 col2\" >73</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
-       "                        <td id=\"T_57323_row40_col0\" class=\"data row40 col0\" >rr6</td>\n",
-       "                        <td id=\"T_57323_row40_col1\" class=\"data row40 col1\" >Précipitations dans les 6 dernières heures</td>\n",
-       "                        <td id=\"T_57323_row40_col2\" class=\"data row40 col2\" >10869</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
-       "                        <td id=\"T_57323_row41_col0\" class=\"data row41 col0\" >rr12</td>\n",
-       "                        <td id=\"T_57323_row41_col1\" class=\"data row41 col1\" >Précipitations dans les 12 dernières heures</td>\n",
-       "                        <td id=\"T_57323_row41_col2\" class=\"data row41 col2\" >10919</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
-       "                        <td id=\"T_57323_row42_col0\" class=\"data row42 col0\" >rr24</td>\n",
-       "                        <td id=\"T_57323_row42_col1\" class=\"data row42 col1\" >Précipitations dans les 24 dernières heures</td>\n",
-       "                        <td id=\"T_57323_row42_col2\" class=\"data row42 col2\" >12730</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
-       "                        <td id=\"T_57323_row43_col0\" class=\"data row43 col0\" >phenspe1</td>\n",
-       "                        <td id=\"T_57323_row43_col1\" class=\"data row43 col1\" >Phénomène spécial 1</td>\n",
-       "                        <td id=\"T_57323_row43_col2\" class=\"data row43 col2\" >14818</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
-       "                        <td id=\"T_57323_row44_col0\" class=\"data row44 col0\" >phenspe2</td>\n",
-       "                        <td id=\"T_57323_row44_col1\" class=\"data row44 col1\" >Phénomène spécial 2</td>\n",
-       "                        <td id=\"T_57323_row44_col2\" class=\"data row44 col2\" >14826</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
-       "                        <td id=\"T_57323_row45_col0\" class=\"data row45 col0\" >phenspe3</td>\n",
-       "                        <td id=\"T_57323_row45_col1\" class=\"data row45 col1\" >Phénomène spécial 3</td>\n",
-       "                        <td id=\"T_57323_row45_col2\" class=\"data row45 col2\" >15515</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
-       "                        <td id=\"T_57323_row46_col0\" class=\"data row46 col0\" >phenspe4</td>\n",
-       "                        <td id=\"T_57323_row46_col1\" class=\"data row46 col1\" >Phénomène spécial 4</td>\n",
-       "                        <td id=\"T_57323_row46_col2\" class=\"data row46 col2\" >28869</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
-       "                        <td id=\"T_57323_row47_col0\" class=\"data row47 col0\" >nnuage1</td>\n",
-       "                        <td id=\"T_57323_row47_col1\" class=\"data row47 col1\" >Nébulosité couche nuageuse 1</td>\n",
-       "                        <td id=\"T_57323_row47_col2\" class=\"data row47 col2\" >4753</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
-       "                        <td id=\"T_57323_row48_col0\" class=\"data row48 col0\" >ctype1</td>\n",
-       "                        <td id=\"T_57323_row48_col1\" class=\"data row48 col1\" >Type nuage 1</td>\n",
-       "                        <td id=\"T_57323_row48_col2\" class=\"data row48 col2\" >5699</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
-       "                        <td id=\"T_57323_row49_col0\" class=\"data row49 col0\" >hnuage1</td>\n",
-       "                        <td id=\"T_57323_row49_col1\" class=\"data row49 col1\" >Hauteur de base 1</td>\n",
-       "                        <td id=\"T_57323_row49_col2\" class=\"data row49 col2\" >5439</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
-       "                        <td id=\"T_57323_row50_col0\" class=\"data row50 col0\" >nnuage2</td>\n",
-       "                        <td id=\"T_57323_row50_col1\" class=\"data row50 col1\" >Nébulosité couche nuageuse 2</td>\n",
-       "                        <td id=\"T_57323_row50_col2\" class=\"data row50 col2\" >16112</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
-       "                        <td id=\"T_57323_row51_col0\" class=\"data row51 col0\" >ctype2</td>\n",
-       "                        <td id=\"T_57323_row51_col1\" class=\"data row51 col1\" >Type nuage 2</td>\n",
-       "                        <td id=\"T_57323_row51_col2\" class=\"data row51 col2\" >16643</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
-       "                        <td id=\"T_57323_row52_col0\" class=\"data row52 col0\" >hnuage2</td>\n",
-       "                        <td id=\"T_57323_row52_col1\" class=\"data row52 col1\" >Hauteur de base 2</td>\n",
-       "                        <td id=\"T_57323_row52_col2\" class=\"data row52 col2\" >16317</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
-       "                        <td id=\"T_57323_row53_col0\" class=\"data row53 col0\" >nnuage3</td>\n",
-       "                        <td id=\"T_57323_row53_col1\" class=\"data row53 col1\" >Nébulosité couche nuageuse 3</td>\n",
-       "                        <td id=\"T_57323_row53_col2\" class=\"data row53 col2\" >25387</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
-       "                        <td id=\"T_57323_row54_col0\" class=\"data row54 col0\" >ctype3</td>\n",
-       "                        <td id=\"T_57323_row54_col1\" class=\"data row54 col1\" >Type nuage 3</td>\n",
-       "                        <td id=\"T_57323_row54_col2\" class=\"data row54 col2\" >25642</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
-       "                        <td id=\"T_57323_row55_col0\" class=\"data row55 col0\" >hnuage3</td>\n",
-       "                        <td id=\"T_57323_row55_col1\" class=\"data row55 col1\" >Hauteur de base 3</td>\n",
-       "                        <td id=\"T_57323_row55_col2\" class=\"data row55 col2\" >25431</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
-       "                        <td id=\"T_57323_row56_col0\" class=\"data row56 col0\" >nnuage4</td>\n",
-       "                        <td id=\"T_57323_row56_col1\" class=\"data row56 col1\" >Nébulosité couche nuageuse 4</td>\n",
-       "                        <td id=\"T_57323_row56_col2\" class=\"data row56 col2\" >28850</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
-       "                        <td id=\"T_57323_row57_col0\" class=\"data row57 col0\" >ctype4</td>\n",
-       "                        <td id=\"T_57323_row57_col1\" class=\"data row57 col1\" >Type nuage 4</td>\n",
-       "                        <td id=\"T_57323_row57_col2\" class=\"data row57 col2\" >28780</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row58\" class=\"row_heading level0 row58\" >58</th>\n",
-       "                        <td id=\"T_57323_row58_col0\" class=\"data row58 col0\" >hnuage4</td>\n",
-       "                        <td id=\"T_57323_row58_col1\" class=\"data row58 col1\" >Hauteur de base 4</td>\n",
-       "                        <td id=\"T_57323_row58_col2\" class=\"data row58 col2\" >28850</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row59\" class=\"row_heading level0 row59\" >59</th>\n",
-       "                        <td id=\"T_57323_row59_col0\" class=\"data row59 col0\" >coordonnees</td>\n",
-       "                        <td id=\"T_57323_row59_col1\" class=\"data row59 col1\" >Coordonnees</td>\n",
-       "                        <td id=\"T_57323_row59_col2\" class=\"data row59 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row60\" class=\"row_heading level0 row60\" >60</th>\n",
-       "                        <td id=\"T_57323_row60_col0\" class=\"data row60 col0\" >nom</td>\n",
-       "                        <td id=\"T_57323_row60_col1\" class=\"data row60 col1\" >Nom</td>\n",
-       "                        <td id=\"T_57323_row60_col2\" class=\"data row60 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row61\" class=\"row_heading level0 row61\" >61</th>\n",
-       "                        <td id=\"T_57323_row61_col0\" class=\"data row61 col0\" >type_de_tendance_barometrique</td>\n",
-       "                        <td id=\"T_57323_row61_col1\" class=\"data row61 col1\" >Type de tendance barométrique.1</td>\n",
-       "                        <td id=\"T_57323_row61_col2\" class=\"data row61 col2\" >2</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row62\" class=\"row_heading level0 row62\" >62</th>\n",
-       "                        <td id=\"T_57323_row62_col0\" class=\"data row62 col0\" >temps_passe_1</td>\n",
-       "                        <td id=\"T_57323_row62_col1\" class=\"data row62 col1\" >Temps passé 1.1</td>\n",
-       "                        <td id=\"T_57323_row62_col2\" class=\"data row62 col2\" >542</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row63\" class=\"row_heading level0 row63\" >63</th>\n",
-       "                        <td id=\"T_57323_row63_col0\" class=\"data row63 col0\" >temps_present</td>\n",
-       "                        <td id=\"T_57323_row63_col1\" class=\"data row63 col1\" >Temps présent.1</td>\n",
-       "                        <td id=\"T_57323_row63_col2\" class=\"data row63 col2\" >1</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row64\" class=\"row_heading level0 row64\" >64</th>\n",
-       "                        <td id=\"T_57323_row64_col0\" class=\"data row64 col0\" >tc</td>\n",
-       "                        <td id=\"T_57323_row64_col1\" class=\"data row64 col1\" >Température (°C)</td>\n",
-       "                        <td id=\"T_57323_row64_col2\" class=\"data row64 col2\" >14</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row65\" class=\"row_heading level0 row65\" >65</th>\n",
-       "                        <td id=\"T_57323_row65_col0\" class=\"data row65 col0\" >tn12c</td>\n",
-       "                        <td id=\"T_57323_row65_col1\" class=\"data row65 col1\" >Température minimale sur 12 heures (°C)</td>\n",
-       "                        <td id=\"T_57323_row65_col2\" class=\"data row65 col2\" >21883</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row66\" class=\"row_heading level0 row66\" >66</th>\n",
-       "                        <td id=\"T_57323_row66_col0\" class=\"data row66 col0\" >tn24c</td>\n",
-       "                        <td id=\"T_57323_row66_col1\" class=\"data row66 col1\" >Température minimale sur 24 heures (°C)</td>\n",
-       "                        <td id=\"T_57323_row66_col2\" class=\"data row66 col2\" >29165</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row67\" class=\"row_heading level0 row67\" >67</th>\n",
-       "                        <td id=\"T_57323_row67_col0\" class=\"data row67 col0\" >tx12c</td>\n",
-       "                        <td id=\"T_57323_row67_col1\" class=\"data row67 col1\" >Température maximale sur 12 heures (°C)</td>\n",
-       "                        <td id=\"T_57323_row67_col2\" class=\"data row67 col2\" >21883</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row68\" class=\"row_heading level0 row68\" >68</th>\n",
-       "                        <td id=\"T_57323_row68_col0\" class=\"data row68 col0\" >tx24c</td>\n",
-       "                        <td id=\"T_57323_row68_col1\" class=\"data row68 col1\" >Température maximale sur 24 heures (°C)</td>\n",
-       "                        <td id=\"T_57323_row68_col2\" class=\"data row68 col2\" >29165</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row69\" class=\"row_heading level0 row69\" >69</th>\n",
-       "                        <td id=\"T_57323_row69_col0\" class=\"data row69 col0\" >tminsolc</td>\n",
-       "                        <td id=\"T_57323_row69_col1\" class=\"data row69 col1\" >Température minimale du sol sur 12 heures (en °C)</td>\n",
-       "                        <td id=\"T_57323_row69_col2\" class=\"data row69 col2\" >27364</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row70\" class=\"row_heading level0 row70\" >70</th>\n",
-       "                        <td id=\"T_57323_row70_col0\" class=\"data row70 col0\" >altitude</td>\n",
-       "                        <td id=\"T_57323_row70_col1\" class=\"data row70 col1\" >Altitude</td>\n",
-       "                        <td id=\"T_57323_row70_col2\" class=\"data row70 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row71\" class=\"row_heading level0 row71\" >71</th>\n",
-       "                        <td id=\"T_57323_row71_col0\" class=\"data row71 col0\" >longitude</td>\n",
-       "                        <td id=\"T_57323_row71_col1\" class=\"data row71 col1\" >Longitude</td>\n",
-       "                        <td id=\"T_57323_row71_col2\" class=\"data row71 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row72\" class=\"row_heading level0 row72\" >72</th>\n",
-       "                        <td id=\"T_57323_row72_col0\" class=\"data row72 col0\" >latitude</td>\n",
-       "                        <td id=\"T_57323_row72_col1\" class=\"data row72 col1\" >Latitude</td>\n",
-       "                        <td id=\"T_57323_row72_col2\" class=\"data row72 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row73\" class=\"row_heading level0 row73\" >73</th>\n",
-       "                        <td id=\"T_57323_row73_col0\" class=\"data row73 col0\" >libgeo</td>\n",
-       "                        <td id=\"T_57323_row73_col1\" class=\"data row73 col1\" >communes (name)</td>\n",
-       "                        <td id=\"T_57323_row73_col2\" class=\"data row73 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row74\" class=\"row_heading level0 row74\" >74</th>\n",
-       "                        <td id=\"T_57323_row74_col0\" class=\"data row74 col0\" >codegeo</td>\n",
-       "                        <td id=\"T_57323_row74_col1\" class=\"data row74 col1\" >communes (code)</td>\n",
-       "                        <td id=\"T_57323_row74_col2\" class=\"data row74 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row75\" class=\"row_heading level0 row75\" >75</th>\n",
-       "                        <td id=\"T_57323_row75_col0\" class=\"data row75 col0\" >nom_epci</td>\n",
-       "                        <td id=\"T_57323_row75_col1\" class=\"data row75 col1\" >EPCI (name)</td>\n",
-       "                        <td id=\"T_57323_row75_col2\" class=\"data row75 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row76\" class=\"row_heading level0 row76\" >76</th>\n",
-       "                        <td id=\"T_57323_row76_col0\" class=\"data row76 col0\" >code_epci</td>\n",
-       "                        <td id=\"T_57323_row76_col1\" class=\"data row76 col1\" >EPCI (code)</td>\n",
-       "                        <td id=\"T_57323_row76_col2\" class=\"data row76 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row77\" class=\"row_heading level0 row77\" >77</th>\n",
-       "                        <td id=\"T_57323_row77_col0\" class=\"data row77 col0\" >nom_dept</td>\n",
-       "                        <td id=\"T_57323_row77_col1\" class=\"data row77 col1\" >department (name)</td>\n",
-       "                        <td id=\"T_57323_row77_col2\" class=\"data row77 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row78\" class=\"row_heading level0 row78\" >78</th>\n",
-       "                        <td id=\"T_57323_row78_col0\" class=\"data row78 col0\" >code_dep</td>\n",
-       "                        <td id=\"T_57323_row78_col1\" class=\"data row78 col1\" >department (code)</td>\n",
-       "                        <td id=\"T_57323_row78_col2\" class=\"data row78 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row79\" class=\"row_heading level0 row79\" >79</th>\n",
-       "                        <td id=\"T_57323_row79_col0\" class=\"data row79 col0\" >nom_reg</td>\n",
-       "                        <td id=\"T_57323_row79_col1\" class=\"data row79 col1\" >region (name)</td>\n",
-       "                        <td id=\"T_57323_row79_col2\" class=\"data row79 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_57323_level0_row80\" class=\"row_heading level0 row80\" >80</th>\n",
-       "                        <td id=\"T_57323_row80_col0\" class=\"data row80 col0\" >code_reg</td>\n",
-       "                        <td id=\"T_57323_row80_col1\" class=\"data row80 col1\" >region (code)</td>\n",
-       "                        <td id=\"T_57323_row80_col2\" class=\"data row80 col2\" >0</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f720857b760>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shape is :  (29165, 81)\n"
-     ]
-    }
-   ],
-   "source": [
-    "df = pd.read_csv(f'{datasets_dir}/SYNOP/{data_filename}', header=0, sep=';')\n",
-    "pwk.subtitle('Raw data :')\n",
-    "display(df.tail(10))\n",
-    "\n",
-    "# ---- Get the columns name as descriptions\n",
-    "synop_desc = list(df.columns)\n",
-    "\n",
-    "# ---- Set Codes as columns name\n",
-    "df.columns   = synop_codes\n",
-    "code2desc    = dict(zip(synop_codes, synop_desc))\n",
-    "\n",
-    "# ---- Count the na values by columns\n",
-    "columns_na = df.isna().sum().tolist()\n",
-    "\n",
-    "# ---- Show all of that\n",
-    "df_desc=pd.DataFrame({'Code':synop_codes, 'Description':synop_desc, 'Na':columns_na})\n",
-    "\n",
-    "pwk.subtitle('List of columns :')\n",
-    "display(df_desc.style.set_properties(**{'text-align': 'left'}))\n",
-    "\n",
-    "print('Shape is : ', df.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4 - Prepare dataset\n",
-    "### 4.1 - Keep only certain columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**Our selected columns :**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: left;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>pmer</th>\n",
-       "      <th>tend</th>\n",
-       "      <th>cod_tend</th>\n",
-       "      <th>dd</th>\n",
-       "      <th>ff</th>\n",
-       "      <th>td</th>\n",
-       "      <th>u</th>\n",
-       "      <th>ww</th>\n",
-       "      <th>pres</th>\n",
-       "      <th>rafper</th>\n",
-       "      <th>per</th>\n",
-       "      <th>rr1</th>\n",
-       "      <th>rr3</th>\n",
-       "      <th>tc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2015-06-12T17:00:00+02:00</td>\n",
-       "      <td>101050.0</td>\n",
-       "      <td>-230.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>286.25</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98330.0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>24.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2015-06-05T17:00:00+02:00</td>\n",
-       "      <td>101590.0</td>\n",
-       "      <td>-220.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>3.9</td>\n",
-       "      <td>286.95</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>98930.0</td>\n",
-       "      <td>9.9</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>32.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2015-06-15T11:00:00+02:00</td>\n",
-       "      <td>101420.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>270.0</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>286.85</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>98660.0</td>\n",
-       "      <td>4.5</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>20.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2015-06-15T14:00:00+02:00</td>\n",
-       "      <td>101430.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>286.45</td>\n",
-       "      <td>55.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98680.0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>22.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2015-06-20T05:00:00+02:00</td>\n",
-       "      <td>102030.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>282.95</td>\n",
-       "      <td>82.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>99170.0</td>\n",
-       "      <td>2.4</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>2015-06-22T05:00:00+02:00</td>\n",
-       "      <td>101680.0</td>\n",
-       "      <td>-120.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>286.15</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98870.0</td>\n",
-       "      <td>4.7</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>16.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>2015-06-23T02:00:00+02:00</td>\n",
-       "      <td>101270.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>4.5</td>\n",
-       "      <td>282.95</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>98490.0</td>\n",
-       "      <td>10.2</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>2015-06-25T14:00:00+02:00</td>\n",
-       "      <td>102180.0</td>\n",
-       "      <td>-40.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>283.25</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>99430.0</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>25.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>2015-07-05T20:00:00+02:00</td>\n",
-       "      <td>101410.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>8.3</td>\n",
-       "      <td>288.05</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>98760.0</td>\n",
-       "      <td>13.4</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>33.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>2015-05-14T17:00:00+02:00</td>\n",
-       "      <td>101070.0</td>\n",
-       "      <td>-150.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>284.95</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>98300.0</td>\n",
-       "      <td>11.1</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>2015-03-16T22:00:00+01:00</td>\n",
-       "      <td>102150.0</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>275.05</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>99240.0</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>2015-03-26T01:00:00+01:00</td>\n",
-       "      <td>101140.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>330.0</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>275.45</td>\n",
-       "      <td>82.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98220.0</td>\n",
-       "      <td>8.1</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>2015-04-03T17:00:00+02:00</td>\n",
-       "      <td>101690.0</td>\n",
-       "      <td>-250.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>340.0</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>278.15</td>\n",
-       "      <td>55.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98850.0</td>\n",
-       "      <td>6.4</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>2015-04-05T20:00:00+02:00</td>\n",
-       "      <td>101850.0</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>7.8</td>\n",
-       "      <td>268.45</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98950.0</td>\n",
-       "      <td>13.5</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>2014-10-22T17:00:00+02:00</td>\n",
-       "      <td>102670.0</td>\n",
-       "      <td>-70.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>275.35</td>\n",
-       "      <td>55.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>99770.0</td>\n",
-       "      <td>7.2</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>2015-02-08T16:00:00+01:00</td>\n",
-       "      <td>102570.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>12.3</td>\n",
-       "      <td>271.55</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>99590.0</td>\n",
-       "      <td>19.9</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>2015-02-11T07:00:00+01:00</td>\n",
-       "      <td>102670.0</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>290.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>267.55</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>99610.0</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-3.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>2015-02-07T04:00:00+01:00</td>\n",
-       "      <td>101900.0</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>310.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>268.65</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98900.0</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>2015-02-13T04:00:00+01:00</td>\n",
-       "      <td>102140.0</td>\n",
-       "      <td>-50.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>2.1</td>\n",
-       "      <td>273.55</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>99190.0</td>\n",
-       "      <td>4.9</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>2015-02-16T19:00:00+01:00</td>\n",
-       "      <td>102060.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>275.65</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>99110.0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   date                        pmer      tend   cod_tend  dd     ff    td      \\\n",
-       "0   2015-06-12T17:00:00+02:00  101050.0 -230.0  6.0       140.0   3.6  286.25   \n",
-       "1   2015-06-05T17:00:00+02:00  101590.0 -220.0  8.0       190.0   3.9  286.95   \n",
-       "2   2015-06-15T11:00:00+02:00  101420.0   90.0  1.0       270.0   1.5  286.85   \n",
-       "3   2015-06-15T14:00:00+02:00  101430.0   20.0  1.0        10.0   2.5  286.45   \n",
-       "4   2015-06-20T05:00:00+02:00  102030.0    0.0  4.0        50.0   0.7  282.95   \n",
-       "5   2015-06-22T05:00:00+02:00  101680.0 -120.0  6.0       180.0   0.7  286.15   \n",
-       "6   2015-06-23T02:00:00+02:00  101270.0  150.0  2.0        20.0   4.5  282.95   \n",
-       "7   2015-06-25T14:00:00+02:00  102180.0  -40.0  8.0        10.0   2.3  283.25   \n",
-       "8   2015-07-05T20:00:00+02:00  101410.0   50.0  3.0       190.0   8.3  288.05   \n",
-       "9   2015-05-14T17:00:00+02:00  101070.0 -150.0  6.0        20.0   6.2  284.95   \n",
-       "10  2015-03-16T22:00:00+01:00  102150.0   40.0  1.0        50.0   1.7  275.05   \n",
-       "11  2015-03-26T01:00:00+01:00  101140.0  100.0  1.0       330.0   5.9  275.45   \n",
-       "12  2015-04-03T17:00:00+02:00  101690.0 -250.0  7.0       340.0   3.5  278.15   \n",
-       "13  2015-04-05T20:00:00+02:00  101850.0  140.0  3.0        10.0   7.8  268.45   \n",
-       "14  2014-10-22T17:00:00+02:00  102670.0  -70.0  8.0        20.0   4.6  275.35   \n",
-       "15  2015-02-08T16:00:00+01:00  102570.0   20.0  3.0       350.0  12.3  271.55   \n",
-       "16  2015-02-11T07:00:00+01:00  102670.0  -10.0  7.0       290.0   2.0  267.55   \n",
-       "17  2015-02-07T04:00:00+01:00  101900.0  160.0  1.0       310.0   2.0  268.65   \n",
-       "18  2015-02-13T04:00:00+01:00  102140.0  -50.0  8.0       140.0   2.1  273.55   \n",
-       "19  2015-02-16T19:00:00+01:00  102060.0  100.0  3.0        20.0   3.2  275.65   \n",
-       "\n",
-       "    u     ww    pres     rafper  per   rr1  rr3  tc    \n",
-       "0   50.0   2.0  98330.0   5.1   -10.0  0.0 -0.1  24.2  \n",
-       "1   32.0   3.0  98930.0   9.9   -10.0  0.0  0.0  32.6  \n",
-       "2   64.0   3.0  98660.0   4.5   -10.0  0.0  0.0  20.8  \n",
-       "3   55.0   1.0  98680.0   5.1   -10.0  0.0  0.0  22.8  \n",
-       "4   82.0   2.0  99170.0   2.4   -10.0  0.0  0.0  12.8  \n",
-       "5   80.0   1.0  98870.0   4.7   -10.0  0.0 -0.1  16.5  \n",
-       "6   54.0   0.0  98490.0  10.2   -10.0  0.0  0.0  19.3  \n",
-       "7   38.0   1.0  99430.0   7.5   -10.0  0.0  0.0  25.5  \n",
-       "8   33.0   3.0  98760.0  13.4   -10.0  0.0  0.0  33.4  \n",
-       "9   60.0   3.0  98300.0  11.1   -10.0  0.0  0.0  19.8  \n",
-       "10  62.0   1.0  99240.0   4.6   -10.0  0.0  0.0   8.8  \n",
-       "11  82.0   1.0  98220.0   8.1   -10.0  0.0  0.0   5.1  \n",
-       "12  55.0   1.0  98850.0   6.4   -10.0  0.0  0.0  13.9  \n",
-       "13  38.0   1.0  98950.0  13.5   -10.0  0.0  0.0   8.9  \n",
-       "14  55.0   1.0  99770.0   7.2   -10.0  0.0  0.0  10.9  \n",
-       "15  68.0   0.0  99590.0  19.9   -10.0  0.0  0.0   3.8  \n",
-       "16  88.0  10.0  99610.0   3.3   -10.0  0.0  0.0  -3.9  \n",
-       "17  74.0   2.0  98900.0   3.2   -10.0  0.0  0.0  -0.4  \n",
-       "18  74.0   0.0  99190.0   4.9   -10.0  0.0  0.0   4.6  \n",
-       "19  88.0   1.0  99110.0   5.1   -10.0  0.0  0.0   4.3  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**Few statistics :**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style  type=\"text/css\" >\n",
-       "</style><table id=\"T_2b7dd_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >pmer</th>        <th class=\"col_heading level0 col1\" >tend</th>        <th class=\"col_heading level0 col2\" >cod_tend</th>        <th class=\"col_heading level0 col3\" >dd</th>        <th class=\"col_heading level0 col4\" >ff</th>        <th class=\"col_heading level0 col5\" >td</th>        <th class=\"col_heading level0 col6\" >u</th>        <th class=\"col_heading level0 col7\" >ww</th>        <th class=\"col_heading level0 col8\" >pres</th>        <th class=\"col_heading level0 col9\" >rafper</th>        <th class=\"col_heading level0 col10\" >per</th>        <th class=\"col_heading level0 col11\" >rr1</th>        <th class=\"col_heading level0 col12\" >rr3</th>        <th class=\"col_heading level0 col13\" >tc</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_2b7dd_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
-       "                        <td id=\"T_2b7dd_row0_col0\" class=\"data row0 col0\" >29148.00</td>\n",
-       "                        <td id=\"T_2b7dd_row0_col1\" class=\"data row0 col1\" >29163.00</td>\n",
-       "                        <td id=\"T_2b7dd_row0_col2\" class=\"data row0 col2\" >29163.00</td>\n",
-       "                        <td id=\"T_2b7dd_row0_col3\" class=\"data row0 col3\" >29162.00</td>\n",
-       "                        <td id=\"T_2b7dd_row0_col4\" class=\"data row0 col4\" >29163.00</td>\n",
-       "                        <td id=\"T_2b7dd_row0_col5\" class=\"data row0 col5\" >29148.00</td>\n",
-       "                        <td id=\"T_2b7dd_row0_col6\" class=\"data row0 col6\" >29148.00</td>\n",
-       "                        <td id=\"T_2b7dd_row0_col7\" class=\"data row0 col7\" >29164.00</td>\n",
-       "                        <td id=\"T_2b7dd_row0_col8\" class=\"data row0 col8\" >29165.00</td>\n",
-       "                        <td id=\"T_2b7dd_row0_col9\" class=\"data row0 col9\" >29156.00</td>\n",
-       "                        <td id=\"T_2b7dd_row0_col10\" class=\"data row0 col10\" >29157.00</td>\n",
-       "                        <td id=\"T_2b7dd_row0_col11\" class=\"data row0 col11\" >29070.00</td>\n",
-       "                        <td id=\"T_2b7dd_row0_col12\" class=\"data row0 col12\" >29092.00</td>\n",
-       "                        <td id=\"T_2b7dd_row0_col13\" class=\"data row0 col13\" >29151.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_2b7dd_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
-       "                        <td id=\"T_2b7dd_row1_col0\" class=\"data row1 col0\" >101753.55</td>\n",
-       "                        <td id=\"T_2b7dd_row1_col1\" class=\"data row1 col1\" >0.26</td>\n",
-       "                        <td id=\"T_2b7dd_row1_col2\" class=\"data row1 col2\" >4.31</td>\n",
-       "                        <td id=\"T_2b7dd_row1_col3\" class=\"data row1 col3\" >204.09</td>\n",
-       "                        <td id=\"T_2b7dd_row1_col4\" class=\"data row1 col4\" >3.40</td>\n",
-       "                        <td id=\"T_2b7dd_row1_col5\" class=\"data row1 col5\" >280.03</td>\n",
-       "                        <td id=\"T_2b7dd_row1_col6\" class=\"data row1 col6\" >71.02</td>\n",
-       "                        <td id=\"T_2b7dd_row1_col7\" class=\"data row1 col7\" >10.11</td>\n",
-       "                        <td id=\"T_2b7dd_row1_col8\" class=\"data row1 col8\" >98894.60</td>\n",
-       "                        <td id=\"T_2b7dd_row1_col9\" class=\"data row1 col9\" >6.30</td>\n",
-       "                        <td id=\"T_2b7dd_row1_col10\" class=\"data row1 col10\" >-10.00</td>\n",
-       "                        <td id=\"T_2b7dd_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
-       "                        <td id=\"T_2b7dd_row1_col12\" class=\"data row1 col12\" >0.28</td>\n",
-       "                        <td id=\"T_2b7dd_row1_col13\" class=\"data row1 col13\" >12.69</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_2b7dd_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
-       "                        <td id=\"T_2b7dd_row2_col0\" class=\"data row2 col0\" >798.09</td>\n",
-       "                        <td id=\"T_2b7dd_row2_col1\" class=\"data row2 col1\" >111.44</td>\n",
-       "                        <td id=\"T_2b7dd_row2_col2\" class=\"data row2 col2\" >2.72</td>\n",
-       "                        <td id=\"T_2b7dd_row2_col3\" class=\"data row2 col3\" >115.42</td>\n",
-       "                        <td id=\"T_2b7dd_row2_col4\" class=\"data row2 col4\" >2.47</td>\n",
-       "                        <td id=\"T_2b7dd_row2_col5\" class=\"data row2 col5\" >5.86</td>\n",
-       "                        <td id=\"T_2b7dd_row2_col6\" class=\"data row2 col6\" >18.28</td>\n",
-       "                        <td id=\"T_2b7dd_row2_col7\" class=\"data row2 col7\" >19.40</td>\n",
-       "                        <td id=\"T_2b7dd_row2_col8\" class=\"data row2 col8\" >761.59</td>\n",
-       "                        <td id=\"T_2b7dd_row2_col9\" class=\"data row2 col9\" >3.85</td>\n",
-       "                        <td id=\"T_2b7dd_row2_col10\" class=\"data row2 col10\" >0.00</td>\n",
-       "                        <td id=\"T_2b7dd_row2_col11\" class=\"data row2 col11\" >0.61</td>\n",
-       "                        <td id=\"T_2b7dd_row2_col12\" class=\"data row2 col12\" >1.41</td>\n",
-       "                        <td id=\"T_2b7dd_row2_col13\" class=\"data row2 col13\" >8.15</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_2b7dd_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
-       "                        <td id=\"T_2b7dd_row3_col0\" class=\"data row3 col0\" >97960.00</td>\n",
-       "                        <td id=\"T_2b7dd_row3_col1\" class=\"data row3 col1\" >-750.00</td>\n",
-       "                        <td id=\"T_2b7dd_row3_col2\" class=\"data row3 col2\" >0.00</td>\n",
-       "                        <td id=\"T_2b7dd_row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
-       "                        <td id=\"T_2b7dd_row3_col4\" class=\"data row3 col4\" >0.00</td>\n",
-       "                        <td id=\"T_2b7dd_row3_col5\" class=\"data row3 col5\" >249.25</td>\n",
-       "                        <td id=\"T_2b7dd_row3_col6\" class=\"data row3 col6\" >2.00</td>\n",
-       "                        <td id=\"T_2b7dd_row3_col7\" class=\"data row3 col7\" >0.00</td>\n",
-       "                        <td id=\"T_2b7dd_row3_col8\" class=\"data row3 col8\" >95170.00</td>\n",
-       "                        <td id=\"T_2b7dd_row3_col9\" class=\"data row3 col9\" >0.00</td>\n",
-       "                        <td id=\"T_2b7dd_row3_col10\" class=\"data row3 col10\" >-10.00</td>\n",
-       "                        <td id=\"T_2b7dd_row3_col11\" class=\"data row3 col11\" >-0.10</td>\n",
-       "                        <td id=\"T_2b7dd_row3_col12\" class=\"data row3 col12\" >-0.10</td>\n",
-       "                        <td id=\"T_2b7dd_row3_col13\" class=\"data row3 col13\" >-12.10</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_2b7dd_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
-       "                        <td id=\"T_2b7dd_row4_col0\" class=\"data row4 col0\" >101300.00</td>\n",
-       "                        <td id=\"T_2b7dd_row4_col1\" class=\"data row4 col1\" >-70.00</td>\n",
-       "                        <td id=\"T_2b7dd_row4_col2\" class=\"data row4 col2\" >2.00</td>\n",
-       "                        <td id=\"T_2b7dd_row4_col3\" class=\"data row4 col3\" >130.00</td>\n",
-       "                        <td id=\"T_2b7dd_row4_col4\" class=\"data row4 col4\" >1.50</td>\n",
-       "                        <td id=\"T_2b7dd_row4_col5\" class=\"data row4 col5\" >275.83</td>\n",
-       "                        <td id=\"T_2b7dd_row4_col6\" class=\"data row4 col6\" >58.00</td>\n",
-       "                        <td id=\"T_2b7dd_row4_col7\" class=\"data row4 col7\" >2.00</td>\n",
-       "                        <td id=\"T_2b7dd_row4_col8\" class=\"data row4 col8\" >98480.00</td>\n",
-       "                        <td id=\"T_2b7dd_row4_col9\" class=\"data row4 col9\" >3.60</td>\n",
-       "                        <td id=\"T_2b7dd_row4_col10\" class=\"data row4 col10\" >-10.00</td>\n",
-       "                        <td id=\"T_2b7dd_row4_col11\" class=\"data row4 col11\" >0.00</td>\n",
-       "                        <td id=\"T_2b7dd_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
-       "                        <td id=\"T_2b7dd_row4_col13\" class=\"data row4 col13\" >6.60</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_2b7dd_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
-       "                        <td id=\"T_2b7dd_row5_col0\" class=\"data row5 col0\" >101740.00</td>\n",
-       "                        <td id=\"T_2b7dd_row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
-       "                        <td id=\"T_2b7dd_row5_col2\" class=\"data row5 col2\" >3.00</td>\n",
-       "                        <td id=\"T_2b7dd_row5_col3\" class=\"data row5 col3\" >190.00</td>\n",
-       "                        <td id=\"T_2b7dd_row5_col4\" class=\"data row5 col4\" >2.90</td>\n",
-       "                        <td id=\"T_2b7dd_row5_col5\" class=\"data row5 col5\" >280.25</td>\n",
-       "                        <td id=\"T_2b7dd_row5_col6\" class=\"data row5 col6\" >74.00</td>\n",
-       "                        <td id=\"T_2b7dd_row5_col7\" class=\"data row5 col7\" >2.00</td>\n",
-       "                        <td id=\"T_2b7dd_row5_col8\" class=\"data row5 col8\" >98920.00</td>\n",
-       "                        <td id=\"T_2b7dd_row5_col9\" class=\"data row5 col9\" >5.30</td>\n",
-       "                        <td id=\"T_2b7dd_row5_col10\" class=\"data row5 col10\" >-10.00</td>\n",
-       "                        <td id=\"T_2b7dd_row5_col11\" class=\"data row5 col11\" >0.00</td>\n",
-       "                        <td id=\"T_2b7dd_row5_col12\" class=\"data row5 col12\" >0.00</td>\n",
-       "                        <td id=\"T_2b7dd_row5_col13\" class=\"data row5 col13\" >12.50</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_2b7dd_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
-       "                        <td id=\"T_2b7dd_row6_col0\" class=\"data row6 col0\" >102240.00</td>\n",
-       "                        <td id=\"T_2b7dd_row6_col1\" class=\"data row6 col1\" >70.00</td>\n",
-       "                        <td id=\"T_2b7dd_row6_col2\" class=\"data row6 col2\" >7.00</td>\n",
-       "                        <td id=\"T_2b7dd_row6_col3\" class=\"data row6 col3\" >330.00</td>\n",
-       "                        <td id=\"T_2b7dd_row6_col4\" class=\"data row6 col4\" >4.60</td>\n",
-       "                        <td id=\"T_2b7dd_row6_col5\" class=\"data row6 col5\" >284.55</td>\n",
-       "                        <td id=\"T_2b7dd_row6_col6\" class=\"data row6 col6\" >86.00</td>\n",
-       "                        <td id=\"T_2b7dd_row6_col7\" class=\"data row6 col7\" >3.00</td>\n",
-       "                        <td id=\"T_2b7dd_row6_col8\" class=\"data row6 col8\" >99360.00</td>\n",
-       "                        <td id=\"T_2b7dd_row6_col9\" class=\"data row6 col9\" >8.20</td>\n",
-       "                        <td id=\"T_2b7dd_row6_col10\" class=\"data row6 col10\" >-10.00</td>\n",
-       "                        <td id=\"T_2b7dd_row6_col11\" class=\"data row6 col11\" >0.00</td>\n",
-       "                        <td id=\"T_2b7dd_row6_col12\" class=\"data row6 col12\" >0.00</td>\n",
-       "                        <td id=\"T_2b7dd_row6_col13\" class=\"data row6 col13\" >18.50</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_2b7dd_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
-       "                        <td id=\"T_2b7dd_row7_col0\" class=\"data row7 col0\" >104280.00</td>\n",
-       "                        <td id=\"T_2b7dd_row7_col1\" class=\"data row7 col1\" >810.00</td>\n",
-       "                        <td id=\"T_2b7dd_row7_col2\" class=\"data row7 col2\" >8.00</td>\n",
-       "                        <td id=\"T_2b7dd_row7_col3\" class=\"data row7 col3\" >360.00</td>\n",
-       "                        <td id=\"T_2b7dd_row7_col4\" class=\"data row7 col4\" >18.80</td>\n",
-       "                        <td id=\"T_2b7dd_row7_col5\" class=\"data row7 col5\" >295.95</td>\n",
-       "                        <td id=\"T_2b7dd_row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
-       "                        <td id=\"T_2b7dd_row7_col7\" class=\"data row7 col7\" >97.00</td>\n",
-       "                        <td id=\"T_2b7dd_row7_col8\" class=\"data row7 col8\" >101210.00</td>\n",
-       "                        <td id=\"T_2b7dd_row7_col9\" class=\"data row7 col9\" >30.20</td>\n",
-       "                        <td id=\"T_2b7dd_row7_col10\" class=\"data row7 col10\" >-10.00</td>\n",
-       "                        <td id=\"T_2b7dd_row7_col11\" class=\"data row7 col11\" >19.00</td>\n",
-       "                        <td id=\"T_2b7dd_row7_col12\" class=\"data row7 col12\" >45.00</td>\n",
-       "                        <td id=\"T_2b7dd_row7_col13\" class=\"data row7 col13\" >38.90</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f7185b47fa0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "columns_used=['date','pmer','tend','cod_tend','dd','ff','td','u','ww','pres','rafper','per','rr1','rr3','tc']\n",
-    "\n",
-    "# ---- Drop unused columns\n",
-    "\n",
-    "to_drop = df.columns.difference(columns_used)\n",
-    "df.drop( to_drop, axis=1, inplace=True)\n",
-    "\n",
-    "# ---- Show all of that\n",
-    "\n",
-    "pwk.subtitle('Our selected columns :')\n",
-    "display(df.head(20))\n",
-    "\n",
-    "pwk.subtitle('Few statistics :')\n",
-    "display(df.describe().style.format('{:.2f}'))\n",
-    "\n",
-    "# ---- 'per' column is constant, we can drop it\n",
-    "\n",
-    "df.drop(['per'],axis=1,inplace=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.2 - Cleanup dataset\n",
-    "Let's sort it and cook up some NaN values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**Before :**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: left;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>pmer</th>\n",
-       "      <th>tend</th>\n",
-       "      <th>cod_tend</th>\n",
-       "      <th>dd</th>\n",
-       "      <th>ff</th>\n",
-       "      <th>td</th>\n",
-       "      <th>u</th>\n",
-       "      <th>ww</th>\n",
-       "      <th>pres</th>\n",
-       "      <th>rafper</th>\n",
-       "      <th>rr1</th>\n",
-       "      <th>rr3</th>\n",
-       "      <th>tc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>396</th>\n",
-       "      <td>2010-02-19T16:00:00+01:00</td>\n",
-       "      <td>99760.0</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>330.0</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>275.85</td>\n",
-       "      <td>79.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>96890.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>434</th>\n",
-       "      <td>2010-02-24T10:00:00+01:00</td>\n",
-       "      <td>100310.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>279.25</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>97470.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>9.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>477</th>\n",
-       "      <td>2010-03-01T19:00:00+01:00</td>\n",
-       "      <td>101400.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>340.0</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>275.45</td>\n",
-       "      <td>61.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98520.0</td>\n",
-       "      <td>5.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>734</th>\n",
-       "      <td>2010-04-03T02:00:00+02:00</td>\n",
-       "      <td>101550.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>7.7</td>\n",
-       "      <td>277.55</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98680.0</td>\n",
-       "      <td>12.3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>10.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1061</th>\n",
-       "      <td>2010-05-13T23:00:00+02:00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>330.0</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98220.0</td>\n",
-       "      <td>7.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>9.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1063</th>\n",
-       "      <td>2010-05-14T05:00:00+02:00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-50.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>4.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98110.0</td>\n",
-       "      <td>7.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1064</th>\n",
-       "      <td>2010-05-14T08:00:00+02:00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98110.0</td>\n",
-       "      <td>6.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2268</th>\n",
-       "      <td>2010-10-11T20:00:00+02:00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98060.0</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2269</th>\n",
-       "      <td>2010-10-11T23:00:00+02:00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>130.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98190.0</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2270</th>\n",
-       "      <td>2010-10-12T02:00:00+02:00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98260.0</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     date                        pmer      tend   cod_tend  dd     ff   \\\n",
-       "396   2010-02-19T16:00:00+01:00   99760.0  180.0  3.0       330.0  4.6   \n",
-       "434   2010-02-24T10:00:00+01:00  100310.0   60.0  1.0         NaN  NaN   \n",
-       "477   2010-03-01T19:00:00+01:00  101400.0    NaN  NaN       340.0  2.6   \n",
-       "734   2010-04-03T02:00:00+02:00  101550.0   50.0  0.0       190.0  7.7   \n",
-       "1061  2010-05-13T23:00:00+02:00       NaN   60.0  2.0       330.0  4.6   \n",
-       "1063  2010-05-14T05:00:00+02:00       NaN  -50.0  5.0       350.0  4.1   \n",
-       "1064  2010-05-14T08:00:00+02:00       NaN    0.0  5.0       350.0  4.6   \n",
-       "2268  2010-10-11T20:00:00+02:00       NaN  150.0  2.0        10.0  1.0   \n",
-       "2269  2010-10-11T23:00:00+02:00       NaN  130.0  3.0        80.0  1.0   \n",
-       "2270  2010-10-12T02:00:00+02:00       NaN   70.0  1.0         0.0  0.0   \n",
-       "\n",
-       "      td      u     ww    pres     rafper  rr1  rr3  tc    \n",
-       "396   275.85  79.0  21.0  96890.0   NaN    0.0  1.0   6.1  \n",
-       "434   279.25  77.0   2.0  97470.0   NaN    0.2  0.2   9.9  \n",
-       "477   275.45  61.0   2.0  98520.0   5.7    0.0  NaN   9.4  \n",
-       "734   277.55  64.0   2.0  98680.0  12.3    NaN  NaN  10.9  \n",
-       "1061     NaN   NaN   2.0  98220.0   7.7    0.0  0.0   9.9  \n",
-       "1063     NaN   NaN   2.0  98110.0   7.2    0.0  0.0   8.1  \n",
-       "1064     NaN   NaN   2.0  98110.0   6.7    0.0  0.0   8.1  \n",
-       "2268     NaN   NaN   2.0  98060.0   3.1    NaN  NaN   NaN  \n",
-       "2269     NaN   NaN   2.0  98190.0   2.6    NaN  NaN   NaN  \n",
-       "2270     NaN   NaN   2.0  98260.0   1.5    NaN  NaN   NaN  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**After :**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "        vertical-align: top;\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: left;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>pmer</th>\n",
-       "      <th>tend</th>\n",
-       "      <th>cod_tend</th>\n",
-       "      <th>dd</th>\n",
-       "      <th>ff</th>\n",
-       "      <th>td</th>\n",
-       "      <th>u</th>\n",
-       "      <th>ww</th>\n",
-       "      <th>pres</th>\n",
-       "      <th>rafper</th>\n",
-       "      <th>rr1</th>\n",
-       "      <th>rr3</th>\n",
-       "      <th>tc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>396</th>\n",
-       "      <td>2010-02-19T16:00:00+01:00</td>\n",
-       "      <td>99760.000000</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>330.0</td>\n",
-       "      <td>4.60</td>\n",
-       "      <td>275.85</td>\n",
-       "      <td>79.000000</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>96890.0</td>\n",
-       "      <td>8.25</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>434</th>\n",
-       "      <td>2010-02-24T10:00:00+01:00</td>\n",
-       "      <td>100310.000000</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>170.0</td>\n",
-       "      <td>4.15</td>\n",
-       "      <td>279.25</td>\n",
-       "      <td>77.000000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>97470.0</td>\n",
-       "      <td>6.65</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>9.90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>477</th>\n",
-       "      <td>2010-03-01T19:00:00+01:00</td>\n",
-       "      <td>101400.000000</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>340.0</td>\n",
-       "      <td>2.60</td>\n",
-       "      <td>275.45</td>\n",
-       "      <td>61.000000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98520.0</td>\n",
-       "      <td>5.70</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>9.40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>734</th>\n",
-       "      <td>2010-04-03T02:00:00+02:00</td>\n",
-       "      <td>101550.000000</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>7.70</td>\n",
-       "      <td>277.55</td>\n",
-       "      <td>64.000000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98680.0</td>\n",
-       "      <td>12.30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1061</th>\n",
-       "      <td>2010-05-13T23:00:00+02:00</td>\n",
-       "      <td>101020.000000</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>330.0</td>\n",
-       "      <td>4.60</td>\n",
-       "      <td>281.25</td>\n",
-       "      <td>86.500000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98220.0</td>\n",
-       "      <td>7.70</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>9.90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1063</th>\n",
-       "      <td>2010-05-14T05:00:00+02:00</td>\n",
-       "      <td>101040.000000</td>\n",
-       "      <td>-50.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>4.10</td>\n",
-       "      <td>279.15</td>\n",
-       "      <td>80.666667</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98110.0</td>\n",
-       "      <td>7.20</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1064</th>\n",
-       "      <td>2010-05-14T08:00:00+02:00</td>\n",
-       "      <td>101040.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>4.60</td>\n",
-       "      <td>279.35</td>\n",
-       "      <td>79.333333</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98110.0</td>\n",
-       "      <td>6.70</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2268</th>\n",
-       "      <td>2010-10-11T20:00:00+02:00</td>\n",
-       "      <td>100786.666667</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>284.75</td>\n",
-       "      <td>83.333333</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98060.0</td>\n",
-       "      <td>3.10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>14.45</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2269</th>\n",
-       "      <td>2010-10-11T23:00:00+02:00</td>\n",
-       "      <td>100863.333333</td>\n",
-       "      <td>130.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>284.45</td>\n",
-       "      <td>84.666667</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98190.0</td>\n",
-       "      <td>2.60</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13.90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2270</th>\n",
-       "      <td>2010-10-12T02:00:00+02:00</td>\n",
-       "      <td>100940.000000</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>284.15</td>\n",
-       "      <td>86.000000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98260.0</td>\n",
-       "      <td>1.50</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13.35</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     date                        pmer           tend   cod_tend  dd     ff    \\\n",
-       "396   2010-02-19T16:00:00+01:00   99760.000000  180.0  3.0       330.0  4.60   \n",
-       "434   2010-02-24T10:00:00+01:00  100310.000000   60.0  1.0       170.0  4.15   \n",
-       "477   2010-03-01T19:00:00+01:00  101400.000000  195.0  4.0       340.0  2.60   \n",
-       "734   2010-04-03T02:00:00+02:00  101550.000000   50.0  0.0       190.0  7.70   \n",
-       "1061  2010-05-13T23:00:00+02:00  101020.000000   60.0  2.0       330.0  4.60   \n",
-       "1063  2010-05-14T05:00:00+02:00  101040.000000  -50.0  5.0       350.0  4.10   \n",
-       "1064  2010-05-14T08:00:00+02:00  101040.000000    0.0  5.0       350.0  4.60   \n",
-       "2268  2010-10-11T20:00:00+02:00  100786.666667  150.0  2.0        10.0  1.00   \n",
-       "2269  2010-10-11T23:00:00+02:00  100863.333333  130.0  3.0        80.0  1.00   \n",
-       "2270  2010-10-12T02:00:00+02:00  100940.000000   70.0  1.0         0.0  0.00   \n",
-       "\n",
-       "      td      u          ww    pres     rafper  rr1  rr3  tc     \n",
-       "396   275.85  79.000000  21.0  96890.0   8.25   0.0  1.0   6.10  \n",
-       "434   279.25  77.000000   2.0  97470.0   6.65   0.2  0.2   9.90  \n",
-       "477   275.45  61.000000   2.0  98520.0   5.70   0.0  0.5   9.40  \n",
-       "734   277.55  64.000000   2.0  98680.0  12.30   0.0  0.0  10.90  \n",
-       "1061  281.25  86.500000   2.0  98220.0   7.70   0.0  0.0   9.90  \n",
-       "1063  279.15  80.666667   2.0  98110.0   7.20   0.0  0.0   8.10  \n",
-       "1064  279.35  79.333333   2.0  98110.0   6.70   0.0  0.0   8.10  \n",
-       "2268  284.75  83.333333   2.0  98060.0   3.10   0.0  0.0  14.45  \n",
-       "2269  284.45  84.666667   2.0  98190.0   2.60   0.0  0.0  13.90  \n",
-       "2270  284.15  86.000000   2.0  98260.0   1.50   0.0  0.0  13.35  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ---- First of all, we have to sort on the date\n",
-    "\n",
-    "df.sort_values(['date'],  inplace=True)\n",
-    "df.reset_index(drop=True, inplace=True)\n",
-    "\n",
-    "# ---- Before : Lines with NaN\n",
-    "\n",
-    "na_rows=df.isna().any(axis=1)\n",
-    "pwk.subtitle('Before :')\n",
-    "display( df[na_rows].head(10) )\n",
-    "\n",
-    "# ---- Nice interpolation for plugging holes\n",
-    "\n",
-    "df.interpolate(inplace=True)\n",
-    "\n",
-    "# ---- After\n",
-    "\n",
-    "pwk.subtitle('After :')\n",
-    "display(df[na_rows].head(10))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 5 - About our enhanced dataset\n",
-    "### 5.1 - Summarize it"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**Dataset columns :**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "<style  type=\"text/css\" >\n",
-       "#T_cbb81_row0_col0,#T_cbb81_row0_col1,#T_cbb81_row0_col2,#T_cbb81_row1_col0,#T_cbb81_row1_col1,#T_cbb81_row1_col2,#T_cbb81_row2_col0,#T_cbb81_row2_col1,#T_cbb81_row2_col2,#T_cbb81_row3_col0,#T_cbb81_row3_col1,#T_cbb81_row3_col2,#T_cbb81_row4_col0,#T_cbb81_row4_col1,#T_cbb81_row4_col2,#T_cbb81_row5_col0,#T_cbb81_row5_col1,#T_cbb81_row5_col2,#T_cbb81_row6_col0,#T_cbb81_row6_col1,#T_cbb81_row6_col2,#T_cbb81_row7_col0,#T_cbb81_row7_col1,#T_cbb81_row7_col2,#T_cbb81_row8_col0,#T_cbb81_row8_col1,#T_cbb81_row8_col2,#T_cbb81_row9_col0,#T_cbb81_row9_col1,#T_cbb81_row9_col2,#T_cbb81_row10_col0,#T_cbb81_row10_col1,#T_cbb81_row10_col2,#T_cbb81_row11_col0,#T_cbb81_row11_col1,#T_cbb81_row11_col2,#T_cbb81_row12_col0,#T_cbb81_row12_col1,#T_cbb81_row12_col2,#T_cbb81_row13_col0,#T_cbb81_row13_col1,#T_cbb81_row13_col2{\n",
-       "            text-align:  left;\n",
-       "        }</style><table id=\"T_cbb81_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Columns</th>        <th class=\"col_heading level0 col1\" >Description</th>        <th class=\"col_heading level0 col2\" >Na</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_cbb81_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "                        <td id=\"T_cbb81_row0_col0\" class=\"data row0 col0\" >date</td>\n",
-       "                        <td id=\"T_cbb81_row0_col1\" class=\"data row0 col1\" >Date</td>\n",
-       "                        <td id=\"T_cbb81_row0_col2\" class=\"data row0 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_cbb81_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
-       "                        <td id=\"T_cbb81_row1_col0\" class=\"data row1 col0\" >pmer</td>\n",
-       "                        <td id=\"T_cbb81_row1_col1\" class=\"data row1 col1\" >Pression au niveau mer</td>\n",
-       "                        <td id=\"T_cbb81_row1_col2\" class=\"data row1 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_cbb81_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "                        <td id=\"T_cbb81_row2_col0\" class=\"data row2 col0\" >tend</td>\n",
-       "                        <td id=\"T_cbb81_row2_col1\" class=\"data row2 col1\" >Variation de pression en 3 heures</td>\n",
-       "                        <td id=\"T_cbb81_row2_col2\" class=\"data row2 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_cbb81_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
-       "                        <td id=\"T_cbb81_row3_col0\" class=\"data row3 col0\" >cod_tend</td>\n",
-       "                        <td id=\"T_cbb81_row3_col1\" class=\"data row3 col1\" >Type de tendance barométrique</td>\n",
-       "                        <td id=\"T_cbb81_row3_col2\" class=\"data row3 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_cbb81_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "                        <td id=\"T_cbb81_row4_col0\" class=\"data row4 col0\" >dd</td>\n",
-       "                        <td id=\"T_cbb81_row4_col1\" class=\"data row4 col1\" >Direction du vent moyen 10 mn</td>\n",
-       "                        <td id=\"T_cbb81_row4_col2\" class=\"data row4 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_cbb81_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
-       "                        <td id=\"T_cbb81_row5_col0\" class=\"data row5 col0\" >ff</td>\n",
-       "                        <td id=\"T_cbb81_row5_col1\" class=\"data row5 col1\" >Vitesse du vent moyen 10 mn</td>\n",
-       "                        <td id=\"T_cbb81_row5_col2\" class=\"data row5 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_cbb81_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "                        <td id=\"T_cbb81_row6_col0\" class=\"data row6 col0\" >td</td>\n",
-       "                        <td id=\"T_cbb81_row6_col1\" class=\"data row6 col1\" >Point de rosée</td>\n",
-       "                        <td id=\"T_cbb81_row6_col2\" class=\"data row6 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_cbb81_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "                        <td id=\"T_cbb81_row7_col0\" class=\"data row7 col0\" >u</td>\n",
-       "                        <td id=\"T_cbb81_row7_col1\" class=\"data row7 col1\" >Humidité</td>\n",
-       "                        <td id=\"T_cbb81_row7_col2\" class=\"data row7 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_cbb81_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
-       "                        <td id=\"T_cbb81_row8_col0\" class=\"data row8 col0\" >ww</td>\n",
-       "                        <td id=\"T_cbb81_row8_col1\" class=\"data row8 col1\" >Temps présent</td>\n",
-       "                        <td id=\"T_cbb81_row8_col2\" class=\"data row8 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_cbb81_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
-       "                        <td id=\"T_cbb81_row9_col0\" class=\"data row9 col0\" >pres</td>\n",
-       "                        <td id=\"T_cbb81_row9_col1\" class=\"data row9 col1\" >Pression station</td>\n",
-       "                        <td id=\"T_cbb81_row9_col2\" class=\"data row9 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_cbb81_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
-       "                        <td id=\"T_cbb81_row10_col0\" class=\"data row10 col0\" >rafper</td>\n",
-       "                        <td id=\"T_cbb81_row10_col1\" class=\"data row10 col1\" >Rafales sur une période</td>\n",
-       "                        <td id=\"T_cbb81_row10_col2\" class=\"data row10 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_cbb81_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
-       "                        <td id=\"T_cbb81_row11_col0\" class=\"data row11 col0\" >rr1</td>\n",
-       "                        <td id=\"T_cbb81_row11_col1\" class=\"data row11 col1\" >Précipitations dans la dernière heure</td>\n",
-       "                        <td id=\"T_cbb81_row11_col2\" class=\"data row11 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_cbb81_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
-       "                        <td id=\"T_cbb81_row12_col0\" class=\"data row12 col0\" >rr3</td>\n",
-       "                        <td id=\"T_cbb81_row12_col1\" class=\"data row12 col1\" >Précipitations dans les 3 dernières heures</td>\n",
-       "                        <td id=\"T_cbb81_row12_col2\" class=\"data row12 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_cbb81_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
-       "                        <td id=\"T_cbb81_row13_col0\" class=\"data row13 col0\" >tc</td>\n",
-       "                        <td id=\"T_cbb81_row13_col1\" class=\"data row13 col1\" >Température (°C)</td>\n",
-       "                        <td id=\"T_cbb81_row13_col2\" class=\"data row13 col2\" >0</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f7183f120d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**Have a look :**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
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-       "    <tr style=\"text-align: left;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>pmer</th>\n",
-       "      <th>tend</th>\n",
-       "      <th>cod_tend</th>\n",
-       "      <th>dd</th>\n",
-       "      <th>ff</th>\n",
-       "      <th>td</th>\n",
-       "      <th>u</th>\n",
-       "      <th>ww</th>\n",
-       "      <th>pres</th>\n",
-       "      <th>rafper</th>\n",
-       "      <th>rr1</th>\n",
-       "      <th>rr3</th>\n",
-       "      <th>tc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>29145</th>\n",
-       "      <td>2020-02-24T13:00:00+01:00</td>\n",
-       "      <td>102380.0</td>\n",
-       "      <td>-220.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>281.15</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>99540.0</td>\n",
-       "      <td>3.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>16.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29146</th>\n",
-       "      <td>2020-02-24T16:00:00+01:00</td>\n",
-       "      <td>101990.0</td>\n",
-       "      <td>-350.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>110.0</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>281.55</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>99190.0</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19.1</td>\n",
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-       "    <tr>\n",
-       "      <th>29147</th>\n",
-       "      <td>2020-02-24T19:00:00+01:00</td>\n",
-       "      <td>101800.0</td>\n",
-       "      <td>-220.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>2.9</td>\n",
-       "      <td>280.05</td>\n",
-       "      <td>55.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>98970.0</td>\n",
-       "      <td>4.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>15.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29148</th>\n",
-       "      <td>2020-02-24T22:00:00+01:00</td>\n",
-       "      <td>101740.0</td>\n",
-       "      <td>-80.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>170.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>280.35</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98890.0</td>\n",
-       "      <td>4.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29149</th>\n",
-       "      <td>2020-02-25T01:00:00+01:00</td>\n",
-       "      <td>101640.0</td>\n",
-       "      <td>-150.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>170.0</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>278.85</td>\n",
-       "      <td>83.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98740.0</td>\n",
-       "      <td>4.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29150</th>\n",
-       "      <td>2020-02-25T04:00:00+01:00</td>\n",
-       "      <td>101450.0</td>\n",
-       "      <td>-200.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>3.7</td>\n",
-       "      <td>277.75</td>\n",
-       "      <td>87.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98540.0</td>\n",
-       "      <td>4.8</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29151</th>\n",
-       "      <td>2020-02-25T07:00:00+01:00</td>\n",
-       "      <td>101530.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>276.95</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>98600.0</td>\n",
-       "      <td>6.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29152</th>\n",
-       "      <td>2020-02-25T10:00:00+01:00</td>\n",
-       "      <td>101490.0</td>\n",
-       "      <td>-20.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>200.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>277.55</td>\n",
-       "      <td>87.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>98580.0</td>\n",
-       "      <td>5.5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29153</th>\n",
-       "      <td>2020-02-25T13:00:00+01:00</td>\n",
-       "      <td>101330.0</td>\n",
-       "      <td>-140.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>3.8</td>\n",
-       "      <td>278.95</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>98440.0</td>\n",
-       "      <td>7.1</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>8.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29154</th>\n",
-       "      <td>2020-02-25T16:00:00+01:00</td>\n",
-       "      <td>100990.0</td>\n",
-       "      <td>-290.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>4.4</td>\n",
-       "      <td>279.55</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>98150.0</td>\n",
-       "      <td>7.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29155</th>\n",
-       "      <td>2020-02-25T19:00:00+01:00</td>\n",
-       "      <td>100910.0</td>\n",
-       "      <td>-90.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>260.0</td>\n",
-       "      <td>4.3</td>\n",
-       "      <td>278.95</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>98060.0</td>\n",
-       "      <td>8.4</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>11.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29156</th>\n",
-       "      <td>2020-02-25T22:00:00+01:00</td>\n",
-       "      <td>100980.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>280.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>273.65</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98120.0</td>\n",
-       "      <td>11.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29157</th>\n",
-       "      <td>2020-02-26T01:00:00+01:00</td>\n",
-       "      <td>101040.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>275.65</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>98150.0</td>\n",
-       "      <td>10.7</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>7.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29158</th>\n",
-       "      <td>2020-02-26T04:00:00+01:00</td>\n",
-       "      <td>101060.0</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>275.85</td>\n",
-       "      <td>86.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>98140.0</td>\n",
-       "      <td>13.6</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>4.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29159</th>\n",
-       "      <td>2020-02-26T07:00:00+01:00</td>\n",
-       "      <td>100940.0</td>\n",
-       "      <td>-110.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>274.85</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>98030.0</td>\n",
-       "      <td>7.4</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>5.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29160</th>\n",
-       "      <td>2020-02-26T10:00:00+01:00</td>\n",
-       "      <td>101100.0</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>6.8</td>\n",
-       "      <td>274.45</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98190.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29161</th>\n",
-       "      <td>2020-02-26T13:00:00+01:00</td>\n",
-       "      <td>101200.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>310.0</td>\n",
-       "      <td>10.3</td>\n",
-       "      <td>270.55</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98290.0</td>\n",
-       "      <td>19.5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>6.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29162</th>\n",
-       "      <td>2020-02-26T16:00:00+01:00</td>\n",
-       "      <td>101290.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>310.0</td>\n",
-       "      <td>8.9</td>\n",
-       "      <td>270.55</td>\n",
-       "      <td>47.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98390.0</td>\n",
-       "      <td>14.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29163</th>\n",
-       "      <td>2020-02-26T19:00:00+01:00</td>\n",
-       "      <td>101550.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>300.0</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>272.05</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98620.0</td>\n",
-       "      <td>7.4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29164</th>\n",
-       "      <td>2020-02-26T22:00:00+01:00</td>\n",
-       "      <td>101780.0</td>\n",
-       "      <td>200.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>274.05</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98820.0</td>\n",
-       "      <td>8.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      date                        pmer      tend   cod_tend  dd     ff    \\\n",
-       "29145  2020-02-24T13:00:00+01:00  102380.0 -220.0  8.0       120.0   1.6   \n",
-       "29146  2020-02-24T16:00:00+01:00  101990.0 -350.0  6.0       110.0   1.6   \n",
-       "29147  2020-02-24T19:00:00+01:00  101800.0 -220.0  6.0       150.0   2.9   \n",
-       "29148  2020-02-24T22:00:00+01:00  101740.0  -80.0  6.0       170.0   1.8   \n",
-       "29149  2020-02-25T01:00:00+01:00  101640.0 -150.0  8.0       170.0   2.5   \n",
-       "29150  2020-02-25T04:00:00+01:00  101450.0 -200.0  6.0       150.0   3.7   \n",
-       "29151  2020-02-25T07:00:00+01:00  101530.0   60.0  3.0        30.0   4.0   \n",
-       "29152  2020-02-25T10:00:00+01:00  101490.0  -20.0  8.0       200.0   1.8   \n",
-       "29153  2020-02-25T13:00:00+01:00  101330.0 -140.0  8.0       150.0   3.8   \n",
-       "29154  2020-02-25T16:00:00+01:00  100990.0 -290.0  6.0       140.0   4.4   \n",
-       "29155  2020-02-25T19:00:00+01:00  100910.0  -90.0  5.0       260.0   4.3   \n",
-       "29156  2020-02-25T22:00:00+01:00  100980.0   60.0  3.0       280.0   8.0   \n",
-       "29157  2020-02-26T01:00:00+01:00  101040.0   30.0  2.0       230.0   2.8   \n",
-       "29158  2020-02-26T04:00:00+01:00  101060.0  -10.0  8.0       230.0   3.0   \n",
-       "29159  2020-02-26T07:00:00+01:00  100940.0 -110.0  6.0       210.0   3.3   \n",
-       "29160  2020-02-26T10:00:00+01:00  101100.0  160.0  3.0       230.0   6.8   \n",
-       "29161  2020-02-26T13:00:00+01:00  101200.0  100.0  3.0       310.0  10.3   \n",
-       "29162  2020-02-26T16:00:00+01:00  101290.0  100.0  3.0       310.0   8.9   \n",
-       "29163  2020-02-26T19:00:00+01:00  101550.0  230.0  2.0       300.0   2.8   \n",
-       "29164  2020-02-26T22:00:00+01:00  101780.0  200.0  2.0        50.0   3.2   \n",
-       "\n",
-       "       td      u     ww    pres     rafper  rr1  rr3  tc    \n",
-       "29145  281.15  59.0   0.0  99540.0   3.7    0.0  0.0  16.0  \n",
-       "29146  281.55  50.0   3.0  99190.0   3.3    0.0  0.0  19.1  \n",
-       "29147  280.05  55.0   3.0  98970.0   4.1    0.0  0.0  15.9  \n",
-       "29148  280.35  67.0   2.0  98890.0   4.3    0.0  0.0  13.2  \n",
-       "29149  278.85  83.0   2.0  98740.0   4.7    0.0  0.0   8.4  \n",
-       "29150  277.75  87.0   2.0  98540.0   4.8    0.0  0.0   6.6  \n",
-       "29151  276.95  92.0   3.0  98600.0   6.1    0.0  0.0   5.0  \n",
-       "29152  277.55  87.0   3.0  98580.0   5.5    0.0  0.0   6.4  \n",
-       "29153  278.95  85.0  21.0  98440.0   7.1    0.6  2.0   8.2  \n",
-       "29154  279.55  69.0   3.0  98150.0   7.2    0.0  0.0  11.9  \n",
-       "29155  278.95  69.0  25.0  98060.0   8.4   -0.1 -0.1  11.3  \n",
-       "29156  273.65  51.0   1.0  98120.0  11.3    0.0  0.0  10.2  \n",
-       "29157  275.65  69.0  25.0  98150.0  10.7   -0.1 -0.1   7.8  \n",
-       "29158  275.85  86.0  25.0  98140.0  13.6    0.4  1.8   4.8  \n",
-       "29159  274.85  78.0  21.0  98030.0   7.4   -0.1 -0.1   5.2  \n",
-       "29160  274.45  74.0   1.0  98190.0  10.0    0.0  0.0   5.6  \n",
-       "29161  270.55  52.0   1.0  98290.0  19.5    0.0 -0.1   6.6  \n",
-       "29162  270.55  47.0   1.0  98390.0  14.3    0.0  0.0   8.0  \n",
-       "29163  272.05  64.0   1.0  98620.0   7.4    0.0  0.0   5.2  \n",
-       "29164  274.05  84.0   1.0  98820.0   8.2    0.0  0.0   3.3  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shape is :  (29165, 14)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ---- Count the na values by columns\n",
-    "dataset_na    = df.isna().sum().tolist()\n",
-    "dataset_cols  = df.columns.tolist()\n",
-    "dataset_desc  = [ code2desc[c] for c in dataset_cols ]\n",
-    "\n",
-    "# ---- Show all of that\n",
-    "df_desc=pd.DataFrame({'Columns':dataset_cols, 'Description':dataset_desc, 'Na':dataset_na})\n",
-    "pwk.subtitle('Dataset columns :')\n",
-    "display(df_desc.style.set_properties(**{'text-align': 'left'}))\n",
-    "\n",
-    "pwk.subtitle('Have a look :')\n",
-    "display(df.tail(20))\n",
-    "print('Shape is : ', df.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 5.2 - Have a look (1 month)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x1440 with 13 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "i=random.randint(0,len(df)-240)\n",
-    "df.iloc[i:i+240].plot(subplots=True, fontsize=12, figsize=(16,20))\n",
-    "pwk.save_fig('01-one-month')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 6 - Save it"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset saved. (3.0 Mo)\n",
-      "Synop description saved.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ---- Save it\n",
-    "#\n",
-    "pwk.mkdir(output_dir)\n",
-    "\n",
-    "filedata = f'{output_dir}/{dataset_name}'\n",
-    "filedesc = f'{output_dir}/{dataset_desc}'\n",
-    "\n",
-    "df.to_csv(filedata, sep=';', index=False)\n",
-    "size=os.path.getsize(filedata)/(1024*1024)\n",
-    "print(f'Dataset saved. ({size:0.1f} Mo)')\n",
-    "\n",
-    "with open(filedesc, 'w', encoding='utf-8') as f:\n",
-    "    json.dump(code2desc, f, indent=4)\n",
-    "print('Synop description saved.')\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "End time is : Friday 05 March 2021, 16:36:30\n",
-      "Duration is : 00:24:03 093ms\n",
-      "This notebook ends here\n"
-     ]
-    }
-   ],
-   "source": [
-    "pwk.end()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/SYNOP/01-Preparation-of-data==done==.ipynb b/SYNOP/01-Preparation-of-data==done==.ipynb
deleted file mode 100644
index 5700958c720d66a9eb2bee0682ea9b214b29c107..0000000000000000000000000000000000000000
--- a/SYNOP/01-Preparation-of-data==done==.ipynb
+++ /dev/null
@@ -1,3203 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
-    "\n",
-    "# <!-- TITLE --> [SYNOP1] - Preparation of data\n",
-    "<!-- DESC --> Episode 1 : Data analysis and preparation of a meteorological dataset (SYNOP)\n",
-    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
-    "\n",
-    "## Objectives :\n",
-    " - Undestand the data\n",
-    " - cleanup a usable dataset\n",
-    "\n",
-    "\n",
-    "SYNOP meteorological data, available at: https://public.opendatasoft.com  \n",
-    "This dataset contains a set of measurements (temperature, pressure, ...) made every 3 hours at the LYS airport.  \n",
-    "The objective will be to predict the evolution of the weather !\n",
-    "\n",
-    "## What we're going to do :\n",
-    "\n",
-    " - Read the data\n",
-    " - Cleanup and build a usable dataset\n",
-    "\n",
-    "## Step 1 - Import and init\n",
-    "### 1.1 - Python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:29:55.915134Z",
-     "iopub.status.busy": "2021-03-01T19:29:55.914653Z",
-     "iopub.status.idle": "2021-03-01T19:29:58.693994Z",
-     "shell.execute_reply": "2021-03-01T19:29:58.694485Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       "\n",
-       "div.warn {    \n",
-       "    background-color: #fcf2f2;\n",
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-       "    border-left: 5px solid #dfb5b4;\n",
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-       "    font-weight: bold;\n",
-       "    font-size: 1.1em;;\n",
-       "    }\n",
-       "\n",
-       "\n",
-       "\n",
-       "div.nota {    \n",
-       "    background-color: #DAFFDE;\n",
-       "    border-left: 5px solid #92CC99;\n",
-       "    padding: 0.5em;\n",
-       "    }\n",
-       "\n",
-       "div.todo:before { content:url(data:image/svg+xml;base64,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-       "    margin-right:20px;\n",
-       "    margin-top:-20px;\n",
-       "    margin-bottom:20px;\n",
-       "}\n",
-       "div.todo{\n",
-       "    font-weight: bold;\n",
-       "    font-size: 1.1em;\n",
-       "    margin-top:40px;\n",
-       "}\n",
-       "div.todo ul{\n",
-       "    margin: 0.2em;\n",
-       "}\n",
-       "div.todo li{\n",
-       "    margin-left:60px;\n",
-       "    margin-top:0;\n",
-       "    margin-bottom:0;\n",
-       "}\n",
-       "\n",
-       "div .comment{\n",
-       "    font-size:0.8em;\n",
-       "    color:#696969;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "</style>\n",
-       "\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**FIDLE 2020 - Practical Work Module**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Version              : 2.0.17\n",
-      "Notebook id          : SYNOP1\n",
-      "Run time             : Monday 01 March 2021, 20:29:58\n",
-      "TensorFlow version   : 2.4.0\n",
-      "Keras version        : 2.4.0\n",
-      "Datasets dir         : /gpfswork/rech/mlh/uja62cb/datasets\n",
-      "Run dir              : ./run\n",
-      "Update keras cache   : False\n",
-      "Save figs            : True\n",
-      "Path figs            : ./run/figs\n"
-     ]
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from tensorflow import keras\n",
-    "from tensorflow.keras.callbacks import TensorBoard\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import pandas as pd\n",
-    "import h5py, json\n",
-    "import os,time,sys\n",
-    "import math, random\n",
-    "\n",
-    "from importlib import reload\n",
-    "\n",
-    "sys.path.append('..')\n",
-    "import fidle.pwk as pwk\n",
-    "\n",
-    "datasets_dir = pwk.init('SYNOP1')\n",
-    "\n",
-    "pd.set_option('display.max_rows',200)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2 - Read the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:29:58.697722Z",
-     "iopub.status.busy": "2021-03-01T19:29:58.697257Z",
-     "iopub.status.idle": "2021-03-01T19:29:58.698889Z",
-     "shell.execute_reply": "2021-03-01T19:29:58.699366Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data_filename   = 'origine/donnees-synop-essentielles-omm-LYS.csv'\n",
-    "schema_filename = 'origine/schema.json'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.1 - Read columns code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:29:58.702915Z",
-     "iopub.status.busy": "2021-03-01T19:29:58.702436Z",
-     "iopub.status.idle": "2021-03-01T19:29:58.717980Z",
-     "shell.execute_reply": "2021-03-01T19:29:58.718453Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "with open(f'{datasets_dir}/SYNOP/{schema_filename}','r') as json_file:\n",
-    "    schema = json.load(json_file)\n",
-    "\n",
-    "synop_codes=list( schema['definitions']['donnees-synop-essentielles-omm_records']['properties']['fields']['properties'].keys() )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.2 - Read data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:29:58.755116Z",
-     "iopub.status.busy": "2021-03-01T19:29:58.754639Z",
-     "iopub.status.idle": "2021-03-01T19:29:59.234724Z",
-     "shell.execute_reply": "2021-03-01T19:29:59.235229Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**Raw data :**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ID OMM station</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Pression au niveau mer</th>\n",
-       "      <th>Variation de pression en 3 heures</th>\n",
-       "      <th>Type de tendance barométrique</th>\n",
-       "      <th>Direction du vent moyen 10 mn</th>\n",
-       "      <th>Vitesse du vent moyen 10 mn</th>\n",
-       "      <th>Température</th>\n",
-       "      <th>Point de rosée</th>\n",
-       "      <th>Humidité</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Longitude</th>\n",
-       "      <th>Latitude</th>\n",
-       "      <th>communes (name)</th>\n",
-       "      <th>communes (code)</th>\n",
-       "      <th>EPCI (name)</th>\n",
-       "      <th>EPCI (code)</th>\n",
-       "      <th>department (name)</th>\n",
-       "      <th>department (code)</th>\n",
-       "      <th>region (name)</th>\n",
-       "      <th>region (code)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>29155</th>\n",
-       "      <td>7481</td>\n",
-       "      <td>2019-11-16T01:00:00+01:00</td>\n",
-       "      <td>100640.0</td>\n",
-       "      <td>130.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>272.75</td>\n",
-       "      <td>272.75</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.077833</td>\n",
-       "      <td>45.7265</td>\n",
-       "      <td>Colombier-Saugnieu</td>\n",
-       "      <td>69299</td>\n",
-       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
-       "      <td>246900575</td>\n",
-       "      <td>Rhône</td>\n",
-       "      <td>69</td>\n",
-       "      <td>Auvergne-Rhône-Alpes</td>\n",
-       "      <td>84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29156</th>\n",
-       "      <td>7481</td>\n",
-       "      <td>2019-11-16T19:00:00+01:00</td>\n",
-       "      <td>101090.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>130.0</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>276.95</td>\n",
-       "      <td>274.65</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.077833</td>\n",
-       "      <td>45.7265</td>\n",
-       "      <td>Colombier-Saugnieu</td>\n",
-       "      <td>69299</td>\n",
-       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
-       "      <td>246900575</td>\n",
-       "      <td>Rhône</td>\n",
-       "      <td>69</td>\n",
-       "      <td>Auvergne-Rhône-Alpes</td>\n",
-       "      <td>84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29157</th>\n",
-       "      <td>7481</td>\n",
-       "      <td>2020-02-12T16:00:00+01:00</td>\n",
-       "      <td>102460.0</td>\n",
-       "      <td>-180.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>360.0</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>283.45</td>\n",
-       "      <td>271.75</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.077833</td>\n",
-       "      <td>45.7265</td>\n",
-       "      <td>Colombier-Saugnieu</td>\n",
-       "      <td>69299</td>\n",
-       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
-       "      <td>246900575</td>\n",
-       "      <td>Rhône</td>\n",
-       "      <td>69</td>\n",
-       "      <td>Auvergne-Rhône-Alpes</td>\n",
-       "      <td>84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29158</th>\n",
-       "      <td>7481</td>\n",
-       "      <td>2020-02-13T04:00:00+01:00</td>\n",
-       "      <td>102100.0</td>\n",
-       "      <td>-240.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>4.9</td>\n",
-       "      <td>274.75</td>\n",
-       "      <td>271.15</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.077833</td>\n",
-       "      <td>45.7265</td>\n",
-       "      <td>Colombier-Saugnieu</td>\n",
-       "      <td>69299</td>\n",
-       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
-       "      <td>246900575</td>\n",
-       "      <td>Rhône</td>\n",
-       "      <td>69</td>\n",
-       "      <td>Auvergne-Rhône-Alpes</td>\n",
-       "      <td>84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29159</th>\n",
-       "      <td>7481</td>\n",
-       "      <td>2020-02-14T01:00:00+01:00</td>\n",
-       "      <td>102080.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>280.0</td>\n",
-       "      <td>4.5</td>\n",
-       "      <td>283.15</td>\n",
-       "      <td>276.15</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.077833</td>\n",
-       "      <td>45.7265</td>\n",
-       "      <td>Colombier-Saugnieu</td>\n",
-       "      <td>69299</td>\n",
-       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
-       "      <td>246900575</td>\n",
-       "      <td>Rhône</td>\n",
-       "      <td>69</td>\n",
-       "      <td>Auvergne-Rhône-Alpes</td>\n",
-       "      <td>84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29160</th>\n",
-       "      <td>7481</td>\n",
-       "      <td>2020-02-14T07:00:00+01:00</td>\n",
-       "      <td>102430.0</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>280.15</td>\n",
-       "      <td>278.45</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.077833</td>\n",
-       "      <td>45.7265</td>\n",
-       "      <td>Colombier-Saugnieu</td>\n",
-       "      <td>69299</td>\n",
-       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
-       "      <td>246900575</td>\n",
-       "      <td>Rhône</td>\n",
-       "      <td>69</td>\n",
-       "      <td>Auvergne-Rhône-Alpes</td>\n",
-       "      <td>84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29161</th>\n",
-       "      <td>7481</td>\n",
-       "      <td>2020-02-15T16:00:00+01:00</td>\n",
-       "      <td>102190.0</td>\n",
-       "      <td>-160.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>6.9</td>\n",
-       "      <td>290.15</td>\n",
-       "      <td>273.75</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.077833</td>\n",
-       "      <td>45.7265</td>\n",
-       "      <td>Colombier-Saugnieu</td>\n",
-       "      <td>69299</td>\n",
-       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
-       "      <td>246900575</td>\n",
-       "      <td>Rhône</td>\n",
-       "      <td>69</td>\n",
-       "      <td>Auvergne-Rhône-Alpes</td>\n",
-       "      <td>84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29162</th>\n",
-       "      <td>7481</td>\n",
-       "      <td>2020-01-25T22:00:00+01:00</td>\n",
-       "      <td>102030.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>4.9</td>\n",
-       "      <td>281.45</td>\n",
-       "      <td>278.55</td>\n",
-       "      <td>82.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.077833</td>\n",
-       "      <td>45.7265</td>\n",
-       "      <td>Colombier-Saugnieu</td>\n",
-       "      <td>69299</td>\n",
-       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
-       "      <td>246900575</td>\n",
-       "      <td>Rhône</td>\n",
-       "      <td>69</td>\n",
-       "      <td>Auvergne-Rhône-Alpes</td>\n",
-       "      <td>84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29163</th>\n",
-       "      <td>7481</td>\n",
-       "      <td>2020-01-26T19:00:00+01:00</td>\n",
-       "      <td>102010.0</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>170.0</td>\n",
-       "      <td>3.7</td>\n",
-       "      <td>282.85</td>\n",
-       "      <td>279.15</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.077833</td>\n",
-       "      <td>45.7265</td>\n",
-       "      <td>Colombier-Saugnieu</td>\n",
-       "      <td>69299</td>\n",
-       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
-       "      <td>246900575</td>\n",
-       "      <td>Rhône</td>\n",
-       "      <td>69</td>\n",
-       "      <td>Auvergne-Rhône-Alpes</td>\n",
-       "      <td>84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29164</th>\n",
-       "      <td>7481</td>\n",
-       "      <td>2020-02-08T19:00:00+01:00</td>\n",
-       "      <td>102540.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>283.75</td>\n",
-       "      <td>277.65</td>\n",
-       "      <td>66.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.077833</td>\n",
-       "      <td>45.7265</td>\n",
-       "      <td>Colombier-Saugnieu</td>\n",
-       "      <td>69299</td>\n",
-       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
-       "      <td>246900575</td>\n",
-       "      <td>Rhône</td>\n",
-       "      <td>69</td>\n",
-       "      <td>Auvergne-Rhône-Alpes</td>\n",
-       "      <td>84</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>10 rows × 81 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       ID OMM station                       Date  Pression au niveau mer  \\\n",
-       "29155            7481  2019-11-16T01:00:00+01:00                100640.0   \n",
-       "29156            7481  2019-11-16T19:00:00+01:00                101090.0   \n",
-       "29157            7481  2020-02-12T16:00:00+01:00                102460.0   \n",
-       "29158            7481  2020-02-13T04:00:00+01:00                102100.0   \n",
-       "29159            7481  2020-02-14T01:00:00+01:00                102080.0   \n",
-       "29160            7481  2020-02-14T07:00:00+01:00                102430.0   \n",
-       "29161            7481  2020-02-15T16:00:00+01:00                102190.0   \n",
-       "29162            7481  2020-01-25T22:00:00+01:00                102030.0   \n",
-       "29163            7481  2020-01-26T19:00:00+01:00                102010.0   \n",
-       "29164            7481  2020-02-08T19:00:00+01:00                102540.0   \n",
-       "\n",
-       "       Variation de pression en 3 heures  Type de tendance barométrique  \\\n",
-       "29155                              130.0                            1.0   \n",
-       "29156                               90.0                            3.0   \n",
-       "29157                             -180.0                            6.0   \n",
-       "29158                             -240.0                            8.0   \n",
-       "29159                              230.0                            1.0   \n",
-       "29160                              210.0                            2.0   \n",
-       "29161                             -160.0                            6.0   \n",
-       "29162                               20.0                            1.0   \n",
-       "29163                               80.0                            3.0   \n",
-       "29164                              150.0                            2.0   \n",
-       "\n",
-       "       Direction du vent moyen 10 mn  Vitesse du vent moyen 10 mn  \\\n",
-       "29155                          190.0                          1.0   \n",
-       "29156                          130.0                          3.5   \n",
-       "29157                          360.0                          2.3   \n",
-       "29158                          150.0                          4.9   \n",
-       "29159                          280.0                          4.5   \n",
-       "29160                          140.0                          3.4   \n",
-       "29161                          180.0                          6.9   \n",
-       "29162                          140.0                          4.9   \n",
-       "29163                          170.0                          3.7   \n",
-       "29164                          190.0                          6.2   \n",
-       "\n",
-       "       Température  Point de rosée  Humidité  ...  Longitude  Latitude  \\\n",
-       "29155       272.75          272.75     100.0  ...   5.077833   45.7265   \n",
-       "29156       276.95          274.65      85.0  ...   5.077833   45.7265   \n",
-       "29157       283.45          271.75      44.0  ...   5.077833   45.7265   \n",
-       "29158       274.75          271.15      77.0  ...   5.077833   45.7265   \n",
-       "29159       283.15          276.15      62.0  ...   5.077833   45.7265   \n",
-       "29160       280.15          278.45      89.0  ...   5.077833   45.7265   \n",
-       "29161       290.15          273.75      33.0  ...   5.077833   45.7265   \n",
-       "29162       281.45          278.55      82.0  ...   5.077833   45.7265   \n",
-       "29163       282.85          279.15      78.0  ...   5.077833   45.7265   \n",
-       "29164       283.75          277.65      66.0  ...   5.077833   45.7265   \n",
-       "\n",
-       "          communes (name)  communes (code)                  EPCI (name)  \\\n",
-       "29155  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
-       "29156  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
-       "29157  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
-       "29158  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
-       "29159  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
-       "29160  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
-       "29161  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
-       "29162  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
-       "29163  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
-       "29164  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
-       "\n",
-       "       EPCI (code)  department (name)  department (code)  \\\n",
-       "29155    246900575              Rhône                 69   \n",
-       "29156    246900575              Rhône                 69   \n",
-       "29157    246900575              Rhône                 69   \n",
-       "29158    246900575              Rhône                 69   \n",
-       "29159    246900575              Rhône                 69   \n",
-       "29160    246900575              Rhône                 69   \n",
-       "29161    246900575              Rhône                 69   \n",
-       "29162    246900575              Rhône                 69   \n",
-       "29163    246900575              Rhône                 69   \n",
-       "29164    246900575              Rhône                 69   \n",
-       "\n",
-       "              region (name)  region (code)  \n",
-       "29155  Auvergne-Rhône-Alpes             84  \n",
-       "29156  Auvergne-Rhône-Alpes             84  \n",
-       "29157  Auvergne-Rhône-Alpes             84  \n",
-       "29158  Auvergne-Rhône-Alpes             84  \n",
-       "29159  Auvergne-Rhône-Alpes             84  \n",
-       "29160  Auvergne-Rhône-Alpes             84  \n",
-       "29161  Auvergne-Rhône-Alpes             84  \n",
-       "29162  Auvergne-Rhône-Alpes             84  \n",
-       "29163  Auvergne-Rhône-Alpes             84  \n",
-       "29164  Auvergne-Rhône-Alpes             84  \n",
-       "\n",
-       "[10 rows x 81 columns]"
-      ]
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-       "            text-align:  left;\n",
-       "        }</style><table id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44f\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Code</th>        <th class=\"col_heading level0 col1\" >Description</th>        <th class=\"col_heading level0 col2\" >Na</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow0_col0\" class=\"data row0 col0\" >numer_sta</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow0_col1\" class=\"data row0 col1\" >ID OMM station</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow0_col2\" class=\"data row0 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow1_col0\" class=\"data row1 col0\" >date</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow1_col1\" class=\"data row1 col1\" >Date</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow1_col2\" class=\"data row1 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow2_col0\" class=\"data row2 col0\" >pmer</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow2_col1\" class=\"data row2 col1\" >Pression au niveau mer</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow2_col2\" class=\"data row2 col2\" >17</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow3_col0\" class=\"data row3 col0\" >tend</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow3_col1\" class=\"data row3 col1\" >Variation de pression en 3 heures</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow3_col2\" class=\"data row3 col2\" >2</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow4_col0\" class=\"data row4 col0\" >cod_tend</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow4_col1\" class=\"data row4 col1\" >Type de tendance barométrique</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow4_col2\" class=\"data row4 col2\" >2</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow5_col0\" class=\"data row5 col0\" >dd</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow5_col1\" class=\"data row5 col1\" >Direction du vent moyen 10 mn</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow5_col2\" class=\"data row5 col2\" >3</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow6_col0\" class=\"data row6 col0\" >ff</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow6_col1\" class=\"data row6 col1\" >Vitesse du vent moyen 10 mn</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow6_col2\" class=\"data row6 col2\" >2</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow7_col0\" class=\"data row7 col0\" >t</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow7_col1\" class=\"data row7 col1\" >Température</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow7_col2\" class=\"data row7 col2\" >14</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow8_col0\" class=\"data row8 col0\" >td</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow8_col1\" class=\"data row8 col1\" >Point de rosée</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow8_col2\" class=\"data row8 col2\" >17</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow9_col0\" class=\"data row9 col0\" >u</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow9_col1\" class=\"data row9 col1\" >Humidité</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow9_col2\" class=\"data row9 col2\" >17</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow10_col0\" class=\"data row10 col0\" >vv</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow10_col1\" class=\"data row10 col1\" >Visibilité horizontale</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow10_col2\" class=\"data row10 col2\" >31</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow11_col0\" class=\"data row11 col0\" >ww</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow11_col1\" class=\"data row11 col1\" >Temps présent</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow11_col2\" class=\"data row11 col2\" >1</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow12_col0\" class=\"data row12 col0\" >w1</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow12_col1\" class=\"data row12 col1\" >Temps passé 1</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow12_col2\" class=\"data row12 col2\" >542</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow13_col0\" class=\"data row13 col0\" >w2</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow13_col1\" class=\"data row13 col1\" >Temps passé 2</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow13_col2\" class=\"data row13 col2\" >552</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow14_col0\" class=\"data row14 col0\" >n</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow14_col1\" class=\"data row14 col1\" >Nebulosité totale</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow14_col2\" class=\"data row14 col2\" >801</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow15_col0\" class=\"data row15 col0\" >nbas</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow15_col1\" class=\"data row15 col1\" >Nébulosité  des nuages de l' étage inférieur</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow15_col2\" class=\"data row15 col2\" >2381</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow16_col0\" class=\"data row16 col0\" >hbas</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow16_col1\" class=\"data row16 col1\" >Hauteur de la base des nuages de l'étage inférieur</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow16_col2\" class=\"data row16 col2\" >8861</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow17_col0\" class=\"data row17 col0\" >cl</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow17_col1\" class=\"data row17 col1\" >Type des nuages de l'étage inférieur</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow17_col2\" class=\"data row17 col2\" >3377</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow18_col0\" class=\"data row18 col0\" >cm</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow18_col1\" class=\"data row18 col1\" >Type des nuages de l'étage moyen</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow18_col2\" class=\"data row18 col2\" >6912</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow19_col0\" class=\"data row19 col0\" >ch</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow19_col1\" class=\"data row19 col1\" >Type des nuages de l'étage supérieur</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow19_col2\" class=\"data row19 col2\" >8494</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow20_col0\" class=\"data row20 col0\" >pres</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow20_col1\" class=\"data row20 col1\" >Pression station</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow20_col2\" class=\"data row20 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow21_col0\" class=\"data row21 col0\" >niv_bar</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow21_col1\" class=\"data row21 col1\" >Niveau barométrique</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow21_col2\" class=\"data row21 col2\" >29165</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow22_col0\" class=\"data row22 col0\" >geop</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow22_col1\" class=\"data row22 col1\" >Géopotentiel</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow22_col2\" class=\"data row22 col2\" >29165</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow23_col0\" class=\"data row23 col0\" >tend24</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow23_col1\" class=\"data row23 col1\" >Variation de pression en 24 heures</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow23_col2\" class=\"data row23 col2\" >14443</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow24_col0\" class=\"data row24 col0\" >tn12</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow24_col1\" class=\"data row24 col1\" >Température minimale sur 12 heures</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow24_col2\" class=\"data row24 col2\" >21883</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow25_col0\" class=\"data row25 col0\" >tn24</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow25_col1\" class=\"data row25 col1\" >Température minimale sur 24 heures</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow25_col2\" class=\"data row25 col2\" >29165</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow26_col0\" class=\"data row26 col0\" >tx12</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow26_col1\" class=\"data row26 col1\" >Température maximale sur 12 heures</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow26_col2\" class=\"data row26 col2\" >21883</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow27_col0\" class=\"data row27 col0\" >tx24</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow27_col1\" class=\"data row27 col1\" >Température maximale sur 24 heures</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow27_col2\" class=\"data row27 col2\" >29165</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow28_col0\" class=\"data row28 col0\" >tminsol</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow28_col1\" class=\"data row28 col1\" >Température minimale du sol sur 12 heures</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow28_col2\" class=\"data row28 col2\" >27364</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow29_col0\" class=\"data row29 col0\" >sw</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow29_col1\" class=\"data row29 col1\" >Méthode de mesure Température du thermomètre mouillé</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow29_col2\" class=\"data row29 col2\" >29165</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow30_col0\" class=\"data row30 col0\" >tw</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow30_col1\" class=\"data row30 col1\" >Température du thermomètre mouillé</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow30_col2\" class=\"data row30 col2\" >29165</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow31_col0\" class=\"data row31 col0\" >raf10</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow31_col1\" class=\"data row31 col1\" >Rafale sur les 10 dernières minutes</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow31_col2\" class=\"data row31 col2\" >14127</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow32_col0\" class=\"data row32 col0\" >rafper</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow32_col1\" class=\"data row32 col1\" >Rafales sur une période</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow32_col2\" class=\"data row32 col2\" >9</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow33_col0\" class=\"data row33 col0\" >per</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow33_col1\" class=\"data row33 col1\" >Periode de mesure de la rafale</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow33_col2\" class=\"data row33 col2\" >8</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow34_col0\" class=\"data row34 col0\" >etat_sol</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow34_col1\" class=\"data row34 col1\" >Etat du sol</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow34_col2\" class=\"data row34 col2\" >12278</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow35_col0\" class=\"data row35 col0\" >ht_neige</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow35_col1\" class=\"data row35 col1\" >Hauteur totale de la couche de neige, glace, autre au sol</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow35_col2\" class=\"data row35 col2\" >12083</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow36_col0\" class=\"data row36 col0\" >ssfrai</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow36_col1\" class=\"data row36 col1\" >Hauteur de la neige fraîche</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow36_col2\" class=\"data row36 col2\" >2914</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow37_col0\" class=\"data row37 col0\" >perssfrai</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow37_col1\" class=\"data row37 col1\" >Periode de mesure de la neige fraiche</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow37_col2\" class=\"data row37 col2\" >4489</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow38_col0\" class=\"data row38 col0\" >rr1</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow38_col1\" class=\"data row38 col1\" >Précipitations dans la dernière heure</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow38_col2\" class=\"data row38 col2\" >95</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow39_col0\" class=\"data row39 col0\" >rr3</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow39_col1\" class=\"data row39 col1\" >Précipitations dans les 3 dernières heures</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow39_col2\" class=\"data row39 col2\" >73</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow40_col0\" class=\"data row40 col0\" >rr6</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow40_col1\" class=\"data row40 col1\" >Précipitations dans les 6 dernières heures</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow40_col2\" class=\"data row40 col2\" >10869</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow41_col0\" class=\"data row41 col0\" >rr12</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow41_col1\" class=\"data row41 col1\" >Précipitations dans les 12 dernières heures</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow41_col2\" class=\"data row41 col2\" >10919</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow42_col0\" class=\"data row42 col0\" >rr24</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow42_col1\" class=\"data row42 col1\" >Précipitations dans les 24 dernières heures</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow42_col2\" class=\"data row42 col2\" >12730</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow43_col0\" class=\"data row43 col0\" >phenspe1</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow43_col1\" class=\"data row43 col1\" >Phénomène spécial 1</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow43_col2\" class=\"data row43 col2\" >14818</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow44_col0\" class=\"data row44 col0\" >phenspe2</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow44_col1\" class=\"data row44 col1\" >Phénomène spécial 2</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow44_col2\" class=\"data row44 col2\" >14826</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow45_col0\" class=\"data row45 col0\" >phenspe3</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow45_col1\" class=\"data row45 col1\" >Phénomène spécial 3</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow45_col2\" class=\"data row45 col2\" >15515</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow46_col0\" class=\"data row46 col0\" >phenspe4</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow46_col1\" class=\"data row46 col1\" >Phénomène spécial 4</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow46_col2\" class=\"data row46 col2\" >28869</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow47_col0\" class=\"data row47 col0\" >nnuage1</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow47_col1\" class=\"data row47 col1\" >Nébulosité couche nuageuse 1</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow47_col2\" class=\"data row47 col2\" >4753</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow48_col0\" class=\"data row48 col0\" >ctype1</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow48_col1\" class=\"data row48 col1\" >Type nuage 1</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow48_col2\" class=\"data row48 col2\" >5699</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow49_col0\" class=\"data row49 col0\" >hnuage1</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow49_col1\" class=\"data row49 col1\" >Hauteur de base 1</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow49_col2\" class=\"data row49 col2\" >5439</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow50_col0\" class=\"data row50 col0\" >nnuage2</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow50_col1\" class=\"data row50 col1\" >Nébulosité couche nuageuse 2</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow50_col2\" class=\"data row50 col2\" >16112</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow51_col0\" class=\"data row51 col0\" >ctype2</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow51_col1\" class=\"data row51 col1\" >Type nuage 2</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow51_col2\" class=\"data row51 col2\" >16643</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow52_col0\" class=\"data row52 col0\" >hnuage2</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow52_col1\" class=\"data row52 col1\" >Hauteur de base 2</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow52_col2\" class=\"data row52 col2\" >16317</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow53_col0\" class=\"data row53 col0\" >nnuage3</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow53_col1\" class=\"data row53 col1\" >Nébulosité couche nuageuse 3</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow53_col2\" class=\"data row53 col2\" >25387</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow54_col0\" class=\"data row54 col0\" >ctype3</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow54_col1\" class=\"data row54 col1\" >Type nuage 3</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow54_col2\" class=\"data row54 col2\" >25642</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow55_col0\" class=\"data row55 col0\" >hnuage3</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow55_col1\" class=\"data row55 col1\" >Hauteur de base 3</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow55_col2\" class=\"data row55 col2\" >25431</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow56_col0\" class=\"data row56 col0\" >nnuage4</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow56_col1\" class=\"data row56 col1\" >Nébulosité couche nuageuse 4</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow56_col2\" class=\"data row56 col2\" >28850</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow57_col0\" class=\"data row57 col0\" >ctype4</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow57_col1\" class=\"data row57 col1\" >Type nuage 4</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow57_col2\" class=\"data row57 col2\" >28780</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row58\" class=\"row_heading level0 row58\" >58</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow58_col0\" class=\"data row58 col0\" >hnuage4</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow58_col1\" class=\"data row58 col1\" >Hauteur de base 4</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow58_col2\" class=\"data row58 col2\" >28850</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row59\" class=\"row_heading level0 row59\" >59</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow59_col0\" class=\"data row59 col0\" >coordonnees</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow59_col1\" class=\"data row59 col1\" >Coordonnees</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow59_col2\" class=\"data row59 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row60\" class=\"row_heading level0 row60\" >60</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow60_col0\" class=\"data row60 col0\" >nom</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow60_col1\" class=\"data row60 col1\" >Nom</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow60_col2\" class=\"data row60 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row61\" class=\"row_heading level0 row61\" >61</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow61_col0\" class=\"data row61 col0\" >type_de_tendance_barometrique</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow61_col1\" class=\"data row61 col1\" >Type de tendance barométrique.1</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow61_col2\" class=\"data row61 col2\" >2</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row62\" class=\"row_heading level0 row62\" >62</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow62_col0\" class=\"data row62 col0\" >temps_passe_1</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow62_col1\" class=\"data row62 col1\" >Temps passé 1.1</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow62_col2\" class=\"data row62 col2\" >542</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row63\" class=\"row_heading level0 row63\" >63</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow63_col0\" class=\"data row63 col0\" >temps_present</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow63_col1\" class=\"data row63 col1\" >Temps présent.1</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow63_col2\" class=\"data row63 col2\" >1</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row64\" class=\"row_heading level0 row64\" >64</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow64_col0\" class=\"data row64 col0\" >tc</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow64_col1\" class=\"data row64 col1\" >Température (°C)</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow64_col2\" class=\"data row64 col2\" >14</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row65\" class=\"row_heading level0 row65\" >65</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow65_col0\" class=\"data row65 col0\" >tn12c</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow65_col1\" class=\"data row65 col1\" >Température minimale sur 12 heures (°C)</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow65_col2\" class=\"data row65 col2\" >21883</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row66\" class=\"row_heading level0 row66\" >66</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow66_col0\" class=\"data row66 col0\" >tn24c</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow66_col1\" class=\"data row66 col1\" >Température minimale sur 24 heures (°C)</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow66_col2\" class=\"data row66 col2\" >29165</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row67\" class=\"row_heading level0 row67\" >67</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow67_col0\" class=\"data row67 col0\" >tx12c</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow67_col1\" class=\"data row67 col1\" >Température maximale sur 12 heures (°C)</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow67_col2\" class=\"data row67 col2\" >21883</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row68\" class=\"row_heading level0 row68\" >68</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow68_col0\" class=\"data row68 col0\" >tx24c</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow68_col1\" class=\"data row68 col1\" >Température maximale sur 24 heures (°C)</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow68_col2\" class=\"data row68 col2\" >29165</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row69\" class=\"row_heading level0 row69\" >69</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow69_col0\" class=\"data row69 col0\" >tminsolc</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow69_col1\" class=\"data row69 col1\" >Température minimale du sol sur 12 heures (en °C)</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow69_col2\" class=\"data row69 col2\" >27364</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row70\" class=\"row_heading level0 row70\" >70</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow70_col0\" class=\"data row70 col0\" >altitude</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow70_col1\" class=\"data row70 col1\" >Altitude</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow70_col2\" class=\"data row70 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row71\" class=\"row_heading level0 row71\" >71</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow71_col0\" class=\"data row71 col0\" >longitude</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow71_col1\" class=\"data row71 col1\" >Longitude</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow71_col2\" class=\"data row71 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row72\" class=\"row_heading level0 row72\" >72</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow72_col0\" class=\"data row72 col0\" >latitude</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow72_col1\" class=\"data row72 col1\" >Latitude</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow72_col2\" class=\"data row72 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row73\" class=\"row_heading level0 row73\" >73</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow73_col0\" class=\"data row73 col0\" >libgeo</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow73_col1\" class=\"data row73 col1\" >communes (name)</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow73_col2\" class=\"data row73 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row74\" class=\"row_heading level0 row74\" >74</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow74_col0\" class=\"data row74 col0\" >codegeo</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow74_col1\" class=\"data row74 col1\" >communes (code)</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow74_col2\" class=\"data row74 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row75\" class=\"row_heading level0 row75\" >75</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow75_col0\" class=\"data row75 col0\" >nom_epci</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow75_col1\" class=\"data row75 col1\" >EPCI (name)</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow75_col2\" class=\"data row75 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row76\" class=\"row_heading level0 row76\" >76</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow76_col0\" class=\"data row76 col0\" >code_epci</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow76_col1\" class=\"data row76 col1\" >EPCI (code)</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow76_col2\" class=\"data row76 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row77\" class=\"row_heading level0 row77\" >77</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow77_col0\" class=\"data row77 col0\" >nom_dept</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow77_col1\" class=\"data row77 col1\" >department (name)</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow77_col2\" class=\"data row77 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row78\" class=\"row_heading level0 row78\" >78</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow78_col0\" class=\"data row78 col0\" >code_dep</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow78_col1\" class=\"data row78 col1\" >department (code)</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow78_col2\" class=\"data row78 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row79\" class=\"row_heading level0 row79\" >79</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow79_col0\" class=\"data row79 col0\" >nom_reg</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow79_col1\" class=\"data row79 col1\" >region (name)</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow79_col2\" class=\"data row79 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44flevel0_row80\" class=\"row_heading level0 row80\" >80</th>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow80_col0\" class=\"data row80 col0\" >code_reg</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow80_col1\" class=\"data row80 col1\" >region (code)</td>\n",
-       "                        <td id=\"T_82d2ad52_7ac4_11eb_8ba3_0cc47af5a44frow80_col2\" class=\"data row80 col2\" >0</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x15151bd2d290>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shape is :  (29165, 81)\n"
-     ]
-    }
-   ],
-   "source": [
-    "df = pd.read_csv(f'{datasets_dir}/SYNOP/{data_filename}', header=0, sep=';')\n",
-    "pwk.subtitle('Raw data :')\n",
-    "display(df.tail(10))\n",
-    "\n",
-    "# ---- Get the columns name as descriptions\n",
-    "synop_desc = list(df.columns)\n",
-    "\n",
-    "# ---- Set Codes as columns name\n",
-    "df.columns   = synop_codes\n",
-    "code2desc    = dict(zip(synop_codes, synop_desc))\n",
-    "\n",
-    "# ---- Count the na values by columns\n",
-    "columns_na = df.isna().sum().tolist()\n",
-    "\n",
-    "# ---- Show all of that\n",
-    "df_desc=pd.DataFrame({'Code':synop_codes, 'Description':synop_desc, 'Na':columns_na})\n",
-    "\n",
-    "pwk.subtitle('List of columns :')\n",
-    "display(df_desc.style.set_properties(**{'text-align': 'left'}))\n",
-    "\n",
-    "print('Shape is : ', df.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Keep only certain columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:29:59.244773Z",
-     "iopub.status.busy": "2021-03-01T19:29:59.244304Z",
-     "iopub.status.idle": "2021-03-01T19:29:59.337808Z",
-     "shell.execute_reply": "2021-03-01T19:29:59.338308Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**Our selected columns :**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>pmer</th>\n",
-       "      <th>tend</th>\n",
-       "      <th>cod_tend</th>\n",
-       "      <th>dd</th>\n",
-       "      <th>ff</th>\n",
-       "      <th>td</th>\n",
-       "      <th>u</th>\n",
-       "      <th>ww</th>\n",
-       "      <th>pres</th>\n",
-       "      <th>rafper</th>\n",
-       "      <th>per</th>\n",
-       "      <th>rr1</th>\n",
-       "      <th>rr3</th>\n",
-       "      <th>tc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2015-06-12T17:00:00+02:00</td>\n",
-       "      <td>101050.0</td>\n",
-       "      <td>-230.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>286.25</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98330.0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>24.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2015-06-05T17:00:00+02:00</td>\n",
-       "      <td>101590.0</td>\n",
-       "      <td>-220.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>3.9</td>\n",
-       "      <td>286.95</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>98930.0</td>\n",
-       "      <td>9.9</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>32.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2015-06-15T11:00:00+02:00</td>\n",
-       "      <td>101420.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>270.0</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>286.85</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>98660.0</td>\n",
-       "      <td>4.5</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>20.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2015-06-15T14:00:00+02:00</td>\n",
-       "      <td>101430.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>286.45</td>\n",
-       "      <td>55.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98680.0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>22.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2015-06-20T05:00:00+02:00</td>\n",
-       "      <td>102030.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>282.95</td>\n",
-       "      <td>82.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>99170.0</td>\n",
-       "      <td>2.4</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>2015-06-22T05:00:00+02:00</td>\n",
-       "      <td>101680.0</td>\n",
-       "      <td>-120.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>286.15</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98870.0</td>\n",
-       "      <td>4.7</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>16.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>2015-06-23T02:00:00+02:00</td>\n",
-       "      <td>101270.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>4.5</td>\n",
-       "      <td>282.95</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>98490.0</td>\n",
-       "      <td>10.2</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>2015-06-25T14:00:00+02:00</td>\n",
-       "      <td>102180.0</td>\n",
-       "      <td>-40.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>283.25</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>99430.0</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>25.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>2015-07-05T20:00:00+02:00</td>\n",
-       "      <td>101410.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>8.3</td>\n",
-       "      <td>288.05</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>98760.0</td>\n",
-       "      <td>13.4</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>33.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>2015-05-14T17:00:00+02:00</td>\n",
-       "      <td>101070.0</td>\n",
-       "      <td>-150.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>284.95</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>98300.0</td>\n",
-       "      <td>11.1</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>2015-03-16T22:00:00+01:00</td>\n",
-       "      <td>102150.0</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>275.05</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>99240.0</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>2015-03-26T01:00:00+01:00</td>\n",
-       "      <td>101140.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>330.0</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>275.45</td>\n",
-       "      <td>82.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98220.0</td>\n",
-       "      <td>8.1</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>2015-04-03T17:00:00+02:00</td>\n",
-       "      <td>101690.0</td>\n",
-       "      <td>-250.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>340.0</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>278.15</td>\n",
-       "      <td>55.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98850.0</td>\n",
-       "      <td>6.4</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>2015-04-05T20:00:00+02:00</td>\n",
-       "      <td>101850.0</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>7.8</td>\n",
-       "      <td>268.45</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98950.0</td>\n",
-       "      <td>13.5</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>2014-10-22T17:00:00+02:00</td>\n",
-       "      <td>102670.0</td>\n",
-       "      <td>-70.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>275.35</td>\n",
-       "      <td>55.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>99770.0</td>\n",
-       "      <td>7.2</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>2015-02-08T16:00:00+01:00</td>\n",
-       "      <td>102570.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>12.3</td>\n",
-       "      <td>271.55</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>99590.0</td>\n",
-       "      <td>19.9</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>2015-02-11T07:00:00+01:00</td>\n",
-       "      <td>102670.0</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>290.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>267.55</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>99610.0</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-3.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>2015-02-07T04:00:00+01:00</td>\n",
-       "      <td>101900.0</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>310.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>268.65</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98900.0</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>2015-02-13T04:00:00+01:00</td>\n",
-       "      <td>102140.0</td>\n",
-       "      <td>-50.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>2.1</td>\n",
-       "      <td>273.55</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>99190.0</td>\n",
-       "      <td>4.9</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>2015-02-16T19:00:00+01:00</td>\n",
-       "      <td>102060.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>275.65</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>99110.0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                         date      pmer   tend  cod_tend     dd    ff      td  \\\n",
-       "0   2015-06-12T17:00:00+02:00  101050.0 -230.0       6.0  140.0   3.6  286.25   \n",
-       "1   2015-06-05T17:00:00+02:00  101590.0 -220.0       8.0  190.0   3.9  286.95   \n",
-       "2   2015-06-15T11:00:00+02:00  101420.0   90.0       1.0  270.0   1.5  286.85   \n",
-       "3   2015-06-15T14:00:00+02:00  101430.0   20.0       1.0   10.0   2.5  286.45   \n",
-       "4   2015-06-20T05:00:00+02:00  102030.0    0.0       4.0   50.0   0.7  282.95   \n",
-       "5   2015-06-22T05:00:00+02:00  101680.0 -120.0       6.0  180.0   0.7  286.15   \n",
-       "6   2015-06-23T02:00:00+02:00  101270.0  150.0       2.0   20.0   4.5  282.95   \n",
-       "7   2015-06-25T14:00:00+02:00  102180.0  -40.0       8.0   10.0   2.3  283.25   \n",
-       "8   2015-07-05T20:00:00+02:00  101410.0   50.0       3.0  190.0   8.3  288.05   \n",
-       "9   2015-05-14T17:00:00+02:00  101070.0 -150.0       6.0   20.0   6.2  284.95   \n",
-       "10  2015-03-16T22:00:00+01:00  102150.0   40.0       1.0   50.0   1.7  275.05   \n",
-       "11  2015-03-26T01:00:00+01:00  101140.0  100.0       1.0  330.0   5.9  275.45   \n",
-       "12  2015-04-03T17:00:00+02:00  101690.0 -250.0       7.0  340.0   3.5  278.15   \n",
-       "13  2015-04-05T20:00:00+02:00  101850.0  140.0       3.0   10.0   7.8  268.45   \n",
-       "14  2014-10-22T17:00:00+02:00  102670.0  -70.0       8.0   20.0   4.6  275.35   \n",
-       "15  2015-02-08T16:00:00+01:00  102570.0   20.0       3.0  350.0  12.3  271.55   \n",
-       "16  2015-02-11T07:00:00+01:00  102670.0  -10.0       7.0  290.0   2.0  267.55   \n",
-       "17  2015-02-07T04:00:00+01:00  101900.0  160.0       1.0  310.0   2.0  268.65   \n",
-       "18  2015-02-13T04:00:00+01:00  102140.0  -50.0       8.0  140.0   2.1  273.55   \n",
-       "19  2015-02-16T19:00:00+01:00  102060.0  100.0       3.0   20.0   3.2  275.65   \n",
-       "\n",
-       "       u    ww     pres  rafper   per  rr1  rr3    tc  \n",
-       "0   50.0   2.0  98330.0     5.1 -10.0  0.0 -0.1  24.2  \n",
-       "1   32.0   3.0  98930.0     9.9 -10.0  0.0  0.0  32.6  \n",
-       "2   64.0   3.0  98660.0     4.5 -10.0  0.0  0.0  20.8  \n",
-       "3   55.0   1.0  98680.0     5.1 -10.0  0.0  0.0  22.8  \n",
-       "4   82.0   2.0  99170.0     2.4 -10.0  0.0  0.0  12.8  \n",
-       "5   80.0   1.0  98870.0     4.7 -10.0  0.0 -0.1  16.5  \n",
-       "6   54.0   0.0  98490.0    10.2 -10.0  0.0  0.0  19.3  \n",
-       "7   38.0   1.0  99430.0     7.5 -10.0  0.0  0.0  25.5  \n",
-       "8   33.0   3.0  98760.0    13.4 -10.0  0.0  0.0  33.4  \n",
-       "9   60.0   3.0  98300.0    11.1 -10.0  0.0  0.0  19.8  \n",
-       "10  62.0   1.0  99240.0     4.6 -10.0  0.0  0.0   8.8  \n",
-       "11  82.0   1.0  98220.0     8.1 -10.0  0.0  0.0   5.1  \n",
-       "12  55.0   1.0  98850.0     6.4 -10.0  0.0  0.0  13.9  \n",
-       "13  38.0   1.0  98950.0    13.5 -10.0  0.0  0.0   8.9  \n",
-       "14  55.0   1.0  99770.0     7.2 -10.0  0.0  0.0  10.9  \n",
-       "15  68.0   0.0  99590.0    19.9 -10.0  0.0  0.0   3.8  \n",
-       "16  88.0  10.0  99610.0     3.3 -10.0  0.0  0.0  -3.9  \n",
-       "17  74.0   2.0  98900.0     3.2 -10.0  0.0  0.0  -0.4  \n",
-       "18  74.0   0.0  99190.0     4.9 -10.0  0.0  0.0   4.6  \n",
-       "19  88.0   1.0  99110.0     5.1 -10.0  0.0  0.0   4.3  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**Few statistics :**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>pmer</th>\n",
-       "      <th>tend</th>\n",
-       "      <th>cod_tend</th>\n",
-       "      <th>dd</th>\n",
-       "      <th>ff</th>\n",
-       "      <th>td</th>\n",
-       "      <th>u</th>\n",
-       "      <th>ww</th>\n",
-       "      <th>pres</th>\n",
-       "      <th>rafper</th>\n",
-       "      <th>per</th>\n",
-       "      <th>rr1</th>\n",
-       "      <th>rr3</th>\n",
-       "      <th>tc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>29148.000000</td>\n",
-       "      <td>29163.000000</td>\n",
-       "      <td>29163.000000</td>\n",
-       "      <td>29162.000000</td>\n",
-       "      <td>29163.00000</td>\n",
-       "      <td>29148.000000</td>\n",
-       "      <td>29148.000000</td>\n",
-       "      <td>29164.000000</td>\n",
-       "      <td>29165.000000</td>\n",
-       "      <td>29156.000000</td>\n",
-       "      <td>29157.0</td>\n",
-       "      <td>29070.000000</td>\n",
-       "      <td>29092.000000</td>\n",
-       "      <td>29151.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>101753.552902</td>\n",
-       "      <td>0.255118</td>\n",
-       "      <td>4.306930</td>\n",
-       "      <td>204.088197</td>\n",
-       "      <td>3.39653</td>\n",
-       "      <td>280.027865</td>\n",
-       "      <td>71.021614</td>\n",
-       "      <td>10.106158</td>\n",
-       "      <td>98894.598320</td>\n",
-       "      <td>6.299005</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.092886</td>\n",
-       "      <td>0.279008</td>\n",
-       "      <td>12.688261</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>798.093804</td>\n",
-       "      <td>111.438232</td>\n",
-       "      <td>2.716149</td>\n",
-       "      <td>115.422508</td>\n",
-       "      <td>2.46898</td>\n",
-       "      <td>5.857534</td>\n",
-       "      <td>18.275755</td>\n",
-       "      <td>19.404573</td>\n",
-       "      <td>761.586766</td>\n",
-       "      <td>3.852478</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.605673</td>\n",
-       "      <td>1.414611</td>\n",
-       "      <td>8.146390</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>97960.000000</td>\n",
-       "      <td>-750.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>249.250000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>95170.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>-0.100000</td>\n",
-       "      <td>-0.100000</td>\n",
-       "      <td>-12.100000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>101300.000000</td>\n",
-       "      <td>-70.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>130.000000</td>\n",
-       "      <td>1.50000</td>\n",
-       "      <td>275.825000</td>\n",
-       "      <td>58.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>98480.000000</td>\n",
-       "      <td>3.600000</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>6.600000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>101740.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>190.000000</td>\n",
-       "      <td>2.90000</td>\n",
-       "      <td>280.250000</td>\n",
-       "      <td>74.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>98920.000000</td>\n",
-       "      <td>5.300000</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>12.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>102240.000000</td>\n",
-       "      <td>70.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>330.000000</td>\n",
-       "      <td>4.60000</td>\n",
-       "      <td>284.550000</td>\n",
-       "      <td>86.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>99360.000000</td>\n",
-       "      <td>8.200000</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>18.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>104280.000000</td>\n",
-       "      <td>810.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>360.000000</td>\n",
-       "      <td>18.80000</td>\n",
-       "      <td>295.950000</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>97.000000</td>\n",
-       "      <td>101210.000000</td>\n",
-       "      <td>30.200000</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>19.000000</td>\n",
-       "      <td>45.000000</td>\n",
-       "      <td>38.900000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                pmer          tend      cod_tend            dd           ff  \\\n",
-       "count   29148.000000  29163.000000  29163.000000  29162.000000  29163.00000   \n",
-       "mean   101753.552902      0.255118      4.306930    204.088197      3.39653   \n",
-       "std       798.093804    111.438232      2.716149    115.422508      2.46898   \n",
-       "min     97960.000000   -750.000000      0.000000      0.000000      0.00000   \n",
-       "25%    101300.000000    -70.000000      2.000000    130.000000      1.50000   \n",
-       "50%    101740.000000      0.000000      3.000000    190.000000      2.90000   \n",
-       "75%    102240.000000     70.000000      7.000000    330.000000      4.60000   \n",
-       "max    104280.000000    810.000000      8.000000    360.000000     18.80000   \n",
-       "\n",
-       "                 td             u            ww           pres        rafper  \\\n",
-       "count  29148.000000  29148.000000  29164.000000   29165.000000  29156.000000   \n",
-       "mean     280.027865     71.021614     10.106158   98894.598320      6.299005   \n",
-       "std        5.857534     18.275755     19.404573     761.586766      3.852478   \n",
-       "min      249.250000      2.000000      0.000000   95170.000000      0.000000   \n",
-       "25%      275.825000     58.000000      2.000000   98480.000000      3.600000   \n",
-       "50%      280.250000     74.000000      2.000000   98920.000000      5.300000   \n",
-       "75%      284.550000     86.000000      3.000000   99360.000000      8.200000   \n",
-       "max      295.950000    100.000000     97.000000  101210.000000     30.200000   \n",
-       "\n",
-       "           per           rr1           rr3            tc  \n",
-       "count  29157.0  29070.000000  29092.000000  29151.000000  \n",
-       "mean     -10.0      0.092886      0.279008     12.688261  \n",
-       "std        0.0      0.605673      1.414611      8.146390  \n",
-       "min      -10.0     -0.100000     -0.100000    -12.100000  \n",
-       "25%      -10.0      0.000000      0.000000      6.600000  \n",
-       "50%      -10.0      0.000000      0.000000     12.500000  \n",
-       "75%      -10.0      0.000000      0.000000     18.500000  \n",
-       "max      -10.0     19.000000     45.000000     38.900000  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "columns_used=['date','pmer','tend','cod_tend','dd','ff','td','u','ww','pres','rafper','per','rr1','rr3','tc']\n",
-    "\n",
-    "# ---- Drop unused columns\n",
-    "\n",
-    "to_drop = df.columns.difference(columns_used)\n",
-    "df.drop( to_drop, axis=1, inplace=True)\n",
-    "\n",
-    "# ---- Show all of that\n",
-    "\n",
-    "pwk.subtitle('Our selected columns :')\n",
-    "display(df.head(20))\n",
-    "\n",
-    "pwk.subtitle('Few statistics :')\n",
-    "display(df.describe())\n",
-    "\n",
-    "# ---- 'per' column is constant, we can drop it\n",
-    "\n",
-    "df.drop(['per'],axis=1,inplace=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4 - Cleanup session : Let's sort it and cook up some NaN values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:29:59.344191Z",
-     "iopub.status.busy": "2021-03-01T19:29:59.343708Z",
-     "iopub.status.idle": "2021-03-01T19:29:59.412779Z",
-     "shell.execute_reply": "2021-03-01T19:29:59.413269Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**Before :**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
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-     "metadata": {},
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>pmer</th>\n",
-       "      <th>tend</th>\n",
-       "      <th>cod_tend</th>\n",
-       "      <th>dd</th>\n",
-       "      <th>ff</th>\n",
-       "      <th>td</th>\n",
-       "      <th>u</th>\n",
-       "      <th>ww</th>\n",
-       "      <th>pres</th>\n",
-       "      <th>rafper</th>\n",
-       "      <th>rr1</th>\n",
-       "      <th>rr3</th>\n",
-       "      <th>tc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>396</th>\n",
-       "      <td>2010-02-19T16:00:00+01:00</td>\n",
-       "      <td>99760.0</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>330.0</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>275.85</td>\n",
-       "      <td>79.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>96890.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>434</th>\n",
-       "      <td>2010-02-24T10:00:00+01:00</td>\n",
-       "      <td>100310.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>279.25</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>97470.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>9.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>477</th>\n",
-       "      <td>2010-03-01T19:00:00+01:00</td>\n",
-       "      <td>101400.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>340.0</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>275.45</td>\n",
-       "      <td>61.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98520.0</td>\n",
-       "      <td>5.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>734</th>\n",
-       "      <td>2010-04-03T02:00:00+02:00</td>\n",
-       "      <td>101550.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>7.7</td>\n",
-       "      <td>277.55</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98680.0</td>\n",
-       "      <td>12.3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>10.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1061</th>\n",
-       "      <td>2010-05-13T23:00:00+02:00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>330.0</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98220.0</td>\n",
-       "      <td>7.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>9.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1063</th>\n",
-       "      <td>2010-05-14T05:00:00+02:00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-50.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>4.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98110.0</td>\n",
-       "      <td>7.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1064</th>\n",
-       "      <td>2010-05-14T08:00:00+02:00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98110.0</td>\n",
-       "      <td>6.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2268</th>\n",
-       "      <td>2010-10-11T20:00:00+02:00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98060.0</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2269</th>\n",
-       "      <td>2010-10-11T23:00:00+02:00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>130.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98190.0</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2270</th>\n",
-       "      <td>2010-10-12T02:00:00+02:00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98260.0</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           date      pmer   tend  cod_tend     dd   ff  \\\n",
-       "396   2010-02-19T16:00:00+01:00   99760.0  180.0       3.0  330.0  4.6   \n",
-       "434   2010-02-24T10:00:00+01:00  100310.0   60.0       1.0    NaN  NaN   \n",
-       "477   2010-03-01T19:00:00+01:00  101400.0    NaN       NaN  340.0  2.6   \n",
-       "734   2010-04-03T02:00:00+02:00  101550.0   50.0       0.0  190.0  7.7   \n",
-       "1061  2010-05-13T23:00:00+02:00       NaN   60.0       2.0  330.0  4.6   \n",
-       "1063  2010-05-14T05:00:00+02:00       NaN  -50.0       5.0  350.0  4.1   \n",
-       "1064  2010-05-14T08:00:00+02:00       NaN    0.0       5.0  350.0  4.6   \n",
-       "2268  2010-10-11T20:00:00+02:00       NaN  150.0       2.0   10.0  1.0   \n",
-       "2269  2010-10-11T23:00:00+02:00       NaN  130.0       3.0   80.0  1.0   \n",
-       "2270  2010-10-12T02:00:00+02:00       NaN   70.0       1.0    0.0  0.0   \n",
-       "\n",
-       "          td     u    ww     pres  rafper  rr1  rr3    tc  \n",
-       "396   275.85  79.0  21.0  96890.0     NaN  0.0  1.0   6.1  \n",
-       "434   279.25  77.0   2.0  97470.0     NaN  0.2  0.2   9.9  \n",
-       "477   275.45  61.0   2.0  98520.0     5.7  0.0  NaN   9.4  \n",
-       "734   277.55  64.0   2.0  98680.0    12.3  NaN  NaN  10.9  \n",
-       "1061     NaN   NaN   2.0  98220.0     7.7  0.0  0.0   9.9  \n",
-       "1063     NaN   NaN   2.0  98110.0     7.2  0.0  0.0   8.1  \n",
-       "1064     NaN   NaN   2.0  98110.0     6.7  0.0  0.0   8.1  \n",
-       "2268     NaN   NaN   2.0  98060.0     3.1  NaN  NaN   NaN  \n",
-       "2269     NaN   NaN   2.0  98190.0     2.6  NaN  NaN   NaN  \n",
-       "2270     NaN   NaN   2.0  98260.0     1.5  NaN  NaN   NaN  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**After :**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>396</th>\n",
-       "      <td>2010-02-19T16:00:00+01:00</td>\n",
-       "      <td>99760.000000</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>330.0</td>\n",
-       "      <td>4.60</td>\n",
-       "      <td>275.85</td>\n",
-       "      <td>79.000000</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>96890.0</td>\n",
-       "      <td>8.25</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>434</th>\n",
-       "      <td>2010-02-24T10:00:00+01:00</td>\n",
-       "      <td>100310.000000</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>170.0</td>\n",
-       "      <td>4.15</td>\n",
-       "      <td>279.25</td>\n",
-       "      <td>77.000000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>97470.0</td>\n",
-       "      <td>6.65</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>9.90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>477</th>\n",
-       "      <td>2010-03-01T19:00:00+01:00</td>\n",
-       "      <td>101400.000000</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>340.0</td>\n",
-       "      <td>2.60</td>\n",
-       "      <td>275.45</td>\n",
-       "      <td>61.000000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98520.0</td>\n",
-       "      <td>5.70</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>9.40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>734</th>\n",
-       "      <td>2010-04-03T02:00:00+02:00</td>\n",
-       "      <td>101550.000000</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>7.70</td>\n",
-       "      <td>277.55</td>\n",
-       "      <td>64.000000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98680.0</td>\n",
-       "      <td>12.30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1061</th>\n",
-       "      <td>2010-05-13T23:00:00+02:00</td>\n",
-       "      <td>101020.000000</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>330.0</td>\n",
-       "      <td>4.60</td>\n",
-       "      <td>281.25</td>\n",
-       "      <td>86.500000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98220.0</td>\n",
-       "      <td>7.70</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>9.90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1063</th>\n",
-       "      <td>2010-05-14T05:00:00+02:00</td>\n",
-       "      <td>101040.000000</td>\n",
-       "      <td>-50.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>4.10</td>\n",
-       "      <td>279.15</td>\n",
-       "      <td>80.666667</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98110.0</td>\n",
-       "      <td>7.20</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1064</th>\n",
-       "      <td>2010-05-14T08:00:00+02:00</td>\n",
-       "      <td>101040.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>4.60</td>\n",
-       "      <td>279.35</td>\n",
-       "      <td>79.333333</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98110.0</td>\n",
-       "      <td>6.70</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2268</th>\n",
-       "      <td>2010-10-11T20:00:00+02:00</td>\n",
-       "      <td>100786.666667</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>284.75</td>\n",
-       "      <td>83.333333</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98060.0</td>\n",
-       "      <td>3.10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>14.45</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2269</th>\n",
-       "      <td>2010-10-11T23:00:00+02:00</td>\n",
-       "      <td>100863.333333</td>\n",
-       "      <td>130.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>284.45</td>\n",
-       "      <td>84.666667</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98190.0</td>\n",
-       "      <td>2.60</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13.90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2270</th>\n",
-       "      <td>2010-10-12T02:00:00+02:00</td>\n",
-       "      <td>100940.000000</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>284.15</td>\n",
-       "      <td>86.000000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98260.0</td>\n",
-       "      <td>1.50</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13.35</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           date           pmer   tend  cod_tend     dd    ff  \\\n",
-       "396   2010-02-19T16:00:00+01:00   99760.000000  180.0       3.0  330.0  4.60   \n",
-       "434   2010-02-24T10:00:00+01:00  100310.000000   60.0       1.0  170.0  4.15   \n",
-       "477   2010-03-01T19:00:00+01:00  101400.000000  195.0       4.0  340.0  2.60   \n",
-       "734   2010-04-03T02:00:00+02:00  101550.000000   50.0       0.0  190.0  7.70   \n",
-       "1061  2010-05-13T23:00:00+02:00  101020.000000   60.0       2.0  330.0  4.60   \n",
-       "1063  2010-05-14T05:00:00+02:00  101040.000000  -50.0       5.0  350.0  4.10   \n",
-       "1064  2010-05-14T08:00:00+02:00  101040.000000    0.0       5.0  350.0  4.60   \n",
-       "2268  2010-10-11T20:00:00+02:00  100786.666667  150.0       2.0   10.0  1.00   \n",
-       "2269  2010-10-11T23:00:00+02:00  100863.333333  130.0       3.0   80.0  1.00   \n",
-       "2270  2010-10-12T02:00:00+02:00  100940.000000   70.0       1.0    0.0  0.00   \n",
-       "\n",
-       "          td          u    ww     pres  rafper  rr1  rr3     tc  \n",
-       "396   275.85  79.000000  21.0  96890.0    8.25  0.0  1.0   6.10  \n",
-       "434   279.25  77.000000   2.0  97470.0    6.65  0.2  0.2   9.90  \n",
-       "477   275.45  61.000000   2.0  98520.0    5.70  0.0  0.5   9.40  \n",
-       "734   277.55  64.000000   2.0  98680.0   12.30  0.0  0.0  10.90  \n",
-       "1061  281.25  86.500000   2.0  98220.0    7.70  0.0  0.0   9.90  \n",
-       "1063  279.15  80.666667   2.0  98110.0    7.20  0.0  0.0   8.10  \n",
-       "1064  279.35  79.333333   2.0  98110.0    6.70  0.0  0.0   8.10  \n",
-       "2268  284.75  83.333333   2.0  98060.0    3.10  0.0  0.0  14.45  \n",
-       "2269  284.45  84.666667   2.0  98190.0    2.60  0.0  0.0  13.90  \n",
-       "2270  284.15  86.000000   2.0  98260.0    1.50  0.0  0.0  13.35  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ---- First of all, we have to sort on the date\n",
-    "\n",
-    "df.sort_values(['date'],  inplace=True)\n",
-    "df.reset_index(drop=True, inplace=True)\n",
-    "\n",
-    "# ---- Before : Lines with NaN\n",
-    "\n",
-    "na_rows=df.isna().any(axis=1)\n",
-    "pwk.subtitle('Before :')\n",
-    "display( df[na_rows].head(10) )\n",
-    "\n",
-    "# ---- Nice interpolation for plugging holes\n",
-    "\n",
-    "df.interpolate(inplace=True)\n",
-    "\n",
-    "# ---- After\n",
-    "\n",
-    "pwk.subtitle('After :')\n",
-    "display(df[na_rows].head(10))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 5 - Final dataset\n",
-    "### 5.1 - Summarize it"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:29:59.420052Z",
-     "iopub.status.busy": "2021-03-01T19:29:59.419569Z",
-     "iopub.status.idle": "2021-03-01T19:29:59.454774Z",
-     "shell.execute_reply": "2021-03-01T19:29:59.455259Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**Dataset columns :**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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-       "            text-align:  left;\n",
-       "        }</style><table id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44f\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Columns</th>        <th class=\"col_heading level0 col1\" >Description</th>        <th class=\"col_heading level0 col2\" >Na</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44flevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow0_col0\" class=\"data row0 col0\" >date</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow0_col1\" class=\"data row0 col1\" >Date</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow0_col2\" class=\"data row0 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44flevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow1_col0\" class=\"data row1 col0\" >pmer</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow1_col1\" class=\"data row1 col1\" >Pression au niveau mer</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow1_col2\" class=\"data row1 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44flevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow2_col0\" class=\"data row2 col0\" >tend</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow2_col1\" class=\"data row2 col1\" >Variation de pression en 3 heures</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow2_col2\" class=\"data row2 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44flevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow3_col0\" class=\"data row3 col0\" >cod_tend</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow3_col1\" class=\"data row3 col1\" >Type de tendance barométrique</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow3_col2\" class=\"data row3 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44flevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow4_col0\" class=\"data row4 col0\" >dd</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow4_col1\" class=\"data row4 col1\" >Direction du vent moyen 10 mn</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow4_col2\" class=\"data row4 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44flevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow5_col0\" class=\"data row5 col0\" >ff</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow5_col1\" class=\"data row5 col1\" >Vitesse du vent moyen 10 mn</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow5_col2\" class=\"data row5 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44flevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow6_col0\" class=\"data row6 col0\" >td</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow6_col1\" class=\"data row6 col1\" >Point de rosée</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow6_col2\" class=\"data row6 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44flevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow7_col0\" class=\"data row7 col0\" >u</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow7_col1\" class=\"data row7 col1\" >Humidité</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow7_col2\" class=\"data row7 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44flevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow8_col0\" class=\"data row8 col0\" >ww</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow8_col1\" class=\"data row8 col1\" >Temps présent</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow8_col2\" class=\"data row8 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44flevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow9_col0\" class=\"data row9 col0\" >pres</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow9_col1\" class=\"data row9 col1\" >Pression station</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow9_col2\" class=\"data row9 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44flevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow10_col0\" class=\"data row10 col0\" >rafper</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow10_col1\" class=\"data row10 col1\" >Rafales sur une période</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow10_col2\" class=\"data row10 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44flevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow11_col0\" class=\"data row11 col0\" >rr1</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow11_col1\" class=\"data row11 col1\" >Précipitations dans la dernière heure</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow11_col2\" class=\"data row11 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44flevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow12_col0\" class=\"data row12 col0\" >rr3</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow12_col1\" class=\"data row12 col1\" >Précipitations dans les 3 dernières heures</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow12_col2\" class=\"data row12 col2\" >0</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44flevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow13_col0\" class=\"data row13 col0\" >tc</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow13_col1\" class=\"data row13 col1\" >Température (°C)</td>\n",
-       "                        <td id=\"T_82f5dffe_7ac4_11eb_9d35_0cc47af5a44frow13_col2\" class=\"data row13 col2\" >0</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x151517d38610>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**Have a look :**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>pmer</th>\n",
-       "      <th>tend</th>\n",
-       "      <th>cod_tend</th>\n",
-       "      <th>dd</th>\n",
-       "      <th>ff</th>\n",
-       "      <th>td</th>\n",
-       "      <th>u</th>\n",
-       "      <th>ww</th>\n",
-       "      <th>pres</th>\n",
-       "      <th>rafper</th>\n",
-       "      <th>rr1</th>\n",
-       "      <th>rr3</th>\n",
-       "      <th>tc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>29145</th>\n",
-       "      <td>2020-02-24T13:00:00+01:00</td>\n",
-       "      <td>102380.0</td>\n",
-       "      <td>-220.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>281.15</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>99540.0</td>\n",
-       "      <td>3.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>16.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29146</th>\n",
-       "      <td>2020-02-24T16:00:00+01:00</td>\n",
-       "      <td>101990.0</td>\n",
-       "      <td>-350.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>110.0</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>281.55</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>99190.0</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19.1</td>\n",
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-       "    <tr>\n",
-       "      <th>29147</th>\n",
-       "      <td>2020-02-24T19:00:00+01:00</td>\n",
-       "      <td>101800.0</td>\n",
-       "      <td>-220.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>2.9</td>\n",
-       "      <td>280.05</td>\n",
-       "      <td>55.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>98970.0</td>\n",
-       "      <td>4.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>15.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29148</th>\n",
-       "      <td>2020-02-24T22:00:00+01:00</td>\n",
-       "      <td>101740.0</td>\n",
-       "      <td>-80.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>170.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>280.35</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98890.0</td>\n",
-       "      <td>4.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29149</th>\n",
-       "      <td>2020-02-25T01:00:00+01:00</td>\n",
-       "      <td>101640.0</td>\n",
-       "      <td>-150.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>170.0</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>278.85</td>\n",
-       "      <td>83.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98740.0</td>\n",
-       "      <td>4.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29150</th>\n",
-       "      <td>2020-02-25T04:00:00+01:00</td>\n",
-       "      <td>101450.0</td>\n",
-       "      <td>-200.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>3.7</td>\n",
-       "      <td>277.75</td>\n",
-       "      <td>87.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98540.0</td>\n",
-       "      <td>4.8</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29151</th>\n",
-       "      <td>2020-02-25T07:00:00+01:00</td>\n",
-       "      <td>101530.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>276.95</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>98600.0</td>\n",
-       "      <td>6.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29152</th>\n",
-       "      <td>2020-02-25T10:00:00+01:00</td>\n",
-       "      <td>101490.0</td>\n",
-       "      <td>-20.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>200.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>277.55</td>\n",
-       "      <td>87.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>98580.0</td>\n",
-       "      <td>5.5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29153</th>\n",
-       "      <td>2020-02-25T13:00:00+01:00</td>\n",
-       "      <td>101330.0</td>\n",
-       "      <td>-140.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>3.8</td>\n",
-       "      <td>278.95</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>98440.0</td>\n",
-       "      <td>7.1</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>8.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29154</th>\n",
-       "      <td>2020-02-25T16:00:00+01:00</td>\n",
-       "      <td>100990.0</td>\n",
-       "      <td>-290.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>4.4</td>\n",
-       "      <td>279.55</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>98150.0</td>\n",
-       "      <td>7.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29155</th>\n",
-       "      <td>2020-02-25T19:00:00+01:00</td>\n",
-       "      <td>100910.0</td>\n",
-       "      <td>-90.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>260.0</td>\n",
-       "      <td>4.3</td>\n",
-       "      <td>278.95</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>98060.0</td>\n",
-       "      <td>8.4</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>11.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29156</th>\n",
-       "      <td>2020-02-25T22:00:00+01:00</td>\n",
-       "      <td>100980.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>280.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>273.65</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98120.0</td>\n",
-       "      <td>11.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29157</th>\n",
-       "      <td>2020-02-26T01:00:00+01:00</td>\n",
-       "      <td>101040.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>275.65</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>98150.0</td>\n",
-       "      <td>10.7</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>7.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29158</th>\n",
-       "      <td>2020-02-26T04:00:00+01:00</td>\n",
-       "      <td>101060.0</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>275.85</td>\n",
-       "      <td>86.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>98140.0</td>\n",
-       "      <td>13.6</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>4.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29159</th>\n",
-       "      <td>2020-02-26T07:00:00+01:00</td>\n",
-       "      <td>100940.0</td>\n",
-       "      <td>-110.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>274.85</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>98030.0</td>\n",
-       "      <td>7.4</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>5.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29160</th>\n",
-       "      <td>2020-02-26T10:00:00+01:00</td>\n",
-       "      <td>101100.0</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>6.8</td>\n",
-       "      <td>274.45</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98190.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29161</th>\n",
-       "      <td>2020-02-26T13:00:00+01:00</td>\n",
-       "      <td>101200.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>310.0</td>\n",
-       "      <td>10.3</td>\n",
-       "      <td>270.55</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98290.0</td>\n",
-       "      <td>19.5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>6.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29162</th>\n",
-       "      <td>2020-02-26T16:00:00+01:00</td>\n",
-       "      <td>101290.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>310.0</td>\n",
-       "      <td>8.9</td>\n",
-       "      <td>270.55</td>\n",
-       "      <td>47.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98390.0</td>\n",
-       "      <td>14.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29163</th>\n",
-       "      <td>2020-02-26T19:00:00+01:00</td>\n",
-       "      <td>101550.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>300.0</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>272.05</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98620.0</td>\n",
-       "      <td>7.4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29164</th>\n",
-       "      <td>2020-02-26T22:00:00+01:00</td>\n",
-       "      <td>101780.0</td>\n",
-       "      <td>200.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>274.05</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>98820.0</td>\n",
-       "      <td>8.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                            date      pmer   tend  cod_tend     dd    ff  \\\n",
-       "29145  2020-02-24T13:00:00+01:00  102380.0 -220.0       8.0  120.0   1.6   \n",
-       "29146  2020-02-24T16:00:00+01:00  101990.0 -350.0       6.0  110.0   1.6   \n",
-       "29147  2020-02-24T19:00:00+01:00  101800.0 -220.0       6.0  150.0   2.9   \n",
-       "29148  2020-02-24T22:00:00+01:00  101740.0  -80.0       6.0  170.0   1.8   \n",
-       "29149  2020-02-25T01:00:00+01:00  101640.0 -150.0       8.0  170.0   2.5   \n",
-       "29150  2020-02-25T04:00:00+01:00  101450.0 -200.0       6.0  150.0   3.7   \n",
-       "29151  2020-02-25T07:00:00+01:00  101530.0   60.0       3.0   30.0   4.0   \n",
-       "29152  2020-02-25T10:00:00+01:00  101490.0  -20.0       8.0  200.0   1.8   \n",
-       "29153  2020-02-25T13:00:00+01:00  101330.0 -140.0       8.0  150.0   3.8   \n",
-       "29154  2020-02-25T16:00:00+01:00  100990.0 -290.0       6.0  140.0   4.4   \n",
-       "29155  2020-02-25T19:00:00+01:00  100910.0  -90.0       5.0  260.0   4.3   \n",
-       "29156  2020-02-25T22:00:00+01:00  100980.0   60.0       3.0  280.0   8.0   \n",
-       "29157  2020-02-26T01:00:00+01:00  101040.0   30.0       2.0  230.0   2.8   \n",
-       "29158  2020-02-26T04:00:00+01:00  101060.0  -10.0       8.0  230.0   3.0   \n",
-       "29159  2020-02-26T07:00:00+01:00  100940.0 -110.0       6.0  210.0   3.3   \n",
-       "29160  2020-02-26T10:00:00+01:00  101100.0  160.0       3.0  230.0   6.8   \n",
-       "29161  2020-02-26T13:00:00+01:00  101200.0  100.0       3.0  310.0  10.3   \n",
-       "29162  2020-02-26T16:00:00+01:00  101290.0  100.0       3.0  310.0   8.9   \n",
-       "29163  2020-02-26T19:00:00+01:00  101550.0  230.0       2.0  300.0   2.8   \n",
-       "29164  2020-02-26T22:00:00+01:00  101780.0  200.0       2.0   50.0   3.2   \n",
-       "\n",
-       "           td     u    ww     pres  rafper  rr1  rr3    tc  \n",
-       "29145  281.15  59.0   0.0  99540.0     3.7  0.0  0.0  16.0  \n",
-       "29146  281.55  50.0   3.0  99190.0     3.3  0.0  0.0  19.1  \n",
-       "29147  280.05  55.0   3.0  98970.0     4.1  0.0  0.0  15.9  \n",
-       "29148  280.35  67.0   2.0  98890.0     4.3  0.0  0.0  13.2  \n",
-       "29149  278.85  83.0   2.0  98740.0     4.7  0.0  0.0   8.4  \n",
-       "29150  277.75  87.0   2.0  98540.0     4.8  0.0  0.0   6.6  \n",
-       "29151  276.95  92.0   3.0  98600.0     6.1  0.0  0.0   5.0  \n",
-       "29152  277.55  87.0   3.0  98580.0     5.5  0.0  0.0   6.4  \n",
-       "29153  278.95  85.0  21.0  98440.0     7.1  0.6  2.0   8.2  \n",
-       "29154  279.55  69.0   3.0  98150.0     7.2  0.0  0.0  11.9  \n",
-       "29155  278.95  69.0  25.0  98060.0     8.4 -0.1 -0.1  11.3  \n",
-       "29156  273.65  51.0   1.0  98120.0    11.3  0.0  0.0  10.2  \n",
-       "29157  275.65  69.0  25.0  98150.0    10.7 -0.1 -0.1   7.8  \n",
-       "29158  275.85  86.0  25.0  98140.0    13.6  0.4  1.8   4.8  \n",
-       "29159  274.85  78.0  21.0  98030.0     7.4 -0.1 -0.1   5.2  \n",
-       "29160  274.45  74.0   1.0  98190.0    10.0  0.0  0.0   5.6  \n",
-       "29161  270.55  52.0   1.0  98290.0    19.5  0.0 -0.1   6.6  \n",
-       "29162  270.55  47.0   1.0  98390.0    14.3  0.0  0.0   8.0  \n",
-       "29163  272.05  64.0   1.0  98620.0     7.4  0.0  0.0   5.2  \n",
-       "29164  274.05  84.0   1.0  98820.0     8.2  0.0  0.0   3.3  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shape is :  (29165, 14)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ---- Count the na values by columns\n",
-    "dataset_na    = df.isna().sum().tolist()\n",
-    "dataset_cols  = df.columns.tolist()\n",
-    "dataset_desc  = [ code2desc[c] for c in dataset_cols ]\n",
-    "\n",
-    "# ---- Show all of that\n",
-    "df_desc=pd.DataFrame({'Columns':dataset_cols, 'Description':dataset_desc, 'Na':dataset_na})\n",
-    "pwk.subtitle('Dataset columns :')\n",
-    "display(df_desc.style.set_properties(**{'text-align': 'left'}))\n",
-    "\n",
-    "pwk.subtitle('Have a look :')\n",
-    "display(df.tail(20))\n",
-    "print('Shape is : ', df.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 5.2 - Have a look (1 month)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:29:59.459329Z",
-     "iopub.status.busy": "2021-03-01T19:29:59.458858Z",
-     "iopub.status.idle": "2021-03-01T19:30:03.855832Z",
-     "shell.execute_reply": "2021-03-01T19:30:03.856341Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div class=\"comment\">Saved: ./run/figs/SYNOP1-01-one-month</div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x1440 with 13 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "i=random.randint(0,len(df)-240)\n",
-    "df.iloc[i:i+240].plot(subplots=True, fontsize=12, figsize=(16,20))\n",
-    "pwk.save_fig('01-one-month')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Save it"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:30:03.861352Z",
-     "iopub.status.busy": "2021-03-01T19:30:03.860880Z",
-     "iopub.status.idle": "2021-03-01T19:30:04.493145Z",
-     "shell.execute_reply": "2021-03-01T19:30:04.493633Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset saved. (3.0 Mo)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Synop description saved.\n"
-     ]
-    }
-   ],
-   "source": [
-    "dataset_name = 'synop-LYS.csv'\n",
-    "dataset_desc = 'synop.json'\n",
-    "output_dir   = './data'\n",
-    "\n",
-    "# ---- Save it\n",
-    "#\n",
-    "\n",
-    "pwk.mkdir(output_dir)\n",
-    "\n",
-    "filedata = f'{output_dir}/{dataset_name}'\n",
-    "filedesc = f'{output_dir}/{dataset_desc}'\n",
-    "\n",
-    "df.to_csv(filedata, sep=';', index=False)\n",
-    "size=os.path.getsize(filedata)/(1024*1024)\n",
-    "print(f'Dataset saved. ({size:0.1f} Mo)')\n",
-    "\n",
-    "with open(filedesc, 'w', encoding='utf-8') as f:\n",
-    "    json.dump(code2desc, f, indent=4)\n",
-    "print('Synop description saved.')\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:30:04.497126Z",
-     "iopub.status.busy": "2021-03-01T19:30:04.496656Z",
-     "iopub.status.idle": "2021-03-01T19:30:04.499000Z",
-     "shell.execute_reply": "2021-03-01T19:30:04.499478Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "End time is : Monday 01 March 2021, 20:30:04\n",
-      "Duration is : 00:00:06 807ms\n",
-      "This notebook ends here\n"
-     ]
-    }
-   ],
-   "source": [
-    "pwk.end()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/SYNOP/03-12h-predictions==done==.ipynb b/SYNOP/03-12h-predictions==done==.ipynb
deleted file mode 100644
index e50b1b2b81181bf9cea2add47c383957dc5a231f..0000000000000000000000000000000000000000
--- a/SYNOP/03-12h-predictions==done==.ipynb
+++ /dev/null
@@ -1,584 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
-    "\n",
-    "# <!-- TITLE --> [SYNOP3] - 12h predictions\n",
-    "<!-- DESC --> Episode 3: Attempt to predict in a more longer term \n",
-    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
-    "\n",
-    "## Objectives :\n",
-    " - Prediction at 12:00\n",
-    " - Understand the principle of using recurrent neurons... and the limitations of our example !\n",
-    "\n",
-    "\n",
-    "SYNOP meteorological data, available at: https://public.opendatasoft.com\n",
-    "\n",
-    "## What we're going to do :\n",
-    "\n",
-    " - Read the data\n",
-    " - Make a reccurent prediction\n",
-    "\n",
-    "## Step 1 - Import and init\n",
-    "### 1.1 - Python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:32:26.826042Z",
-     "iopub.status.busy": "2021-03-01T19:32:26.825564Z",
-     "iopub.status.idle": "2021-03-01T19:32:29.451327Z",
-     "shell.execute_reply": "2021-03-01T19:32:29.451816Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       "\n",
-       "div.warn {    \n",
-       "    background-color: #fcf2f2;\n",
-       "    border-color: #dFb5b4;\n",
-       "    border-left: 5px solid #dfb5b4;\n",
-       "    padding: 0.5em;\n",
-       "    font-weight: bold;\n",
-       "    font-size: 1.1em;;\n",
-       "    }\n",
-       "\n",
-       "\n",
-       "\n",
-       "div.nota {    \n",
-       "    background-color: #DAFFDE;\n",
-       "    border-left: 5px solid #92CC99;\n",
-       "    padding: 0.5em;\n",
-       "    }\n",
-       "\n",
-       "div.todo:before { 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-       "    margin-top:-20px;\n",
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-       "div.todo{\n",
-       "    font-weight: bold;\n",
-       "    font-size: 1.1em;\n",
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-       "div.todo ul{\n",
-       "    margin: 0.2em;\n",
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-       "div.todo li{\n",
-       "    margin-left:60px;\n",
-       "    margin-top:0;\n",
-       "    margin-bottom:0;\n",
-       "}\n",
-       "\n",
-       "div .comment{\n",
-       "    font-size:0.8em;\n",
-       "    color:#696969;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "</style>\n",
-       "\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**FIDLE 2020 - Practical Work Module**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Version              : 2.0.17\n",
-      "Notebook id          : SYNOP3\n",
-      "Run time             : Monday 01 March 2021, 20:32:29\n",
-      "TensorFlow version   : 2.4.0\n",
-      "Keras version        : 2.4.0\n",
-      "Datasets dir         : /gpfswork/rech/mlh/uja62cb/datasets\n",
-      "Run dir              : ./run\n",
-      "Update keras cache   : False\n",
-      "Save figs            : True\n",
-      "Path figs            : ./run/figs\n"
-     ]
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from tensorflow import keras\n",
-    "from tensorflow.keras.callbacks import TensorBoard\n",
-    "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
-    "\n",
-    "import numpy as np\n",
-    "import math, random\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import pandas as pd\n",
-    "import h5py, json\n",
-    "import os,time,sys\n",
-    "\n",
-    "from importlib import reload\n",
-    "\n",
-    "sys.path.append('..')\n",
-    "import fidle.pwk as pwk\n",
-    "\n",
-    "datasets_dir = pwk.init('SYNOP3')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1.2 - Parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:32:29.456470Z",
-     "iopub.status.busy": "2021-03-01T19:32:29.455998Z",
-     "iopub.status.idle": "2021-03-01T19:32:29.457580Z",
-     "shell.execute_reply": "2021-03-01T19:32:29.458059Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# ---- About dataset\n",
-    "#\n",
-    "dataset_dir      = './data'\n",
-    "dataset_filename = 'synop-LYS.csv'\n",
-    "schema_filename  = 'synop.json'\n",
-    "features         = ['tend', 'cod_tend', 'dd', 'ff', 'td', 'u', 'ww', 'pres', 'rafper', 'rr1', 'rr3', 'tc']\n",
-    "features_len     = len(features)\n",
-    "\n",
-    "# ---- About training\n",
-    "#\n",
-    "iterations       = 4        # number of iterations for prediction (1 iteration = 3h)\n",
-    "\n",
-    "scale            = 1        # Percentage of dataset to be used (1=all)\n",
-    "train_prop       = .8       # Percentage for train (the rest being for the test)\n",
-    "sequence_len     = 16\n",
-    "batch_size       = 32\n",
-    "epochs           = 10"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Override parameters (batch mode) - Just forget this cell"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:32:29.461294Z",
-     "iopub.status.busy": "2021-03-01T19:32:29.460826Z",
-     "iopub.status.idle": "2021-03-01T19:32:29.464737Z",
-     "shell.execute_reply": "2021-03-01T19:32:29.464253Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "**\\*\\* Overrided parameters : \\*\\***"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iterations           : 4\n",
-      "scale                : 1\n",
-      "train_prop           : 0.8\n",
-      "sequence_len         : 16\n",
-      "batch_size           : 32\n",
-      "epochs               : 10\n"
-     ]
-    }
-   ],
-   "source": [
-    "pwk.override('iterations', 'scale', 'train_prop', 'sequence_len', 'batch_size', 'epochs')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2 - Read and prepare dataset\n",
-    "As before, in episode 2... ;-)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:32:29.470231Z",
-     "iopub.status.busy": "2021-03-01T19:32:29.469753Z",
-     "iopub.status.idle": "2021-03-01T19:32:29.535392Z",
-     "shell.execute_reply": "2021-03-01T19:32:29.535889Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset       :  (29165, 14)\n",
-      "Train dataset :  (23332, 12)\n",
-      "Test  dataset :  (5833, 12)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ---- Read dataset\n",
-    "\n",
-    "df = pd.read_csv(f'{dataset_dir}/{dataset_filename}', header=0, sep=';')\n",
-    "\n",
-    "# ---- Scaling\n",
-    "\n",
-    "df = df[:int(scale*len(df))]\n",
-    "train_len=int(train_prop*len(df))\n",
-    "\n",
-    "# ---- Train / Test\n",
-    "dataset_train = df.loc[ :train_len-1, features ]\n",
-    "dataset_test  = df.loc[train_len:,    features ]\n",
-    "\n",
-    "# ---- Normalize, and convert to numpy array\n",
-    "mean = dataset_train.mean()\n",
-    "std  = dataset_train.std()\n",
-    "dataset_train = np.array( (dataset_train - mean) / std )\n",
-    "dataset_test  = np.array( (dataset_test  - mean) / std )\n",
-    "\n",
-    "print('Dataset       : ',df.shape)\n",
-    "print('Train dataset : ',dataset_train.shape)\n",
-    "print('Test  dataset : ',dataset_test.shape)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Predict"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3.1 - Load model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:32:29.539062Z",
-     "iopub.status.busy": "2021-03-01T19:32:29.538589Z",
-     "iopub.status.idle": "2021-03-01T19:32:30.756275Z",
-     "shell.execute_reply": "2021-03-01T19:32:30.756780Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n"
-     ]
-    }
-   ],
-   "source": [
-    "loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3.2 Make a 12h prediction\n",
-    "Note : Our predictions are normalized"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:32:30.762304Z",
-     "iopub.status.busy": "2021-03-01T19:32:30.761819Z",
-     "iopub.status.idle": "2021-03-01T19:32:34.804192Z",
-     "shell.execute_reply": "2021-03-01T19:32:34.804694Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div class=\"comment\">Saved: ./run/figs/SYNOP3-01-prediction-norm</div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 1080x1152 with 12 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "# ---- Initial sequence\n",
-    "\n",
-    "s=random.randint(0,len(dataset_test)-sequence_len-iterations)\n",
-    "\n",
-    "sequence_pred = dataset_test[s:s+sequence_len].copy()\n",
-    "sequence_true = dataset_test[s:s+sequence_len+iterations].copy()\n",
-    "\n",
-    "# ---- Iterate on 4 predictions\n",
-    "\n",
-    "sequence_pred=list(sequence_pred)\n",
-    "\n",
-    "for i in range(iterations):\n",
-    "    sequence=sequence_pred[-sequence_len:]\n",
-    "    pred = loaded_model.predict( np.array([sequence]) )\n",
-    "    sequence_pred.append(pred[0])\n",
-    "\n",
-    "# ---- Extract the predictions    \n",
-    "\n",
-    "pred=np.array(sequence_pred[-iterations:])\n",
-    "       \n",
-    "# ---- Show result\n",
-    "\n",
-    "pwk.plot_multivariate_serie(sequence_true, predictions=pred, labels=features, save_as='01-prediction-norm')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3.3 Full prediction\n",
-    "#### Some cool functions that do the job"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:32:34.811682Z",
-     "iopub.status.busy": "2021-03-01T19:32:34.811205Z",
-     "iopub.status.idle": "2021-03-01T19:32:34.812843Z",
-     "shell.execute_reply": "2021-03-01T19:32:34.813317Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def denormalize(mean,std,seq):\n",
-    "    nseq = seq.copy()\n",
-    "    for i,s in enumerate(nseq):\n",
-    "        s = s*std + mean\n",
-    "        nseq[i]=s\n",
-    "    return nseq\n",
-    "\n",
-    "\n",
-    "def get_prediction(dataset, model, iterations=4,sequence_len=16):\n",
-    "\n",
-    "    # ---- Initial sequence\n",
-    "\n",
-    "    s=random.randint(0,len(dataset)-sequence_len-iterations)\n",
-    "\n",
-    "    sequence_pred = dataset[s:s+sequence_len].copy()\n",
-    "    sequence_true = dataset[s:s+sequence_len+iterations].copy()\n",
-    "\n",
-    "    # ---- Iterate\n",
-    "\n",
-    "    sequence_pred=list(sequence_pred)\n",
-    "\n",
-    "    for i in range(iterations):\n",
-    "        sequence=sequence_pred[-sequence_len:]\n",
-    "        pred = model.predict( np.array([sequence]) )\n",
-    "        sequence_pred.append(pred[0])\n",
-    "\n",
-    "    # ---- Extract the predictions    \n",
-    "\n",
-    "    pred=np.array(sequence_pred[-iterations:])\n",
-    "\n",
-    "    # ---- De-normalization\n",
-    "\n",
-    "    sequence_true = denormalize(mean,std, sequence_true)\n",
-    "    pred          = denormalize(mean,std, pred)\n",
-    "\n",
-    "    return sequence_true,pred"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### And the result is..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:32:34.817120Z",
-     "iopub.status.busy": "2021-03-01T19:32:34.816649Z",
-     "iopub.status.idle": "2021-03-01T19:32:35.853404Z",
-     "shell.execute_reply": "2021-03-01T19:32:35.853905Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div class=\"comment\">Saved: ./run/figs/SYNOP3-02-prediction</div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 3024x2304 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "  \n",
-    "sequence_true, pred = get_prediction(dataset_test, loaded_model,iterations=4)\n",
-    "\n",
-    "feat=11\n",
-    "\n",
-    "pwk.plot_multivariate_serie(sequence_true, predictions=pred, labels=features, only_features=[feat],width=14, height=8, save_as='02-prediction')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-03-01T19:32:35.857217Z",
-     "iopub.status.busy": "2021-03-01T19:32:35.856739Z",
-     "iopub.status.idle": "2021-03-01T19:32:35.859074Z",
-     "shell.execute_reply": "2021-03-01T19:32:35.859553Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "End time is : Monday 01 March 2021, 20:32:35\n",
-      "Duration is : 00:00:06 410ms\n",
-      "This notebook ends here\n"
-     ]
-    }
-   ],
-   "source": [
-    "pwk.end()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"todo\">\n",
-    "    What you can do:\n",
-    "    <ul>\n",
-    "        <li>Trying to increase the forecasting time</li>\n",
-    "        <li>What could we do to try to improve our forecasts?</li>\n",
-    "    </ul>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/SYNOP/LADYB1-Ladybug.ipynb b/SYNOP/LADYB1-Ladybug.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a2eabb2f7239949e0ebcefacc80b4621e51d3df5
--- /dev/null
+++ b/SYNOP/LADYB1-Ladybug.ipynb
@@ -0,0 +1,489 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+    "\n",
+    "# <!-- TITLE --> [LADYB1] - Prediction of a 2D trajectory via RNN\n",
+    "<!-- DESC --> Artificial dataset generation and prediction attempt via a recurrent network\n",
+    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+    "\n",
+    "## Objectives :\n",
+    " - Understanding the use of a recurrent neural network\n",
+    "\n",
+    "## What we're going to do :\n",
+    "\n",
+    " - Generate an artificial dataset\n",
+    " - dataset preparation\n",
+    " - Doing our training\n",
+    " - Making predictions\n",
+    "\n",
+    "## Step 1 - Import and init\n",
+    "### 1.1 - Python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras.callbacks import TensorBoard\n",
+    "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
+    "\n",
+    "import numpy as np\n",
+    "import math, random\n",
+    "from math import sin,cos,pi\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import pandas as pd\n",
+    "import h5py, json\n",
+    "import os,time,sys\n",
+    "\n",
+    "from importlib import reload\n",
+    "\n",
+    "sys.path.append('..')\n",
+    "import fidle.pwk as pwk\n",
+    "\n",
+    "run_dir = './run/LADYBUG1'\n",
+    "datasets_dir = pwk.init('LADYBUG1', run_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2 - Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ---- About dataset\n",
+    "#\n",
+    "max_t        = 1000\n",
+    "delta_t      = 0.02\n",
+    "features_len = 2\n",
+    "\n",
+    "\n",
+    "sequence_len = 20\n",
+    "predict_len  = 5\n",
+    "\n",
+    "# ---- About training\n",
+    "#\n",
+    "scale        = 1        # Percentage of dataset to be used (1=all)\n",
+    "train_prop   = .8       # Percentage for train (the rest being for the test)\n",
+    "batch_size   = 32\n",
+    "epochs       = 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Override parameters (batch mode) - Just forget this cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pwk.override('scale', 'train_prop', 'sequence_len', 'predict_len', 'batch_size', 'epochs')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Generation of a fun dataset\n",
+    "### 2.1 - Virtual trajectory of our ladybug"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ladybug_init(s=122):\n",
+    "    \n",
+    "    if s>0 : random.seed(s)\n",
+    "    ladybug_init.params_x = [ random.gauss(0.,1.) for u in range(8)]\n",
+    "    ladybug_init.params_y = [ random.gauss(0.,1.) for u in range(8)]\n",
+    "    \n",
+    "def ladybug_move(t):\n",
+    "    k=0.5\n",
+    "    [ax1, ax2, ax3, ax4, kx1, kx2, kx3, kx4] = ladybug_init.params_x\n",
+    "    [ay1, ay2, ay3, ay4, ky1, ky2, ky3, ky4] = ladybug_init.params_y\n",
+    "    \n",
+    "    x = ax1*sin(t*(kx1+20)) + ax2*cos(t*(kx2+10)) + ax3*sin(t*(kx3+5)) + ax4*cos(t*(kx4+5))\n",
+    "    y = ay1*cos(t*(ky1+20)) + ay2*sin(t*(ky2+10)) + ay3*cos(t*(ky3+5)) + ay4*sin(t*(ky4+5)) \n",
+    "\n",
+    "\n",
+    "    return x,y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2 - Get some positions, and build a rescaled and normalized dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ---- Get positions\n",
+    "#\n",
+    "ladybug_init(s=16)\n",
+    "x,y = 0,0\n",
+    "positions=[]\n",
+    "for t in np.arange(0., max_t, delta_t):\n",
+    "    positions.append([x,y])\n",
+    "    x,y = ladybug_move(t)\n",
+    "#     (x,y) = (x+dx, y+dy)\n",
+    "\n",
+    "# ---- Build rescaled dataset\n",
+    "#\n",
+    "n = int( len(positions)*scale )\n",
+    "dataset = np.array(positions[:n])\n",
+    "\n",
+    "k = int(len(dataset)*train_prop)\n",
+    "x_train = dataset[:k]\n",
+    "x_test  = dataset[k:]\n",
+    "\n",
+    "# ---- Normalize\n",
+    "#\n",
+    "mean = x_train.mean()\n",
+    "std  = x_train.std()\n",
+    "x_train = (x_train - mean) / std\n",
+    "x_test  = (x_test  - mean) / std\n",
+    "\n",
+    "print(\"Dataset generated.\")\n",
+    "print(\"Train shape is : \", x_train.shape)\n",
+    "print(\"Test  shape is : \", x_test.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3 - Have a look\n",
+    "An extract from the data we have: the virtual trajectory of our ladybug   \n",
+    "And what we want to predict (in red), from a segment (in blue)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pwk.plot_2d_serie(x_train[:1000], figsize=(12,12), lw=1,ms=4,save_as='01-dataset')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "k1,k2 = sequence_len, predict_len\n",
+    "i = random.randint(0,len(x_test)-k1-k2)\n",
+    "j = i+k1\n",
+    "\n",
+    "pwk.plot_2d_segment( x_test[i:j+k2], x_test[j:j+k2],ms=6, save_as='02-objectives')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4 - Prepare some nice data generator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ---- Train generator\n",
+    "#\n",
+    "train_generator = TimeseriesGenerator(x_train, x_train, length=sequence_len,  batch_size=batch_size)\n",
+    "test_generator  = TimeseriesGenerator(x_test,  x_test,  length=sequence_len,  batch_size=batch_size)\n",
+    "\n",
+    "# ---- About\n",
+    "#\n",
+    "pwk.subtitle('About the splitting of our dataset :')\n",
+    "\n",
+    "x,y=train_generator[0]\n",
+    "print(f'Number of batch trains available : ', len(train_generator))\n",
+    "print('batch x shape : ',x.shape)\n",
+    "print('batch y shape : ',y.shape)\n",
+    "\n",
+    "x,y=train_generator[0]\n",
+    "pwk.subtitle('What a batch looks like (x) :')\n",
+    "pwk.np_print(x[0] )\n",
+    "pwk.subtitle('What a batch looks like (y) :')\n",
+    "pwk.np_print(y[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Create a model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = keras.models.Sequential()\n",
+    "model.add( keras.layers.InputLayer(input_shape=(sequence_len, features_len)) )\n",
+    "# model.add( keras.layers.GRU(200, dropout=.1, recurrent_dropout=0.5, return_sequences=False, activation='relu') )\n",
+    "model.add( keras.layers.GRU(200, return_sequences=False, activation='relu') )\n",
+    "model.add( keras.layers.Dense(features_len) )\n",
+    "\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Compile and run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.1 - Add callback"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pwk.mkdir('./run/models')\n",
+    "save_dir = './run/models/best_model.h5'\n",
+    "bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2 - Compile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer='rmsprop', \n",
+    "              loss='mse', \n",
+    "              metrics   = ['mae'] )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.3 - Fit\n",
+    "3' with a CPU (laptop)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pwk.chrono_start()\n",
+    "\n",
+    "history=model.fit(train_generator, \n",
+    "                  epochs=epochs, \n",
+    "                  verbose=1,\n",
+    "                  validation_data = test_generator,\n",
+    "                  callbacks = [bestmodel_callback])\n",
+    "\n",
+    "pwk.chrono_show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pwk.plot_history(history,plot={'loss':['loss','val_loss'], 'mae':['mae','val_mae']}, save_as='03-history')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5 - Predict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.1 - Load model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')\n",
+    "print('Loaded.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.2 - Make a 1-step prediction\n",
+    "A simple prediction on a single iteration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s=random.randint(0,len(x_test)-sequence_len)\n",
+    "\n",
+    "sequence      = x_test[s:s+sequence_len]\n",
+    "sequence_true = x_test[s:s+sequence_len+1]\n",
+    "\n",
+    "sequence_pred = loaded_model.predict( np.array([sequence]) )\n",
+    "\n",
+    "pwk.plot_2d_segment(sequence_true, sequence_pred)\n",
+    "pwk.plot_multivariate_serie(sequence_true, predictions=sequence_pred, labels=['Axis=0', 'Axis=1'],save_as='04-one-step-prediction')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.3 - Make n-steps prediction\n",
+    "A longer term prediction, via a nice iteration function :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_prediction(dataset, model, iterations=4):\n",
+    "\n",
+    "    # ---- Initial sequence\n",
+    "    #\n",
+    "    s=random.randint(0,len(dataset)-sequence_len-iterations)\n",
+    "\n",
+    "    sequence_pred = dataset[s:s+sequence_len].copy()\n",
+    "    sequence_true = dataset[s:s+sequence_len+iterations].copy()\n",
+    "\n",
+    "    # ---- Iterate \n",
+    "    #\n",
+    "    sequence_pred = list(sequence_pred)\n",
+    "\n",
+    "    for i in range(iterations):\n",
+    "        sequence   = sequence_pred[-sequence_len:]\n",
+    "        prediction = model.predict( np.array([sequence]) )\n",
+    "        sequence_pred.append(prediction[0])\n",
+    "\n",
+    "    # ---- Extract the predictions    \n",
+    "    #\n",
+    "    prediction = np.array(sequence_pred[-iterations:])\n",
+    "\n",
+    "    return sequence_true,prediction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An n-steps prediction :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sequence_true, sequence_pred = get_prediction(x_test, loaded_model, iterations=5)\n",
+    "\n",
+    "pwk.plot_2d_segment(sequence_true, sequence_pred, ms=8, save_as='02-prediction-norm')\n",
+    "pwk.plot_multivariate_serie(sequence_true, predictions=sequence_pred, hide_ticks=True, labels=['Axis=0', 'Axis=1'],save_as='02-prediction-norm')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pwk.end()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/SYNOP/LADYB1-Ladybug==done==.ipynb b/SYNOP/LADYB1-Ladybug==done==.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..33f3bcc4ed05aec8ee96464849e0027c7d258ebf
--- /dev/null
+++ b/SYNOP/LADYB1-Ladybug==done==.ipynb
@@ -0,0 +1,17924 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+    "\n",
+    "# <!-- TITLE --> [LADYB1] - Prediction of a 2D trajectory via RNN\n",
+    "<!-- DESC --> Artificial dataset generation and prediction attempt via a recurrent network\n",
+    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+    "\n",
+    "## Objectives :\n",
+    " - Understanding the use of a recurrent neural network\n",
+    "\n",
+    "## What we're going to do :\n",
+    "\n",
+    " - Generate an artificial dataset\n",
+    " - dataset preparation\n",
+    " - Doing our training\n",
+    " - Making predictions\n",
+    "\n",
+    "## Step 1 - Import and init\n",
+    "### 1.1 - Python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:13:23.614441Z",
+     "iopub.status.busy": "2021-03-07T20:13:23.614016Z",
+     "iopub.status.idle": "2021-03-07T20:13:25.134363Z",
+     "shell.execute_reply": "2021-03-07T20:13:25.134690Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "\n",
+       "div.warn {    \n",
+       "    background-color: #fcf2f2;\n",
+       "    border-color: #dFb5b4;\n",
+       "    border-left: 5px solid #dfb5b4;\n",
+       "    padding: 0.5em;\n",
+       "    font-weight: bold;\n",
+       "    font-size: 1.1em;;\n",
+       "    }\n",
+       "\n",
+       "\n",
+       "\n",
+       "div.nota {    \n",
+       "    background-color: #DAFFDE;\n",
+       "    border-left: 5px solid #92CC99;\n",
+       "    padding: 0.5em;\n",
+       "    }\n",
+       "\n",
+       "div.todo:before { content:url(data:image/svg+xml;base64,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);\n",
+       "    float:left;\n",
+       "    margin-right:20px;\n",
+       "    margin-top:-20px;\n",
+       "    margin-bottom:20px;\n",
+       "}\n",
+       "div.todo{\n",
+       "    font-weight: bold;\n",
+       "    font-size: 1.1em;\n",
+       "    margin-top:40px;\n",
+       "}\n",
+       "div.todo ul{\n",
+       "    margin: 0.2em;\n",
+       "}\n",
+       "div.todo li{\n",
+       "    margin-left:60px;\n",
+       "    margin-top:0;\n",
+       "    margin-bottom:0;\n",
+       "}\n",
+       "\n",
+       "div .comment{\n",
+       "    font-size:0.8em;\n",
+       "    color:#696969;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "</style>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "<br>**FIDLE 2020 - Practical Work Module**"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Version              : 2.0.18\n",
+      "Notebook id          : LADYBUG1\n",
+      "Run time             : Sunday 07 March 2021, 21:13:25\n",
+      "TensorFlow version   : 2.2.0\n",
+      "Keras version        : 2.3.0-tf\n",
+      "Datasets dir         : /home/pjluc/datasets/fidle\n",
+      "Run dir              : ./run/LADYBUG1\n",
+      "Update keras cache   : False\n",
+      "Save figs            : True\n",
+      "Path figs            : ./run/LADYBUG1/figs\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras.callbacks import TensorBoard\n",
+    "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
+    "\n",
+    "import numpy as np\n",
+    "import math, random\n",
+    "from math import sin,cos,pi\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import pandas as pd\n",
+    "import h5py, json\n",
+    "import os,time,sys\n",
+    "\n",
+    "from importlib import reload\n",
+    "\n",
+    "sys.path.append('..')\n",
+    "import fidle.pwk as pwk\n",
+    "\n",
+    "run_dir = './run/LADYBUG1'\n",
+    "datasets_dir = pwk.init('LADYBUG1', run_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2 - Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:13:25.138412Z",
+     "iopub.status.busy": "2021-03-07T20:13:25.138029Z",
+     "iopub.status.idle": "2021-03-07T20:13:25.140203Z",
+     "shell.execute_reply": "2021-03-07T20:13:25.139877Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# ---- About dataset\n",
+    "#\n",
+    "max_t        = 1000\n",
+    "delta_t      = 0.02\n",
+    "features_len = 2\n",
+    "\n",
+    "\n",
+    "sequence_len = 20\n",
+    "predict_len  = 5\n",
+    "\n",
+    "# ---- About training\n",
+    "#\n",
+    "scale        = 1        # Percentage of dataset to be used (1=all)\n",
+    "train_prop   = .8       # Percentage for train (the rest being for the test)\n",
+    "batch_size   = 32\n",
+    "epochs       = 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Override parameters (batch mode) - Just forget this cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:13:25.143675Z",
+     "iopub.status.busy": "2021-03-07T20:13:25.143329Z",
+     "iopub.status.idle": "2021-03-07T20:13:25.147331Z",
+     "shell.execute_reply": "2021-03-07T20:13:25.148486Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pwk.override('scale', 'train_prop', 'sequence_len', 'predict_len', 'batch_size', 'epochs')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Generation of a fun dataset\n",
+    "### 2.1 - Virtual trajectory of our ladybug"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:13:25.156304Z",
+     "iopub.status.busy": "2021-03-07T20:13:25.155913Z",
+     "iopub.status.idle": "2021-03-07T20:13:25.158152Z",
+     "shell.execute_reply": "2021-03-07T20:13:25.158422Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def ladybug_init(s=122):\n",
+    "    \n",
+    "    if s>0 : random.seed(s)\n",
+    "    ladybug_init.params_x = [ random.gauss(0.,1.) for u in range(8)]\n",
+    "    ladybug_init.params_y = [ random.gauss(0.,1.) for u in range(8)]\n",
+    "    \n",
+    "def ladybug_move(t):\n",
+    "    k=0.5\n",
+    "    [ax1, ax2, ax3, ax4, kx1, kx2, kx3, kx4] = ladybug_init.params_x\n",
+    "    [ay1, ay2, ay3, ay4, ky1, ky2, ky3, ky4] = ladybug_init.params_y\n",
+    "    \n",
+    "    x = ax1*sin(t*(kx1+20)) + ax2*cos(t*(kx2+10)) + ax3*sin(t*(kx3+5)) + ax4*cos(t*(kx4+5))\n",
+    "    y = ay1*cos(t*(ky1+20)) + ay2*sin(t*(ky2+10)) + ay3*cos(t*(ky3+5)) + ay4*sin(t*(ky4+5)) \n",
+    "\n",
+    "\n",
+    "    return x,y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2 - Get some positions, and build a rescaled and normalized dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:13:25.229641Z",
+     "iopub.status.busy": "2021-03-07T20:13:25.162436Z",
+     "iopub.status.idle": "2021-03-07T20:13:25.407737Z",
+     "shell.execute_reply": "2021-03-07T20:13:25.407382Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset generated.\n",
+      "Train shape is :  (40000, 2)\n",
+      "Test  shape is :  (10000, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ---- Get positions\n",
+    "#\n",
+    "ladybug_init(s=16)\n",
+    "x,y = 0,0\n",
+    "positions=[]\n",
+    "for t in np.arange(0., max_t, delta_t):\n",
+    "    positions.append([x,y])\n",
+    "    x,y = ladybug_move(t)\n",
+    "#     (x,y) = (x+dx, y+dy)\n",
+    "\n",
+    "# ---- Build rescaled dataset\n",
+    "#\n",
+    "n = int( len(positions)*scale )\n",
+    "dataset = np.array(positions[:n])\n",
+    "\n",
+    "k = int(len(dataset)*train_prop)\n",
+    "x_train = dataset[:k]\n",
+    "x_test  = dataset[k:]\n",
+    "\n",
+    "# ---- Normalize\n",
+    "#\n",
+    "mean = x_train.mean()\n",
+    "std  = x_train.std()\n",
+    "x_train = (x_train - mean) / std\n",
+    "x_test  = (x_test  - mean) / std\n",
+    "\n",
+    "print(\"Dataset generated.\")\n",
+    "print(\"Train shape is : \", x_train.shape)\n",
+    "print(\"Test  shape is : \", x_test.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3 - Have a look\n",
+    "An extract from the data we have: the virtual trajectory of our ladybug   \n",
+    "And what we want to predict (in red), from a segment (in blue)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:13:25.423705Z",
+     "iopub.status.busy": "2021-03-07T20:13:25.423353Z",
+     "iopub.status.idle": "2021-03-07T20:13:26.003515Z",
+     "shell.execute_reply": "2021-03-07T20:13:26.003849Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"comment\">Saved: ./run/LADYBUG1/figs/LADYBUG1-01-dataset</div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pwk.plot_2d_serie(x_train[:1000], figsize=(12,12), lw=1,ms=4,save_as='01-dataset')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:13:26.016760Z",
+     "iopub.status.busy": "2021-03-07T20:13:26.016440Z",
+     "iopub.status.idle": "2021-03-07T20:13:26.218617Z",
+     "shell.execute_reply": "2021-03-07T20:13:26.218935Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"comment\">Saved: ./run/LADYBUG1/figs/LADYBUG1-02-objectives</div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "k1,k2 = sequence_len, predict_len\n",
+    "i = random.randint(0,len(x_test)-k1-k2)\n",
+    "j = i+k1\n",
+    "\n",
+    "pwk.plot_2d_segment( x_test[i:j+k2], x_test[j:j+k2],ms=6, save_as='02-objectives')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4 - Prepare some nice data generator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:13:26.223070Z",
+     "iopub.status.busy": "2021-03-07T20:13:26.222628Z",
+     "iopub.status.idle": "2021-03-07T20:13:26.231296Z",
+     "shell.execute_reply": "2021-03-07T20:13:26.230974Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "<br>**About the splitting of our dataset :**"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of batch trains available :  1250\n",
+      "batch x shape :  (32, 20, 2)\n",
+      "batch y shape :  (32, 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "<br>**What a batch looks like (x) :**"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1.973e-04  1.973e-04]\n",
+      " [ 1.511e+00  6.019e-01]\n",
+      " [ 1.097e+00  5.694e-01]\n",
+      " [ 6.732e-01  4.095e-01]\n",
+      " [ 2.772e-01  1.534e-01]\n",
+      " [-6.037e-02 -1.521e-01]\n",
+      " [-3.214e-01 -4.520e-01]\n",
+      " [-5.014e-01 -6.919e-01]\n",
+      " [-6.096e-01 -8.267e-01]\n",
+      " [-6.663e-01 -8.277e-01]\n",
+      " [-6.991e-01 -6.870e-01]\n",
+      " [-7.382e-01 -4.191e-01]\n",
+      " [-8.108e-01 -5.863e-02]\n",
+      " [-9.365e-01  3.454e-01]\n",
+      " [-1.124e+00  7.367e-01]\n",
+      " [-1.368e+00  1.061e+00]\n",
+      " [-1.652e+00  1.274e+00]\n",
+      " [-1.946e+00  1.349e+00]\n",
+      " [-2.214e+00  1.281e+00]\n",
+      " [-2.420e+00  1.086e+00]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "<br>**What a batch looks like (y) :**"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-2.528  0.801]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ---- Train generator\n",
+    "#\n",
+    "train_generator = TimeseriesGenerator(x_train, x_train, length=sequence_len,  batch_size=batch_size)\n",
+    "test_generator  = TimeseriesGenerator(x_test,  x_test,  length=sequence_len,  batch_size=batch_size)\n",
+    "\n",
+    "# ---- About\n",
+    "#\n",
+    "pwk.subtitle('About the splitting of our dataset :')\n",
+    "\n",
+    "x,y=train_generator[0]\n",
+    "print(f'Number of batch trains available : ', len(train_generator))\n",
+    "print('batch x shape : ',x.shape)\n",
+    "print('batch y shape : ',y.shape)\n",
+    "\n",
+    "x,y=train_generator[0]\n",
+    "pwk.subtitle('What a batch looks like (x) :')\n",
+    "pwk.np_print(x[0] )\n",
+    "pwk.subtitle('What a batch looks like (y) :')\n",
+    "pwk.np_print(y[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Create a model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:13:26.237193Z",
+     "iopub.status.busy": "2021-03-07T20:13:26.236874Z",
+     "iopub.status.idle": "2021-03-07T20:13:26.322582Z",
+     "shell.execute_reply": "2021-03-07T20:13:26.322838Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "gru (GRU)                    (None, 200)               122400    \n",
+      "_________________________________________________________________\n",
+      "dense (Dense)                (None, 2)                 402       \n",
+      "=================================================================\n",
+      "Total params: 122,802\n",
+      "Trainable params: 122,802\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = keras.models.Sequential()\n",
+    "model.add( keras.layers.InputLayer(input_shape=(sequence_len, features_len)) )\n",
+    "# model.add( keras.layers.GRU(200, dropout=.1, recurrent_dropout=0.5, return_sequences=False, activation='relu') )\n",
+    "model.add( keras.layers.GRU(200, return_sequences=False, activation='relu') )\n",
+    "model.add( keras.layers.Dense(features_len) )\n",
+    "\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Compile and run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.1 - Add callback"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:13:26.325943Z",
+     "iopub.status.busy": "2021-03-07T20:13:26.325630Z",
+     "iopub.status.idle": "2021-03-07T20:13:26.328136Z",
+     "shell.execute_reply": "2021-03-07T20:13:26.327743Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pwk.mkdir('./run/models')\n",
+    "save_dir = './run/models/best_model.h5'\n",
+    "bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2 - Compile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:13:26.335152Z",
+     "iopub.status.busy": "2021-03-07T20:13:26.334830Z",
+     "iopub.status.idle": "2021-03-07T20:13:26.339120Z",
+     "shell.execute_reply": "2021-03-07T20:13:26.339375Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer='rmsprop', \n",
+    "              loss='mse', \n",
+    "              metrics   = ['mae'] )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.3 - Fit\n",
+    "3' with a CPU (laptop)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:13:26.342748Z",
+     "iopub.status.busy": "2021-03-07T20:13:26.342437Z",
+     "iopub.status.idle": "2021-03-07T20:15:52.824246Z",
+     "shell.execute_reply": "2021-03-07T20:15:52.824702Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "   1/1250 [..............................] - ETA: 0s - loss: 0.5276 - mae: 0.6221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "   3/1250 [..............................] - ETA: 22s - loss: 0.7849 - mae: 0.7437"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "   5/1250 [..............................] - ETA: 26s - loss: 0.8883 - mae: 0.7830"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "   8/1250 [..............................] - ETA: 27s - loss: 0.7637 - mae: 0.7235"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  11/1250 [..............................] - ETA: 26s - loss: 0.6855 - mae: 0.6845"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  14/1250 [..............................] - ETA: 25s - loss: 0.6042 - mae: 0.6351"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  17/1250 [..............................] - ETA: 25s - loss: 0.5703 - mae: 0.6149"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  20/1250 [..............................] - ETA: 25s - loss: 0.5310 - mae: 0.5930"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  23/1250 [..............................] - ETA: 24s - loss: 0.5066 - mae: 0.5769"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  26/1250 [..............................] - ETA: 24s - loss: 0.4785 - mae: 0.5594"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  29/1250 [..............................] - ETA: 24s - loss: 0.5359 - mae: 0.5753"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  32/1250 [..............................] - ETA: 24s - loss: 0.5153 - mae: 0.5653"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  35/1250 [..............................] - ETA: 24s - loss: 0.4956 - mae: 0.5550"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  38/1250 [..............................] - ETA: 24s - loss: 0.4793 - mae: 0.5452"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  41/1250 [..............................] - ETA: 24s - loss: 0.4629 - mae: 0.5358"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  44/1250 [>.............................] - ETA: 23s - loss: 0.4547 - mae: 0.5316"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  47/1250 [>.............................] - ETA: 23s - loss: 0.4401 - mae: 0.5228"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  50/1250 [>.............................] - ETA: 23s - loss: 0.4280 - mae: 0.5153"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  53/1250 [>.............................] - ETA: 23s - loss: 0.4181 - mae: 0.5102"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  56/1250 [>.............................] - ETA: 23s - loss: 0.4068 - mae: 0.5036"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  59/1250 [>.............................] - ETA: 23s - loss: 0.3945 - mae: 0.4957"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  62/1250 [>.............................] - ETA: 23s - loss: 0.3828 - mae: 0.4876"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  65/1250 [>.............................] - ETA: 23s - loss: 0.3818 - mae: 0.4871"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  68/1250 [>.............................] - ETA: 23s - loss: 0.3776 - mae: 0.4846"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  71/1250 [>.............................] - ETA: 23s - loss: 0.3712 - mae: 0.4800"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  74/1250 [>.............................] - ETA: 23s - loss: 0.3659 - mae: 0.4762"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  76/1250 [>.............................] - ETA: 23s - loss: 0.3610 - mae: 0.4731"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  79/1250 [>.............................] - ETA: 23s - loss: 0.3542 - mae: 0.4690"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  82/1250 [>.............................] - ETA: 23s - loss: 0.3481 - mae: 0.4657"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  85/1250 [=>............................] - ETA: 23s - loss: 0.3408 - mae: 0.4602"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  88/1250 [=>............................] - ETA: 22s - loss: 0.3342 - mae: 0.4557"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  91/1250 [=>............................] - ETA: 22s - loss: 0.3278 - mae: 0.4511"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  94/1250 [=>............................] - ETA: 22s - loss: 0.3216 - mae: 0.4461"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  97/1250 [=>............................] - ETA: 22s - loss: 0.3158 - mae: 0.4418"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 100/1250 [=>............................] - ETA: 22s - loss: 0.3097 - mae: 0.4370"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 103/1250 [=>............................] - ETA: 22s - loss: 0.3053 - mae: 0.4341"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 106/1250 [=>............................] - ETA: 22s - loss: 0.2996 - mae: 0.4294"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 109/1250 [=>............................] - ETA: 22s - loss: 0.2937 - mae: 0.4243"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 112/1250 [=>............................] - ETA: 22s - loss: 0.2899 - mae: 0.4215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 115/1250 [=>............................] - ETA: 22s - loss: 0.2850 - mae: 0.4177"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 118/1250 [=>............................] - ETA: 22s - loss: 0.2801 - mae: 0.4135"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 121/1250 [=>............................] - ETA: 22s - loss: 0.2757 - mae: 0.4098"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 124/1250 [=>............................] - ETA: 21s - loss: 0.2705 - mae: 0.4050"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 127/1250 [==>...........................] - ETA: 21s - loss: 0.2669 - mae: 0.4024"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 130/1250 [==>...........................] - ETA: 21s - loss: 0.2629 - mae: 0.3992"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 133/1250 [==>...........................] - ETA: 21s - loss: 0.2589 - mae: 0.3959"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 136/1250 [==>...........................] - ETA: 21s - loss: 0.2567 - mae: 0.3944"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 139/1250 [==>...........................] - ETA: 21s - loss: 0.2542 - mae: 0.3927"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 142/1250 [==>...........................] - ETA: 21s - loss: 0.2507 - mae: 0.3898"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 145/1250 [==>...........................] - ETA: 21s - loss: 0.2468 - mae: 0.3862"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 148/1250 [==>...........................] - ETA: 21s - loss: 0.2428 - mae: 0.3821"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 151/1250 [==>...........................] - ETA: 21s - loss: 0.2389 - mae: 0.3781"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 154/1250 [==>...........................] - ETA: 21s - loss: 0.2369 - mae: 0.3756"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 157/1250 [==>...........................] - ETA: 21s - loss: 0.2348 - mae: 0.3740"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 160/1250 [==>...........................] - ETA: 21s - loss: 0.2322 - mae: 0.3720"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 163/1250 [==>...........................] - ETA: 21s - loss: 0.2293 - mae: 0.3693"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 166/1250 [==>...........................] - ETA: 21s - loss: 0.2266 - mae: 0.3669"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 169/1250 [===>..........................] - ETA: 20s - loss: 0.2236 - mae: 0.3639"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 172/1250 [===>..........................] - ETA: 20s - loss: 0.2205 - mae: 0.3607"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 175/1250 [===>..........................] - ETA: 20s - loss: 0.2178 - mae: 0.3580"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 178/1250 [===>..........................] - ETA: 20s - loss: 0.2146 - mae: 0.3544"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 181/1250 [===>..........................] - ETA: 20s - loss: 0.2115 - mae: 0.3509"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 184/1250 [===>..........................] - ETA: 20s - loss: 0.2086 - mae: 0.3476"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 187/1250 [===>..........................] - ETA: 20s - loss: 0.2057 - mae: 0.3442"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 190/1250 [===>..........................] - ETA: 20s - loss: 0.2035 - mae: 0.3420"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 193/1250 [===>..........................] - ETA: 20s - loss: 0.2007 - mae: 0.3390"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 196/1250 [===>..........................] - ETA: 20s - loss: 0.1983 - mae: 0.3364"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 199/1250 [===>..........................] - ETA: 20s - loss: 0.1957 - mae: 0.3334"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 202/1250 [===>..........................] - ETA: 20s - loss: 0.1932 - mae: 0.3303"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 205/1250 [===>..........................] - ETA: 20s - loss: 0.1910 - mae: 0.3279"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 208/1250 [===>..........................] - ETA: 20s - loss: 0.1886 - mae: 0.3250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 211/1250 [====>.........................] - ETA: 20s - loss: 0.1864 - mae: 0.3226"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 214/1250 [====>.........................] - ETA: 20s - loss: 0.1845 - mae: 0.3207"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 217/1250 [====>.........................] - ETA: 19s - loss: 0.1823 - mae: 0.3183"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 220/1250 [====>.........................] - ETA: 19s - loss: 0.1801 - mae: 0.3156"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 223/1250 [====>.........................] - ETA: 19s - loss: 0.1793 - mae: 0.3151"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 226/1250 [====>.........................] - ETA: 19s - loss: 0.1774 - mae: 0.3130"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 229/1250 [====>.........................] - ETA: 19s - loss: 0.1754 - mae: 0.3106"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 232/1250 [====>.........................] - ETA: 19s - loss: 0.1734 - mae: 0.3083"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 235/1250 [====>.........................] - ETA: 19s - loss: 0.1715 - mae: 0.3060"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 238/1250 [====>.........................] - ETA: 19s - loss: 0.1695 - mae: 0.3033"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 241/1250 [====>.........................] - ETA: 19s - loss: 0.1679 - mae: 0.3016"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 244/1250 [====>.........................] - ETA: 19s - loss: 0.1661 - mae: 0.2994"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 247/1250 [====>.........................] - ETA: 19s - loss: 0.1646 - mae: 0.2977"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 250/1250 [=====>........................] - ETA: 19s - loss: 0.1628 - mae: 0.2951"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 253/1250 [=====>........................] - ETA: 19s - loss: 0.1613 - mae: 0.2937"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 256/1250 [=====>........................] - ETA: 19s - loss: 0.1598 - mae: 0.2917"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 259/1250 [=====>........................] - ETA: 19s - loss: 0.1580 - mae: 0.2893"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 262/1250 [=====>........................] - ETA: 19s - loss: 0.1566 - mae: 0.2875"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 265/1250 [=====>........................] - ETA: 18s - loss: 0.1550 - mae: 0.2855"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 268/1250 [=====>........................] - ETA: 18s - loss: 0.1535 - mae: 0.2836"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 271/1250 [=====>........................] - ETA: 18s - loss: 0.1521 - mae: 0.2820"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 274/1250 [=====>........................] - ETA: 18s - loss: 0.1507 - mae: 0.2804"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 277/1250 [=====>........................] - ETA: 18s - loss: 0.1493 - mae: 0.2787"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 280/1250 [=====>........................] - ETA: 18s - loss: 0.1479 - mae: 0.2769"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 283/1250 [=====>........................] - ETA: 18s - loss: 0.1465 - mae: 0.2749"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 286/1250 [=====>........................] - ETA: 18s - loss: 0.1451 - mae: 0.2729"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 289/1250 [=====>........................] - ETA: 18s - loss: 0.1436 - mae: 0.2707"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 292/1250 [======>.......................] - ETA: 18s - loss: 0.1425 - mae: 0.2696"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 295/1250 [======>.......................] - ETA: 18s - loss: 0.1413 - mae: 0.2680"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 298/1250 [======>.......................] - ETA: 18s - loss: 0.1401 - mae: 0.2666"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 301/1250 [======>.......................] - ETA: 18s - loss: 0.1388 - mae: 0.2649"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 304/1250 [======>.......................] - ETA: 18s - loss: 0.1377 - mae: 0.2636"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 307/1250 [======>.......................] - ETA: 18s - loss: 0.1365 - mae: 0.2620"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 310/1250 [======>.......................] - ETA: 18s - loss: 0.1353 - mae: 0.2604"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 313/1250 [======>.......................] - ETA: 18s - loss: 0.1342 - mae: 0.2590"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 316/1250 [======>.......................] - ETA: 17s - loss: 0.1331 - mae: 0.2574"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 319/1250 [======>.......................] - ETA: 17s - loss: 0.1321 - mae: 0.2562"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 322/1250 [======>.......................] - ETA: 17s - loss: 0.1309 - mae: 0.2545"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 325/1250 [======>.......................] - ETA: 17s - loss: 0.1300 - mae: 0.2535"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 328/1250 [======>.......................] - ETA: 17s - loss: 0.1289 - mae: 0.2519"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 331/1250 [======>.......................] - ETA: 17s - loss: 0.1279 - mae: 0.2509"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 334/1250 [=======>......................] - ETA: 17s - loss: 0.1270 - mae: 0.2497"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 337/1250 [=======>......................] - ETA: 17s - loss: 0.1259 - mae: 0.2481"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 340/1250 [=======>......................] - ETA: 17s - loss: 0.1249 - mae: 0.2467"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 343/1250 [=======>......................] - ETA: 17s - loss: 0.1239 - mae: 0.2453"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 346/1250 [=======>......................] - ETA: 17s - loss: 0.1229 - mae: 0.2440"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 349/1250 [=======>......................] - ETA: 17s - loss: 0.1220 - mae: 0.2428"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 352/1250 [=======>......................] - ETA: 17s - loss: 0.1211 - mae: 0.2413"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 355/1250 [=======>......................] - ETA: 17s - loss: 0.1201 - mae: 0.2399"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 358/1250 [=======>......................] - ETA: 17s - loss: 0.1193 - mae: 0.2388"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 361/1250 [=======>......................] - ETA: 17s - loss: 0.1184 - mae: 0.2375"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 364/1250 [=======>......................] - ETA: 17s - loss: 0.1176 - mae: 0.2365"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 367/1250 [=======>......................] - ETA: 16s - loss: 0.1167 - mae: 0.2355"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 370/1250 [=======>......................] - ETA: 16s - loss: 0.1159 - mae: 0.2343"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 373/1250 [=======>......................] - ETA: 16s - loss: 0.1150 - mae: 0.2329"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 376/1250 [========>.....................] - ETA: 16s - loss: 0.1141 - mae: 0.2315"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 379/1250 [========>.....................] - ETA: 16s - loss: 0.1134 - mae: 0.2305"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 382/1250 [========>.....................] - ETA: 16s - loss: 0.1126 - mae: 0.2295"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 385/1250 [========>.....................] - ETA: 16s - loss: 0.1118 - mae: 0.2281"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 388/1250 [========>.....................] - ETA: 16s - loss: 0.1110 - mae: 0.2269"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 391/1250 [========>.....................] - ETA: 16s - loss: 0.1102 - mae: 0.2259"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 394/1250 [========>.....................] - ETA: 16s - loss: 0.1095 - mae: 0.2248"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 397/1250 [========>.....................] - ETA: 16s - loss: 0.1087 - mae: 0.2237"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 400/1250 [========>.....................] - ETA: 16s - loss: 0.1080 - mae: 0.2226"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 403/1250 [========>.....................] - ETA: 16s - loss: 0.1073 - mae: 0.2217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 406/1250 [========>.....................] - ETA: 16s - loss: 0.1066 - mae: 0.2207"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 409/1250 [========>.....................] - ETA: 16s - loss: 0.1059 - mae: 0.2197"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 412/1250 [========>.....................] - ETA: 16s - loss: 0.1052 - mae: 0.2185"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 415/1250 [========>.....................] - ETA: 16s - loss: 0.1046 - mae: 0.2178"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 418/1250 [=========>....................] - ETA: 15s - loss: 0.1039 - mae: 0.2168"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 421/1250 [=========>....................] - ETA: 15s - loss: 0.1032 - mae: 0.2159"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 424/1250 [=========>....................] - ETA: 15s - loss: 0.1026 - mae: 0.2150"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 427/1250 [=========>....................] - ETA: 15s - loss: 0.1019 - mae: 0.2140"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 430/1250 [=========>....................] - ETA: 15s - loss: 0.1013 - mae: 0.2130"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 433/1250 [=========>....................] - ETA: 15s - loss: 0.1006 - mae: 0.2119"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 436/1250 [=========>....................] - ETA: 15s - loss: 0.1001 - mae: 0.2112"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 439/1250 [=========>....................] - ETA: 15s - loss: 0.0994 - mae: 0.2102"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 442/1250 [=========>....................] - ETA: 15s - loss: 0.0988 - mae: 0.2093"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 445/1250 [=========>....................] - ETA: 15s - loss: 0.0982 - mae: 0.2082"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 448/1250 [=========>....................] - ETA: 15s - loss: 0.0976 - mae: 0.2073"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 451/1250 [=========>....................] - ETA: 15s - loss: 0.0970 - mae: 0.2065"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 454/1250 [=========>....................] - ETA: 15s - loss: 0.0964 - mae: 0.2057"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 457/1250 [=========>....................] - ETA: 15s - loss: 0.0959 - mae: 0.2050"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 460/1250 [==========>...................] - ETA: 15s - loss: 0.0954 - mae: 0.2043"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 463/1250 [==========>...................] - ETA: 15s - loss: 0.0948 - mae: 0.2033"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 466/1250 [==========>...................] - ETA: 15s - loss: 0.0942 - mae: 0.2024"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 469/1250 [==========>...................] - ETA: 14s - loss: 0.0937 - mae: 0.2016"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 472/1250 [==========>...................] - ETA: 14s - loss: 0.0931 - mae: 0.2008"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 475/1250 [==========>...................] - ETA: 14s - loss: 0.0926 - mae: 0.2001"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 478/1250 [==========>...................] - ETA: 14s - loss: 0.0921 - mae: 0.1993"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 481/1250 [==========>...................] - ETA: 14s - loss: 0.0916 - mae: 0.1986"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 484/1250 [==========>...................] - ETA: 14s - loss: 0.0911 - mae: 0.1978"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 487/1250 [==========>...................] - ETA: 14s - loss: 0.0905 - mae: 0.1970"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 490/1250 [==========>...................] - ETA: 14s - loss: 0.0900 - mae: 0.1962"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 493/1250 [==========>...................] - ETA: 14s - loss: 0.0896 - mae: 0.1957"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 496/1250 [==========>...................] - ETA: 14s - loss: 0.0891 - mae: 0.1949"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 499/1250 [==========>...................] - ETA: 14s - loss: 0.0886 - mae: 0.1940"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 502/1250 [===========>..................] - ETA: 14s - loss: 0.0881 - mae: 0.1934"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 505/1250 [===========>..................] - ETA: 14s - loss: 0.0876 - mae: 0.1928"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 508/1250 [===========>..................] - ETA: 14s - loss: 0.0872 - mae: 0.1922"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 511/1250 [===========>..................] - ETA: 14s - loss: 0.0867 - mae: 0.1913"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 514/1250 [===========>..................] - ETA: 14s - loss: 0.0863 - mae: 0.1907"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 517/1250 [===========>..................] - ETA: 14s - loss: 0.0858 - mae: 0.1901"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 520/1250 [===========>..................] - ETA: 13s - loss: 0.0854 - mae: 0.1892"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 523/1250 [===========>..................] - ETA: 13s - loss: 0.0849 - mae: 0.1884"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 526/1250 [===========>..................] - ETA: 13s - loss: 0.0844 - mae: 0.1877"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 529/1250 [===========>..................] - ETA: 13s - loss: 0.0840 - mae: 0.1871"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 532/1250 [===========>..................] - ETA: 13s - loss: 0.0836 - mae: 0.1865"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 535/1250 [===========>..................] - ETA: 13s - loss: 0.0832 - mae: 0.1858"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 538/1250 [===========>..................] - ETA: 13s - loss: 0.0828 - mae: 0.1852"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 541/1250 [===========>..................] - ETA: 13s - loss: 0.0823 - mae: 0.1845"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 544/1250 [============>.................] - ETA: 13s - loss: 0.0819 - mae: 0.1839"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 547/1250 [============>.................] - ETA: 13s - loss: 0.0815 - mae: 0.1833"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 550/1250 [============>.................] - ETA: 13s - loss: 0.0811 - mae: 0.1825"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 553/1250 [============>.................] - ETA: 13s - loss: 0.0807 - mae: 0.1819"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 556/1250 [============>.................] - ETA: 13s - loss: 0.0803 - mae: 0.1813"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 559/1250 [============>.................] - ETA: 13s - loss: 0.0799 - mae: 0.1807"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 562/1250 [============>.................] - ETA: 13s - loss: 0.0795 - mae: 0.1800"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 565/1250 [============>.................] - ETA: 13s - loss: 0.0791 - mae: 0.1793"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 568/1250 [============>.................] - ETA: 13s - loss: 0.0787 - mae: 0.1787"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 571/1250 [============>.................] - ETA: 12s - loss: 0.0783 - mae: 0.1781"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 574/1250 [============>.................] - ETA: 12s - loss: 0.0780 - mae: 0.1776"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 577/1250 [============>.................] - ETA: 12s - loss: 0.0776 - mae: 0.1769"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 580/1250 [============>.................] - ETA: 12s - loss: 0.0772 - mae: 0.1764"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 583/1250 [============>.................] - ETA: 12s - loss: 0.0769 - mae: 0.1760"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 586/1250 [=============>................] - ETA: 12s - loss: 0.0765 - mae: 0.1755"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 589/1250 [=============>................] - ETA: 12s - loss: 0.0762 - mae: 0.1748"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 592/1250 [=============>................] - ETA: 12s - loss: 0.0758 - mae: 0.1741"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 595/1250 [=============>................] - ETA: 12s - loss: 0.0755 - mae: 0.1736"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 598/1250 [=============>................] - ETA: 12s - loss: 0.0751 - mae: 0.1730"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 601/1250 [=============>................] - ETA: 12s - loss: 0.0747 - mae: 0.1724"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 604/1250 [=============>................] - ETA: 12s - loss: 0.0744 - mae: 0.1719"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 607/1250 [=============>................] - ETA: 12s - loss: 0.0740 - mae: 0.1712"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 610/1250 [=============>................] - ETA: 12s - loss: 0.0737 - mae: 0.1706"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 613/1250 [=============>................] - ETA: 12s - loss: 0.0734 - mae: 0.1701"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 616/1250 [=============>................] - ETA: 12s - loss: 0.0730 - mae: 0.1696"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 619/1250 [=============>................] - ETA: 12s - loss: 0.0727 - mae: 0.1691"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 622/1250 [=============>................] - ETA: 12s - loss: 0.0724 - mae: 0.1686"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 625/1250 [==============>...............] - ETA: 11s - loss: 0.0721 - mae: 0.1682"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 628/1250 [==============>...............] - ETA: 11s - loss: 0.0718 - mae: 0.1676"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 631/1250 [==============>...............] - ETA: 11s - loss: 0.0715 - mae: 0.1670"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 634/1250 [==============>...............] - ETA: 11s - loss: 0.0712 - mae: 0.1665"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 637/1250 [==============>...............] - ETA: 11s - loss: 0.0708 - mae: 0.1660"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 640/1250 [==============>...............] - ETA: 11s - loss: 0.0705 - mae: 0.1655"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 643/1250 [==============>...............] - ETA: 11s - loss: 0.0702 - mae: 0.1650"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 646/1250 [==============>...............] - ETA: 11s - loss: 0.0699 - mae: 0.1645"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 649/1250 [==============>...............] - ETA: 11s - loss: 0.0696 - mae: 0.1641"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 652/1250 [==============>...............] - ETA: 11s - loss: 0.0694 - mae: 0.1637"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 655/1250 [==============>...............] - ETA: 11s - loss: 0.0691 - mae: 0.1632"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 658/1250 [==============>...............] - ETA: 11s - loss: 0.0688 - mae: 0.1626"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 661/1250 [==============>...............] - ETA: 11s - loss: 0.0685 - mae: 0.1622"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 664/1250 [==============>...............] - ETA: 11s - loss: 0.0682 - mae: 0.1617"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 667/1250 [===============>..............] - ETA: 11s - loss: 0.0679 - mae: 0.1614"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 670/1250 [===============>..............] - ETA: 11s - loss: 0.0677 - mae: 0.1609"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 673/1250 [===============>..............] - ETA: 11s - loss: 0.0674 - mae: 0.1605"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 676/1250 [===============>..............] - ETA: 10s - loss: 0.0671 - mae: 0.1599"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 679/1250 [===============>..............] - ETA: 10s - loss: 0.0668 - mae: 0.1595"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 682/1250 [===============>..............] - ETA: 10s - loss: 0.0666 - mae: 0.1590"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 685/1250 [===============>..............] - ETA: 10s - loss: 0.0663 - mae: 0.1586"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 688/1250 [===============>..............] - ETA: 10s - loss: 0.0660 - mae: 0.1582"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 691/1250 [===============>..............] - ETA: 10s - loss: 0.0658 - mae: 0.1578"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 694/1250 [===============>..............] - ETA: 10s - loss: 0.0655 - mae: 0.1573"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 697/1250 [===============>..............] - ETA: 10s - loss: 0.0653 - mae: 0.1569"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 700/1250 [===============>..............] - ETA: 10s - loss: 0.0650 - mae: 0.1565"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 703/1250 [===============>..............] - ETA: 10s - loss: 0.0647 - mae: 0.1560"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 706/1250 [===============>..............] - ETA: 10s - loss: 0.0645 - mae: 0.1556"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 709/1250 [================>.............] - ETA: 10s - loss: 0.0642 - mae: 0.1551"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 712/1250 [================>.............] - ETA: 10s - loss: 0.0640 - mae: 0.1548"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 715/1250 [================>.............] - ETA: 10s - loss: 0.0638 - mae: 0.1544"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 718/1250 [================>.............] - ETA: 10s - loss: 0.0635 - mae: 0.1539"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 721/1250 [================>.............] - ETA: 10s - loss: 0.0633 - mae: 0.1536"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 724/1250 [================>.............] - ETA: 10s - loss: 0.0631 - mae: 0.1532"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 727/1250 [================>.............] - ETA: 9s - loss: 0.0628 - mae: 0.1528 "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 730/1250 [================>.............] - ETA: 9s - loss: 0.0626 - mae: 0.1524"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 733/1250 [================>.............] - ETA: 9s - loss: 0.0624 - mae: 0.1521"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 736/1250 [================>.............] - ETA: 9s - loss: 0.0621 - mae: 0.1517"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 739/1250 [================>.............] - ETA: 9s - loss: 0.0619 - mae: 0.1513"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 742/1250 [================>.............] - ETA: 9s - loss: 0.0617 - mae: 0.1509"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 745/1250 [================>.............] - ETA: 9s - loss: 0.0614 - mae: 0.1505"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 748/1250 [================>.............] - ETA: 9s - loss: 0.0612 - mae: 0.1500"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 751/1250 [=================>............] - ETA: 9s - loss: 0.0610 - mae: 0.1496"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 754/1250 [=================>............] - ETA: 9s - loss: 0.0608 - mae: 0.1494"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 757/1250 [=================>............] - ETA: 9s - loss: 0.0605 - mae: 0.1489"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 760/1250 [=================>............] - ETA: 9s - loss: 0.0603 - mae: 0.1486"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 763/1250 [=================>............] - ETA: 9s - loss: 0.0601 - mae: 0.1482"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 766/1250 [=================>............] - ETA: 9s - loss: 0.0599 - mae: 0.1479"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 769/1250 [=================>............] - ETA: 9s - loss: 0.0597 - mae: 0.1475"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 772/1250 [=================>............] - ETA: 9s - loss: 0.0594 - mae: 0.1472"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 775/1250 [=================>............] - ETA: 9s - loss: 0.0592 - mae: 0.1467"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 778/1250 [=================>............] - ETA: 9s - loss: 0.0590 - mae: 0.1464"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 781/1250 [=================>............] - ETA: 8s - loss: 0.0588 - mae: 0.1460"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 784/1250 [=================>............] - ETA: 8s - loss: 0.0586 - mae: 0.1456"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 787/1250 [=================>............] - ETA: 8s - loss: 0.0584 - mae: 0.1452"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 790/1250 [=================>............] - ETA: 8s - loss: 0.0582 - mae: 0.1448"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 793/1250 [==================>...........] - ETA: 8s - loss: 0.0580 - mae: 0.1445"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 796/1250 [==================>...........] - ETA: 8s - loss: 0.0578 - mae: 0.1442"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 799/1250 [==================>...........] - ETA: 8s - loss: 0.0576 - mae: 0.1438"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 802/1250 [==================>...........] - ETA: 8s - loss: 0.0574 - mae: 0.1434"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 805/1250 [==================>...........] - ETA: 8s - loss: 0.0572 - mae: 0.1432"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 808/1250 [==================>...........] - ETA: 8s - loss: 0.0570 - mae: 0.1429"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 811/1250 [==================>...........] - ETA: 8s - loss: 0.0568 - mae: 0.1425"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 814/1250 [==================>...........] - ETA: 8s - loss: 0.0566 - mae: 0.1421"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 817/1250 [==================>...........] - ETA: 8s - loss: 0.0564 - mae: 0.1417"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 820/1250 [==================>...........] - ETA: 8s - loss: 0.0562 - mae: 0.1414"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 823/1250 [==================>...........] - ETA: 8s - loss: 0.0560 - mae: 0.1411"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 826/1250 [==================>...........] - ETA: 8s - loss: 0.0558 - mae: 0.1408"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 829/1250 [==================>...........] - ETA: 8s - loss: 0.0557 - mae: 0.1404"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 832/1250 [==================>...........] - ETA: 7s - loss: 0.0555 - mae: 0.1401"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 835/1250 [===================>..........] - ETA: 7s - loss: 0.0553 - mae: 0.1398"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 838/1250 [===================>..........] - ETA: 7s - loss: 0.0551 - mae: 0.1395"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 841/1250 [===================>..........] - ETA: 7s - loss: 0.0549 - mae: 0.1392"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 844/1250 [===================>..........] - ETA: 7s - loss: 0.0547 - mae: 0.1388"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 847/1250 [===================>..........] - ETA: 7s - loss: 0.0545 - mae: 0.1385"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 850/1250 [===================>..........] - ETA: 7s - loss: 0.0544 - mae: 0.1382"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 853/1250 [===================>..........] - ETA: 7s - loss: 0.0542 - mae: 0.1379"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 856/1250 [===================>..........] - ETA: 7s - loss: 0.0540 - mae: 0.1376"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 859/1250 [===================>..........] - ETA: 7s - loss: 0.0538 - mae: 0.1372"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 862/1250 [===================>..........] - ETA: 7s - loss: 0.0537 - mae: 0.1369"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 865/1250 [===================>..........] - ETA: 7s - loss: 0.0535 - mae: 0.1366"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 868/1250 [===================>..........] - ETA: 7s - loss: 0.0533 - mae: 0.1363"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 871/1250 [===================>..........] - ETA: 7s - loss: 0.0531 - mae: 0.1360"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 874/1250 [===================>..........] - ETA: 7s - loss: 0.0530 - mae: 0.1357"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 877/1250 [====================>.........] - ETA: 7s - loss: 0.0528 - mae: 0.1355"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 880/1250 [====================>.........] - ETA: 7s - loss: 0.0527 - mae: 0.1352"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 883/1250 [====================>.........] - ETA: 6s - loss: 0.0525 - mae: 0.1350"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 886/1250 [====================>.........] - ETA: 6s - loss: 0.0523 - mae: 0.1346"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 889/1250 [====================>.........] - ETA: 6s - loss: 0.0522 - mae: 0.1343"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 892/1250 [====================>.........] - ETA: 6s - loss: 0.0520 - mae: 0.1340"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 895/1250 [====================>.........] - ETA: 6s - loss: 0.0518 - mae: 0.1337"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 898/1250 [====================>.........] - ETA: 6s - loss: 0.0517 - mae: 0.1334"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 901/1250 [====================>.........] - ETA: 6s - loss: 0.0515 - mae: 0.1331"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 904/1250 [====================>.........] - ETA: 6s - loss: 0.0514 - mae: 0.1328"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 907/1250 [====================>.........] - ETA: 6s - loss: 0.0512 - mae: 0.1325"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 910/1250 [====================>.........] - ETA: 6s - loss: 0.0510 - mae: 0.1323"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 913/1250 [====================>.........] - ETA: 6s - loss: 0.0509 - mae: 0.1320"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 916/1250 [====================>.........] - ETA: 6s - loss: 0.0507 - mae: 0.1317"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 919/1250 [=====================>........] - ETA: 6s - loss: 0.0506 - mae: 0.1314"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 922/1250 [=====================>........] - ETA: 6s - loss: 0.0504 - mae: 0.1311"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 925/1250 [=====================>........] - ETA: 6s - loss: 0.0503 - mae: 0.1309"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 928/1250 [=====================>........] - ETA: 6s - loss: 0.0501 - mae: 0.1306"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 931/1250 [=====================>........] - ETA: 6s - loss: 0.0500 - mae: 0.1303"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 934/1250 [=====================>........] - ETA: 6s - loss: 0.0498 - mae: 0.1300"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 937/1250 [=====================>........] - ETA: 5s - loss: 0.0497 - mae: 0.1297"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 940/1250 [=====================>........] - ETA: 5s - loss: 0.0495 - mae: 0.1295"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 943/1250 [=====================>........] - ETA: 5s - loss: 0.0494 - mae: 0.1293"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 946/1250 [=====================>........] - ETA: 5s - loss: 0.0492 - mae: 0.1290"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 949/1250 [=====================>........] - ETA: 5s - loss: 0.0491 - mae: 0.1287"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 952/1250 [=====================>........] - ETA: 5s - loss: 0.0490 - mae: 0.1285"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 955/1250 [=====================>........] - ETA: 5s - loss: 0.0488 - mae: 0.1282"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 958/1250 [=====================>........] - ETA: 5s - loss: 0.0487 - mae: 0.1280"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 961/1250 [======================>.......] - ETA: 5s - loss: 0.0485 - mae: 0.1277"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 964/1250 [======================>.......] - ETA: 5s - loss: 0.0484 - mae: 0.1274"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 967/1250 [======================>.......] - ETA: 5s - loss: 0.0482 - mae: 0.1271"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 970/1250 [======================>.......] - ETA: 5s - loss: 0.0481 - mae: 0.1269"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 973/1250 [======================>.......] - ETA: 5s - loss: 0.0480 - mae: 0.1266"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 976/1250 [======================>.......] - ETA: 5s - loss: 0.0478 - mae: 0.1263"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 979/1250 [======================>.......] - ETA: 5s - loss: 0.0477 - mae: 0.1261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 982/1250 [======================>.......] - ETA: 5s - loss: 0.0476 - mae: 0.1258"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 985/1250 [======================>.......] - ETA: 5s - loss: 0.0474 - mae: 0.1255"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 988/1250 [======================>.......] - ETA: 4s - loss: 0.0473 - mae: 0.1253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 991/1250 [======================>.......] - ETA: 4s - loss: 0.0472 - mae: 0.1251"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 994/1250 [======================>.......] - ETA: 4s - loss: 0.0470 - mae: 0.1248"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 997/1250 [======================>.......] - ETA: 4s - loss: 0.0469 - mae: 0.1246"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1000/1250 [=======================>......] - ETA: 4s - loss: 0.0468 - mae: 0.1244"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1003/1250 [=======================>......] - ETA: 4s - loss: 0.0466 - mae: 0.1242"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1006/1250 [=======================>......] - ETA: 4s - loss: 0.0465 - mae: 0.1239"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1009/1250 [=======================>......] - ETA: 4s - loss: 0.0464 - mae: 0.1236"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1012/1250 [=======================>......] - ETA: 4s - loss: 0.0462 - mae: 0.1234"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1015/1250 [=======================>......] - ETA: 4s - loss: 0.0461 - mae: 0.1232"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1018/1250 [=======================>......] - ETA: 4s - loss: 0.0460 - mae: 0.1229"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1021/1250 [=======================>......] - ETA: 4s - loss: 0.0459 - mae: 0.1227"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1024/1250 [=======================>......] - ETA: 4s - loss: 0.0457 - mae: 0.1225"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1027/1250 [=======================>......] - ETA: 4s - loss: 0.0456 - mae: 0.1222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1030/1250 [=======================>......] - ETA: 4s - loss: 0.0455 - mae: 0.1219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1033/1250 [=======================>......] - ETA: 4s - loss: 0.0454 - mae: 0.1217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1036/1250 [=======================>......] - ETA: 4s - loss: 0.0452 - mae: 0.1215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1039/1250 [=======================>......] - ETA: 4s - loss: 0.0451 - mae: 0.1212"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1042/1250 [========================>.....] - ETA: 3s - loss: 0.0450 - mae: 0.1210"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1045/1250 [========================>.....] - ETA: 3s - loss: 0.0449 - mae: 0.1208"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1048/1250 [========================>.....] - ETA: 3s - loss: 0.0448 - mae: 0.1206"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1051/1250 [========================>.....] - ETA: 3s - loss: 0.0446 - mae: 0.1203"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1054/1250 [========================>.....] - ETA: 3s - loss: 0.0445 - mae: 0.1201"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1057/1250 [========================>.....] - ETA: 3s - loss: 0.0444 - mae: 0.1199"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1060/1250 [========================>.....] - ETA: 3s - loss: 0.0443 - mae: 0.1197"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1063/1250 [========================>.....] - ETA: 3s - loss: 0.0442 - mae: 0.1194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1066/1250 [========================>.....] - ETA: 3s - loss: 0.0440 - mae: 0.1192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1069/1250 [========================>.....] - ETA: 3s - loss: 0.0439 - mae: 0.1190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1072/1250 [========================>.....] - ETA: 3s - loss: 0.0438 - mae: 0.1188"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1075/1250 [========================>.....] - ETA: 3s - loss: 0.0437 - mae: 0.1185"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1078/1250 [========================>.....] - ETA: 3s - loss: 0.0436 - mae: 0.1184"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1081/1250 [========================>.....] - ETA: 3s - loss: 0.0435 - mae: 0.1182"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1084/1250 [=========================>....] - ETA: 3s - loss: 0.0434 - mae: 0.1180"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1087/1250 [=========================>....] - ETA: 3s - loss: 0.0433 - mae: 0.1178"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1090/1250 [=========================>....] - ETA: 3s - loss: 0.0431 - mae: 0.1175"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1093/1250 [=========================>....] - ETA: 2s - loss: 0.0430 - mae: 0.1174"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1096/1250 [=========================>....] - ETA: 2s - loss: 0.0429 - mae: 0.1171"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1099/1250 [=========================>....] - ETA: 2s - loss: 0.0428 - mae: 0.1169"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1102/1250 [=========================>....] - ETA: 2s - loss: 0.0427 - mae: 0.1168"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1105/1250 [=========================>....] - ETA: 2s - loss: 0.0426 - mae: 0.1165"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1108/1250 [=========================>....] - ETA: 2s - loss: 0.0425 - mae: 0.1164"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1111/1250 [=========================>....] - ETA: 2s - loss: 0.0424 - mae: 0.1162"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1114/1250 [=========================>....] - ETA: 2s - loss: 0.0423 - mae: 0.1160"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1117/1250 [=========================>....] - ETA: 2s - loss: 0.0422 - mae: 0.1157"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1120/1250 [=========================>....] - ETA: 2s - loss: 0.0421 - mae: 0.1155"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1123/1250 [=========================>....] - ETA: 2s - loss: 0.0420 - mae: 0.1153"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1126/1250 [==========================>...] - ETA: 2s - loss: 0.0419 - mae: 0.1151"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1129/1250 [==========================>...] - ETA: 2s - loss: 0.0418 - mae: 0.1149"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1132/1250 [==========================>...] - ETA: 2s - loss: 0.0416 - mae: 0.1147"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1135/1250 [==========================>...] - ETA: 2s - loss: 0.0416 - mae: 0.1146"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1138/1250 [==========================>...] - ETA: 2s - loss: 0.0415 - mae: 0.1144"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1141/1250 [==========================>...] - ETA: 2s - loss: 0.0414 - mae: 0.1142"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1144/1250 [==========================>...] - ETA: 2s - loss: 0.0413 - mae: 0.1140"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1147/1250 [==========================>...] - ETA: 1s - loss: 0.0411 - mae: 0.1138"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1150/1250 [==========================>...] - ETA: 1s - loss: 0.0410 - mae: 0.1136"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1153/1250 [==========================>...] - ETA: 1s - loss: 0.0409 - mae: 0.1134"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1156/1250 [==========================>...] - ETA: 1s - loss: 0.0408 - mae: 0.1132"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1159/1250 [==========================>...] - ETA: 1s - loss: 0.0407 - mae: 0.1130"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1162/1250 [==========================>...] - ETA: 1s - loss: 0.0407 - mae: 0.1128"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1165/1250 [==========================>...] - ETA: 1s - loss: 0.0406 - mae: 0.1127"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1168/1250 [===========================>..] - ETA: 1s - loss: 0.0405 - mae: 0.1124"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1171/1250 [===========================>..] - ETA: 1s - loss: 0.0404 - mae: 0.1122"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1174/1250 [===========================>..] - ETA: 1s - loss: 0.0403 - mae: 0.1120"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1177/1250 [===========================>..] - ETA: 1s - loss: 0.0402 - mae: 0.1118"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1180/1250 [===========================>..] - ETA: 1s - loss: 0.0401 - mae: 0.1117"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1183/1250 [===========================>..] - ETA: 1s - loss: 0.0400 - mae: 0.1115"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1186/1250 [===========================>..] - ETA: 1s - loss: 0.0399 - mae: 0.1113"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1189/1250 [===========================>..] - ETA: 1s - loss: 0.0398 - mae: 0.1112"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1192/1250 [===========================>..] - ETA: 1s - loss: 0.0397 - mae: 0.1110"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1195/1250 [===========================>..] - ETA: 1s - loss: 0.0396 - mae: 0.1108"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1198/1250 [===========================>..] - ETA: 0s - loss: 0.0395 - mae: 0.1106"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1201/1250 [===========================>..] - ETA: 0s - loss: 0.0394 - mae: 0.1104"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1204/1250 [===========================>..] - ETA: 0s - loss: 0.0393 - mae: 0.1102"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1207/1250 [===========================>..] - ETA: 0s - loss: 0.0392 - mae: 0.1100"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1210/1250 [============================>.] - ETA: 0s - loss: 0.0391 - mae: 0.1098"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1213/1250 [============================>.] - ETA: 0s - loss: 0.0391 - mae: 0.1097"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1216/1250 [============================>.] - ETA: 0s - loss: 0.0390 - mae: 0.1095"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1219/1250 [============================>.] - ETA: 0s - loss: 0.0389 - mae: 0.1093"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1222/1250 [============================>.] - ETA: 0s - loss: 0.0388 - mae: 0.1092"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1225/1250 [============================>.] - ETA: 0s - loss: 0.0387 - mae: 0.1090"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1228/1250 [============================>.] - ETA: 0s - loss: 0.0386 - mae: 0.1088"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1231/1250 [============================>.] - ETA: 0s - loss: 0.0385 - mae: 0.1086"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1234/1250 [============================>.] - ETA: 0s - loss: 0.0384 - mae: 0.1085"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1237/1250 [============================>.] - ETA: 0s - loss: 0.0383 - mae: 0.1083"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1240/1250 [============================>.] - ETA: 0s - loss: 0.0383 - mae: 0.1081"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1243/1250 [============================>.] - ETA: 0s - loss: 0.0382 - mae: 0.1079"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1246/1250 [============================>.] - ETA: 0s - loss: 0.0381 - mae: 0.1078"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1249/1250 [============================>.] - ETA: 0s - loss: 0.0380 - mae: 0.1077"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1250/1250 [==============================] - 26s 21ms/step - loss: 0.0380 - mae: 0.1076 - val_loss: 0.0017 - val_mae: 0.0323\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2/5\n",
+      "\r",
+      "   1/1250 [..............................] - ETA: 0s - loss: 0.0014 - mae: 0.0272"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "   4/1250 [..............................] - ETA: 20s - loss: 9.6141e-04 - mae: 0.0232"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "   7/1250 [..............................] - ETA: 23s - loss: 0.0027 - mae: 0.0367    "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  10/1250 [..............................] - ETA: 24s - loss: 0.0025 - mae: 0.0360"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  13/1250 [..............................] - ETA: 25s - loss: 0.0023 - mae: 0.0348"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  16/1250 [..............................] - ETA: 25s - loss: 0.0024 - mae: 0.0363"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  19/1250 [..............................] - ETA: 25s - loss: 0.0025 - mae: 0.0374"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  22/1250 [..............................] - ETA: 25s - loss: 0.0023 - mae: 0.0360"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  25/1250 [..............................] - ETA: 24s - loss: 0.0023 - mae: 0.0359"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  28/1250 [..............................] - ETA: 24s - loss: 0.0022 - mae: 0.0356"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  31/1250 [..............................] - ETA: 24s - loss: 0.0025 - mae: 0.0376"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  34/1250 [..............................] - ETA: 24s - loss: 0.0025 - mae: 0.0373"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  37/1250 [..............................] - ETA: 24s - loss: 0.0024 - mae: 0.0371"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  40/1250 [..............................] - ETA: 23s - loss: 0.0025 - mae: 0.0375"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  43/1250 [>.............................] - ETA: 23s - loss: 0.0025 - mae: 0.0377"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  46/1250 [>.............................] - ETA: 23s - loss: 0.0024 - mae: 0.0374"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  49/1250 [>.............................] - ETA: 23s - loss: 0.0023 - mae: 0.0367"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  52/1250 [>.............................] - ETA: 23s - loss: 0.0025 - mae: 0.0377"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  55/1250 [>.............................] - ETA: 23s - loss: 0.0025 - mae: 0.0374"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  58/1250 [>.............................] - ETA: 23s - loss: 0.0024 - mae: 0.0368"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  61/1250 [>.............................] - ETA: 23s - loss: 0.0023 - mae: 0.0366"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  64/1250 [>.............................] - ETA: 23s - loss: 0.0023 - mae: 0.0366"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  67/1250 [>.............................] - ETA: 23s - loss: 0.0024 - mae: 0.0373"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  70/1250 [>.............................] - ETA: 22s - loss: 0.0024 - mae: 0.0376"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  73/1250 [>.............................] - ETA: 22s - loss: 0.0024 - mae: 0.0374"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  76/1250 [>.............................] - ETA: 22s - loss: 0.0023 - mae: 0.0369"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  79/1250 [>.............................] - ETA: 22s - loss: 0.0023 - mae: 0.0368"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  82/1250 [>.............................] - ETA: 22s - loss: 0.0025 - mae: 0.0379"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  85/1250 [=>............................] - ETA: 22s - loss: 0.0025 - mae: 0.0379"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  88/1250 [=>............................] - ETA: 22s - loss: 0.0024 - mae: 0.0377"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  91/1250 [=>............................] - ETA: 22s - loss: 0.0024 - mae: 0.0379"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  94/1250 [=>............................] - ETA: 22s - loss: 0.0025 - mae: 0.0384"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  97/1250 [=>............................] - ETA: 22s - loss: 0.0024 - mae: 0.0381"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 100/1250 [=>............................] - ETA: 22s - loss: 0.0024 - mae: 0.0379"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 103/1250 [=>............................] - ETA: 22s - loss: 0.0024 - mae: 0.0378"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 106/1250 [=>............................] - ETA: 22s - loss: 0.0024 - mae: 0.0377"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 109/1250 [=>............................] - ETA: 22s - loss: 0.0025 - mae: 0.0382"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 112/1250 [=>............................] - ETA: 22s - loss: 0.0025 - mae: 0.0382"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 115/1250 [=>............................] - ETA: 22s - loss: 0.0024 - mae: 0.0378"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 118/1250 [=>............................] - ETA: 21s - loss: 0.0024 - mae: 0.0379"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 121/1250 [=>............................] - ETA: 21s - loss: 0.0025 - mae: 0.0381"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 124/1250 [=>............................] - ETA: 21s - loss: 0.0025 - mae: 0.0382"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 127/1250 [==>...........................] - ETA: 21s - loss: 0.0024 - mae: 0.0379"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 130/1250 [==>...........................] - ETA: 21s - loss: 0.0024 - mae: 0.0375"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 133/1250 [==>...........................] - ETA: 21s - loss: 0.0024 - mae: 0.0372"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 136/1250 [==>...........................] - ETA: 21s - loss: 0.0024 - mae: 0.0373"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 139/1250 [==>...........................] - ETA: 21s - loss: 0.0023 - mae: 0.0371"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 142/1250 [==>...........................] - ETA: 21s - loss: 0.0023 - mae: 0.0370"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 145/1250 [==>...........................] - ETA: 21s - loss: 0.0024 - mae: 0.0372"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 148/1250 [==>...........................] - ETA: 21s - loss: 0.0024 - mae: 0.0377"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 151/1250 [==>...........................] - ETA: 21s - loss: 0.0024 - mae: 0.0376"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 154/1250 [==>...........................] - ETA: 21s - loss: 0.0024 - mae: 0.0373"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 157/1250 [==>...........................] - ETA: 21s - loss: 0.0024 - mae: 0.0375"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 160/1250 [==>...........................] - ETA: 21s - loss: 0.0024 - mae: 0.0375"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 163/1250 [==>...........................] - ETA: 20s - loss: 0.0024 - mae: 0.0372"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 166/1250 [==>...........................] - ETA: 20s - loss: 0.0024 - mae: 0.0372"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 169/1250 [===>..........................] - ETA: 20s - loss: 0.0024 - mae: 0.0372"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 172/1250 [===>..........................] - ETA: 20s - loss: 0.0024 - mae: 0.0374"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 175/1250 [===>..........................] - ETA: 20s - loss: 0.0024 - mae: 0.0372"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 178/1250 [===>..........................] - ETA: 20s - loss: 0.0023 - mae: 0.0370"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 181/1250 [===>..........................] - ETA: 20s - loss: 0.0023 - mae: 0.0370"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 184/1250 [===>..........................] - ETA: 20s - loss: 0.0023 - mae: 0.0371"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 187/1250 [===>..........................] - ETA: 20s - loss: 0.0024 - mae: 0.0372"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 190/1250 [===>..........................] - ETA: 20s - loss: 0.0023 - mae: 0.0371"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 193/1250 [===>..........................] - ETA: 20s - loss: 0.0023 - mae: 0.0371"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 196/1250 [===>..........................] - ETA: 20s - loss: 0.0023 - mae: 0.0372"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 199/1250 [===>..........................] - ETA: 20s - loss: 0.0023 - mae: 0.0369"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 202/1250 [===>..........................] - ETA: 20s - loss: 0.0023 - mae: 0.0368"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 205/1250 [===>..........................] - ETA: 20s - loss: 0.0024 - mae: 0.0371"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 208/1250 [===>..........................] - ETA: 20s - loss: 0.0024 - mae: 0.0371"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 211/1250 [====>.........................] - ETA: 20s - loss: 0.0024 - mae: 0.0371"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 214/1250 [====>.........................] - ETA: 19s - loss: 0.0023 - mae: 0.0369"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 217/1250 [====>.........................] - ETA: 19s - loss: 0.0023 - mae: 0.0368"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 220/1250 [====>.........................] - ETA: 19s - loss: 0.0023 - mae: 0.0366"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 223/1250 [====>.........................] - ETA: 19s - loss: 0.0023 - mae: 0.0364"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 226/1250 [====>.........................] - ETA: 19s - loss: 0.0023 - mae: 0.0365"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 229/1250 [====>.........................] - ETA: 19s - loss: 0.0023 - mae: 0.0365"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 232/1250 [====>.........................] - ETA: 19s - loss: 0.0023 - mae: 0.0364"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 235/1250 [====>.........................] - ETA: 19s - loss: 0.0023 - mae: 0.0366"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 238/1250 [====>.........................] - ETA: 19s - loss: 0.0023 - mae: 0.0367"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 241/1250 [====>.........................] - ETA: 19s - loss: 0.0023 - mae: 0.0366"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 244/1250 [====>.........................] - ETA: 19s - loss: 0.0023 - mae: 0.0365"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 247/1250 [====>.........................] - ETA: 19s - loss: 0.0023 - mae: 0.0364"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 250/1250 [=====>........................] - ETA: 19s - loss: 0.0023 - mae: 0.0364"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 253/1250 [=====>........................] - ETA: 19s - loss: 0.0023 - mae: 0.0363"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 256/1250 [=====>........................] - ETA: 19s - loss: 0.0023 - mae: 0.0363"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 259/1250 [=====>........................] - ETA: 19s - loss: 0.0023 - mae: 0.0363"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 262/1250 [=====>........................] - ETA: 18s - loss: 0.0023 - mae: 0.0364"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 265/1250 [=====>........................] - ETA: 18s - loss: 0.0023 - mae: 0.0362"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 268/1250 [=====>........................] - ETA: 18s - loss: 0.0023 - mae: 0.0364"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 271/1250 [=====>........................] - ETA: 18s - loss: 0.0023 - mae: 0.0363"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 274/1250 [=====>........................] - ETA: 18s - loss: 0.0023 - mae: 0.0363"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 277/1250 [=====>........................] - ETA: 18s - loss: 0.0023 - mae: 0.0363"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 280/1250 [=====>........................] - ETA: 18s - loss: 0.0023 - mae: 0.0362"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 283/1250 [=====>........................] - ETA: 18s - loss: 0.0023 - mae: 0.0361"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 286/1250 [=====>........................] - ETA: 18s - loss: 0.0023 - mae: 0.0360"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 289/1250 [=====>........................] - ETA: 18s - loss: 0.0022 - mae: 0.0359"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 292/1250 [======>.......................] - ETA: 18s - loss: 0.0023 - mae: 0.0360"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 295/1250 [======>.......................] - ETA: 18s - loss: 0.0023 - mae: 0.0361"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 298/1250 [======>.......................] - ETA: 18s - loss: 0.0023 - mae: 0.0361"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 301/1250 [======>.......................] - ETA: 18s - loss: 0.0023 - mae: 0.0360"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 304/1250 [======>.......................] - ETA: 18s - loss: 0.0023 - mae: 0.0361"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 307/1250 [======>.......................] - ETA: 18s - loss: 0.0023 - mae: 0.0360"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 310/1250 [======>.......................] - ETA: 18s - loss: 0.0023 - mae: 0.0360"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 313/1250 [======>.......................] - ETA: 17s - loss: 0.0022 - mae: 0.0359"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 316/1250 [======>.......................] - ETA: 17s - loss: 0.0022 - mae: 0.0358"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 319/1250 [======>.......................] - ETA: 17s - loss: 0.0022 - mae: 0.0358"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 322/1250 [======>.......................] - ETA: 17s - loss: 0.0023 - mae: 0.0359"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 325/1250 [======>.......................] - ETA: 17s - loss: 0.0022 - mae: 0.0359"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 328/1250 [======>.......................] - ETA: 17s - loss: 0.0022 - mae: 0.0357"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 331/1250 [======>.......................] - ETA: 17s - loss: 0.0022 - mae: 0.0357"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 334/1250 [=======>......................] - ETA: 17s - loss: 0.0022 - mae: 0.0358"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 337/1250 [=======>......................] - ETA: 17s - loss: 0.0023 - mae: 0.0359"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 340/1250 [=======>......................] - ETA: 17s - loss: 0.0022 - mae: 0.0358"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 343/1250 [=======>......................] - ETA: 17s - loss: 0.0022 - mae: 0.0359"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 346/1250 [=======>......................] - ETA: 17s - loss: 0.0022 - mae: 0.0357"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 349/1250 [=======>......................] - ETA: 17s - loss: 0.0022 - mae: 0.0357"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 352/1250 [=======>......................] - ETA: 17s - loss: 0.0022 - mae: 0.0356"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 355/1250 [=======>......................] - ETA: 17s - loss: 0.0022 - mae: 0.0355"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 358/1250 [=======>......................] - ETA: 17s - loss: 0.0022 - mae: 0.0355"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 361/1250 [=======>......................] - ETA: 17s - loss: 0.0022 - mae: 0.0355"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 364/1250 [=======>......................] - ETA: 17s - loss: 0.0022 - mae: 0.0355"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 367/1250 [=======>......................] - ETA: 17s - loss: 0.0022 - mae: 0.0356"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 370/1250 [=======>......................] - ETA: 17s - loss: 0.0022 - mae: 0.0355"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 373/1250 [=======>......................] - ETA: 17s - loss: 0.0022 - mae: 0.0355"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 376/1250 [========>.....................] - ETA: 16s - loss: 0.0022 - mae: 0.0355"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 379/1250 [========>.....................] - ETA: 16s - loss: 0.0022 - mae: 0.0355"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 382/1250 [========>.....................] - ETA: 16s - loss: 0.0022 - mae: 0.0355"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 385/1250 [========>.....................] - ETA: 16s - loss: 0.0022 - mae: 0.0353"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 388/1250 [========>.....................] - ETA: 16s - loss: 0.0022 - mae: 0.0353"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 391/1250 [========>.....................] - ETA: 16s - loss: 0.0022 - mae: 0.0352"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 394/1250 [========>.....................] - ETA: 16s - loss: 0.0022 - mae: 0.0353"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 397/1250 [========>.....................] - ETA: 16s - loss: 0.0022 - mae: 0.0352"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 400/1250 [========>.....................] - ETA: 16s - loss: 0.0022 - mae: 0.0354"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 403/1250 [========>.....................] - ETA: 16s - loss: 0.0022 - mae: 0.0352"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 406/1250 [========>.....................] - ETA: 16s - loss: 0.0022 - mae: 0.0352"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 409/1250 [========>.....................] - ETA: 16s - loss: 0.0022 - mae: 0.0352"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 412/1250 [========>.....................] - ETA: 16s - loss: 0.0022 - mae: 0.0351"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 415/1250 [========>.....................] - ETA: 16s - loss: 0.0022 - mae: 0.0352"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 418/1250 [=========>....................] - ETA: 16s - loss: 0.0022 - mae: 0.0354"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 421/1250 [=========>....................] - ETA: 16s - loss: 0.0022 - mae: 0.0353"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 424/1250 [=========>....................] - ETA: 16s - loss: 0.0022 - mae: 0.0353"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 427/1250 [=========>....................] - ETA: 16s - loss: 0.0022 - mae: 0.0352"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 430/1250 [=========>....................] - ETA: 16s - loss: 0.0022 - mae: 0.0352"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 433/1250 [=========>....................] - ETA: 16s - loss: 0.0022 - mae: 0.0352"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 436/1250 [=========>....................] - ETA: 15s - loss: 0.0021 - mae: 0.0351"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 439/1250 [=========>....................] - ETA: 15s - loss: 0.0021 - mae: 0.0351"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 442/1250 [=========>....................] - ETA: 15s - loss: 0.0021 - mae: 0.0351"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 445/1250 [=========>....................] - ETA: 15s - loss: 0.0021 - mae: 0.0350"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 448/1250 [=========>....................] - ETA: 15s - loss: 0.0021 - mae: 0.0351"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 451/1250 [=========>....................] - ETA: 15s - loss: 0.0021 - mae: 0.0350"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 454/1250 [=========>....................] - ETA: 15s - loss: 0.0021 - mae: 0.0350"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 457/1250 [=========>....................] - ETA: 15s - loss: 0.0021 - mae: 0.0350"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 460/1250 [==========>...................] - ETA: 15s - loss: 0.0021 - mae: 0.0350"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 463/1250 [==========>...................] - ETA: 15s - loss: 0.0021 - mae: 0.0349"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 466/1250 [==========>...................] - ETA: 15s - loss: 0.0021 - mae: 0.0349"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 469/1250 [==========>...................] - ETA: 15s - loss: 0.0021 - mae: 0.0349"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 472/1250 [==========>...................] - ETA: 15s - loss: 0.0021 - mae: 0.0349"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 475/1250 [==========>...................] - ETA: 15s - loss: 0.0021 - mae: 0.0348"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 478/1250 [==========>...................] - ETA: 15s - loss: 0.0021 - mae: 0.0348"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 481/1250 [==========>...................] - ETA: 15s - loss: 0.0021 - mae: 0.0348"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 484/1250 [==========>...................] - ETA: 15s - loss: 0.0021 - mae: 0.0347"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 487/1250 [==========>...................] - ETA: 15s - loss: 0.0021 - mae: 0.0348"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 490/1250 [==========>...................] - ETA: 15s - loss: 0.0021 - mae: 0.0348"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 493/1250 [==========>...................] - ETA: 15s - loss: 0.0021 - mae: 0.0349"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 496/1250 [==========>...................] - ETA: 14s - loss: 0.0021 - mae: 0.0348"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 499/1250 [==========>...................] - ETA: 14s - loss: 0.0021 - mae: 0.0347"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 502/1250 [===========>..................] - ETA: 14s - loss: 0.0021 - mae: 0.0347"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 505/1250 [===========>..................] - ETA: 14s - loss: 0.0021 - mae: 0.0347"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 508/1250 [===========>..................] - ETA: 14s - loss: 0.0021 - mae: 0.0347"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 511/1250 [===========>..................] - ETA: 14s - loss: 0.0021 - mae: 0.0348"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 514/1250 [===========>..................] - ETA: 14s - loss: 0.0021 - mae: 0.0349"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 517/1250 [===========>..................] - ETA: 14s - loss: 0.0021 - mae: 0.0348"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 520/1250 [===========>..................] - ETA: 14s - loss: 0.0021 - mae: 0.0348"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 523/1250 [===========>..................] - ETA: 14s - loss: 0.0021 - mae: 0.0347"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 526/1250 [===========>..................] - ETA: 14s - loss: 0.0021 - mae: 0.0347"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 529/1250 [===========>..................] - ETA: 14s - loss: 0.0021 - mae: 0.0347"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 532/1250 [===========>..................] - ETA: 14s - loss: 0.0021 - mae: 0.0347"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 535/1250 [===========>..................] - ETA: 14s - loss: 0.0021 - mae: 0.0347"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 538/1250 [===========>..................] - ETA: 14s - loss: 0.0021 - mae: 0.0346"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 541/1250 [===========>..................] - ETA: 14s - loss: 0.0021 - mae: 0.0347"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 544/1250 [============>.................] - ETA: 14s - loss: 0.0021 - mae: 0.0346"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 547/1250 [============>.................] - ETA: 14s - loss: 0.0021 - mae: 0.0345"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 550/1250 [============>.................] - ETA: 13s - loss: 0.0021 - mae: 0.0346"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 553/1250 [============>.................] - ETA: 13s - loss: 0.0021 - mae: 0.0345"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 556/1250 [============>.................] - ETA: 13s - loss: 0.0021 - mae: 0.0346"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 559/1250 [============>.................] - ETA: 13s - loss: 0.0021 - mae: 0.0346"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 562/1250 [============>.................] - ETA: 13s - loss: 0.0021 - mae: 0.0345"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 565/1250 [============>.................] - ETA: 13s - loss: 0.0021 - mae: 0.0344"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 568/1250 [============>.................] - ETA: 13s - loss: 0.0021 - mae: 0.0345"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 571/1250 [============>.................] - ETA: 13s - loss: 0.0021 - mae: 0.0345"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 574/1250 [============>.................] - ETA: 13s - loss: 0.0021 - mae: 0.0345"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 577/1250 [============>.................] - ETA: 13s - loss: 0.0021 - mae: 0.0345"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 580/1250 [============>.................] - ETA: 13s - loss: 0.0021 - mae: 0.0345"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 583/1250 [============>.................] - ETA: 13s - loss: 0.0021 - mae: 0.0344"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 586/1250 [=============>................] - ETA: 13s - loss: 0.0021 - mae: 0.0344"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 589/1250 [=============>................] - ETA: 13s - loss: 0.0021 - mae: 0.0344"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 592/1250 [=============>................] - ETA: 13s - loss: 0.0021 - mae: 0.0343"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 595/1250 [=============>................] - ETA: 13s - loss: 0.0021 - mae: 0.0343"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 598/1250 [=============>................] - ETA: 13s - loss: 0.0021 - mae: 0.0343"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 601/1250 [=============>................] - ETA: 13s - loss: 0.0021 - mae: 0.0343"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 604/1250 [=============>................] - ETA: 12s - loss: 0.0021 - mae: 0.0343"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 607/1250 [=============>................] - ETA: 12s - loss: 0.0021 - mae: 0.0343"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 610/1250 [=============>................] - ETA: 12s - loss: 0.0020 - mae: 0.0342"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 613/1250 [=============>................] - ETA: 12s - loss: 0.0020 - mae: 0.0341"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 616/1250 [=============>................] - ETA: 12s - loss: 0.0020 - mae: 0.0341"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 619/1250 [=============>................] - ETA: 12s - loss: 0.0020 - mae: 0.0341"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 622/1250 [=============>................] - ETA: 12s - loss: 0.0020 - mae: 0.0340"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 625/1250 [==============>...............] - ETA: 12s - loss: 0.0020 - mae: 0.0340"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 628/1250 [==============>...............] - ETA: 12s - loss: 0.0020 - mae: 0.0340"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 631/1250 [==============>...............] - ETA: 12s - loss: 0.0020 - mae: 0.0340"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 634/1250 [==============>...............] - ETA: 12s - loss: 0.0020 - mae: 0.0340"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 637/1250 [==============>...............] - ETA: 12s - loss: 0.0020 - mae: 0.0340"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 640/1250 [==============>...............] - ETA: 12s - loss: 0.0020 - mae: 0.0339"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 643/1250 [==============>...............] - ETA: 12s - loss: 0.0020 - mae: 0.0340"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 646/1250 [==============>...............] - ETA: 12s - loss: 0.0020 - mae: 0.0339"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 649/1250 [==============>...............] - ETA: 12s - loss: 0.0020 - mae: 0.0339"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 652/1250 [==============>...............] - ETA: 12s - loss: 0.0020 - mae: 0.0338"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 655/1250 [==============>...............] - ETA: 11s - loss: 0.0020 - mae: 0.0338"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 658/1250 [==============>...............] - ETA: 11s - loss: 0.0020 - mae: 0.0339"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 661/1250 [==============>...............] - ETA: 11s - loss: 0.0020 - mae: 0.0339"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 664/1250 [==============>...............] - ETA: 11s - loss: 0.0020 - mae: 0.0338"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 667/1250 [===============>..............] - ETA: 11s - loss: 0.0020 - mae: 0.0337"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 670/1250 [===============>..............] - ETA: 11s - loss: 0.0020 - mae: 0.0337"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 673/1250 [===============>..............] - ETA: 11s - loss: 0.0020 - mae: 0.0337"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 676/1250 [===============>..............] - ETA: 11s - loss: 0.0020 - mae: 0.0337"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 679/1250 [===============>..............] - ETA: 11s - loss: 0.0020 - mae: 0.0337"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 682/1250 [===============>..............] - ETA: 11s - loss: 0.0020 - mae: 0.0337"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 685/1250 [===============>..............] - ETA: 11s - loss: 0.0020 - mae: 0.0337"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 688/1250 [===============>..............] - ETA: 11s - loss: 0.0020 - mae: 0.0336"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 691/1250 [===============>..............] - ETA: 11s - loss: 0.0020 - mae: 0.0337"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 694/1250 [===============>..............] - ETA: 11s - loss: 0.0020 - mae: 0.0337"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 697/1250 [===============>..............] - ETA: 11s - loss: 0.0020 - mae: 0.0337"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 700/1250 [===============>..............] - ETA: 11s - loss: 0.0020 - mae: 0.0337"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 703/1250 [===============>..............] - ETA: 11s - loss: 0.0020 - mae: 0.0337"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 706/1250 [===============>..............] - ETA: 10s - loss: 0.0020 - mae: 0.0336"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 709/1250 [================>.............] - ETA: 10s - loss: 0.0020 - mae: 0.0335"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 712/1250 [================>.............] - ETA: 10s - loss: 0.0020 - mae: 0.0335"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 715/1250 [================>.............] - ETA: 10s - loss: 0.0020 - mae: 0.0336"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 718/1250 [================>.............] - ETA: 10s - loss: 0.0020 - mae: 0.0335"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 721/1250 [================>.............] - ETA: 10s - loss: 0.0020 - mae: 0.0335"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 724/1250 [================>.............] - ETA: 10s - loss: 0.0020 - mae: 0.0335"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 727/1250 [================>.............] - ETA: 10s - loss: 0.0020 - mae: 0.0336"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 730/1250 [================>.............] - ETA: 10s - loss: 0.0020 - mae: 0.0335"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 733/1250 [================>.............] - ETA: 10s - loss: 0.0020 - mae: 0.0335"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 736/1250 [================>.............] - ETA: 10s - loss: 0.0020 - mae: 0.0335"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 739/1250 [================>.............] - ETA: 10s - loss: 0.0020 - mae: 0.0335"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 742/1250 [================>.............] - ETA: 10s - loss: 0.0020 - mae: 0.0335"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 745/1250 [================>.............] - ETA: 10s - loss: 0.0020 - mae: 0.0334"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 748/1250 [================>.............] - ETA: 10s - loss: 0.0020 - mae: 0.0334"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 751/1250 [=================>............] - ETA: 10s - loss: 0.0020 - mae: 0.0334"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 754/1250 [=================>............] - ETA: 10s - loss: 0.0020 - mae: 0.0333"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 757/1250 [=================>............] - ETA: 9s - loss: 0.0020 - mae: 0.0333 "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 760/1250 [=================>............] - ETA: 9s - loss: 0.0020 - mae: 0.0333"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 763/1250 [=================>............] - ETA: 9s - loss: 0.0020 - mae: 0.0333"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 766/1250 [=================>............] - ETA: 9s - loss: 0.0020 - mae: 0.0334"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 769/1250 [=================>............] - ETA: 9s - loss: 0.0020 - mae: 0.0333"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 772/1250 [=================>............] - ETA: 9s - loss: 0.0020 - mae: 0.0333"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 775/1250 [=================>............] - ETA: 9s - loss: 0.0020 - mae: 0.0333"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 778/1250 [=================>............] - ETA: 9s - loss: 0.0020 - mae: 0.0333"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 781/1250 [=================>............] - ETA: 9s - loss: 0.0020 - mae: 0.0333"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 784/1250 [=================>............] - ETA: 9s - loss: 0.0019 - mae: 0.0333"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 787/1250 [=================>............] - ETA: 9s - loss: 0.0019 - mae: 0.0332"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 790/1250 [=================>............] - ETA: 9s - loss: 0.0019 - mae: 0.0333"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 793/1250 [==================>...........] - ETA: 9s - loss: 0.0019 - mae: 0.0332"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 796/1250 [==================>...........] - ETA: 9s - loss: 0.0019 - mae: 0.0332"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 799/1250 [==================>...........] - ETA: 9s - loss: 0.0019 - mae: 0.0332"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 802/1250 [==================>...........] - ETA: 9s - loss: 0.0019 - mae: 0.0332"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 805/1250 [==================>...........] - ETA: 9s - loss: 0.0019 - mae: 0.0331"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 808/1250 [==================>...........] - ETA: 8s - loss: 0.0019 - mae: 0.0332"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 811/1250 [==================>...........] - ETA: 8s - loss: 0.0019 - mae: 0.0332"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 814/1250 [==================>...........] - ETA: 8s - loss: 0.0019 - mae: 0.0332"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 817/1250 [==================>...........] - ETA: 8s - loss: 0.0019 - mae: 0.0332"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 820/1250 [==================>...........] - ETA: 8s - loss: 0.0019 - mae: 0.0331"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 823/1250 [==================>...........] - ETA: 8s - loss: 0.0019 - mae: 0.0331"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 826/1250 [==================>...........] - ETA: 8s - loss: 0.0019 - mae: 0.0330"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 829/1250 [==================>...........] - ETA: 8s - loss: 0.0019 - mae: 0.0331"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 832/1250 [==================>...........] - ETA: 8s - loss: 0.0019 - mae: 0.0330"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 835/1250 [===================>..........] - ETA: 8s - loss: 0.0019 - mae: 0.0330"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 838/1250 [===================>..........] - ETA: 8s - loss: 0.0019 - mae: 0.0330"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 841/1250 [===================>..........] - ETA: 8s - loss: 0.0019 - mae: 0.0330"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 844/1250 [===================>..........] - ETA: 8s - loss: 0.0019 - mae: 0.0330"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 847/1250 [===================>..........] - ETA: 8s - loss: 0.0019 - mae: 0.0330"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 850/1250 [===================>..........] - ETA: 8s - loss: 0.0019 - mae: 0.0329"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 853/1250 [===================>..........] - ETA: 8s - loss: 0.0019 - mae: 0.0329"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 856/1250 [===================>..........] - ETA: 8s - loss: 0.0019 - mae: 0.0329"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 859/1250 [===================>..........] - ETA: 7s - loss: 0.0019 - mae: 0.0329"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 862/1250 [===================>..........] - ETA: 7s - loss: 0.0019 - mae: 0.0329"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 865/1250 [===================>..........] - ETA: 7s - loss: 0.0019 - mae: 0.0328"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 868/1250 [===================>..........] - ETA: 7s - loss: 0.0019 - mae: 0.0328"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 871/1250 [===================>..........] - ETA: 7s - loss: 0.0019 - mae: 0.0328"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 874/1250 [===================>..........] - ETA: 7s - loss: 0.0019 - mae: 0.0328"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 877/1250 [====================>.........] - ETA: 7s - loss: 0.0019 - mae: 0.0328"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 880/1250 [====================>.........] - ETA: 7s - loss: 0.0019 - mae: 0.0328"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 883/1250 [====================>.........] - ETA: 7s - loss: 0.0019 - mae: 0.0328"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 886/1250 [====================>.........] - ETA: 7s - loss: 0.0019 - mae: 0.0328"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 889/1250 [====================>.........] - ETA: 7s - loss: 0.0019 - mae: 0.0328"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 892/1250 [====================>.........] - ETA: 7s - loss: 0.0019 - mae: 0.0328"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 895/1250 [====================>.........] - ETA: 7s - loss: 0.0019 - mae: 0.0329"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 898/1250 [====================>.........] - ETA: 7s - loss: 0.0019 - mae: 0.0328"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 901/1250 [====================>.........] - ETA: 7s - loss: 0.0019 - mae: 0.0328"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 904/1250 [====================>.........] - ETA: 7s - loss: 0.0019 - mae: 0.0328"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 907/1250 [====================>.........] - ETA: 7s - loss: 0.0019 - mae: 0.0327"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 910/1250 [====================>.........] - ETA: 6s - loss: 0.0019 - mae: 0.0327"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 913/1250 [====================>.........] - ETA: 6s - loss: 0.0019 - mae: 0.0326"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 916/1250 [====================>.........] - ETA: 6s - loss: 0.0019 - mae: 0.0326"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 919/1250 [=====================>........] - ETA: 6s - loss: 0.0019 - mae: 0.0327"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 922/1250 [=====================>........] - ETA: 6s - loss: 0.0019 - mae: 0.0327"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 925/1250 [=====================>........] - ETA: 6s - loss: 0.0019 - mae: 0.0327"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 928/1250 [=====================>........] - ETA: 6s - loss: 0.0019 - mae: 0.0326"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 931/1250 [=====================>........] - ETA: 6s - loss: 0.0019 - mae: 0.0326"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 934/1250 [=====================>........] - ETA: 6s - loss: 0.0019 - mae: 0.0326"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 937/1250 [=====================>........] - ETA: 6s - loss: 0.0019 - mae: 0.0326"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 940/1250 [=====================>........] - ETA: 6s - loss: 0.0019 - mae: 0.0326"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 943/1250 [=====================>........] - ETA: 6s - loss: 0.0019 - mae: 0.0326"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 946/1250 [=====================>........] - ETA: 6s - loss: 0.0019 - mae: 0.0325"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 949/1250 [=====================>........] - ETA: 6s - loss: 0.0019 - mae: 0.0325"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 952/1250 [=====================>........] - ETA: 6s - loss: 0.0019 - mae: 0.0325"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 955/1250 [=====================>........] - ETA: 6s - loss: 0.0019 - mae: 0.0325"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 958/1250 [=====================>........] - ETA: 5s - loss: 0.0019 - mae: 0.0325"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 961/1250 [======================>.......] - ETA: 5s - loss: 0.0019 - mae: 0.0325"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 964/1250 [======================>.......] - ETA: 5s - loss: 0.0019 - mae: 0.0324"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 967/1250 [======================>.......] - ETA: 5s - loss: 0.0019 - mae: 0.0325"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 970/1250 [======================>.......] - ETA: 5s - loss: 0.0019 - mae: 0.0324"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 973/1250 [======================>.......] - ETA: 5s - loss: 0.0019 - mae: 0.0324"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 976/1250 [======================>.......] - ETA: 5s - loss: 0.0019 - mae: 0.0324"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 979/1250 [======================>.......] - ETA: 5s - loss: 0.0019 - mae: 0.0324"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 982/1250 [======================>.......] - ETA: 5s - loss: 0.0019 - mae: 0.0324"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 985/1250 [======================>.......] - ETA: 5s - loss: 0.0019 - mae: 0.0324"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 988/1250 [======================>.......] - ETA: 5s - loss: 0.0018 - mae: 0.0323"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 991/1250 [======================>.......] - ETA: 5s - loss: 0.0018 - mae: 0.0323"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 994/1250 [======================>.......] - ETA: 5s - loss: 0.0018 - mae: 0.0322"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 997/1250 [======================>.......] - ETA: 5s - loss: 0.0018 - mae: 0.0322"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1000/1250 [=======================>......] - ETA: 5s - loss: 0.0018 - mae: 0.0323"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1003/1250 [=======================>......] - ETA: 5s - loss: 0.0018 - mae: 0.0323"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1006/1250 [=======================>......] - ETA: 4s - loss: 0.0018 - mae: 0.0323"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1009/1250 [=======================>......] - ETA: 4s - loss: 0.0018 - mae: 0.0323"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1012/1250 [=======================>......] - ETA: 4s - loss: 0.0018 - mae: 0.0323"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1015/1250 [=======================>......] - ETA: 4s - loss: 0.0018 - mae: 0.0322"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1018/1250 [=======================>......] - ETA: 4s - loss: 0.0018 - mae: 0.0322"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1021/1250 [=======================>......] - ETA: 4s - loss: 0.0018 - mae: 0.0322"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1024/1250 [=======================>......] - ETA: 4s - loss: 0.0018 - mae: 0.0322"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1027/1250 [=======================>......] - ETA: 4s - loss: 0.0018 - mae: 0.0322"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1030/1250 [=======================>......] - ETA: 4s - loss: 0.0018 - mae: 0.0322"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1033/1250 [=======================>......] - ETA: 4s - loss: 0.0018 - mae: 0.0321"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1036/1250 [=======================>......] - ETA: 4s - loss: 0.0018 - mae: 0.0321"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1039/1250 [=======================>......] - ETA: 4s - loss: 0.0018 - mae: 0.0321"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1042/1250 [========================>.....] - ETA: 4s - loss: 0.0018 - mae: 0.0321"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1045/1250 [========================>.....] - ETA: 4s - loss: 0.0018 - mae: 0.0321"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1048/1250 [========================>.....] - ETA: 4s - loss: 0.0018 - mae: 0.0321"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1051/1250 [========================>.....] - ETA: 4s - loss: 0.0018 - mae: 0.0320"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1054/1250 [========================>.....] - ETA: 4s - loss: 0.0018 - mae: 0.0321"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1057/1250 [========================>.....] - ETA: 3s - loss: 0.0018 - mae: 0.0321"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1060/1250 [========================>.....] - ETA: 3s - loss: 0.0018 - mae: 0.0321"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1063/1250 [========================>.....] - ETA: 3s - loss: 0.0018 - mae: 0.0321"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1066/1250 [========================>.....] - ETA: 3s - loss: 0.0018 - mae: 0.0321"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1069/1250 [========================>.....] - ETA: 3s - loss: 0.0018 - mae: 0.0320"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1072/1250 [========================>.....] - ETA: 3s - loss: 0.0018 - mae: 0.0320"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1075/1250 [========================>.....] - ETA: 3s - loss: 0.0018 - mae: 0.0319"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1078/1250 [========================>.....] - ETA: 3s - loss: 0.0018 - mae: 0.0319"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1081/1250 [========================>.....] - ETA: 3s - loss: 0.0018 - mae: 0.0319"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1084/1250 [=========================>....] - ETA: 3s - loss: 0.0018 - mae: 0.0319"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1087/1250 [=========================>....] - ETA: 3s - loss: 0.0018 - mae: 0.0319"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1090/1250 [=========================>....] - ETA: 3s - loss: 0.0018 - mae: 0.0319"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1093/1250 [=========================>....] - ETA: 3s - loss: 0.0018 - mae: 0.0319"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1096/1250 [=========================>....] - ETA: 3s - loss: 0.0018 - mae: 0.0319"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1099/1250 [=========================>....] - ETA: 3s - loss: 0.0018 - mae: 0.0319"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1102/1250 [=========================>....] - ETA: 3s - loss: 0.0018 - mae: 0.0319"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1105/1250 [=========================>....] - ETA: 2s - loss: 0.0018 - mae: 0.0319"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1108/1250 [=========================>....] - ETA: 2s - loss: 0.0018 - mae: 0.0319"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1111/1250 [=========================>....] - ETA: 2s - loss: 0.0018 - mae: 0.0319"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1114/1250 [=========================>....] - ETA: 2s - loss: 0.0018 - mae: 0.0318"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1117/1250 [=========================>....] - ETA: 2s - loss: 0.0018 - mae: 0.0318"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1120/1250 [=========================>....] - ETA: 2s - loss: 0.0018 - mae: 0.0318"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1123/1250 [=========================>....] - ETA: 2s - loss: 0.0018 - mae: 0.0318"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1126/1250 [==========================>...] - ETA: 2s - loss: 0.0018 - mae: 0.0318"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1129/1250 [==========================>...] - ETA: 2s - loss: 0.0018 - mae: 0.0318"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1132/1250 [==========================>...] - ETA: 2s - loss: 0.0018 - mae: 0.0318"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1135/1250 [==========================>...] - ETA: 2s - loss: 0.0018 - mae: 0.0318"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1138/1250 [==========================>...] - ETA: 2s - loss: 0.0018 - mae: 0.0318"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1141/1250 [==========================>...] - ETA: 2s - loss: 0.0018 - mae: 0.0317"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1144/1250 [==========================>...] - ETA: 2s - loss: 0.0018 - mae: 0.0317"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1147/1250 [==========================>...] - ETA: 2s - loss: 0.0018 - mae: 0.0317"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1150/1250 [==========================>...] - ETA: 2s - loss: 0.0018 - mae: 0.0316"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1153/1250 [==========================>...] - ETA: 1s - loss: 0.0018 - mae: 0.0316"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1156/1250 [==========================>...] - ETA: 1s - loss: 0.0018 - mae: 0.0317"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1159/1250 [==========================>...] - ETA: 1s - loss: 0.0018 - mae: 0.0317"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1162/1250 [==========================>...] - ETA: 1s - loss: 0.0018 - mae: 0.0316"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1165/1250 [==========================>...] - ETA: 1s - loss: 0.0018 - mae: 0.0316"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1168/1250 [===========================>..] - ETA: 1s - loss: 0.0018 - mae: 0.0316"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1171/1250 [===========================>..] - ETA: 1s - loss: 0.0018 - mae: 0.0316"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1174/1250 [===========================>..] - ETA: 1s - loss: 0.0018 - mae: 0.0315"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1177/1250 [===========================>..] - ETA: 1s - loss: 0.0018 - mae: 0.0315"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1180/1250 [===========================>..] - ETA: 1s - loss: 0.0018 - mae: 0.0315"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1183/1250 [===========================>..] - ETA: 1s - loss: 0.0018 - mae: 0.0315"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1186/1250 [===========================>..] - ETA: 1s - loss: 0.0018 - mae: 0.0316"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1189/1250 [===========================>..] - ETA: 1s - loss: 0.0018 - mae: 0.0315"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1192/1250 [===========================>..] - ETA: 1s - loss: 0.0018 - mae: 0.0315"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1195/1250 [===========================>..] - ETA: 1s - loss: 0.0018 - mae: 0.0314"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1198/1250 [===========================>..] - ETA: 1s - loss: 0.0018 - mae: 0.0314"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1201/1250 [===========================>..] - ETA: 1s - loss: 0.0018 - mae: 0.0314"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1204/1250 [===========================>..] - ETA: 0s - loss: 0.0018 - mae: 0.0314"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1207/1250 [===========================>..] - ETA: 0s - loss: 0.0018 - mae: 0.0314"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1210/1250 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0314"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1213/1250 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0314"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1216/1250 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0314"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1219/1250 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0314"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1222/1250 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0314"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1225/1250 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0314"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1228/1250 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0314"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1231/1250 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0314"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1234/1250 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0314"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1237/1250 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0314"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1240/1250 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0314"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1243/1250 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0313"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1246/1250 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0313"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1249/1250 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0313"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1250/1250 [==============================] - 28s 22ms/step - loss: 0.0018 - mae: 0.0313 - val_loss: 0.0016 - val_mae: 0.0304\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3/5\n",
+      "\r",
+      "   1/1250 [..............................] - ETA: 0s - loss: 0.0014 - mae: 0.0276"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "   4/1250 [..............................] - ETA: 20s - loss: 0.0021 - mae: 0.0356"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "   7/1250 [..............................] - ETA: 23s - loss: 0.0015 - mae: 0.0307"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  10/1250 [..............................] - ETA: 24s - loss: 0.0017 - mae: 0.0326"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  13/1250 [..............................] - ETA: 24s - loss: 0.0017 - mae: 0.0324"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  16/1250 [..............................] - ETA: 25s - loss: 0.0020 - mae: 0.0350"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  19/1250 [..............................] - ETA: 25s - loss: 0.0018 - mae: 0.0330"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  22/1250 [..............................] - ETA: 25s - loss: 0.0018 - mae: 0.0330"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  25/1250 [..............................] - ETA: 25s - loss: 0.0017 - mae: 0.0315"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  28/1250 [..............................] - ETA: 26s - loss: 0.0015 - mae: 0.0298"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  31/1250 [..............................] - ETA: 26s - loss: 0.0015 - mae: 0.0286"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  34/1250 [..............................] - ETA: 26s - loss: 0.0014 - mae: 0.0280"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  37/1250 [..............................] - ETA: 26s - loss: 0.0014 - mae: 0.0279"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  40/1250 [..............................] - ETA: 26s - loss: 0.0014 - mae: 0.0286"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  43/1250 [>.............................] - ETA: 26s - loss: 0.0014 - mae: 0.0278"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  46/1250 [>.............................] - ETA: 26s - loss: 0.0016 - mae: 0.0290"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  49/1250 [>.............................] - ETA: 26s - loss: 0.0015 - mae: 0.0281"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  52/1250 [>.............................] - ETA: 25s - loss: 0.0015 - mae: 0.0278"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  55/1250 [>.............................] - ETA: 25s - loss: 0.0015 - mae: 0.0277"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  58/1250 [>.............................] - ETA: 25s - loss: 0.0015 - mae: 0.0281"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  61/1250 [>.............................] - ETA: 25s - loss: 0.0015 - mae: 0.0278"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  64/1250 [>.............................] - ETA: 25s - loss: 0.0014 - mae: 0.0274"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  67/1250 [>.............................] - ETA: 25s - loss: 0.0014 - mae: 0.0273"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  70/1250 [>.............................] - ETA: 25s - loss: 0.0014 - mae: 0.0269"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  73/1250 [>.............................] - ETA: 25s - loss: 0.0014 - mae: 0.0273"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  76/1250 [>.............................] - ETA: 25s - loss: 0.0014 - mae: 0.0273"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  79/1250 [>.............................] - ETA: 25s - loss: 0.0014 - mae: 0.0272"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  82/1250 [>.............................] - ETA: 25s - loss: 0.0014 - mae: 0.0270"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  85/1250 [=>............................] - ETA: 25s - loss: 0.0014 - mae: 0.0269"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  88/1250 [=>............................] - ETA: 25s - loss: 0.0014 - mae: 0.0272"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  91/1250 [=>............................] - ETA: 25s - loss: 0.0014 - mae: 0.0272"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  94/1250 [=>............................] - ETA: 25s - loss: 0.0014 - mae: 0.0272"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  97/1250 [=>............................] - ETA: 25s - loss: 0.0014 - mae: 0.0270"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 100/1250 [=>............................] - ETA: 25s - loss: 0.0014 - mae: 0.0275"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 103/1250 [=>............................] - ETA: 25s - loss: 0.0014 - mae: 0.0275"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 106/1250 [=>............................] - ETA: 24s - loss: 0.0014 - mae: 0.0274"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 109/1250 [=>............................] - ETA: 24s - loss: 0.0014 - mae: 0.0272"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 112/1250 [=>............................] - ETA: 24s - loss: 0.0013 - mae: 0.0270"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 115/1250 [=>............................] - ETA: 24s - loss: 0.0013 - mae: 0.0271"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 118/1250 [=>............................] - ETA: 24s - loss: 0.0013 - mae: 0.0271"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 121/1250 [=>............................] - ETA: 24s - loss: 0.0014 - mae: 0.0277"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 124/1250 [=>............................] - ETA: 24s - loss: 0.0014 - mae: 0.0275"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 126/1250 [==>...........................] - ETA: 24s - loss: 0.0014 - mae: 0.0274"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 128/1250 [==>...........................] - ETA: 25s - loss: 0.0014 - mae: 0.0274"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 130/1250 [==>...........................] - ETA: 25s - loss: 0.0014 - mae: 0.0274"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 132/1250 [==>...........................] - ETA: 25s - loss: 0.0014 - mae: 0.0274"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 134/1250 [==>...........................] - ETA: 25s - loss: 0.0014 - mae: 0.0274"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 136/1250 [==>...........................] - ETA: 25s - loss: 0.0014 - mae: 0.0274"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 138/1250 [==>...........................] - ETA: 25s - loss: 0.0014 - mae: 0.0274"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 140/1250 [==>...........................] - ETA: 25s - loss: 0.0014 - mae: 0.0273"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 142/1250 [==>...........................] - ETA: 25s - loss: 0.0013 - mae: 0.0272"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 144/1250 [==>...........................] - ETA: 25s - loss: 0.0013 - mae: 0.0272"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 146/1250 [==>...........................] - ETA: 25s - loss: 0.0014 - mae: 0.0273"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 148/1250 [==>...........................] - ETA: 25s - loss: 0.0014 - mae: 0.0273"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 150/1250 [==>...........................] - ETA: 25s - loss: 0.0014 - mae: 0.0272"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 152/1250 [==>...........................] - ETA: 25s - loss: 0.0013 - mae: 0.0272"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 154/1250 [==>...........................] - ETA: 25s - loss: 0.0014 - mae: 0.0273"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 156/1250 [==>...........................] - ETA: 25s - loss: 0.0013 - mae: 0.0271"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 158/1250 [==>...........................] - ETA: 25s - loss: 0.0013 - mae: 0.0271"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 160/1250 [==>...........................] - ETA: 25s - loss: 0.0014 - mae: 0.0273"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 162/1250 [==>...........................] - ETA: 25s - loss: 0.0014 - mae: 0.0272"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 165/1250 [==>...........................] - ETA: 25s - loss: 0.0014 - mae: 0.0271"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 167/1250 [===>..........................] - ETA: 25s - loss: 0.0014 - mae: 0.0272"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 169/1250 [===>..........................] - ETA: 25s - loss: 0.0014 - mae: 0.0273"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 172/1250 [===>..........................] - ETA: 25s - loss: 0.0013 - mae: 0.0272"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 175/1250 [===>..........................] - ETA: 25s - loss: 0.0013 - mae: 0.0271"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 177/1250 [===>..........................] - ETA: 25s - loss: 0.0013 - mae: 0.0271"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 179/1250 [===>..........................] - ETA: 25s - loss: 0.0013 - mae: 0.0271"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 182/1250 [===>..........................] - ETA: 25s - loss: 0.0013 - mae: 0.0269"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 185/1250 [===>..........................] - ETA: 25s - loss: 0.0013 - mae: 0.0271"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 188/1250 [===>..........................] - ETA: 25s - loss: 0.0013 - mae: 0.0271"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 191/1250 [===>..........................] - ETA: 25s - loss: 0.0013 - mae: 0.0270"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 194/1250 [===>..........................] - ETA: 25s - loss: 0.0013 - mae: 0.0269"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 197/1250 [===>..........................] - ETA: 24s - loss: 0.0013 - mae: 0.0269"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 200/1250 [===>..........................] - ETA: 24s - loss: 0.0013 - mae: 0.0267"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 203/1250 [===>..........................] - ETA: 24s - loss: 0.0013 - mae: 0.0267"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 206/1250 [===>..........................] - ETA: 24s - loss: 0.0013 - mae: 0.0265"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 209/1250 [====>.........................] - ETA: 24s - loss: 0.0013 - mae: 0.0266"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 212/1250 [====>.........................] - ETA: 24s - loss: 0.0013 - mae: 0.0267"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 215/1250 [====>.........................] - ETA: 24s - loss: 0.0013 - mae: 0.0268"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 218/1250 [====>.........................] - ETA: 24s - loss: 0.0013 - mae: 0.0269"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 221/1250 [====>.........................] - ETA: 24s - loss: 0.0013 - mae: 0.0270"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 224/1250 [====>.........................] - ETA: 24s - loss: 0.0013 - mae: 0.0269"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 227/1250 [====>.........................] - ETA: 24s - loss: 0.0013 - mae: 0.0269"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 230/1250 [====>.........................] - ETA: 23s - loss: 0.0013 - mae: 0.0270"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 233/1250 [====>.........................] - ETA: 23s - loss: 0.0013 - mae: 0.0268"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 236/1250 [====>.........................] - ETA: 23s - loss: 0.0013 - mae: 0.0269"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 239/1250 [====>.........................] - ETA: 23s - loss: 0.0013 - mae: 0.0268"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 242/1250 [====>.........................] - ETA: 23s - loss: 0.0013 - mae: 0.0269"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 245/1250 [====>.........................] - ETA: 23s - loss: 0.0013 - mae: 0.0267"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 248/1250 [====>.........................] - ETA: 23s - loss: 0.0013 - mae: 0.0268"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 251/1250 [=====>........................] - ETA: 23s - loss: 0.0013 - mae: 0.0269"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 254/1250 [=====>........................] - ETA: 23s - loss: 0.0013 - mae: 0.0269"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 257/1250 [=====>........................] - ETA: 23s - loss: 0.0013 - mae: 0.0268"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 260/1250 [=====>........................] - ETA: 23s - loss: 0.0013 - mae: 0.0267"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 263/1250 [=====>........................] - ETA: 22s - loss: 0.0013 - mae: 0.0268"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 266/1250 [=====>........................] - ETA: 22s - loss: 0.0013 - mae: 0.0269"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 269/1250 [=====>........................] - ETA: 22s - loss: 0.0013 - mae: 0.0269"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 272/1250 [=====>........................] - ETA: 22s - loss: 0.0013 - mae: 0.0267"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 275/1250 [=====>........................] - ETA: 22s - loss: 0.0013 - mae: 0.0266"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 278/1250 [=====>........................] - ETA: 22s - loss: 0.0013 - mae: 0.0265"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 281/1250 [=====>........................] - ETA: 22s - loss: 0.0013 - mae: 0.0264"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 284/1250 [=====>........................] - ETA: 22s - loss: 0.0013 - mae: 0.0264"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 287/1250 [=====>........................] - ETA: 22s - loss: 0.0013 - mae: 0.0265"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 290/1250 [=====>........................] - ETA: 22s - loss: 0.0013 - mae: 0.0265"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 293/1250 [======>.......................] - ETA: 22s - loss: 0.0013 - mae: 0.0265"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 296/1250 [======>.......................] - ETA: 22s - loss: 0.0013 - mae: 0.0264"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 299/1250 [======>.......................] - ETA: 22s - loss: 0.0013 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 302/1250 [======>.......................] - ETA: 21s - loss: 0.0013 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 305/1250 [======>.......................] - ETA: 21s - loss: 0.0013 - mae: 0.0263"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 308/1250 [======>.......................] - ETA: 21s - loss: 0.0013 - mae: 0.0263"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 311/1250 [======>.......................] - ETA: 21s - loss: 0.0013 - mae: 0.0264"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 314/1250 [======>.......................] - ETA: 21s - loss: 0.0013 - mae: 0.0263"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 317/1250 [======>.......................] - ETA: 21s - loss: 0.0013 - mae: 0.0263"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 320/1250 [======>.......................] - ETA: 21s - loss: 0.0013 - mae: 0.0263"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 323/1250 [======>.......................] - ETA: 21s - loss: 0.0013 - mae: 0.0263"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 326/1250 [======>.......................] - ETA: 21s - loss: 0.0013 - mae: 0.0264"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 329/1250 [======>.......................] - ETA: 21s - loss: 0.0012 - mae: 0.0263"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 332/1250 [======>.......................] - ETA: 21s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 335/1250 [=======>......................] - ETA: 21s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 338/1250 [=======>......................] - ETA: 20s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 341/1250 [=======>......................] - ETA: 20s - loss: 0.0013 - mae: 0.0263"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 344/1250 [=======>......................] - ETA: 20s - loss: 0.0013 - mae: 0.0263"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 347/1250 [=======>......................] - ETA: 20s - loss: 0.0013 - mae: 0.0264"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 350/1250 [=======>......................] - ETA: 20s - loss: 0.0013 - mae: 0.0263"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 353/1250 [=======>......................] - ETA: 20s - loss: 0.0013 - mae: 0.0263"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 356/1250 [=======>......................] - ETA: 20s - loss: 0.0013 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 359/1250 [=======>......................] - ETA: 20s - loss: 0.0013 - mae: 0.0263"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 362/1250 [=======>......................] - ETA: 20s - loss: 0.0013 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 365/1250 [=======>......................] - ETA: 20s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 368/1250 [=======>......................] - ETA: 20s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 371/1250 [=======>......................] - ETA: 20s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 374/1250 [=======>......................] - ETA: 20s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 377/1250 [========>.....................] - ETA: 19s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 380/1250 [========>.....................] - ETA: 19s - loss: 0.0013 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 383/1250 [========>.....................] - ETA: 19s - loss: 0.0013 - mae: 0.0263"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 386/1250 [========>.....................] - ETA: 19s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 389/1250 [========>.....................] - ETA: 19s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 392/1250 [========>.....................] - ETA: 19s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 395/1250 [========>.....................] - ETA: 19s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 398/1250 [========>.....................] - ETA: 19s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 401/1250 [========>.....................] - ETA: 19s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 404/1250 [========>.....................] - ETA: 19s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 407/1250 [========>.....................] - ETA: 19s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 410/1250 [========>.....................] - ETA: 19s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 413/1250 [========>.....................] - ETA: 19s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 416/1250 [========>.....................] - ETA: 18s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 419/1250 [=========>....................] - ETA: 18s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 422/1250 [=========>....................] - ETA: 18s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 425/1250 [=========>....................] - ETA: 18s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 428/1250 [=========>....................] - ETA: 18s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 431/1250 [=========>....................] - ETA: 18s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 434/1250 [=========>....................] - ETA: 18s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 437/1250 [=========>....................] - ETA: 18s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 440/1250 [=========>....................] - ETA: 18s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 443/1250 [=========>....................] - ETA: 18s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 446/1250 [=========>....................] - ETA: 18s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 449/1250 [=========>....................] - ETA: 18s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 452/1250 [=========>....................] - ETA: 18s - loss: 0.0013 - mae: 0.0263"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 455/1250 [=========>....................] - ETA: 18s - loss: 0.0013 - mae: 0.0263"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 458/1250 [=========>....................] - ETA: 17s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 461/1250 [==========>...................] - ETA: 17s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 464/1250 [==========>...................] - ETA: 17s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 467/1250 [==========>...................] - ETA: 17s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 470/1250 [==========>...................] - ETA: 17s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 473/1250 [==========>...................] - ETA: 17s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 476/1250 [==========>...................] - ETA: 17s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 479/1250 [==========>...................] - ETA: 17s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 482/1250 [==========>...................] - ETA: 17s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 485/1250 [==========>...................] - ETA: 17s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 488/1250 [==========>...................] - ETA: 17s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 491/1250 [==========>...................] - ETA: 17s - loss: 0.0012 - mae: 0.0262"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 494/1250 [==========>...................] - ETA: 17s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 497/1250 [==========>...................] - ETA: 17s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 500/1250 [===========>..................] - ETA: 16s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 503/1250 [===========>..................] - ETA: 16s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 506/1250 [===========>..................] - ETA: 16s - loss: 0.0012 - mae: 0.0260"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 509/1250 [===========>..................] - ETA: 16s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 512/1250 [===========>..................] - ETA: 16s - loss: 0.0012 - mae: 0.0261"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 515/1250 [===========>..................] - ETA: 16s - loss: 0.0012 - mae: 0.0260"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 518/1250 [===========>..................] - ETA: 16s - loss: 0.0012 - mae: 0.0260"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 521/1250 [===========>..................] - ETA: 16s - loss: 0.0012 - mae: 0.0260"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 524/1250 [===========>..................] - ETA: 16s - loss: 0.0012 - mae: 0.0260"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 527/1250 [===========>..................] - ETA: 16s - loss: 0.0012 - mae: 0.0260"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 530/1250 [===========>..................] - ETA: 16s - loss: 0.0012 - mae: 0.0260"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 533/1250 [===========>..................] - ETA: 16s - loss: 0.0012 - mae: 0.0260"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 536/1250 [===========>..................] - ETA: 16s - loss: 0.0012 - mae: 0.0260"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 539/1250 [===========>..................] - ETA: 16s - loss: 0.0012 - mae: 0.0259"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 542/1250 [============>.................] - ETA: 15s - loss: 0.0012 - mae: 0.0259"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 545/1250 [============>.................] - ETA: 15s - loss: 0.0012 - mae: 0.0259"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 548/1250 [============>.................] - ETA: 15s - loss: 0.0012 - mae: 0.0259"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 551/1250 [============>.................] - ETA: 15s - loss: 0.0012 - mae: 0.0259"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 554/1250 [============>.................] - ETA: 15s - loss: 0.0012 - mae: 0.0259"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 557/1250 [============>.................] - ETA: 15s - loss: 0.0012 - mae: 0.0259"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 560/1250 [============>.................] - ETA: 15s - loss: 0.0012 - mae: 0.0259"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 563/1250 [============>.................] - ETA: 15s - loss: 0.0012 - mae: 0.0258"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 566/1250 [============>.................] - ETA: 15s - loss: 0.0012 - mae: 0.0258"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 569/1250 [============>.................] - ETA: 15s - loss: 0.0012 - mae: 0.0258"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 572/1250 [============>.................] - ETA: 15s - loss: 0.0012 - mae: 0.0258"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 575/1250 [============>.................] - ETA: 15s - loss: 0.0012 - mae: 0.0258"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 578/1250 [============>.................] - ETA: 15s - loss: 0.0012 - mae: 0.0258"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 581/1250 [============>.................] - ETA: 15s - loss: 0.0012 - mae: 0.0258"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 584/1250 [=============>................] - ETA: 14s - loss: 0.0012 - mae: 0.0258"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 587/1250 [=============>................] - ETA: 14s - loss: 0.0012 - mae: 0.0258"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 590/1250 [=============>................] - ETA: 14s - loss: 0.0012 - mae: 0.0258"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 593/1250 [=============>................] - ETA: 14s - loss: 0.0012 - mae: 0.0258"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 596/1250 [=============>................] - ETA: 14s - loss: 0.0012 - mae: 0.0258"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 599/1250 [=============>................] - ETA: 14s - loss: 0.0012 - mae: 0.0259"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 602/1250 [=============>................] - ETA: 14s - loss: 0.0012 - mae: 0.0259"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 605/1250 [=============>................] - ETA: 14s - loss: 0.0012 - mae: 0.0258"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 608/1250 [=============>................] - ETA: 14s - loss: 0.0012 - mae: 0.0257"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 611/1250 [=============>................] - ETA: 14s - loss: 0.0012 - mae: 0.0258"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 614/1250 [=============>................] - ETA: 14s - loss: 0.0012 - mae: 0.0257"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 617/1250 [=============>................] - ETA: 14s - loss: 0.0012 - mae: 0.0257"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 620/1250 [=============>................] - ETA: 14s - loss: 0.0012 - mae: 0.0257"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 623/1250 [=============>................] - ETA: 14s - loss: 0.0012 - mae: 0.0257"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 626/1250 [==============>...............] - ETA: 14s - loss: 0.0012 - mae: 0.0257"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 629/1250 [==============>...............] - ETA: 13s - loss: 0.0012 - mae: 0.0257"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 632/1250 [==============>...............] - ETA: 13s - loss: 0.0012 - mae: 0.0257"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 635/1250 [==============>...............] - ETA: 13s - loss: 0.0012 - mae: 0.0257"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 638/1250 [==============>...............] - ETA: 13s - loss: 0.0012 - mae: 0.0257"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 641/1250 [==============>...............] - ETA: 13s - loss: 0.0012 - mae: 0.0256"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 644/1250 [==============>...............] - ETA: 13s - loss: 0.0012 - mae: 0.0257"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 647/1250 [==============>...............] - ETA: 13s - loss: 0.0012 - mae: 0.0257"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 650/1250 [==============>...............] - ETA: 13s - loss: 0.0012 - mae: 0.0257"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 653/1250 [==============>...............] - ETA: 13s - loss: 0.0012 - mae: 0.0257"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 656/1250 [==============>...............] - ETA: 13s - loss: 0.0012 - mae: 0.0257"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 659/1250 [==============>...............] - ETA: 13s - loss: 0.0012 - mae: 0.0256"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 662/1250 [==============>...............] - ETA: 13s - loss: 0.0012 - mae: 0.0256"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 665/1250 [==============>...............] - ETA: 13s - loss: 0.0012 - mae: 0.0256"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 668/1250 [===============>..............] - ETA: 13s - loss: 0.0012 - mae: 0.0256"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 671/1250 [===============>..............] - ETA: 12s - loss: 0.0012 - mae: 0.0256"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 674/1250 [===============>..............] - ETA: 12s - loss: 0.0012 - mae: 0.0257"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 677/1250 [===============>..............] - ETA: 12s - loss: 0.0012 - mae: 0.0256"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 680/1250 [===============>..............] - ETA: 12s - loss: 0.0012 - mae: 0.0256"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 683/1250 [===============>..............] - ETA: 12s - loss: 0.0012 - mae: 0.0255"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 686/1250 [===============>..............] - ETA: 12s - loss: 0.0012 - mae: 0.0256"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 689/1250 [===============>..............] - ETA: 12s - loss: 0.0012 - mae: 0.0256"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 692/1250 [===============>..............] - ETA: 12s - loss: 0.0012 - mae: 0.0256"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 695/1250 [===============>..............] - ETA: 12s - loss: 0.0012 - mae: 0.0255"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 698/1250 [===============>..............] - ETA: 12s - loss: 0.0012 - mae: 0.0255"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 701/1250 [===============>..............] - ETA: 12s - loss: 0.0012 - mae: 0.0255"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 704/1250 [===============>..............] - ETA: 12s - loss: 0.0012 - mae: 0.0255"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 707/1250 [===============>..............] - ETA: 12s - loss: 0.0012 - mae: 0.0255"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 710/1250 [================>.............] - ETA: 12s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 713/1250 [================>.............] - ETA: 12s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 716/1250 [================>.............] - ETA: 11s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 719/1250 [================>.............] - ETA: 11s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 722/1250 [================>.............] - ETA: 11s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 725/1250 [================>.............] - ETA: 11s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 728/1250 [================>.............] - ETA: 11s - loss: 0.0012 - mae: 0.0255"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 731/1250 [================>.............] - ETA: 11s - loss: 0.0012 - mae: 0.0255"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 734/1250 [================>.............] - ETA: 11s - loss: 0.0012 - mae: 0.0255"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 737/1250 [================>.............] - ETA: 11s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 740/1250 [================>.............] - ETA: 11s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 743/1250 [================>.............] - ETA: 11s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 746/1250 [================>.............] - ETA: 11s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 749/1250 [================>.............] - ETA: 11s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 752/1250 [=================>............] - ETA: 11s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 755/1250 [=================>............] - ETA: 11s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 758/1250 [=================>............] - ETA: 11s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 761/1250 [=================>............] - ETA: 10s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 764/1250 [=================>............] - ETA: 10s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 767/1250 [=================>............] - ETA: 10s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 770/1250 [=================>............] - ETA: 10s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 773/1250 [=================>............] - ETA: 10s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 776/1250 [=================>............] - ETA: 10s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 779/1250 [=================>............] - ETA: 10s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 782/1250 [=================>............] - ETA: 10s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 785/1250 [=================>............] - ETA: 10s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 788/1250 [=================>............] - ETA: 10s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 791/1250 [=================>............] - ETA: 10s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 794/1250 [==================>...........] - ETA: 10s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 797/1250 [==================>...........] - ETA: 10s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 800/1250 [==================>...........] - ETA: 10s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 803/1250 [==================>...........] - ETA: 9s - loss: 0.0011 - mae: 0.0253 "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 806/1250 [==================>...........] - ETA: 9s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 809/1250 [==================>...........] - ETA: 9s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 812/1250 [==================>...........] - ETA: 9s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 815/1250 [==================>...........] - ETA: 9s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 818/1250 [==================>...........] - ETA: 9s - loss: 0.0011 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 821/1250 [==================>...........] - ETA: 9s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 824/1250 [==================>...........] - ETA: 9s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 827/1250 [==================>...........] - ETA: 9s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 830/1250 [==================>...........] - ETA: 9s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 833/1250 [==================>...........] - ETA: 9s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 836/1250 [===================>..........] - ETA: 9s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 839/1250 [===================>..........] - ETA: 9s - loss: 0.0012 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 842/1250 [===================>..........] - ETA: 9s - loss: 0.0011 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 845/1250 [===================>..........] - ETA: 9s - loss: 0.0011 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 848/1250 [===================>..........] - ETA: 8s - loss: 0.0011 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 851/1250 [===================>..........] - ETA: 8s - loss: 0.0011 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 854/1250 [===================>..........] - ETA: 8s - loss: 0.0011 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 857/1250 [===================>..........] - ETA: 8s - loss: 0.0011 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 860/1250 [===================>..........] - ETA: 8s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 863/1250 [===================>..........] - ETA: 8s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 866/1250 [===================>..........] - ETA: 8s - loss: 0.0011 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 869/1250 [===================>..........] - ETA: 8s - loss: 0.0011 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 872/1250 [===================>..........] - ETA: 8s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 875/1250 [====================>.........] - ETA: 8s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 878/1250 [====================>.........] - ETA: 8s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 881/1250 [====================>.........] - ETA: 8s - loss: 0.0011 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 884/1250 [====================>.........] - ETA: 8s - loss: 0.0011 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 887/1250 [====================>.........] - ETA: 8s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 890/1250 [====================>.........] - ETA: 8s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 893/1250 [====================>.........] - ETA: 7s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 896/1250 [====================>.........] - ETA: 7s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 899/1250 [====================>.........] - ETA: 7s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 902/1250 [====================>.........] - ETA: 7s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 905/1250 [====================>.........] - ETA: 7s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 908/1250 [====================>.........] - ETA: 7s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 911/1250 [====================>.........] - ETA: 7s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 914/1250 [====================>.........] - ETA: 7s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 917/1250 [=====================>........] - ETA: 7s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 920/1250 [=====================>........] - ETA: 7s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 923/1250 [=====================>........] - ETA: 7s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 926/1250 [=====================>........] - ETA: 7s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 929/1250 [=====================>........] - ETA: 7s - loss: 0.0011 - mae: 0.0253"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 932/1250 [=====================>........] - ETA: 7s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 935/1250 [=====================>........] - ETA: 7s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 938/1250 [=====================>........] - ETA: 6s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 941/1250 [=====================>........] - ETA: 6s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 944/1250 [=====================>........] - ETA: 6s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 947/1250 [=====================>........] - ETA: 6s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 950/1250 [=====================>........] - ETA: 6s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 953/1250 [=====================>........] - ETA: 6s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 956/1250 [=====================>........] - ETA: 6s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 959/1250 [======================>.......] - ETA: 6s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 962/1250 [======================>.......] - ETA: 6s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 965/1250 [======================>.......] - ETA: 6s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 968/1250 [======================>.......] - ETA: 6s - loss: 0.0011 - mae: 0.0251"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 971/1250 [======================>.......] - ETA: 6s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 974/1250 [======================>.......] - ETA: 6s - loss: 0.0011 - mae: 0.0251"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 977/1250 [======================>.......] - ETA: 6s - loss: 0.0011 - mae: 0.0252"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 980/1250 [======================>.......] - ETA: 6s - loss: 0.0011 - mae: 0.0251"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 983/1250 [======================>.......] - ETA: 5s - loss: 0.0011 - mae: 0.0251"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 986/1250 [======================>.......] - ETA: 5s - loss: 0.0011 - mae: 0.0251"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 989/1250 [======================>.......] - ETA: 5s - loss: 0.0011 - mae: 0.0251"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 992/1250 [======================>.......] - ETA: 5s - loss: 0.0011 - mae: 0.0251"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 995/1250 [======================>.......] - ETA: 5s - loss: 0.0011 - mae: 0.0251"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 998/1250 [======================>.......] - ETA: 5s - loss: 0.0011 - mae: 0.0251"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1001/1250 [=======================>......] - ETA: 5s - loss: 0.0011 - mae: 0.0251"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1004/1250 [=======================>......] - ETA: 5s - loss: 0.0011 - mae: 0.0251"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1007/1250 [=======================>......] - ETA: 5s - loss: 0.0011 - mae: 0.0251"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1010/1250 [=======================>......] - ETA: 5s - loss: 0.0011 - mae: 0.0251"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1013/1250 [=======================>......] - ETA: 5s - loss: 0.0011 - mae: 0.0251"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1016/1250 [=======================>......] - ETA: 5s - loss: 0.0011 - mae: 0.0251"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1019/1250 [=======================>......] - ETA: 5s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1022/1250 [=======================>......] - ETA: 5s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1025/1250 [=======================>......] - ETA: 5s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1028/1250 [=======================>......] - ETA: 4s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1031/1250 [=======================>......] - ETA: 4s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1034/1250 [=======================>......] - ETA: 4s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1037/1250 [=======================>......] - ETA: 4s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1040/1250 [=======================>......] - ETA: 4s - loss: 0.0011 - mae: 0.0251"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1043/1250 [========================>.....] - ETA: 4s - loss: 0.0011 - mae: 0.0251"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1046/1250 [========================>.....] - ETA: 4s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1049/1250 [========================>.....] - ETA: 4s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1052/1250 [========================>.....] - ETA: 4s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1055/1250 [========================>.....] - ETA: 4s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1058/1250 [========================>.....] - ETA: 4s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1061/1250 [========================>.....] - ETA: 4s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1064/1250 [========================>.....] - ETA: 4s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1067/1250 [========================>.....] - ETA: 4s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1070/1250 [========================>.....] - ETA: 4s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1073/1250 [========================>.....] - ETA: 3s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1076/1250 [========================>.....] - ETA: 3s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1079/1250 [========================>.....] - ETA: 3s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1082/1250 [========================>.....] - ETA: 3s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1085/1250 [=========================>....] - ETA: 3s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1088/1250 [=========================>....] - ETA: 3s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1091/1250 [=========================>....] - ETA: 3s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1094/1250 [=========================>....] - ETA: 3s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1097/1250 [=========================>....] - ETA: 3s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1100/1250 [=========================>....] - ETA: 3s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1103/1250 [=========================>....] - ETA: 3s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1106/1250 [=========================>....] - ETA: 3s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1109/1250 [=========================>....] - ETA: 3s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1112/1250 [=========================>....] - ETA: 3s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1115/1250 [=========================>....] - ETA: 3s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1118/1250 [=========================>....] - ETA: 2s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1121/1250 [=========================>....] - ETA: 2s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1124/1250 [=========================>....] - ETA: 2s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1127/1250 [==========================>...] - ETA: 2s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1130/1250 [==========================>...] - ETA: 2s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1133/1250 [==========================>...] - ETA: 2s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1136/1250 [==========================>...] - ETA: 2s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1139/1250 [==========================>...] - ETA: 2s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1141/1250 [==========================>...] - ETA: 2s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1144/1250 [==========================>...] - ETA: 2s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1147/1250 [==========================>...] - ETA: 2s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1150/1250 [==========================>...] - ETA: 2s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1153/1250 [==========================>...] - ETA: 2s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1156/1250 [==========================>...] - ETA: 2s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1159/1250 [==========================>...] - ETA: 2s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1162/1250 [==========================>...] - ETA: 1s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1165/1250 [==========================>...] - ETA: 1s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1168/1250 [===========================>..] - ETA: 1s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1171/1250 [===========================>..] - ETA: 1s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1174/1250 [===========================>..] - ETA: 1s - loss: 0.0011 - mae: 0.0250"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1177/1250 [===========================>..] - ETA: 1s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1180/1250 [===========================>..] - ETA: 1s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1183/1250 [===========================>..] - ETA: 1s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1186/1250 [===========================>..] - ETA: 1s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1189/1250 [===========================>..] - ETA: 1s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1192/1250 [===========================>..] - ETA: 1s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1195/1250 [===========================>..] - ETA: 1s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1198/1250 [===========================>..] - ETA: 1s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1201/1250 [===========================>..] - ETA: 1s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1204/1250 [===========================>..] - ETA: 1s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1207/1250 [===========================>..] - ETA: 0s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1210/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0248"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1213/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0248"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1216/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1219/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1222/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0248"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1225/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1228/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1231/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0248"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1234/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1237/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0249"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1240/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0248"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1243/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0248"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1246/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0248"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1249/1250 [============================>.] - ETA: 0s - loss: 0.0011 - mae: 0.0248"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1250/1250 [==============================] - 30s 24ms/step - loss: 0.0011 - mae: 0.0248 - val_loss: 6.1209e-04 - val_mae: 0.0197\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4/5\n",
+      "\r",
+      "   1/1250 [..............................] - ETA: 0s - loss: 7.5856e-04 - mae: 0.0230"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "   4/1250 [..............................] - ETA: 20s - loss: 0.0015 - mae: 0.0294   "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "   7/1250 [..............................] - ETA: 23s - loss: 0.0012 - mae: 0.0254"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  10/1250 [..............................] - ETA: 24s - loss: 0.0010 - mae: 0.0238"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  13/1250 [..............................] - ETA: 25s - loss: 9.3142e-04 - mae: 0.0228"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  16/1250 [..............................] - ETA: 25s - loss: 8.9306e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  19/1250 [..............................] - ETA: 25s - loss: 9.1000e-04 - mae: 0.0226"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  22/1250 [..............................] - ETA: 25s - loss: 9.0141e-04 - mae: 0.0225"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  25/1250 [..............................] - ETA: 26s - loss: 9.0580e-04 - mae: 0.0226"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  28/1250 [..............................] - ETA: 26s - loss: 8.8972e-04 - mae: 0.0228"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  31/1250 [..............................] - ETA: 26s - loss: 8.6000e-04 - mae: 0.0225"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  34/1250 [..............................] - ETA: 26s - loss: 8.9815e-04 - mae: 0.0232"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  37/1250 [..............................] - ETA: 26s - loss: 8.5729e-04 - mae: 0.0226"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  40/1250 [..............................] - ETA: 26s - loss: 9.1217e-04 - mae: 0.0233"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  43/1250 [>.............................] - ETA: 26s - loss: 8.6733e-04 - mae: 0.0226"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  46/1250 [>.............................] - ETA: 26s - loss: 9.4068e-04 - mae: 0.0234"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  49/1250 [>.............................] - ETA: 26s - loss: 0.0010 - mae: 0.0238    "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  52/1250 [>.............................] - ETA: 26s - loss: 9.7511e-04 - mae: 0.0232"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  55/1250 [>.............................] - ETA: 26s - loss: 9.8943e-04 - mae: 0.0233"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  58/1250 [>.............................] - ETA: 26s - loss: 0.0010 - mae: 0.0236    "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  61/1250 [>.............................] - ETA: 26s - loss: 9.7692e-04 - mae: 0.0231"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  64/1250 [>.............................] - ETA: 26s - loss: 9.4311e-04 - mae: 0.0226"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  67/1250 [>.............................] - ETA: 25s - loss: 9.1365e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  70/1250 [>.............................] - ETA: 25s - loss: 8.9332e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  73/1250 [>.............................] - ETA: 25s - loss: 8.8988e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  76/1250 [>.............................] - ETA: 25s - loss: 9.2027e-04 - mae: 0.0225"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  79/1250 [>.............................] - ETA: 25s - loss: 9.0575e-04 - mae: 0.0224"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  82/1250 [>.............................] - ETA: 25s - loss: 8.8603e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  85/1250 [=>............................] - ETA: 25s - loss: 9.0039e-04 - mae: 0.0223"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  88/1250 [=>............................] - ETA: 25s - loss: 8.9897e-04 - mae: 0.0224"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  91/1250 [=>............................] - ETA: 25s - loss: 8.9284e-04 - mae: 0.0224"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  94/1250 [=>............................] - ETA: 25s - loss: 8.7269e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  97/1250 [=>............................] - ETA: 25s - loss: 9.1109e-04 - mae: 0.0225"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  99/1250 [=>............................] - ETA: 25s - loss: 9.0513e-04 - mae: 0.0225"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 102/1250 [=>............................] - ETA: 25s - loss: 9.2520e-04 - mae: 0.0227"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 105/1250 [=>............................] - ETA: 25s - loss: 9.1481e-04 - mae: 0.0225"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 108/1250 [=>............................] - ETA: 25s - loss: 9.0869e-04 - mae: 0.0223"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 111/1250 [=>............................] - ETA: 25s - loss: 8.9776e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 114/1250 [=>............................] - ETA: 25s - loss: 8.9605e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 117/1250 [=>............................] - ETA: 25s - loss: 8.8894e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 120/1250 [=>............................] - ETA: 24s - loss: 8.7713e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 123/1250 [=>............................] - ETA: 24s - loss: 8.7301e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 126/1250 [==>...........................] - ETA: 24s - loss: 8.7037e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 129/1250 [==>...........................] - ETA: 24s - loss: 8.7389e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 132/1250 [==>...........................] - ETA: 24s - loss: 8.8520e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 135/1250 [==>...........................] - ETA: 24s - loss: 8.7959e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 138/1250 [==>...........................] - ETA: 24s - loss: 8.7079e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 141/1250 [==>...........................] - ETA: 24s - loss: 8.6613e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 144/1250 [==>...........................] - ETA: 24s - loss: 8.7933e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 147/1250 [==>...........................] - ETA: 24s - loss: 8.8112e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 150/1250 [==>...........................] - ETA: 24s - loss: 8.8396e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 153/1250 [==>...........................] - ETA: 24s - loss: 8.8431e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 156/1250 [==>...........................] - ETA: 24s - loss: 8.7572e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 159/1250 [==>...........................] - ETA: 24s - loss: 8.7137e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 162/1250 [==>...........................] - ETA: 24s - loss: 8.6878e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 165/1250 [==>...........................] - ETA: 23s - loss: 8.8016e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 168/1250 [===>..........................] - ETA: 23s - loss: 8.7683e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 171/1250 [===>..........................] - ETA: 23s - loss: 8.7040e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 174/1250 [===>..........................] - ETA: 23s - loss: 8.7390e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 177/1250 [===>..........................] - ETA: 23s - loss: 8.7158e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 180/1250 [===>..........................] - ETA: 23s - loss: 8.7720e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 183/1250 [===>..........................] - ETA: 23s - loss: 8.8515e-04 - mae: 0.0224"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 186/1250 [===>..........................] - ETA: 23s - loss: 8.8274e-04 - mae: 0.0223"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 189/1250 [===>..........................] - ETA: 23s - loss: 8.8040e-04 - mae: 0.0223"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 192/1250 [===>..........................] - ETA: 23s - loss: 8.7541e-04 - mae: 0.0223"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 195/1250 [===>..........................] - ETA: 23s - loss: 8.8525e-04 - mae: 0.0224"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 198/1250 [===>..........................] - ETA: 23s - loss: 8.9083e-04 - mae: 0.0225"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 201/1250 [===>..........................] - ETA: 23s - loss: 8.8300e-04 - mae: 0.0224"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 204/1250 [===>..........................] - ETA: 23s - loss: 8.7932e-04 - mae: 0.0223"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 207/1250 [===>..........................] - ETA: 23s - loss: 8.7734e-04 - mae: 0.0223"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 210/1250 [====>.........................] - ETA: 22s - loss: 8.8403e-04 - mae: 0.0224"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 213/1250 [====>.........................] - ETA: 22s - loss: 8.9007e-04 - mae: 0.0225"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 216/1250 [====>.........................] - ETA: 22s - loss: 9.0691e-04 - mae: 0.0226"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 219/1250 [====>.........................] - ETA: 22s - loss: 9.0179e-04 - mae: 0.0225"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 222/1250 [====>.........................] - ETA: 22s - loss: 8.9552e-04 - mae: 0.0225"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 225/1250 [====>.........................] - ETA: 22s - loss: 8.8831e-04 - mae: 0.0224"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 228/1250 [====>.........................] - ETA: 22s - loss: 8.8733e-04 - mae: 0.0224"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 231/1250 [====>.........................] - ETA: 22s - loss: 8.9050e-04 - mae: 0.0225"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 234/1250 [====>.........................] - ETA: 22s - loss: 8.9136e-04 - mae: 0.0225"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 237/1250 [====>.........................] - ETA: 22s - loss: 8.9686e-04 - mae: 0.0226"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 239/1250 [====>.........................] - ETA: 22s - loss: 8.9499e-04 - mae: 0.0226"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 242/1250 [====>.........................] - ETA: 22s - loss: 8.9077e-04 - mae: 0.0225"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 245/1250 [====>.........................] - ETA: 22s - loss: 8.8485e-04 - mae: 0.0225"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 248/1250 [====>.........................] - ETA: 22s - loss: 8.7927e-04 - mae: 0.0224"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 251/1250 [=====>........................] - ETA: 22s - loss: 8.8340e-04 - mae: 0.0224"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 254/1250 [=====>........................] - ETA: 22s - loss: 8.8514e-04 - mae: 0.0225"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 257/1250 [=====>........................] - ETA: 22s - loss: 8.7750e-04 - mae: 0.0223"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 260/1250 [=====>........................] - ETA: 21s - loss: 8.7173e-04 - mae: 0.0223"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 263/1250 [=====>........................] - ETA: 21s - loss: 8.8037e-04 - mae: 0.0224"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 266/1250 [=====>........................] - ETA: 21s - loss: 8.8600e-04 - mae: 0.0224"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 269/1250 [=====>........................] - ETA: 21s - loss: 8.7939e-04 - mae: 0.0223"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 272/1250 [=====>........................] - ETA: 21s - loss: 8.7287e-04 - mae: 0.0223"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 275/1250 [=====>........................] - ETA: 21s - loss: 8.7093e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 278/1250 [=====>........................] - ETA: 21s - loss: 8.7446e-04 - mae: 0.0223"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 281/1250 [=====>........................] - ETA: 21s - loss: 8.7148e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 284/1250 [=====>........................] - ETA: 21s - loss: 8.6848e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 287/1250 [=====>........................] - ETA: 21s - loss: 8.7227e-04 - mae: 0.0223"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 290/1250 [=====>........................] - ETA: 21s - loss: 8.7031e-04 - mae: 0.0223"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 293/1250 [======>.......................] - ETA: 21s - loss: 8.7858e-04 - mae: 0.0224"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 296/1250 [======>.......................] - ETA: 21s - loss: 8.7673e-04 - mae: 0.0223"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 299/1250 [======>.......................] - ETA: 21s - loss: 8.8527e-04 - mae: 0.0224"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 302/1250 [======>.......................] - ETA: 21s - loss: 8.7867e-04 - mae: 0.0223"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 305/1250 [======>.......................] - ETA: 20s - loss: 8.7276e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 308/1250 [======>.......................] - ETA: 20s - loss: 8.8302e-04 - mae: 0.0223"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 311/1250 [======>.......................] - ETA: 20s - loss: 8.9682e-04 - mae: 0.0224"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 314/1250 [======>.......................] - ETA: 20s - loss: 8.9338e-04 - mae: 0.0224"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 317/1250 [======>.......................] - ETA: 20s - loss: 8.8787e-04 - mae: 0.0223"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 320/1250 [======>.......................] - ETA: 20s - loss: 8.8172e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 323/1250 [======>.......................] - ETA: 20s - loss: 8.7542e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 326/1250 [======>.......................] - ETA: 20s - loss: 8.7581e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 329/1250 [======>.......................] - ETA: 20s - loss: 8.7584e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 332/1250 [======>.......................] - ETA: 20s - loss: 8.7318e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 335/1250 [=======>......................] - ETA: 20s - loss: 8.7682e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 338/1250 [=======>......................] - ETA: 20s - loss: 8.7467e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 341/1250 [=======>......................] - ETA: 20s - loss: 8.7746e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 344/1250 [=======>......................] - ETA: 20s - loss: 8.7409e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 347/1250 [=======>......................] - ETA: 20s - loss: 8.7860e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 350/1250 [=======>......................] - ETA: 19s - loss: 8.7734e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 353/1250 [=======>......................] - ETA: 19s - loss: 8.7212e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 356/1250 [=======>......................] - ETA: 19s - loss: 8.6870e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 359/1250 [=======>......................] - ETA: 19s - loss: 8.6861e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 362/1250 [=======>......................] - ETA: 19s - loss: 8.7082e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 365/1250 [=======>......................] - ETA: 19s - loss: 8.6684e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 368/1250 [=======>......................] - ETA: 19s - loss: 8.6279e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 371/1250 [=======>......................] - ETA: 19s - loss: 8.6579e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 374/1250 [=======>......................] - ETA: 19s - loss: 8.6636e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 377/1250 [========>.....................] - ETA: 19s - loss: 8.6818e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 380/1250 [========>.....................] - ETA: 19s - loss: 8.6619e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 383/1250 [========>.....................] - ETA: 19s - loss: 8.6430e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 386/1250 [========>.....................] - ETA: 19s - loss: 8.6129e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 389/1250 [========>.....................] - ETA: 19s - loss: 8.5700e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 392/1250 [========>.....................] - ETA: 19s - loss: 8.5750e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 395/1250 [========>.....................] - ETA: 19s - loss: 8.5715e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 398/1250 [========>.....................] - ETA: 18s - loss: 8.6543e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 401/1250 [========>.....................] - ETA: 18s - loss: 8.6836e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 404/1250 [========>.....................] - ETA: 18s - loss: 8.6795e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 407/1250 [========>.....................] - ETA: 18s - loss: 8.6384e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 410/1250 [========>.....................] - ETA: 18s - loss: 8.6147e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 413/1250 [========>.....................] - ETA: 18s - loss: 8.6398e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 416/1250 [========>.....................] - ETA: 18s - loss: 8.6504e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 419/1250 [=========>....................] - ETA: 18s - loss: 8.6436e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 422/1250 [=========>....................] - ETA: 18s - loss: 8.6460e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 425/1250 [=========>....................] - ETA: 18s - loss: 8.6512e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 428/1250 [=========>....................] - ETA: 18s - loss: 8.6195e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 431/1250 [=========>....................] - ETA: 18s - loss: 8.5994e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 434/1250 [=========>....................] - ETA: 18s - loss: 8.6087e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 437/1250 [=========>....................] - ETA: 18s - loss: 8.5981e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 440/1250 [=========>....................] - ETA: 18s - loss: 8.6094e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 443/1250 [=========>....................] - ETA: 17s - loss: 8.6248e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 446/1250 [=========>....................] - ETA: 17s - loss: 8.6243e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 449/1250 [=========>....................] - ETA: 17s - loss: 8.6173e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 452/1250 [=========>....................] - ETA: 17s - loss: 8.5876e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 455/1250 [=========>....................] - ETA: 17s - loss: 8.5627e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 458/1250 [=========>....................] - ETA: 17s - loss: 8.6463e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 461/1250 [==========>...................] - ETA: 17s - loss: 8.6153e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 464/1250 [==========>...................] - ETA: 17s - loss: 8.6054e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 467/1250 [==========>...................] - ETA: 17s - loss: 8.6276e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 470/1250 [==========>...................] - ETA: 17s - loss: 8.6079e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 473/1250 [==========>...................] - ETA: 17s - loss: 8.6051e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 476/1250 [==========>...................] - ETA: 17s - loss: 8.6505e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 479/1250 [==========>...................] - ETA: 17s - loss: 8.6882e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 482/1250 [==========>...................] - ETA: 17s - loss: 8.6687e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 485/1250 [==========>...................] - ETA: 17s - loss: 8.6789e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 488/1250 [==========>...................] - ETA: 16s - loss: 8.6578e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 491/1250 [==========>...................] - ETA: 16s - loss: 8.6140e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 494/1250 [==========>...................] - ETA: 16s - loss: 8.6172e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 497/1250 [==========>...................] - ETA: 16s - loss: 8.5846e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 500/1250 [===========>..................] - ETA: 16s - loss: 8.5562e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 503/1250 [===========>..................] - ETA: 16s - loss: 8.5274e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 506/1250 [===========>..................] - ETA: 16s - loss: 8.5190e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 509/1250 [===========>..................] - ETA: 16s - loss: 8.5843e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 512/1250 [===========>..................] - ETA: 16s - loss: 8.6207e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 515/1250 [===========>..................] - ETA: 16s - loss: 8.5867e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 518/1250 [===========>..................] - ETA: 16s - loss: 8.5696e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 521/1250 [===========>..................] - ETA: 16s - loss: 8.6833e-04 - mae: 0.0222"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 524/1250 [===========>..................] - ETA: 16s - loss: 8.6557e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 527/1250 [===========>..................] - ETA: 16s - loss: 8.6169e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 530/1250 [===========>..................] - ETA: 16s - loss: 8.5823e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 533/1250 [===========>..................] - ETA: 15s - loss: 8.5830e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 536/1250 [===========>..................] - ETA: 15s - loss: 8.5846e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 539/1250 [===========>..................] - ETA: 15s - loss: 8.5936e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 542/1250 [============>.................] - ETA: 15s - loss: 8.5895e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 545/1250 [============>.................] - ETA: 15s - loss: 8.5729e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 548/1250 [============>.................] - ETA: 15s - loss: 8.5411e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 551/1250 [============>.................] - ETA: 15s - loss: 8.5120e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 554/1250 [============>.................] - ETA: 15s - loss: 8.5718e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 557/1250 [============>.................] - ETA: 15s - loss: 8.5845e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 560/1250 [============>.................] - ETA: 15s - loss: 8.5819e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 563/1250 [============>.................] - ETA: 15s - loss: 8.5723e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 566/1250 [============>.................] - ETA: 15s - loss: 8.5491e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 569/1250 [============>.................] - ETA: 15s - loss: 8.5391e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 572/1250 [============>.................] - ETA: 15s - loss: 8.5279e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 575/1250 [============>.................] - ETA: 15s - loss: 8.5480e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 578/1250 [============>.................] - ETA: 14s - loss: 8.5563e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 581/1250 [============>.................] - ETA: 14s - loss: 8.5677e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 584/1250 [=============>................] - ETA: 14s - loss: 8.5647e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 587/1250 [=============>................] - ETA: 14s - loss: 8.5598e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 590/1250 [=============>................] - ETA: 14s - loss: 8.5522e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 593/1250 [=============>................] - ETA: 14s - loss: 8.6267e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 596/1250 [=============>................] - ETA: 14s - loss: 8.6708e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 598/1250 [=============>................] - ETA: 14s - loss: 8.6502e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 601/1250 [=============>................] - ETA: 14s - loss: 8.6276e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 604/1250 [=============>................] - ETA: 14s - loss: 8.6054e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 607/1250 [=============>................] - ETA: 14s - loss: 8.5721e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 610/1250 [=============>................] - ETA: 14s - loss: 8.5539e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 613/1250 [=============>................] - ETA: 14s - loss: 8.6587e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 616/1250 [=============>................] - ETA: 14s - loss: 8.6409e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 619/1250 [=============>................] - ETA: 14s - loss: 8.6333e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 622/1250 [=============>................] - ETA: 13s - loss: 8.6220e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 625/1250 [==============>...............] - ETA: 13s - loss: 8.6050e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 628/1250 [==============>...............] - ETA: 13s - loss: 8.6111e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 631/1250 [==============>...............] - ETA: 13s - loss: 8.6614e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 634/1250 [==============>...............] - ETA: 13s - loss: 8.6415e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 637/1250 [==============>...............] - ETA: 13s - loss: 8.6648e-04 - mae: 0.0221"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 640/1250 [==============>...............] - ETA: 13s - loss: 8.6387e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 643/1250 [==============>...............] - ETA: 13s - loss: 8.6185e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 646/1250 [==============>...............] - ETA: 13s - loss: 8.5996e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 649/1250 [==============>...............] - ETA: 13s - loss: 8.5783e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 652/1250 [==============>...............] - ETA: 13s - loss: 8.5704e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 655/1250 [==============>...............] - ETA: 13s - loss: 8.6180e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 657/1250 [==============>...............] - ETA: 13s - loss: 8.6278e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 660/1250 [==============>...............] - ETA: 13s - loss: 8.6484e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 663/1250 [==============>...............] - ETA: 13s - loss: 8.6211e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 666/1250 [==============>...............] - ETA: 13s - loss: 8.5908e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 669/1250 [===============>..............] - ETA: 12s - loss: 8.5898e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 672/1250 [===============>..............] - ETA: 12s - loss: 8.5927e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 675/1250 [===============>..............] - ETA: 12s - loss: 8.5890e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 678/1250 [===============>..............] - ETA: 12s - loss: 8.5744e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 681/1250 [===============>..............] - ETA: 12s - loss: 8.5858e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 684/1250 [===============>..............] - ETA: 12s - loss: 8.5905e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 686/1250 [===============>..............] - ETA: 12s - loss: 8.6202e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 689/1250 [===============>..............] - ETA: 12s - loss: 8.5983e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 692/1250 [===============>..............] - ETA: 12s - loss: 8.6082e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 695/1250 [===============>..............] - ETA: 12s - loss: 8.6074e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 698/1250 [===============>..............] - ETA: 12s - loss: 8.5918e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 701/1250 [===============>..............] - ETA: 12s - loss: 8.5737e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 704/1250 [===============>..............] - ETA: 12s - loss: 8.5695e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 707/1250 [===============>..............] - ETA: 12s - loss: 8.5649e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 710/1250 [================>.............] - ETA: 12s - loss: 8.6014e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 713/1250 [================>.............] - ETA: 11s - loss: 8.5957e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 716/1250 [================>.............] - ETA: 11s - loss: 8.5787e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 719/1250 [================>.............] - ETA: 11s - loss: 8.6022e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 722/1250 [================>.............] - ETA: 11s - loss: 8.6105e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 725/1250 [================>.............] - ETA: 11s - loss: 8.6365e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 728/1250 [================>.............] - ETA: 11s - loss: 8.6134e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 731/1250 [================>.............] - ETA: 11s - loss: 8.6297e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 734/1250 [================>.............] - ETA: 11s - loss: 8.6549e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 737/1250 [================>.............] - ETA: 11s - loss: 8.6548e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 740/1250 [================>.............] - ETA: 11s - loss: 8.6627e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 743/1250 [================>.............] - ETA: 11s - loss: 8.6344e-04 - mae: 0.0220"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 746/1250 [================>.............] - ETA: 11s - loss: 8.6123e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 749/1250 [================>.............] - ETA: 11s - loss: 8.6261e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 752/1250 [=================>............] - ETA: 11s - loss: 8.6264e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 755/1250 [=================>............] - ETA: 11s - loss: 8.6115e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 758/1250 [=================>............] - ETA: 10s - loss: 8.5876e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 761/1250 [=================>............] - ETA: 10s - loss: 8.5710e-04 - mae: 0.0219"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 764/1250 [=================>............] - ETA: 10s - loss: 8.5561e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 767/1250 [=================>............] - ETA: 10s - loss: 8.5278e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 770/1250 [=================>............] - ETA: 10s - loss: 8.5045e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 773/1250 [=================>............] - ETA: 10s - loss: 8.4898e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 776/1250 [=================>............] - ETA: 10s - loss: 8.5101e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 779/1250 [=================>............] - ETA: 10s - loss: 8.5609e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 782/1250 [=================>............] - ETA: 10s - loss: 8.5695e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 785/1250 [=================>............] - ETA: 10s - loss: 8.5713e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 788/1250 [=================>............] - ETA: 10s - loss: 8.5494e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 791/1250 [=================>............] - ETA: 10s - loss: 8.5291e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 794/1250 [==================>...........] - ETA: 10s - loss: 8.5068e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 797/1250 [==================>...........] - ETA: 10s - loss: 8.4914e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 800/1250 [==================>...........] - ETA: 10s - loss: 8.5202e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 803/1250 [==================>...........] - ETA: 9s - loss: 8.5136e-04 - mae: 0.0217 "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 806/1250 [==================>...........] - ETA: 9s - loss: 8.4993e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 809/1250 [==================>...........] - ETA: 9s - loss: 8.5141e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 812/1250 [==================>...........] - ETA: 9s - loss: 8.5441e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 815/1250 [==================>...........] - ETA: 9s - loss: 8.5576e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 818/1250 [==================>...........] - ETA: 9s - loss: 8.5685e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 821/1250 [==================>...........] - ETA: 9s - loss: 8.5510e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 824/1250 [==================>...........] - ETA: 9s - loss: 8.5445e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 827/1250 [==================>...........] - ETA: 9s - loss: 8.5392e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 830/1250 [==================>...........] - ETA: 9s - loss: 8.5442e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 833/1250 [==================>...........] - ETA: 9s - loss: 8.5375e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 836/1250 [===================>..........] - ETA: 9s - loss: 8.5317e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 839/1250 [===================>..........] - ETA: 9s - loss: 8.5276e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 842/1250 [===================>..........] - ETA: 9s - loss: 8.5126e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 845/1250 [===================>..........] - ETA: 9s - loss: 8.4972e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 848/1250 [===================>..........] - ETA: 8s - loss: 8.4839e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 851/1250 [===================>..........] - ETA: 8s - loss: 8.4851e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 854/1250 [===================>..........] - ETA: 8s - loss: 8.5370e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 857/1250 [===================>..........] - ETA: 8s - loss: 8.5324e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 860/1250 [===================>..........] - ETA: 8s - loss: 8.5228e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 863/1250 [===================>..........] - ETA: 8s - loss: 8.5220e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 866/1250 [===================>..........] - ETA: 8s - loss: 8.5300e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 869/1250 [===================>..........] - ETA: 8s - loss: 8.5090e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 872/1250 [===================>..........] - ETA: 8s - loss: 8.4960e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 875/1250 [====================>.........] - ETA: 8s - loss: 8.4892e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 878/1250 [====================>.........] - ETA: 8s - loss: 8.4939e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 881/1250 [====================>.........] - ETA: 8s - loss: 8.5080e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 884/1250 [====================>.........] - ETA: 8s - loss: 8.5182e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 887/1250 [====================>.........] - ETA: 8s - loss: 8.5082e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 890/1250 [====================>.........] - ETA: 8s - loss: 8.5287e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 893/1250 [====================>.........] - ETA: 7s - loss: 8.5294e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 896/1250 [====================>.........] - ETA: 7s - loss: 8.5305e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 899/1250 [====================>.........] - ETA: 7s - loss: 8.5160e-04 - mae: 0.0218"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 902/1250 [====================>.........] - ETA: 7s - loss: 8.5010e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 905/1250 [====================>.........] - ETA: 7s - loss: 8.5116e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 908/1250 [====================>.........] - ETA: 7s - loss: 8.5004e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 911/1250 [====================>.........] - ETA: 7s - loss: 8.4824e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 914/1250 [====================>.........] - ETA: 7s - loss: 8.4786e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 917/1250 [=====================>........] - ETA: 7s - loss: 8.5016e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 920/1250 [=====================>........] - ETA: 7s - loss: 8.4849e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 923/1250 [=====================>........] - ETA: 7s - loss: 8.4740e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 926/1250 [=====================>........] - ETA: 7s - loss: 8.5074e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 929/1250 [=====================>........] - ETA: 7s - loss: 8.4938e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 932/1250 [=====================>........] - ETA: 7s - loss: 8.4807e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 935/1250 [=====================>........] - ETA: 7s - loss: 8.4618e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 938/1250 [=====================>........] - ETA: 6s - loss: 8.4640e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 941/1250 [=====================>........] - ETA: 6s - loss: 8.4916e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 944/1250 [=====================>........] - ETA: 6s - loss: 8.4990e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 947/1250 [=====================>........] - ETA: 6s - loss: 8.5025e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 950/1250 [=====================>........] - ETA: 6s - loss: 8.4875e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 953/1250 [=====================>........] - ETA: 6s - loss: 8.4953e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 956/1250 [=====================>........] - ETA: 6s - loss: 8.4794e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 959/1250 [======================>.......] - ETA: 6s - loss: 8.4579e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 962/1250 [======================>.......] - ETA: 6s - loss: 8.4555e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 965/1250 [======================>.......] - ETA: 6s - loss: 8.4530e-04 - mae: 0.0217"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 968/1250 [======================>.......] - ETA: 6s - loss: 8.4443e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 971/1250 [======================>.......] - ETA: 6s - loss: 8.4427e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 974/1250 [======================>.......] - ETA: 6s - loss: 8.4263e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 977/1250 [======================>.......] - ETA: 6s - loss: 8.4183e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 980/1250 [======================>.......] - ETA: 6s - loss: 8.4019e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 983/1250 [======================>.......] - ETA: 5s - loss: 8.3990e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 986/1250 [======================>.......] - ETA: 5s - loss: 8.4480e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 989/1250 [======================>.......] - ETA: 5s - loss: 8.4589e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 992/1250 [======================>.......] - ETA: 5s - loss: 8.4650e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 995/1250 [======================>.......] - ETA: 5s - loss: 8.4505e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 998/1250 [======================>.......] - ETA: 5s - loss: 8.4352e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1001/1250 [=======================>......] - ETA: 5s - loss: 8.4424e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1004/1250 [=======================>......] - ETA: 5s - loss: 8.4499e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1007/1250 [=======================>......] - ETA: 5s - loss: 8.4421e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1010/1250 [=======================>......] - ETA: 5s - loss: 8.4386e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1013/1250 [=======================>......] - ETA: 5s - loss: 8.4459e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1016/1250 [=======================>......] - ETA: 5s - loss: 8.4264e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1019/1250 [=======================>......] - ETA: 5s - loss: 8.4260e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1022/1250 [=======================>......] - ETA: 5s - loss: 8.4246e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1025/1250 [=======================>......] - ETA: 5s - loss: 8.4118e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1028/1250 [=======================>......] - ETA: 4s - loss: 8.4218e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1031/1250 [=======================>......] - ETA: 4s - loss: 8.4140e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1034/1250 [=======================>......] - ETA: 4s - loss: 8.4001e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1037/1250 [=======================>......] - ETA: 4s - loss: 8.3835e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1040/1250 [=======================>......] - ETA: 4s - loss: 8.3771e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1043/1250 [========================>.....] - ETA: 4s - loss: 8.4595e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1046/1250 [========================>.....] - ETA: 4s - loss: 8.4662e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1049/1250 [========================>.....] - ETA: 4s - loss: 8.4515e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1052/1250 [========================>.....] - ETA: 4s - loss: 8.4391e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1055/1250 [========================>.....] - ETA: 4s - loss: 8.4273e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1058/1250 [========================>.....] - ETA: 4s - loss: 8.4400e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1061/1250 [========================>.....] - ETA: 4s - loss: 8.4237e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1064/1250 [========================>.....] - ETA: 4s - loss: 8.4377e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1067/1250 [========================>.....] - ETA: 4s - loss: 8.4623e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1070/1250 [========================>.....] - ETA: 4s - loss: 8.4651e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1073/1250 [========================>.....] - ETA: 3s - loss: 8.4623e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1076/1250 [========================>.....] - ETA: 3s - loss: 8.4502e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1079/1250 [========================>.....] - ETA: 3s - loss: 8.4326e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1082/1250 [========================>.....] - ETA: 3s - loss: 8.4323e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1085/1250 [=========================>....] - ETA: 3s - loss: 8.4209e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1088/1250 [=========================>....] - ETA: 3s - loss: 8.4243e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1091/1250 [=========================>....] - ETA: 3s - loss: 8.4641e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1094/1250 [=========================>....] - ETA: 3s - loss: 8.4572e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1097/1250 [=========================>....] - ETA: 3s - loss: 8.4433e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1099/1250 [=========================>....] - ETA: 3s - loss: 8.4389e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1102/1250 [=========================>....] - ETA: 3s - loss: 8.4507e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1105/1250 [=========================>....] - ETA: 3s - loss: 8.4519e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1108/1250 [=========================>....] - ETA: 3s - loss: 8.4450e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1111/1250 [=========================>....] - ETA: 3s - loss: 8.4303e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1114/1250 [=========================>....] - ETA: 3s - loss: 8.4124e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1117/1250 [=========================>....] - ETA: 2s - loss: 8.3988e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1120/1250 [=========================>....] - ETA: 2s - loss: 8.4183e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1123/1250 [=========================>....] - ETA: 2s - loss: 8.4457e-04 - mae: 0.0216"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1126/1250 [==========================>...] - ETA: 2s - loss: 8.4321e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1129/1250 [==========================>...] - ETA: 2s - loss: 8.4186e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1132/1250 [==========================>...] - ETA: 2s - loss: 8.4027e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1135/1250 [==========================>...] - ETA: 2s - loss: 8.3961e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1138/1250 [==========================>...] - ETA: 2s - loss: 8.3821e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1141/1250 [==========================>...] - ETA: 2s - loss: 8.3823e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1144/1250 [==========================>...] - ETA: 2s - loss: 8.3790e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1147/1250 [==========================>...] - ETA: 2s - loss: 8.3621e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1150/1250 [==========================>...] - ETA: 2s - loss: 8.3507e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1153/1250 [==========================>...] - ETA: 2s - loss: 8.3576e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1156/1250 [==========================>...] - ETA: 2s - loss: 8.3611e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1159/1250 [==========================>...] - ETA: 2s - loss: 8.3509e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1162/1250 [==========================>...] - ETA: 1s - loss: 8.3607e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1165/1250 [==========================>...] - ETA: 1s - loss: 8.3612e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1168/1250 [===========================>..] - ETA: 1s - loss: 8.3639e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1171/1250 [===========================>..] - ETA: 1s - loss: 8.3598e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1174/1250 [===========================>..] - ETA: 1s - loss: 8.3550e-04 - mae: 0.0215"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1177/1250 [===========================>..] - ETA: 1s - loss: 8.3417e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1180/1250 [===========================>..] - ETA: 1s - loss: 8.3264e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1183/1250 [===========================>..] - ETA: 1s - loss: 8.3274e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1186/1250 [===========================>..] - ETA: 1s - loss: 8.3181e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1189/1250 [===========================>..] - ETA: 1s - loss: 8.3041e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1192/1250 [===========================>..] - ETA: 1s - loss: 8.2976e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1195/1250 [===========================>..] - ETA: 1s - loss: 8.3004e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1198/1250 [===========================>..] - ETA: 1s - loss: 8.3243e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1201/1250 [===========================>..] - ETA: 1s - loss: 8.3179e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1204/1250 [===========================>..] - ETA: 1s - loss: 8.3016e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1207/1250 [===========================>..] - ETA: 0s - loss: 8.2912e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1210/1250 [============================>.] - ETA: 0s - loss: 8.2839e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "1213/1250 [============================>.] - ETA: 0s - loss: 8.2740e-04 - mae: 0.0213"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1216/1250 [============================>.] - ETA: 0s - loss: 8.2617e-04 - mae: 0.0213"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1219/1250 [============================>.] - ETA: 0s - loss: 8.2643e-04 - mae: 0.0213"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1222/1250 [============================>.] - ETA: 0s - loss: 8.2778e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1225/1250 [============================>.] - ETA: 0s - loss: 8.2820e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1228/1250 [============================>.] - ETA: 0s - loss: 8.2737e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1231/1250 [============================>.] - ETA: 0s - loss: 8.2945e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1234/1250 [============================>.] - ETA: 0s - loss: 8.2967e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1237/1250 [============================>.] - ETA: 0s - loss: 8.2827e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1240/1250 [============================>.] - ETA: 0s - loss: 8.2674e-04 - mae: 0.0213"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1243/1250 [============================>.] - ETA: 0s - loss: 8.2622e-04 - mae: 0.0213"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1245/1250 [============================>.] - ETA: 0s - loss: 8.2700e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1248/1250 [============================>.] - ETA: 0s - loss: 8.2710e-04 - mae: 0.0214"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1250/1250 [==============================] - 30s 24ms/step - loss: 8.2672e-04 - mae: 0.0214 - val_loss: 5.3541e-04 - val_mae: 0.0166\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5/5\n",
+      "\r",
+      "   1/1250 [..............................] - ETA: 0s - loss: 7.9357e-04 - mae: 0.0206"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "   4/1250 [..............................] - ETA: 20s - loss: 7.6865e-04 - mae: 0.0210"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "   7/1250 [..............................] - ETA: 23s - loss: 6.7867e-04 - mae: 0.0202"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  10/1250 [..............................] - ETA: 24s - loss: 5.8887e-04 - mae: 0.0189"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  13/1250 [..............................] - ETA: 26s - loss: 6.0820e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  16/1250 [..............................] - ETA: 26s - loss: 5.2652e-04 - mae: 0.0174"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  19/1250 [..............................] - ETA: 26s - loss: 4.7571e-04 - mae: 0.0163"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  22/1250 [..............................] - ETA: 26s - loss: 5.6063e-04 - mae: 0.0170"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  25/1250 [..............................] - ETA: 26s - loss: 5.3882e-04 - mae: 0.0168"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  28/1250 [..............................] - ETA: 26s - loss: 5.7033e-04 - mae: 0.0173"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  31/1250 [..............................] - ETA: 26s - loss: 7.0317e-04 - mae: 0.0183"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  34/1250 [..............................] - ETA: 26s - loss: 6.9922e-04 - mae: 0.0186"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  37/1250 [..............................] - ETA: 27s - loss: 6.6955e-04 - mae: 0.0182"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  40/1250 [..............................] - ETA: 27s - loss: 6.7359e-04 - mae: 0.0183"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  43/1250 [>.............................] - ETA: 26s - loss: 6.6123e-04 - mae: 0.0183"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  46/1250 [>.............................] - ETA: 26s - loss: 6.5723e-04 - mae: 0.0185"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "  49/1250 [>.............................] - ETA: 26s - loss: 6.6691e-04 - mae: 0.0188"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  52/1250 [>.............................] - ETA: 26s - loss: 6.9123e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  55/1250 [>.............................] - ETA: 26s - loss: 6.7840e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  58/1250 [>.............................] - ETA: 26s - loss: 6.5446e-04 - mae: 0.0187"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  61/1250 [>.............................] - ETA: 26s - loss: 6.3466e-04 - mae: 0.0184"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  64/1250 [>.............................] - ETA: 26s - loss: 6.4249e-04 - mae: 0.0186"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  67/1250 [>.............................] - ETA: 26s - loss: 6.5156e-04 - mae: 0.0188"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "  70/1250 [>.............................] - ETA: 26s - loss: 6.7056e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  73/1250 [>.............................] - ETA: 26s - loss: 6.5296e-04 - mae: 0.0189"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  76/1250 [>.............................] - ETA: 26s - loss: 6.7859e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  79/1250 [>.............................] - ETA: 26s - loss: 6.6935e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  82/1250 [>.............................] - ETA: 26s - loss: 6.6588e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  85/1250 [=>............................] - ETA: 26s - loss: 6.5556e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  88/1250 [=>............................] - ETA: 26s - loss: 6.7274e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  91/1250 [=>............................] - ETA: 26s - loss: 7.2585e-04 - mae: 0.0196"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  94/1250 [=>............................] - ETA: 26s - loss: 7.4987e-04 - mae: 0.0199"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  97/1250 [=>............................] - ETA: 25s - loss: 7.4203e-04 - mae: 0.0198"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 100/1250 [=>............................] - ETA: 25s - loss: 7.3455e-04 - mae: 0.0198"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 103/1250 [=>............................] - ETA: 25s - loss: 7.3330e-04 - mae: 0.0198"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 106/1250 [=>............................] - ETA: 25s - loss: 7.2150e-04 - mae: 0.0196"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 109/1250 [=>............................] - ETA: 25s - loss: 7.1136e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 112/1250 [=>............................] - ETA: 25s - loss: 7.0156e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 115/1250 [=>............................] - ETA: 25s - loss: 6.9346e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 118/1250 [=>............................] - ETA: 25s - loss: 6.8738e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 121/1250 [=>............................] - ETA: 25s - loss: 6.9392e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 124/1250 [=>............................] - ETA: 25s - loss: 7.3129e-04 - mae: 0.0197"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 127/1250 [==>...........................] - ETA: 25s - loss: 7.4308e-04 - mae: 0.0199"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 130/1250 [==>...........................] - ETA: 25s - loss: 7.3128e-04 - mae: 0.0197"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 133/1250 [==>...........................] - ETA: 25s - loss: 7.1928e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 136/1250 [==>...........................] - ETA: 25s - loss: 7.1469e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 139/1250 [==>...........................] - ETA: 25s - loss: 7.0568e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 142/1250 [==>...........................] - ETA: 24s - loss: 7.0133e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 145/1250 [==>...........................] - ETA: 24s - loss: 7.1784e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 148/1250 [==>...........................] - ETA: 24s - loss: 7.1049e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 151/1250 [==>...........................] - ETA: 24s - loss: 7.1011e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 154/1250 [==>...........................] - ETA: 24s - loss: 7.0090e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 157/1250 [==>...........................] - ETA: 24s - loss: 7.0163e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 159/1250 [==>...........................] - ETA: 24s - loss: 7.0110e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 162/1250 [==>...........................] - ETA: 24s - loss: 7.0126e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 165/1250 [==>...........................] - ETA: 24s - loss: 7.1829e-04 - mae: 0.0196"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 168/1250 [===>..........................] - ETA: 24s - loss: 7.1634e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 171/1250 [===>..........................] - ETA: 24s - loss: 7.2663e-04 - mae: 0.0197"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 174/1250 [===>..........................] - ETA: 24s - loss: 7.2650e-04 - mae: 0.0197"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 177/1250 [===>..........................] - ETA: 24s - loss: 7.1911e-04 - mae: 0.0196"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 180/1250 [===>..........................] - ETA: 24s - loss: 7.1314e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 183/1250 [===>..........................] - ETA: 24s - loss: 7.0628e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 186/1250 [===>..........................] - ETA: 24s - loss: 7.1607e-04 - mae: 0.0196"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 189/1250 [===>..........................] - ETA: 24s - loss: 7.2867e-04 - mae: 0.0197"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 192/1250 [===>..........................] - ETA: 23s - loss: 7.3074e-04 - mae: 0.0198"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 195/1250 [===>..........................] - ETA: 23s - loss: 7.2274e-04 - mae: 0.0196"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 198/1250 [===>..........................] - ETA: 23s - loss: 7.2035e-04 - mae: 0.0196"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 201/1250 [===>..........................] - ETA: 23s - loss: 7.1406e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 204/1250 [===>..........................] - ETA: 23s - loss: 7.1337e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 207/1250 [===>..........................] - ETA: 23s - loss: 7.1626e-04 - mae: 0.0196"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 210/1250 [====>.........................] - ETA: 23s - loss: 7.1552e-04 - mae: 0.0196"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 212/1250 [====>.........................] - ETA: 23s - loss: 7.1133e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 215/1250 [====>.........................] - ETA: 23s - loss: 7.0818e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 218/1250 [====>.........................] - ETA: 23s - loss: 7.0676e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 221/1250 [====>.........................] - ETA: 23s - loss: 6.9966e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 224/1250 [====>.........................] - ETA: 23s - loss: 6.9952e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 227/1250 [====>.........................] - ETA: 23s - loss: 6.9527e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 230/1250 [====>.........................] - ETA: 23s - loss: 6.8822e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 233/1250 [====>.........................] - ETA: 23s - loss: 6.9572e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 236/1250 [====>.........................] - ETA: 23s - loss: 6.9981e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 239/1250 [====>.........................] - ETA: 22s - loss: 7.0210e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 242/1250 [====>.........................] - ETA: 22s - loss: 7.1426e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 245/1250 [====>.........................] - ETA: 22s - loss: 7.1881e-04 - mae: 0.0196"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 248/1250 [====>.........................] - ETA: 22s - loss: 7.2487e-04 - mae: 0.0197"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 251/1250 [=====>........................] - ETA: 22s - loss: 7.1966e-04 - mae: 0.0196"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 254/1250 [=====>........................] - ETA: 22s - loss: 7.1367e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 257/1250 [=====>........................] - ETA: 22s - loss: 7.1076e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 260/1250 [=====>........................] - ETA: 22s - loss: 7.0840e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 263/1250 [=====>........................] - ETA: 22s - loss: 7.1175e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 266/1250 [=====>........................] - ETA: 22s - loss: 7.0777e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 269/1250 [=====>........................] - ETA: 22s - loss: 7.0198e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 272/1250 [=====>........................] - ETA: 22s - loss: 7.0549e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 275/1250 [=====>........................] - ETA: 22s - loss: 7.2355e-04 - mae: 0.0196"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 278/1250 [=====>........................] - ETA: 22s - loss: 7.2081e-04 - mae: 0.0196"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 281/1250 [=====>........................] - ETA: 21s - loss: 7.1649e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 284/1250 [=====>........................] - ETA: 21s - loss: 7.1250e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 287/1250 [=====>........................] - ETA: 21s - loss: 7.1174e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 290/1250 [=====>........................] - ETA: 21s - loss: 7.0601e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 293/1250 [======>.......................] - ETA: 21s - loss: 7.0096e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 296/1250 [======>.......................] - ETA: 21s - loss: 6.9989e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 299/1250 [======>.......................] - ETA: 21s - loss: 7.0989e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 302/1250 [======>.......................] - ETA: 21s - loss: 7.0802e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 305/1250 [======>.......................] - ETA: 21s - loss: 7.0831e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 308/1250 [======>.......................] - ETA: 21s - loss: 7.1330e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 311/1250 [======>.......................] - ETA: 21s - loss: 7.0929e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 314/1250 [======>.......................] - ETA: 21s - loss: 7.0480e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 317/1250 [======>.......................] - ETA: 21s - loss: 7.0031e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 320/1250 [======>.......................] - ETA: 21s - loss: 7.0086e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 323/1250 [======>.......................] - ETA: 21s - loss: 6.9948e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 326/1250 [======>.......................] - ETA: 20s - loss: 7.0036e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 329/1250 [======>.......................] - ETA: 20s - loss: 7.0664e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 332/1250 [======>.......................] - ETA: 20s - loss: 7.0789e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 335/1250 [=======>......................] - ETA: 20s - loss: 7.0412e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 338/1250 [=======>......................] - ETA: 20s - loss: 7.0188e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 341/1250 [=======>......................] - ETA: 20s - loss: 6.9712e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 344/1250 [=======>......................] - ETA: 20s - loss: 7.0103e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 347/1250 [=======>......................] - ETA: 20s - loss: 7.0797e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 350/1250 [=======>......................] - ETA: 20s - loss: 7.0627e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 353/1250 [=======>......................] - ETA: 20s - loss: 7.0365e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 356/1250 [=======>......................] - ETA: 20s - loss: 7.0549e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 359/1250 [=======>......................] - ETA: 20s - loss: 7.0244e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 362/1250 [=======>......................] - ETA: 20s - loss: 7.0454e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 365/1250 [=======>......................] - ETA: 20s - loss: 7.0206e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 368/1250 [=======>......................] - ETA: 19s - loss: 6.9763e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 371/1250 [=======>......................] - ETA: 19s - loss: 6.9522e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 374/1250 [=======>......................] - ETA: 19s - loss: 6.9814e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 377/1250 [========>.....................] - ETA: 19s - loss: 6.9663e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 380/1250 [========>.....................] - ETA: 19s - loss: 7.0152e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 383/1250 [========>.....................] - ETA: 19s - loss: 7.0426e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 386/1250 [========>.....................] - ETA: 19s - loss: 7.0209e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 389/1250 [========>.....................] - ETA: 19s - loss: 7.0946e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 392/1250 [========>.....................] - ETA: 19s - loss: 7.0688e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 395/1250 [========>.....................] - ETA: 19s - loss: 7.0401e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 398/1250 [========>.....................] - ETA: 19s - loss: 7.0059e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 401/1250 [========>.....................] - ETA: 19s - loss: 6.9790e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 404/1250 [========>.....................] - ETA: 19s - loss: 6.9757e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 407/1250 [========>.....................] - ETA: 19s - loss: 7.0157e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 410/1250 [========>.....................] - ETA: 19s - loss: 6.9883e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 413/1250 [========>.....................] - ETA: 18s - loss: 6.9523e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 416/1250 [========>.....................] - ETA: 18s - loss: 6.9422e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 419/1250 [=========>....................] - ETA: 18s - loss: 6.9474e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 422/1250 [=========>....................] - ETA: 18s - loss: 7.0041e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 425/1250 [=========>....................] - ETA: 18s - loss: 7.0124e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 428/1250 [=========>....................] - ETA: 18s - loss: 7.0783e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 431/1250 [=========>....................] - ETA: 18s - loss: 7.0612e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 434/1250 [=========>....................] - ETA: 18s - loss: 7.0355e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 437/1250 [=========>....................] - ETA: 18s - loss: 7.0112e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 440/1250 [=========>....................] - ETA: 18s - loss: 7.0252e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 443/1250 [=========>....................] - ETA: 18s - loss: 7.0016e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 446/1250 [=========>....................] - ETA: 18s - loss: 7.0730e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 448/1250 [=========>....................] - ETA: 18s - loss: 7.0721e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 451/1250 [=========>....................] - ETA: 18s - loss: 7.0426e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 454/1250 [=========>....................] - ETA: 18s - loss: 7.0230e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 457/1250 [=========>....................] - ETA: 17s - loss: 7.0032e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 460/1250 [==========>...................] - ETA: 17s - loss: 6.9687e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 463/1250 [==========>...................] - ETA: 17s - loss: 6.9985e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 466/1250 [==========>...................] - ETA: 17s - loss: 6.9914e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 469/1250 [==========>...................] - ETA: 17s - loss: 6.9594e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 472/1250 [==========>...................] - ETA: 17s - loss: 7.0028e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 475/1250 [==========>...................] - ETA: 17s - loss: 7.0855e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 478/1250 [==========>...................] - ETA: 17s - loss: 7.0734e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 481/1250 [==========>...................] - ETA: 17s - loss: 7.0710e-04 - mae: 0.0195"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 484/1250 [==========>...................] - ETA: 17s - loss: 7.0506e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 487/1250 [==========>...................] - ETA: 17s - loss: 7.0287e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 490/1250 [==========>...................] - ETA: 17s - loss: 7.0101e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 493/1250 [==========>...................] - ETA: 17s - loss: 6.9966e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 496/1250 [==========>...................] - ETA: 17s - loss: 7.0218e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 499/1250 [==========>...................] - ETA: 17s - loss: 7.0304e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 502/1250 [===========>..................] - ETA: 16s - loss: 7.0080e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 505/1250 [===========>..................] - ETA: 16s - loss: 6.9939e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 508/1250 [===========>..................] - ETA: 16s - loss: 6.9718e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 511/1250 [===========>..................] - ETA: 16s - loss: 6.9782e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 514/1250 [===========>..................] - ETA: 16s - loss: 6.9708e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 517/1250 [===========>..................] - ETA: 16s - loss: 6.9816e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 520/1250 [===========>..................] - ETA: 16s - loss: 7.0028e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 523/1250 [===========>..................] - ETA: 16s - loss: 6.9820e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 526/1250 [===========>..................] - ETA: 16s - loss: 6.9515e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 529/1250 [===========>..................] - ETA: 16s - loss: 6.9439e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 532/1250 [===========>..................] - ETA: 16s - loss: 6.9619e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 535/1250 [===========>..................] - ETA: 16s - loss: 6.9812e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 538/1250 [===========>..................] - ETA: 16s - loss: 6.9837e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 541/1250 [===========>..................] - ETA: 16s - loss: 6.9800e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 544/1250 [============>.................] - ETA: 16s - loss: 6.9497e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 547/1250 [============>.................] - ETA: 15s - loss: 6.9475e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 550/1250 [============>.................] - ETA: 15s - loss: 6.9737e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 553/1250 [============>.................] - ETA: 15s - loss: 6.9930e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 556/1250 [============>.................] - ETA: 15s - loss: 6.9708e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 559/1250 [============>.................] - ETA: 15s - loss: 6.9531e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 562/1250 [============>.................] - ETA: 15s - loss: 6.9451e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 565/1250 [============>.................] - ETA: 15s - loss: 7.0339e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 568/1250 [============>.................] - ETA: 15s - loss: 7.0186e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 571/1250 [============>.................] - ETA: 15s - loss: 6.9874e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 574/1250 [============>.................] - ETA: 15s - loss: 6.9596e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 577/1250 [============>.................] - ETA: 15s - loss: 6.9492e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 580/1250 [============>.................] - ETA: 15s - loss: 6.9583e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 583/1250 [============>.................] - ETA: 15s - loss: 6.9499e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 586/1250 [=============>................] - ETA: 15s - loss: 6.9408e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 589/1250 [=============>................] - ETA: 14s - loss: 6.9560e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 592/1250 [=============>................] - ETA: 14s - loss: 6.9470e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 595/1250 [=============>................] - ETA: 14s - loss: 6.9218e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 598/1250 [=============>................] - ETA: 14s - loss: 6.9013e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 601/1250 [=============>................] - ETA: 14s - loss: 6.9531e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 604/1250 [=============>................] - ETA: 14s - loss: 6.9367e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 607/1250 [=============>................] - ETA: 14s - loss: 6.9306e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 610/1250 [=============>................] - ETA: 14s - loss: 6.9503e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 613/1250 [=============>................] - ETA: 14s - loss: 6.9557e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 616/1250 [=============>................] - ETA: 14s - loss: 6.9431e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 619/1250 [=============>................] - ETA: 14s - loss: 6.9348e-04 - mae: 0.0194"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 622/1250 [=============>................] - ETA: 14s - loss: 6.9258e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 625/1250 [==============>...............] - ETA: 14s - loss: 6.8999e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 628/1250 [==============>...............] - ETA: 14s - loss: 6.8804e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 631/1250 [==============>...............] - ETA: 14s - loss: 6.8763e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 634/1250 [==============>...............] - ETA: 13s - loss: 6.8909e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 637/1250 [==============>...............] - ETA: 13s - loss: 6.8722e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 640/1250 [==============>...............] - ETA: 13s - loss: 6.8657e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 643/1250 [==============>...............] - ETA: 13s - loss: 6.8624e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 646/1250 [==============>...............] - ETA: 13s - loss: 6.8433e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 649/1250 [==============>...............] - ETA: 13s - loss: 6.8900e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 652/1250 [==============>...............] - ETA: 13s - loss: 6.9164e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 654/1250 [==============>...............] - ETA: 13s - loss: 6.9083e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 657/1250 [==============>...............] - ETA: 13s - loss: 6.9049e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 660/1250 [==============>...............] - ETA: 13s - loss: 6.9055e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 663/1250 [==============>...............] - ETA: 13s - loss: 6.9195e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 666/1250 [==============>...............] - ETA: 13s - loss: 6.8970e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 669/1250 [===============>..............] - ETA: 13s - loss: 6.8899e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 672/1250 [===============>..............] - ETA: 13s - loss: 6.8699e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 675/1250 [===============>..............] - ETA: 13s - loss: 6.8713e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 678/1250 [===============>..............] - ETA: 12s - loss: 6.8629e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 681/1250 [===============>..............] - ETA: 12s - loss: 6.8518e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 684/1250 [===============>..............] - ETA: 12s - loss: 6.8611e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 687/1250 [===============>..............] - ETA: 12s - loss: 6.8853e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 690/1250 [===============>..............] - ETA: 12s - loss: 6.9016e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 693/1250 [===============>..............] - ETA: 12s - loss: 6.9220e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 696/1250 [===============>..............] - ETA: 12s - loss: 6.9026e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 699/1250 [===============>..............] - ETA: 12s - loss: 6.8835e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 702/1250 [===============>..............] - ETA: 12s - loss: 6.8798e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 705/1250 [===============>..............] - ETA: 12s - loss: 6.8759e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 708/1250 [===============>..............] - ETA: 12s - loss: 6.8570e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 711/1250 [================>.............] - ETA: 12s - loss: 6.8341e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 714/1250 [================>.............] - ETA: 12s - loss: 6.8641e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 717/1250 [================>.............] - ETA: 12s - loss: 6.8828e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 720/1250 [================>.............] - ETA: 12s - loss: 6.8773e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 723/1250 [================>.............] - ETA: 11s - loss: 6.8612e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 726/1250 [================>.............] - ETA: 11s - loss: 6.8657e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 729/1250 [================>.............] - ETA: 11s - loss: 6.8609e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 732/1250 [================>.............] - ETA: 11s - loss: 6.8634e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 735/1250 [================>.............] - ETA: 11s - loss: 6.8654e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 738/1250 [================>.............] - ETA: 11s - loss: 6.8725e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 741/1250 [================>.............] - ETA: 11s - loss: 6.8686e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 744/1250 [================>.............] - ETA: 11s - loss: 6.8556e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 747/1250 [================>.............] - ETA: 11s - loss: 6.8479e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 750/1250 [=================>............] - ETA: 11s - loss: 6.8387e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 753/1250 [=================>............] - ETA: 11s - loss: 6.8220e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 756/1250 [=================>............] - ETA: 11s - loss: 6.8046e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 759/1250 [=================>............] - ETA: 11s - loss: 6.8161e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 762/1250 [=================>............] - ETA: 11s - loss: 6.8199e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 765/1250 [=================>............] - ETA: 11s - loss: 6.8081e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 768/1250 [=================>............] - ETA: 10s - loss: 6.7899e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 771/1250 [=================>............] - ETA: 10s - loss: 6.8020e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 774/1250 [=================>............] - ETA: 10s - loss: 6.8311e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 777/1250 [=================>............] - ETA: 10s - loss: 6.8317e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 780/1250 [=================>............] - ETA: 10s - loss: 6.8249e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 783/1250 [=================>............] - ETA: 10s - loss: 6.8567e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 786/1250 [=================>............] - ETA: 10s - loss: 6.8688e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 789/1250 [=================>............] - ETA: 10s - loss: 6.8629e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 792/1250 [==================>...........] - ETA: 10s - loss: 6.8498e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 795/1250 [==================>...........] - ETA: 10s - loss: 6.8370e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 798/1250 [==================>...........] - ETA: 10s - loss: 6.8206e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 801/1250 [==================>...........] - ETA: 10s - loss: 6.8121e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 804/1250 [==================>...........] - ETA: 10s - loss: 6.8084e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 807/1250 [==================>...........] - ETA: 10s - loss: 6.8016e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 810/1250 [==================>...........] - ETA: 9s - loss: 6.7982e-04 - mae: 0.0192 "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 813/1250 [==================>...........] - ETA: 9s - loss: 6.7857e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 816/1250 [==================>...........] - ETA: 9s - loss: 6.7878e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 819/1250 [==================>...........] - ETA: 9s - loss: 6.8173e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 822/1250 [==================>...........] - ETA: 9s - loss: 6.8051e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 825/1250 [==================>...........] - ETA: 9s - loss: 6.8054e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 828/1250 [==================>...........] - ETA: 9s - loss: 6.8116e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 831/1250 [==================>...........] - ETA: 9s - loss: 6.8068e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 834/1250 [===================>..........] - ETA: 9s - loss: 6.7891e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 837/1250 [===================>..........] - ETA: 9s - loss: 6.7883e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 840/1250 [===================>..........] - ETA: 9s - loss: 6.7970e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 843/1250 [===================>..........] - ETA: 9s - loss: 6.7868e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 846/1250 [===================>..........] - ETA: 9s - loss: 6.8103e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 849/1250 [===================>..........] - ETA: 9s - loss: 6.8004e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 852/1250 [===================>..........] - ETA: 9s - loss: 6.7855e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 855/1250 [===================>..........] - ETA: 8s - loss: 6.7788e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 857/1250 [===================>..........] - ETA: 8s - loss: 6.7762e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 860/1250 [===================>..........] - ETA: 8s - loss: 6.7721e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 863/1250 [===================>..........] - ETA: 8s - loss: 6.7717e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 866/1250 [===================>..........] - ETA: 8s - loss: 6.8031e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 869/1250 [===================>..........] - ETA: 8s - loss: 6.8282e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 872/1250 [===================>..........] - ETA: 8s - loss: 6.8107e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 875/1250 [====================>.........] - ETA: 8s - loss: 6.7925e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 878/1250 [====================>.........] - ETA: 8s - loss: 6.7840e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 881/1250 [====================>.........] - ETA: 8s - loss: 6.7730e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 884/1250 [====================>.........] - ETA: 8s - loss: 6.7840e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 887/1250 [====================>.........] - ETA: 8s - loss: 6.7847e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 890/1250 [====================>.........] - ETA: 8s - loss: 6.7818e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 893/1250 [====================>.........] - ETA: 8s - loss: 6.7791e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 896/1250 [====================>.........] - ETA: 8s - loss: 6.7770e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 899/1250 [====================>.........] - ETA: 7s - loss: 6.7613e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 902/1250 [====================>.........] - ETA: 7s - loss: 6.7533e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 905/1250 [====================>.........] - ETA: 7s - loss: 6.7560e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 908/1250 [====================>.........] - ETA: 7s - loss: 6.7673e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 911/1250 [====================>.........] - ETA: 7s - loss: 6.7571e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 914/1250 [====================>.........] - ETA: 7s - loss: 6.7595e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 917/1250 [=====================>........] - ETA: 7s - loss: 6.7435e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 920/1250 [=====================>........] - ETA: 7s - loss: 6.7605e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 923/1250 [=====================>........] - ETA: 7s - loss: 6.7993e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 926/1250 [=====================>........] - ETA: 7s - loss: 6.7897e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 929/1250 [=====================>........] - ETA: 7s - loss: 6.8142e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 932/1250 [=====================>........] - ETA: 7s - loss: 6.8141e-04 - mae: 0.0193"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 935/1250 [=====================>........] - ETA: 7s - loss: 6.7958e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 938/1250 [=====================>........] - ETA: 7s - loss: 6.7821e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 941/1250 [=====================>........] - ETA: 7s - loss: 6.7761e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 944/1250 [=====================>........] - ETA: 6s - loss: 6.7599e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 947/1250 [=====================>........] - ETA: 6s - loss: 6.7445e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 950/1250 [=====================>........] - ETA: 6s - loss: 6.7309e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 953/1250 [=====================>........] - ETA: 6s - loss: 6.7524e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 956/1250 [=====================>........] - ETA: 6s - loss: 6.7603e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 959/1250 [======================>.......] - ETA: 6s - loss: 6.7552e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 962/1250 [======================>.......] - ETA: 6s - loss: 6.7711e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 965/1250 [======================>.......] - ETA: 6s - loss: 6.7673e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 968/1250 [======================>.......] - ETA: 6s - loss: 6.7584e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 971/1250 [======================>.......] - ETA: 6s - loss: 6.7417e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 974/1250 [======================>.......] - ETA: 6s - loss: 6.7274e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 977/1250 [======================>.......] - ETA: 6s - loss: 6.7195e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 980/1250 [======================>.......] - ETA: 6s - loss: 6.7235e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 983/1250 [======================>.......] - ETA: 6s - loss: 6.7169e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 986/1250 [======================>.......] - ETA: 5s - loss: 6.7331e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 989/1250 [======================>.......] - ETA: 5s - loss: 6.7326e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 992/1250 [======================>.......] - ETA: 5s - loss: 6.7313e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 995/1250 [======================>.......] - ETA: 5s - loss: 6.7372e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 998/1250 [======================>.......] - ETA: 5s - loss: 6.7314e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1001/1250 [=======================>......] - ETA: 5s - loss: 6.7331e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1004/1250 [=======================>......] - ETA: 5s - loss: 6.7380e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1007/1250 [=======================>......] - ETA: 5s - loss: 6.7306e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1010/1250 [=======================>......] - ETA: 5s - loss: 6.7240e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1013/1250 [=======================>......] - ETA: 5s - loss: 6.7112e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1016/1250 [=======================>......] - ETA: 5s - loss: 6.7144e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1019/1250 [=======================>......] - ETA: 5s - loss: 6.7614e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1021/1250 [=======================>......] - ETA: 5s - loss: 6.7535e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1024/1250 [=======================>......] - ETA: 5s - loss: 6.7525e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1027/1250 [=======================>......] - ETA: 5s - loss: 6.7570e-04 - mae: 0.0192"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1030/1250 [=======================>......] - ETA: 4s - loss: 6.7409e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1033/1250 [=======================>......] - ETA: 4s - loss: 6.7295e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1036/1250 [=======================>......] - ETA: 4s - loss: 6.7281e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1039/1250 [=======================>......] - ETA: 4s - loss: 6.7206e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1042/1250 [========================>.....] - ETA: 4s - loss: 6.7168e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1045/1250 [========================>.....] - ETA: 4s - loss: 6.7149e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1048/1250 [========================>.....] - ETA: 4s - loss: 6.7025e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1051/1250 [========================>.....] - ETA: 4s - loss: 6.7021e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1054/1250 [========================>.....] - ETA: 4s - loss: 6.7068e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1057/1250 [========================>.....] - ETA: 4s - loss: 6.7042e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1060/1250 [========================>.....] - ETA: 4s - loss: 6.6900e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1063/1250 [========================>.....] - ETA: 4s - loss: 6.6941e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1066/1250 [========================>.....] - ETA: 4s - loss: 6.6878e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1069/1250 [========================>.....] - ETA: 4s - loss: 6.6936e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1072/1250 [========================>.....] - ETA: 4s - loss: 6.6969e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1075/1250 [========================>.....] - ETA: 3s - loss: 6.7036e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1078/1250 [========================>.....] - ETA: 3s - loss: 6.6994e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1081/1250 [========================>.....] - ETA: 3s - loss: 6.6961e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1084/1250 [=========================>....] - ETA: 3s - loss: 6.6828e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1087/1250 [=========================>....] - ETA: 3s - loss: 6.6758e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1090/1250 [=========================>....] - ETA: 3s - loss: 6.6992e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1093/1250 [=========================>....] - ETA: 3s - loss: 6.6980e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1095/1250 [=========================>....] - ETA: 3s - loss: 6.6888e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1098/1250 [=========================>....] - ETA: 3s - loss: 6.6750e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1100/1250 [=========================>....] - ETA: 3s - loss: 6.6703e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1103/1250 [=========================>....] - ETA: 3s - loss: 6.6591e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1106/1250 [=========================>....] - ETA: 3s - loss: 6.6468e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1109/1250 [=========================>....] - ETA: 3s - loss: 6.6454e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1112/1250 [=========================>....] - ETA: 3s - loss: 6.6480e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1115/1250 [=========================>....] - ETA: 3s - loss: 6.6506e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1118/1250 [=========================>....] - ETA: 2s - loss: 6.6575e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1121/1250 [=========================>....] - ETA: 2s - loss: 6.6997e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1124/1250 [=========================>....] - ETA: 2s - loss: 6.7039e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1127/1250 [==========================>...] - ETA: 2s - loss: 6.7043e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1130/1250 [==========================>...] - ETA: 2s - loss: 6.6930e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1133/1250 [==========================>...] - ETA: 2s - loss: 6.6863e-04 - mae: 0.0191"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1136/1250 [==========================>...] - ETA: 2s - loss: 6.6723e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1139/1250 [==========================>...] - ETA: 2s - loss: 6.6663e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1142/1250 [==========================>...] - ETA: 2s - loss: 6.6584e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1145/1250 [==========================>...] - ETA: 2s - loss: 6.6503e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1148/1250 [==========================>...] - ETA: 2s - loss: 6.6427e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1151/1250 [==========================>...] - ETA: 2s - loss: 6.6557e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1154/1250 [==========================>...] - ETA: 2s - loss: 6.6503e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1157/1250 [==========================>...] - ETA: 2s - loss: 6.6607e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1160/1250 [==========================>...] - ETA: 2s - loss: 6.6689e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1163/1250 [==========================>...] - ETA: 1s - loss: 6.6613e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1166/1250 [==========================>...] - ETA: 1s - loss: 6.6608e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1169/1250 [===========================>..] - ETA: 1s - loss: 6.6612e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1172/1250 [===========================>..] - ETA: 1s - loss: 6.6538e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1175/1250 [===========================>..] - ETA: 1s - loss: 6.6530e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1178/1250 [===========================>..] - ETA: 1s - loss: 6.6408e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1181/1250 [===========================>..] - ETA: 1s - loss: 6.6410e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1184/1250 [===========================>..] - ETA: 1s - loss: 6.6333e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1187/1250 [===========================>..] - ETA: 1s - loss: 6.6421e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1190/1250 [===========================>..] - ETA: 1s - loss: 6.6462e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1193/1250 [===========================>..] - ETA: 1s - loss: 6.6498e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1196/1250 [===========================>..] - ETA: 1s - loss: 6.6479e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1199/1250 [===========================>..] - ETA: 1s - loss: 6.6501e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1202/1250 [===========================>..] - ETA: 1s - loss: 6.6411e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1205/1250 [===========================>..] - ETA: 1s - loss: 6.6353e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1208/1250 [===========================>..] - ETA: 0s - loss: 6.6264e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1211/1250 [============================>.] - ETA: 0s - loss: 6.6234e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1214/1250 [============================>.] - ETA: 0s - loss: 6.6170e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1217/1250 [============================>.] - ETA: 0s - loss: 6.6073e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1220/1250 [============================>.] - ETA: 0s - loss: 6.6050e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1223/1250 [============================>.] - ETA: 0s - loss: 6.5993e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1226/1250 [============================>.] - ETA: 0s - loss: 6.6452e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1229/1250 [============================>.] - ETA: 0s - loss: 6.6442e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1232/1250 [============================>.] - ETA: 0s - loss: 6.6348e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1235/1250 [============================>.] - ETA: 0s - loss: 6.6255e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1238/1250 [============================>.] - ETA: 0s - loss: 6.6158e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1241/1250 [============================>.] - ETA: 0s - loss: 6.6561e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1244/1250 [============================>.] - ETA: 0s - loss: 6.6484e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1247/1250 [============================>.] - ETA: 0s - loss: 6.6413e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1250/1250 [==============================] - ETA: 0s - loss: 6.6283e-04 - mae: 0.0190"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "1250/1250 [==============================] - 31s 25ms/step - loss: 6.6283e-04 - mae: 0.0190 - val_loss: 2.1266e-04 - val_mae: 0.0111\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Duration :  00:02:26 479ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "pwk.chrono_start()\n",
+    "\n",
+    "history=model.fit(train_generator, \n",
+    "                  epochs=epochs, \n",
+    "                  verbose=1,\n",
+    "                  validation_data = test_generator,\n",
+    "                  callbacks = [bestmodel_callback])\n",
+    "\n",
+    "pwk.chrono_show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:15:52.847385Z",
+     "iopub.status.busy": "2021-03-07T20:15:52.841435Z",
+     "iopub.status.idle": "2021-03-07T20:15:53.663901Z",
+     "shell.execute_reply": "2021-03-07T20:15:53.663515Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"comment\">Saved: ./run/LADYBUG1/figs/LADYBUG1-03-history_0</div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"comment\">Saved: ./run/LADYBUG1/figs/LADYBUG1-03-history_1</div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pwk.plot_history(history,plot={'loss':['loss','val_loss'], 'mae':['mae','val_mae']}, save_as='03-history')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5 - Predict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.1 - Load model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:15:53.674003Z",
+     "iopub.status.busy": "2021-03-07T20:15:53.671869Z",
+     "iopub.status.idle": "2021-03-07T20:15:53.748028Z",
+     "shell.execute_reply": "2021-03-07T20:15:53.748288Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded.\n"
+     ]
+    }
+   ],
+   "source": [
+    "loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')\n",
+    "print('Loaded.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.2 - Make a 1-step prediction\n",
+    "A simple prediction on a single iteration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:15:53.753353Z",
+     "iopub.status.busy": "2021-03-07T20:15:53.753042Z",
+     "iopub.status.idle": "2021-03-07T20:15:54.526732Z",
+     "shell.execute_reply": "2021-03-07T20:15:54.526373Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"comment\">Saved: ./run/LADYBUG1/figs/LADYBUG1-fig_00</div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"comment\">Saved: ./run/LADYBUG1/figs/LADYBUG1-04-one-step-prediction</div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "s=random.randint(0,len(x_test)-sequence_len)\n",
+    "\n",
+    "sequence      = x_test[s:s+sequence_len]\n",
+    "sequence_true = x_test[s:s+sequence_len+1]\n",
+    "\n",
+    "sequence_pred = loaded_model.predict( np.array([sequence]) )\n",
+    "\n",
+    "pwk.plot_2d_segment(sequence_true, sequence_pred)\n",
+    "pwk.plot_multivariate_serie(sequence_true, predictions=sequence_pred, labels=['Axis=0', 'Axis=1'],save_as='04-one-step-prediction')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.3 - Make n-steps prediction\n",
+    "A longer term prediction, via a nice iteration function :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:15:54.530797Z",
+     "iopub.status.busy": "2021-03-07T20:15:54.530451Z",
+     "iopub.status.idle": "2021-03-07T20:15:54.533065Z",
+     "shell.execute_reply": "2021-03-07T20:15:54.532747Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def get_prediction(dataset, model, iterations=4):\n",
+    "\n",
+    "    # ---- Initial sequence\n",
+    "    #\n",
+    "    s=random.randint(0,len(dataset)-sequence_len-iterations)\n",
+    "\n",
+    "    sequence_pred = dataset[s:s+sequence_len].copy()\n",
+    "    sequence_true = dataset[s:s+sequence_len+iterations].copy()\n",
+    "\n",
+    "    # ---- Iterate \n",
+    "    #\n",
+    "    sequence_pred = list(sequence_pred)\n",
+    "\n",
+    "    for i in range(iterations):\n",
+    "        sequence   = sequence_pred[-sequence_len:]\n",
+    "        prediction = model.predict( np.array([sequence]) )\n",
+    "        sequence_pred.append(prediction[0])\n",
+    "\n",
+    "    # ---- Extract the predictions    \n",
+    "    #\n",
+    "    prediction = np.array(sequence_pred[-iterations:])\n",
+    "\n",
+    "    return sequence_true,prediction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An n-steps prediction :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:15:54.537139Z",
+     "iopub.status.busy": "2021-03-07T20:15:54.536133Z",
+     "iopub.status.idle": "2021-03-07T20:15:55.130586Z",
+     "shell.execute_reply": "2021-03-07T20:15:55.130261Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"comment\">Saved: ./run/LADYBUG1/figs/LADYBUG1-02-prediction-norm</div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"comment\">Saved: ./run/LADYBUG1/figs/LADYBUG1-02-prediction-norm</div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sequence_true, sequence_pred = get_prediction(x_test, loaded_model, iterations=5)\n",
+    "\n",
+    "pwk.plot_2d_segment(sequence_true, sequence_pred, ms=8, save_as='02-prediction-norm')\n",
+    "pwk.plot_multivariate_serie(sequence_true, predictions=sequence_pred, hide_ticks=True, labels=['Axis=0', 'Axis=1'],save_as='02-prediction-norm')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:15:55.133346Z",
+     "iopub.status.busy": "2021-03-07T20:15:55.132914Z",
+     "iopub.status.idle": "2021-03-07T20:15:55.137257Z",
+     "shell.execute_reply": "2021-03-07T20:15:55.136959Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "End time is : Sunday 07 March 2021, 21:15:55\n",
+      "Duration is : 00:02:30 004ms\n",
+      "This notebook ends here\n"
+     ]
+    }
+   ],
+   "source": [
+    "pwk.end()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/SYNOP/SYNOP1-Preparation-of-data.ipynb b/SYNOP/SYNOP1-Preparation-of-data.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2baa6d3407fafcd1192edd647352a90e540c6629
--- /dev/null
+++ b/SYNOP/SYNOP1-Preparation-of-data.ipynb
@@ -0,0 +1,373 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+    "\n",
+    "# <!-- TITLE --> [SYNOP1] - Preparation of data\n",
+    "<!-- DESC --> Episode 1 : Data analysis and preparation of a meteorological dataset (SYNOP)\n",
+    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+    "\n",
+    "## Objectives :\n",
+    " - Undestand the data\n",
+    " - cleanup a usable dataset\n",
+    "\n",
+    "\n",
+    "SYNOP meteorological data, can be found on :  \n",
+    "https://public.opendatasoft.com  \n",
+    "\n",
+    "About SYNOP datasets :  \n",
+    "https://public.opendatasoft.com/explore/dataset/donnees-synop-essentielles-omm/information/?sort=date\n",
+    "\n",
+    "This dataset contains a set of measurements (temperature, pressure, ...) made every 3 hours at the LYS airport.  \n",
+    "The objective will be to predict the evolution of the weather !\n",
+    "\n",
+    "## What we're going to do :\n",
+    "\n",
+    " - Read the data\n",
+    " - Cleanup and build a usable dataset\n",
+    "\n",
+    "## Step 1 - Import and init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras.callbacks import TensorBoard\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import pandas as pd\n",
+    "import h5py, json\n",
+    "import os,time,sys\n",
+    "import math, random\n",
+    "\n",
+    "from importlib import reload\n",
+    "\n",
+    "sys.path.append('..')\n",
+    "import fidle.pwk as pwk\n",
+    "\n",
+    "datasets_dir = pwk.init('SYNOP1')\n",
+    "\n",
+    "pd.set_option('display.max_rows',200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Parameters\n",
+    "`output_dir` : where to write enhanced dataset, could be :\n",
+    " - `./data`, for simplicity and convenience (best choice because enhanced dataset will be small)\n",
+    " - `<datasets_dir>/SYNOP/enhanced` to save enhanced dataset in your datasets dir.  \n",
+    " \n",
+    "Uncomment the right lines according to what you want :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ---- Our future enhanced dataset\n",
+    "#\n",
+    "dataset_filename     = 'synop-LYS.csv'\n",
+    "description_filename = 'synop.json'\n",
+    "\n",
+    "# ---- For smart tests :\n",
+    "#\n",
+    "output_dir = './data' \n",
+    "\n",
+    "# ---- To save enhanced dataset in the dataset_dir\n",
+    "#\n",
+    "# output_dir = f'{datasets_dir}/SYNOP/enhanced'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Override parameters (batch mode) - Just forget this cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pwk.override('output_dir')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Retrieve the dataset\n",
+    "There are two parts to recover:\n",
+    " - The data itself (csv)\n",
+    " - Description of the data (json)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_filename   = 'origine/donnees-synop-essentielles-omm-LYS.csv'\n",
+    "schema_filename = 'origine/schema.json'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1 - Read dataset description\n",
+    "We need the list and description of the columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(f'{datasets_dir}/SYNOP/{schema_filename}','r') as json_file:\n",
+    "    schema = json.load(json_file)\n",
+    "\n",
+    "synop_codes=list( schema['definitions']['donnees-synop-essentielles-omm_records']['properties']['fields']['properties'].keys() )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2 - Read data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(f'{datasets_dir}/SYNOP/{data_filename}', header=0, sep=';')\n",
+    "pwk.subtitle('Raw data :')\n",
+    "display(df.tail(10))\n",
+    "\n",
+    "# ---- Get the columns name as descriptions\n",
+    "synop_desc = list(df.columns)\n",
+    "\n",
+    "# ---- Set Codes as columns name\n",
+    "df.columns   = synop_codes\n",
+    "code2desc    = dict(zip(synop_codes, synop_desc))\n",
+    "\n",
+    "# ---- Count the na values by columns\n",
+    "columns_na = df.isna().sum().tolist()\n",
+    "\n",
+    "# ---- Show all of that\n",
+    "df_desc=pd.DataFrame({'Code':synop_codes, 'Description':synop_desc, 'Na':columns_na})\n",
+    "\n",
+    "pwk.subtitle('List of columns :')\n",
+    "display(df_desc.style.set_properties(**{'text-align': 'left'}))\n",
+    "\n",
+    "print('Shape is : ', df.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Prepare dataset\n",
+    "### 4.1 - Keep only certain columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns_used=['date','pmer','tend','cod_tend','dd','ff','td','u','ww','pres','rafper','per','rr1','rr3','tc']\n",
+    "\n",
+    "# ---- Drop unused columns\n",
+    "\n",
+    "to_drop = df.columns.difference(columns_used)\n",
+    "df.drop( to_drop, axis=1, inplace=True)\n",
+    "\n",
+    "# ---- Show all of that\n",
+    "\n",
+    "pwk.subtitle('Our selected columns :')\n",
+    "display(df.head(20))\n",
+    "\n",
+    "pwk.subtitle('Few statistics :')\n",
+    "display(df.describe().style.format('{:.2f}'))\n",
+    "\n",
+    "# ---- 'per' column is constant, we can drop it\n",
+    "\n",
+    "df.drop(['per'],axis=1,inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2 - Cleanup dataset\n",
+    "Let's sort it and cook up some NaN values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ---- First of all, we have to sort on the date\n",
+    "\n",
+    "df.sort_values(['date'],  inplace=True)\n",
+    "df.reset_index(drop=True, inplace=True)\n",
+    "\n",
+    "# ---- Before : Lines with NaN\n",
+    "\n",
+    "na_rows=df.isna().any(axis=1)\n",
+    "pwk.subtitle('Before :')\n",
+    "display( df[na_rows].head(10) )\n",
+    "\n",
+    "# ---- Nice interpolation for plugging holes\n",
+    "\n",
+    "df.interpolate(inplace=True)\n",
+    "\n",
+    "# ---- After\n",
+    "\n",
+    "pwk.subtitle('After :')\n",
+    "display(df[na_rows].head(10))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5 - About our enhanced dataset\n",
+    "### 5.1 - Summarize it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ---- Count the na values by columns\n",
+    "dataset_na    = df.isna().sum().tolist()\n",
+    "dataset_cols  = df.columns.tolist()\n",
+    "dataset_desc  = [ code2desc[c] for c in dataset_cols ]\n",
+    "\n",
+    "# ---- Show all of that\n",
+    "df_desc=pd.DataFrame({'Columns':dataset_cols, 'Description':dataset_desc, 'Na':dataset_na})\n",
+    "pwk.subtitle('Dataset columns :')\n",
+    "display(df_desc.style.set_properties(**{'text-align': 'left'}))\n",
+    "\n",
+    "pwk.subtitle('Have a look :')\n",
+    "display(df.tail(20))\n",
+    "print('Shape is : ', df.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.2 - Have a look (1 month)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "i=random.randint(0,len(df)-240)\n",
+    "df.iloc[i:i+240].plot(subplots=True, fontsize=12, figsize=(16,20))\n",
+    "pwk.save_fig('01-one-month')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6 - Save it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ---- Save it\n",
+    "#\n",
+    "pwk.mkdir(output_dir)\n",
+    "\n",
+    "filedata = f'{output_dir}/{dataset_filename}'\n",
+    "filedesc = f'{output_dir}/{description_filename}'\n",
+    "\n",
+    "df.to_csv(filedata, sep=';', index=False)\n",
+    "size=os.path.getsize(filedata)/(1024*1024)\n",
+    "print(f'Dataset saved. ({size:0.1f} Mo)')\n",
+    "\n",
+    "with open(filedesc, 'w', encoding='utf-8') as f:\n",
+    "    json.dump(code2desc, f, indent=4)\n",
+    "print('Synop description saved.')\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pwk.end()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/SYNOP/SYNOP1-Preparation-of-data==done==.ipynb b/SYNOP/SYNOP1-Preparation-of-data==done==.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..9131cc5a2c67fa967d30b024e2e42bb41255084e
--- /dev/null
+++ b/SYNOP/SYNOP1-Preparation-of-data==done==.ipynb
@@ -0,0 +1,3201 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+    "\n",
+    "# <!-- TITLE --> [SYNOP1] - Preparation of data\n",
+    "<!-- DESC --> Episode 1 : Data analysis and preparation of a meteorological dataset (SYNOP)\n",
+    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+    "\n",
+    "## Objectives :\n",
+    " - Undestand the data\n",
+    " - cleanup a usable dataset\n",
+    "\n",
+    "\n",
+    "SYNOP meteorological data, can be found on :  \n",
+    "https://public.opendatasoft.com  \n",
+    "\n",
+    "About SYNOP datasets :  \n",
+    "https://public.opendatasoft.com/explore/dataset/donnees-synop-essentielles-omm/information/?sort=date\n",
+    "\n",
+    "This dataset contains a set of measurements (temperature, pressure, ...) made every 3 hours at the LYS airport.  \n",
+    "The objective will be to predict the evolution of the weather !\n",
+    "\n",
+    "## What we're going to do :\n",
+    "\n",
+    " - Read the data\n",
+    " - Cleanup and build a usable dataset\n",
+    "\n",
+    "## Step 1 - Import and init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:15:56.573994Z",
+     "iopub.status.busy": "2021-03-07T20:15:56.573323Z",
+     "iopub.status.idle": "2021-03-07T20:15:57.927507Z",
+     "shell.execute_reply": "2021-03-07T20:15:57.927177Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "\n",
+       "div.warn {    \n",
+       "    background-color: #fcf2f2;\n",
+       "    border-color: #dFb5b4;\n",
+       "    border-left: 5px solid #dfb5b4;\n",
+       "    padding: 0.5em;\n",
+       "    font-weight: bold;\n",
+       "    font-size: 1.1em;;\n",
+       "    }\n",
+       "\n",
+       "\n",
+       "\n",
+       "div.nota {    \n",
+       "    background-color: #DAFFDE;\n",
+       "    border-left: 5px solid #92CC99;\n",
+       "    padding: 0.5em;\n",
+       "    }\n",
+       "\n",
+       "div.todo:before { 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+       "    float:left;\n",
+       "    margin-right:20px;\n",
+       "    margin-top:-20px;\n",
+       "    margin-bottom:20px;\n",
+       "}\n",
+       "div.todo{\n",
+       "    font-weight: bold;\n",
+       "    font-size: 1.1em;\n",
+       "    margin-top:40px;\n",
+       "}\n",
+       "div.todo ul{\n",
+       "    margin: 0.2em;\n",
+       "}\n",
+       "div.todo li{\n",
+       "    margin-left:60px;\n",
+       "    margin-top:0;\n",
+       "    margin-bottom:0;\n",
+       "}\n",
+       "\n",
+       "div .comment{\n",
+       "    font-size:0.8em;\n",
+       "    color:#696969;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "</style>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "<br>**FIDLE 2020 - Practical Work Module**"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Version              : 2.0.18\n",
+      "Notebook id          : SYNOP1\n",
+      "Run time             : Sunday 07 March 2021, 21:15:57\n",
+      "TensorFlow version   : 2.2.0\n",
+      "Keras version        : 2.3.0-tf\n",
+      "Datasets dir         : /home/pjluc/datasets/fidle\n",
+      "Run dir              : ./run\n",
+      "Update keras cache   : False\n",
+      "Save figs            : True\n",
+      "Path figs            : ./run/figs\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras.callbacks import TensorBoard\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import pandas as pd\n",
+    "import h5py, json\n",
+    "import os,time,sys\n",
+    "import math, random\n",
+    "\n",
+    "from importlib import reload\n",
+    "\n",
+    "sys.path.append('..')\n",
+    "import fidle.pwk as pwk\n",
+    "\n",
+    "datasets_dir = pwk.init('SYNOP1')\n",
+    "\n",
+    "pd.set_option('display.max_rows',200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Parameters\n",
+    "`output_dir` : where to write enhanced dataset, could be :\n",
+    " - `./data`, for simplicity and convenience (best choice because enhanced dataset will be small)\n",
+    " - `<datasets_dir>/SYNOP/enhanced` to save enhanced dataset in your datasets dir.  \n",
+    " \n",
+    "Uncomment the right lines according to what you want :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:15:57.930595Z",
+     "iopub.status.busy": "2021-03-07T20:15:57.930273Z",
+     "iopub.status.idle": "2021-03-07T20:15:57.932729Z",
+     "shell.execute_reply": "2021-03-07T20:15:57.932336Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# ---- Our future enhanced dataset\n",
+    "#\n",
+    "dataset_filename     = 'synop-LYS.csv'\n",
+    "description_filename = 'synop.json'\n",
+    "\n",
+    "# ---- For smart tests :\n",
+    "#\n",
+    "output_dir = './data' \n",
+    "\n",
+    "# ---- To save enhanced dataset in the dataset_dir\n",
+    "#\n",
+    "# output_dir = f'{datasets_dir}/SYNOP/enhanced'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Override parameters (batch mode) - Just forget this cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:15:57.935454Z",
+     "iopub.status.busy": "2021-03-07T20:15:57.935159Z",
+     "iopub.status.idle": "2021-03-07T20:15:57.937348Z",
+     "shell.execute_reply": "2021-03-07T20:15:57.937039Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pwk.override('output_dir')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Retrieve the dataset\n",
+    "There are two parts to recover:\n",
+    " - The data itself (csv)\n",
+    " - Description of the data (json)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:15:57.939870Z",
+     "iopub.status.busy": "2021-03-07T20:15:57.939576Z",
+     "iopub.status.idle": "2021-03-07T20:15:57.942146Z",
+     "shell.execute_reply": "2021-03-07T20:15:57.941836Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "data_filename   = 'origine/donnees-synop-essentielles-omm-LYS.csv'\n",
+    "schema_filename = 'origine/schema.json'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1 - Read dataset description\n",
+    "We need the list and description of the columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:15:57.944963Z",
+     "iopub.status.busy": "2021-03-07T20:15:57.944536Z",
+     "iopub.status.idle": "2021-03-07T20:15:57.947647Z",
+     "shell.execute_reply": "2021-03-07T20:15:57.947330Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with open(f'{datasets_dir}/SYNOP/{schema_filename}','r') as json_file:\n",
+    "    schema = json.load(json_file)\n",
+    "\n",
+    "synop_codes=list( schema['definitions']['donnees-synop-essentielles-omm_records']['properties']['fields']['properties'].keys() )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2 - Read data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:15:57.952858Z",
+     "iopub.status.busy": "2021-03-07T20:15:57.952509Z",
+     "iopub.status.idle": "2021-03-07T20:15:58.277263Z",
+     "shell.execute_reply": "2021-03-07T20:15:58.276973Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "<br>**Raw data :**"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "        vertical-align: top;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID OMM station</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Pression au niveau mer</th>\n",
+       "      <th>Variation de pression en 3 heures</th>\n",
+       "      <th>Type de tendance barométrique</th>\n",
+       "      <th>Direction du vent moyen 10 mn</th>\n",
+       "      <th>Vitesse du vent moyen 10 mn</th>\n",
+       "      <th>Température</th>\n",
+       "      <th>Point de rosée</th>\n",
+       "      <th>Humidité</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>communes (name)</th>\n",
+       "      <th>communes (code)</th>\n",
+       "      <th>EPCI (name)</th>\n",
+       "      <th>EPCI (code)</th>\n",
+       "      <th>department (name)</th>\n",
+       "      <th>department (code)</th>\n",
+       "      <th>region (name)</th>\n",
+       "      <th>region (code)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>29155</th>\n",
+       "      <td>7481</td>\n",
+       "      <td>2019-11-16T01:00:00+01:00</td>\n",
+       "      <td>100640.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>272.75</td>\n",
+       "      <td>272.75</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.077833</td>\n",
+       "      <td>45.7265</td>\n",
+       "      <td>Colombier-Saugnieu</td>\n",
+       "      <td>69299</td>\n",
+       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
+       "      <td>246900575</td>\n",
+       "      <td>Rhône</td>\n",
+       "      <td>69</td>\n",
+       "      <td>Auvergne-Rhône-Alpes</td>\n",
+       "      <td>84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29156</th>\n",
+       "      <td>7481</td>\n",
+       "      <td>2019-11-16T19:00:00+01:00</td>\n",
+       "      <td>101090.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>3.0</td>\n",
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+       "      <td>85.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.077833</td>\n",
+       "      <td>45.7265</td>\n",
+       "      <td>Colombier-Saugnieu</td>\n",
+       "      <td>69299</td>\n",
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+       "      <td>246900575</td>\n",
+       "      <td>Rhône</td>\n",
+       "      <td>69</td>\n",
+       "      <td>Auvergne-Rhône-Alpes</td>\n",
+       "      <td>84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29157</th>\n",
+       "      <td>7481</td>\n",
+       "      <td>2020-02-12T16:00:00+01:00</td>\n",
+       "      <td>102460.0</td>\n",
+       "      <td>-180.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>360.0</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>283.45</td>\n",
+       "      <td>271.75</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.077833</td>\n",
+       "      <td>45.7265</td>\n",
+       "      <td>Colombier-Saugnieu</td>\n",
+       "      <td>69299</td>\n",
+       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
+       "      <td>246900575</td>\n",
+       "      <td>Rhône</td>\n",
+       "      <td>69</td>\n",
+       "      <td>Auvergne-Rhône-Alpes</td>\n",
+       "      <td>84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29158</th>\n",
+       "      <td>7481</td>\n",
+       "      <td>2020-02-13T04:00:00+01:00</td>\n",
+       "      <td>102100.0</td>\n",
+       "      <td>-240.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>274.75</td>\n",
+       "      <td>271.15</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.077833</td>\n",
+       "      <td>45.7265</td>\n",
+       "      <td>Colombier-Saugnieu</td>\n",
+       "      <td>69299</td>\n",
+       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
+       "      <td>246900575</td>\n",
+       "      <td>Rhône</td>\n",
+       "      <td>69</td>\n",
+       "      <td>Auvergne-Rhône-Alpes</td>\n",
+       "      <td>84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29159</th>\n",
+       "      <td>7481</td>\n",
+       "      <td>2020-02-14T01:00:00+01:00</td>\n",
+       "      <td>102080.0</td>\n",
+       "      <td>230.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>283.15</td>\n",
+       "      <td>276.15</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.077833</td>\n",
+       "      <td>45.7265</td>\n",
+       "      <td>Colombier-Saugnieu</td>\n",
+       "      <td>69299</td>\n",
+       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
+       "      <td>246900575</td>\n",
+       "      <td>Rhône</td>\n",
+       "      <td>69</td>\n",
+       "      <td>Auvergne-Rhône-Alpes</td>\n",
+       "      <td>84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29160</th>\n",
+       "      <td>7481</td>\n",
+       "      <td>2020-02-14T07:00:00+01:00</td>\n",
+       "      <td>102430.0</td>\n",
+       "      <td>210.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>280.15</td>\n",
+       "      <td>278.45</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.077833</td>\n",
+       "      <td>45.7265</td>\n",
+       "      <td>Colombier-Saugnieu</td>\n",
+       "      <td>69299</td>\n",
+       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
+       "      <td>246900575</td>\n",
+       "      <td>Rhône</td>\n",
+       "      <td>69</td>\n",
+       "      <td>Auvergne-Rhône-Alpes</td>\n",
+       "      <td>84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29161</th>\n",
+       "      <td>7481</td>\n",
+       "      <td>2020-02-15T16:00:00+01:00</td>\n",
+       "      <td>102190.0</td>\n",
+       "      <td>-160.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>180.0</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>290.15</td>\n",
+       "      <td>273.75</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.077833</td>\n",
+       "      <td>45.7265</td>\n",
+       "      <td>Colombier-Saugnieu</td>\n",
+       "      <td>69299</td>\n",
+       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
+       "      <td>246900575</td>\n",
+       "      <td>Rhône</td>\n",
+       "      <td>69</td>\n",
+       "      <td>Auvergne-Rhône-Alpes</td>\n",
+       "      <td>84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29162</th>\n",
+       "      <td>7481</td>\n",
+       "      <td>2020-01-25T22:00:00+01:00</td>\n",
+       "      <td>102030.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>281.45</td>\n",
+       "      <td>278.55</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.077833</td>\n",
+       "      <td>45.7265</td>\n",
+       "      <td>Colombier-Saugnieu</td>\n",
+       "      <td>69299</td>\n",
+       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
+       "      <td>246900575</td>\n",
+       "      <td>Rhône</td>\n",
+       "      <td>69</td>\n",
+       "      <td>Auvergne-Rhône-Alpes</td>\n",
+       "      <td>84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29163</th>\n",
+       "      <td>7481</td>\n",
+       "      <td>2020-01-26T19:00:00+01:00</td>\n",
+       "      <td>102010.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>170.0</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>282.85</td>\n",
+       "      <td>279.15</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.077833</td>\n",
+       "      <td>45.7265</td>\n",
+       "      <td>Colombier-Saugnieu</td>\n",
+       "      <td>69299</td>\n",
+       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
+       "      <td>246900575</td>\n",
+       "      <td>Rhône</td>\n",
+       "      <td>69</td>\n",
+       "      <td>Auvergne-Rhône-Alpes</td>\n",
+       "      <td>84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29164</th>\n",
+       "      <td>7481</td>\n",
+       "      <td>2020-02-08T19:00:00+01:00</td>\n",
+       "      <td>102540.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>283.75</td>\n",
+       "      <td>277.65</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.077833</td>\n",
+       "      <td>45.7265</td>\n",
+       "      <td>Colombier-Saugnieu</td>\n",
+       "      <td>69299</td>\n",
+       "      <td>CC de l'Est Lyonnais (CCEL)</td>\n",
+       "      <td>246900575</td>\n",
+       "      <td>Rhône</td>\n",
+       "      <td>69</td>\n",
+       "      <td>Auvergne-Rhône-Alpes</td>\n",
+       "      <td>84</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 81 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       ID OMM station                       Date  Pression au niveau mer  \\\n",
+       "29155            7481  2019-11-16T01:00:00+01:00                100640.0   \n",
+       "29156            7481  2019-11-16T19:00:00+01:00                101090.0   \n",
+       "29157            7481  2020-02-12T16:00:00+01:00                102460.0   \n",
+       "29158            7481  2020-02-13T04:00:00+01:00                102100.0   \n",
+       "29159            7481  2020-02-14T01:00:00+01:00                102080.0   \n",
+       "29160            7481  2020-02-14T07:00:00+01:00                102430.0   \n",
+       "29161            7481  2020-02-15T16:00:00+01:00                102190.0   \n",
+       "29162            7481  2020-01-25T22:00:00+01:00                102030.0   \n",
+       "29163            7481  2020-01-26T19:00:00+01:00                102010.0   \n",
+       "29164            7481  2020-02-08T19:00:00+01:00                102540.0   \n",
+       "\n",
+       "       Variation de pression en 3 heures  Type de tendance barométrique  \\\n",
+       "29155                              130.0                            1.0   \n",
+       "29156                               90.0                            3.0   \n",
+       "29157                             -180.0                            6.0   \n",
+       "29158                             -240.0                            8.0   \n",
+       "29159                              230.0                            1.0   \n",
+       "29160                              210.0                            2.0   \n",
+       "29161                             -160.0                            6.0   \n",
+       "29162                               20.0                            1.0   \n",
+       "29163                               80.0                            3.0   \n",
+       "29164                              150.0                            2.0   \n",
+       "\n",
+       "       Direction du vent moyen 10 mn  Vitesse du vent moyen 10 mn  \\\n",
+       "29155                          190.0                          1.0   \n",
+       "29156                          130.0                          3.5   \n",
+       "29157                          360.0                          2.3   \n",
+       "29158                          150.0                          4.9   \n",
+       "29159                          280.0                          4.5   \n",
+       "29160                          140.0                          3.4   \n",
+       "29161                          180.0                          6.9   \n",
+       "29162                          140.0                          4.9   \n",
+       "29163                          170.0                          3.7   \n",
+       "29164                          190.0                          6.2   \n",
+       "\n",
+       "       Température  Point de rosée  Humidité  ...  Longitude  Latitude  \\\n",
+       "29155       272.75          272.75     100.0  ...   5.077833   45.7265   \n",
+       "29156       276.95          274.65      85.0  ...   5.077833   45.7265   \n",
+       "29157       283.45          271.75      44.0  ...   5.077833   45.7265   \n",
+       "29158       274.75          271.15      77.0  ...   5.077833   45.7265   \n",
+       "29159       283.15          276.15      62.0  ...   5.077833   45.7265   \n",
+       "29160       280.15          278.45      89.0  ...   5.077833   45.7265   \n",
+       "29161       290.15          273.75      33.0  ...   5.077833   45.7265   \n",
+       "29162       281.45          278.55      82.0  ...   5.077833   45.7265   \n",
+       "29163       282.85          279.15      78.0  ...   5.077833   45.7265   \n",
+       "29164       283.75          277.65      66.0  ...   5.077833   45.7265   \n",
+       "\n",
+       "          communes (name)  communes (code)                  EPCI (name)  \\\n",
+       "29155  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
+       "29156  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
+       "29157  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
+       "29158  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
+       "29159  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
+       "29160  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
+       "29161  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
+       "29162  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
+       "29163  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
+       "29164  Colombier-Saugnieu            69299  CC de l'Est Lyonnais (CCEL)   \n",
+       "\n",
+       "       EPCI (code)  department (name)  department (code)  \\\n",
+       "29155    246900575              Rhône                 69   \n",
+       "29156    246900575              Rhône                 69   \n",
+       "29157    246900575              Rhône                 69   \n",
+       "29158    246900575              Rhône                 69   \n",
+       "29159    246900575              Rhône                 69   \n",
+       "29160    246900575              Rhône                 69   \n",
+       "29161    246900575              Rhône                 69   \n",
+       "29162    246900575              Rhône                 69   \n",
+       "29163    246900575              Rhône                 69   \n",
+       "29164    246900575              Rhône                 69   \n",
+       "\n",
+       "              region (name)  region (code)  \n",
+       "29155  Auvergne-Rhône-Alpes             84  \n",
+       "29156  Auvergne-Rhône-Alpes             84  \n",
+       "29157  Auvergne-Rhône-Alpes             84  \n",
+       "29158  Auvergne-Rhône-Alpes             84  \n",
+       "29159  Auvergne-Rhône-Alpes             84  \n",
+       "29160  Auvergne-Rhône-Alpes             84  \n",
+       "29161  Auvergne-Rhône-Alpes             84  \n",
+       "29162  Auvergne-Rhône-Alpes             84  \n",
+       "29163  Auvergne-Rhône-Alpes             84  \n",
+       "29164  Auvergne-Rhône-Alpes             84  \n",
+       "\n",
+       "[10 rows x 81 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "<br>**List of columns :**"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
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+       "            text-align:  left;\n",
+       "        }</style><table id=\"T_b4003_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Code</th>        <th class=\"col_heading level0 col1\" >Description</th>        <th class=\"col_heading level0 col2\" >Na</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_b4003_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "                        <td id=\"T_b4003_row0_col0\" class=\"data row0 col0\" >numer_sta</td>\n",
+       "                        <td id=\"T_b4003_row0_col1\" class=\"data row0 col1\" >ID OMM station</td>\n",
+       "                        <td id=\"T_b4003_row0_col2\" class=\"data row0 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "                        <td id=\"T_b4003_row1_col0\" class=\"data row1 col0\" >date</td>\n",
+       "                        <td id=\"T_b4003_row1_col1\" class=\"data row1 col1\" >Date</td>\n",
+       "                        <td id=\"T_b4003_row1_col2\" class=\"data row1 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "                        <td id=\"T_b4003_row2_col0\" class=\"data row2 col0\" >pmer</td>\n",
+       "                        <td id=\"T_b4003_row2_col1\" class=\"data row2 col1\" >Pression au niveau mer</td>\n",
+       "                        <td id=\"T_b4003_row2_col2\" class=\"data row2 col2\" >17</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "                        <td id=\"T_b4003_row3_col0\" class=\"data row3 col0\" >tend</td>\n",
+       "                        <td id=\"T_b4003_row3_col1\" class=\"data row3 col1\" >Variation de pression en 3 heures</td>\n",
+       "                        <td id=\"T_b4003_row3_col2\" class=\"data row3 col2\" >2</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "                        <td id=\"T_b4003_row4_col0\" class=\"data row4 col0\" >cod_tend</td>\n",
+       "                        <td id=\"T_b4003_row4_col1\" class=\"data row4 col1\" >Type de tendance barométrique</td>\n",
+       "                        <td id=\"T_b4003_row4_col2\" class=\"data row4 col2\" >2</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "                        <td id=\"T_b4003_row5_col0\" class=\"data row5 col0\" >dd</td>\n",
+       "                        <td id=\"T_b4003_row5_col1\" class=\"data row5 col1\" >Direction du vent moyen 10 mn</td>\n",
+       "                        <td id=\"T_b4003_row5_col2\" class=\"data row5 col2\" >3</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "                        <td id=\"T_b4003_row6_col0\" class=\"data row6 col0\" >ff</td>\n",
+       "                        <td id=\"T_b4003_row6_col1\" class=\"data row6 col1\" >Vitesse du vent moyen 10 mn</td>\n",
+       "                        <td id=\"T_b4003_row6_col2\" class=\"data row6 col2\" >2</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "                        <td id=\"T_b4003_row7_col0\" class=\"data row7 col0\" >t</td>\n",
+       "                        <td id=\"T_b4003_row7_col1\" class=\"data row7 col1\" >Température</td>\n",
+       "                        <td id=\"T_b4003_row7_col2\" class=\"data row7 col2\" >14</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "                        <td id=\"T_b4003_row8_col0\" class=\"data row8 col0\" >td</td>\n",
+       "                        <td id=\"T_b4003_row8_col1\" class=\"data row8 col1\" >Point de rosée</td>\n",
+       "                        <td id=\"T_b4003_row8_col2\" class=\"data row8 col2\" >17</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "                        <td id=\"T_b4003_row9_col0\" class=\"data row9 col0\" >u</td>\n",
+       "                        <td id=\"T_b4003_row9_col1\" class=\"data row9 col1\" >Humidité</td>\n",
+       "                        <td id=\"T_b4003_row9_col2\" class=\"data row9 col2\" >17</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+       "                        <td id=\"T_b4003_row10_col0\" class=\"data row10 col0\" >vv</td>\n",
+       "                        <td id=\"T_b4003_row10_col1\" class=\"data row10 col1\" >Visibilité horizontale</td>\n",
+       "                        <td id=\"T_b4003_row10_col2\" class=\"data row10 col2\" >31</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+       "                        <td id=\"T_b4003_row11_col0\" class=\"data row11 col0\" >ww</td>\n",
+       "                        <td id=\"T_b4003_row11_col1\" class=\"data row11 col1\" >Temps présent</td>\n",
+       "                        <td id=\"T_b4003_row11_col2\" class=\"data row11 col2\" >1</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+       "                        <td id=\"T_b4003_row12_col0\" class=\"data row12 col0\" >w1</td>\n",
+       "                        <td id=\"T_b4003_row12_col1\" class=\"data row12 col1\" >Temps passé 1</td>\n",
+       "                        <td id=\"T_b4003_row12_col2\" class=\"data row12 col2\" >542</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+       "                        <td id=\"T_b4003_row13_col0\" class=\"data row13 col0\" >w2</td>\n",
+       "                        <td id=\"T_b4003_row13_col1\" class=\"data row13 col1\" >Temps passé 2</td>\n",
+       "                        <td id=\"T_b4003_row13_col2\" class=\"data row13 col2\" >552</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
+       "                        <td id=\"T_b4003_row14_col0\" class=\"data row14 col0\" >n</td>\n",
+       "                        <td id=\"T_b4003_row14_col1\" class=\"data row14 col1\" >Nebulosité totale</td>\n",
+       "                        <td id=\"T_b4003_row14_col2\" class=\"data row14 col2\" >801</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
+       "                        <td id=\"T_b4003_row15_col0\" class=\"data row15 col0\" >nbas</td>\n",
+       "                        <td id=\"T_b4003_row15_col1\" class=\"data row15 col1\" >Nébulosité  des nuages de l' étage inférieur</td>\n",
+       "                        <td id=\"T_b4003_row15_col2\" class=\"data row15 col2\" >2381</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
+       "                        <td id=\"T_b4003_row16_col0\" class=\"data row16 col0\" >hbas</td>\n",
+       "                        <td id=\"T_b4003_row16_col1\" class=\"data row16 col1\" >Hauteur de la base des nuages de l'étage inférieur</td>\n",
+       "                        <td id=\"T_b4003_row16_col2\" class=\"data row16 col2\" >8861</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
+       "                        <td id=\"T_b4003_row17_col0\" class=\"data row17 col0\" >cl</td>\n",
+       "                        <td id=\"T_b4003_row17_col1\" class=\"data row17 col1\" >Type des nuages de l'étage inférieur</td>\n",
+       "                        <td id=\"T_b4003_row17_col2\" class=\"data row17 col2\" >3377</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
+       "                        <td id=\"T_b4003_row18_col0\" class=\"data row18 col0\" >cm</td>\n",
+       "                        <td id=\"T_b4003_row18_col1\" class=\"data row18 col1\" >Type des nuages de l'étage moyen</td>\n",
+       "                        <td id=\"T_b4003_row18_col2\" class=\"data row18 col2\" >6912</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
+       "                        <td id=\"T_b4003_row19_col0\" class=\"data row19 col0\" >ch</td>\n",
+       "                        <td id=\"T_b4003_row19_col1\" class=\"data row19 col1\" >Type des nuages de l'étage supérieur</td>\n",
+       "                        <td id=\"T_b4003_row19_col2\" class=\"data row19 col2\" >8494</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
+       "                        <td id=\"T_b4003_row20_col0\" class=\"data row20 col0\" >pres</td>\n",
+       "                        <td id=\"T_b4003_row20_col1\" class=\"data row20 col1\" >Pression station</td>\n",
+       "                        <td id=\"T_b4003_row20_col2\" class=\"data row20 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
+       "                        <td id=\"T_b4003_row21_col0\" class=\"data row21 col0\" >niv_bar</td>\n",
+       "                        <td id=\"T_b4003_row21_col1\" class=\"data row21 col1\" >Niveau barométrique</td>\n",
+       "                        <td id=\"T_b4003_row21_col2\" class=\"data row21 col2\" >29165</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
+       "                        <td id=\"T_b4003_row22_col0\" class=\"data row22 col0\" >geop</td>\n",
+       "                        <td id=\"T_b4003_row22_col1\" class=\"data row22 col1\" >Géopotentiel</td>\n",
+       "                        <td id=\"T_b4003_row22_col2\" class=\"data row22 col2\" >29165</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
+       "                        <td id=\"T_b4003_row23_col0\" class=\"data row23 col0\" >tend24</td>\n",
+       "                        <td id=\"T_b4003_row23_col1\" class=\"data row23 col1\" >Variation de pression en 24 heures</td>\n",
+       "                        <td id=\"T_b4003_row23_col2\" class=\"data row23 col2\" >14443</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
+       "                        <td id=\"T_b4003_row24_col0\" class=\"data row24 col0\" >tn12</td>\n",
+       "                        <td id=\"T_b4003_row24_col1\" class=\"data row24 col1\" >Température minimale sur 12 heures</td>\n",
+       "                        <td id=\"T_b4003_row24_col2\" class=\"data row24 col2\" >21883</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
+       "                        <td id=\"T_b4003_row25_col0\" class=\"data row25 col0\" >tn24</td>\n",
+       "                        <td id=\"T_b4003_row25_col1\" class=\"data row25 col1\" >Température minimale sur 24 heures</td>\n",
+       "                        <td id=\"T_b4003_row25_col2\" class=\"data row25 col2\" >29165</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
+       "                        <td id=\"T_b4003_row26_col0\" class=\"data row26 col0\" >tx12</td>\n",
+       "                        <td id=\"T_b4003_row26_col1\" class=\"data row26 col1\" >Température maximale sur 12 heures</td>\n",
+       "                        <td id=\"T_b4003_row26_col2\" class=\"data row26 col2\" >21883</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
+       "                        <td id=\"T_b4003_row27_col0\" class=\"data row27 col0\" >tx24</td>\n",
+       "                        <td id=\"T_b4003_row27_col1\" class=\"data row27 col1\" >Température maximale sur 24 heures</td>\n",
+       "                        <td id=\"T_b4003_row27_col2\" class=\"data row27 col2\" >29165</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
+       "                        <td id=\"T_b4003_row28_col0\" class=\"data row28 col0\" >tminsol</td>\n",
+       "                        <td id=\"T_b4003_row28_col1\" class=\"data row28 col1\" >Température minimale du sol sur 12 heures</td>\n",
+       "                        <td id=\"T_b4003_row28_col2\" class=\"data row28 col2\" >27364</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
+       "                        <td id=\"T_b4003_row29_col0\" class=\"data row29 col0\" >sw</td>\n",
+       "                        <td id=\"T_b4003_row29_col1\" class=\"data row29 col1\" >Méthode de mesure Température du thermomètre mouillé</td>\n",
+       "                        <td id=\"T_b4003_row29_col2\" class=\"data row29 col2\" >29165</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
+       "                        <td id=\"T_b4003_row30_col0\" class=\"data row30 col0\" >tw</td>\n",
+       "                        <td id=\"T_b4003_row30_col1\" class=\"data row30 col1\" >Température du thermomètre mouillé</td>\n",
+       "                        <td id=\"T_b4003_row30_col2\" class=\"data row30 col2\" >29165</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
+       "                        <td id=\"T_b4003_row31_col0\" class=\"data row31 col0\" >raf10</td>\n",
+       "                        <td id=\"T_b4003_row31_col1\" class=\"data row31 col1\" >Rafale sur les 10 dernières minutes</td>\n",
+       "                        <td id=\"T_b4003_row31_col2\" class=\"data row31 col2\" >14127</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
+       "                        <td id=\"T_b4003_row32_col0\" class=\"data row32 col0\" >rafper</td>\n",
+       "                        <td id=\"T_b4003_row32_col1\" class=\"data row32 col1\" >Rafales sur une période</td>\n",
+       "                        <td id=\"T_b4003_row32_col2\" class=\"data row32 col2\" >9</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
+       "                        <td id=\"T_b4003_row33_col0\" class=\"data row33 col0\" >per</td>\n",
+       "                        <td id=\"T_b4003_row33_col1\" class=\"data row33 col1\" >Periode de mesure de la rafale</td>\n",
+       "                        <td id=\"T_b4003_row33_col2\" class=\"data row33 col2\" >8</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
+       "                        <td id=\"T_b4003_row34_col0\" class=\"data row34 col0\" >etat_sol</td>\n",
+       "                        <td id=\"T_b4003_row34_col1\" class=\"data row34 col1\" >Etat du sol</td>\n",
+       "                        <td id=\"T_b4003_row34_col2\" class=\"data row34 col2\" >12278</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
+       "                        <td id=\"T_b4003_row35_col0\" class=\"data row35 col0\" >ht_neige</td>\n",
+       "                        <td id=\"T_b4003_row35_col1\" class=\"data row35 col1\" >Hauteur totale de la couche de neige, glace, autre au sol</td>\n",
+       "                        <td id=\"T_b4003_row35_col2\" class=\"data row35 col2\" >12083</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
+       "                        <td id=\"T_b4003_row36_col0\" class=\"data row36 col0\" >ssfrai</td>\n",
+       "                        <td id=\"T_b4003_row36_col1\" class=\"data row36 col1\" >Hauteur de la neige fraîche</td>\n",
+       "                        <td id=\"T_b4003_row36_col2\" class=\"data row36 col2\" >2914</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
+       "                        <td id=\"T_b4003_row37_col0\" class=\"data row37 col0\" >perssfrai</td>\n",
+       "                        <td id=\"T_b4003_row37_col1\" class=\"data row37 col1\" >Periode de mesure de la neige fraiche</td>\n",
+       "                        <td id=\"T_b4003_row37_col2\" class=\"data row37 col2\" >4489</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
+       "                        <td id=\"T_b4003_row38_col0\" class=\"data row38 col0\" >rr1</td>\n",
+       "                        <td id=\"T_b4003_row38_col1\" class=\"data row38 col1\" >Précipitations dans la dernière heure</td>\n",
+       "                        <td id=\"T_b4003_row38_col2\" class=\"data row38 col2\" >95</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
+       "                        <td id=\"T_b4003_row39_col0\" class=\"data row39 col0\" >rr3</td>\n",
+       "                        <td id=\"T_b4003_row39_col1\" class=\"data row39 col1\" >Précipitations dans les 3 dernières heures</td>\n",
+       "                        <td id=\"T_b4003_row39_col2\" class=\"data row39 col2\" >73</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
+       "                        <td id=\"T_b4003_row40_col0\" class=\"data row40 col0\" >rr6</td>\n",
+       "                        <td id=\"T_b4003_row40_col1\" class=\"data row40 col1\" >Précipitations dans les 6 dernières heures</td>\n",
+       "                        <td id=\"T_b4003_row40_col2\" class=\"data row40 col2\" >10869</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
+       "                        <td id=\"T_b4003_row41_col0\" class=\"data row41 col0\" >rr12</td>\n",
+       "                        <td id=\"T_b4003_row41_col1\" class=\"data row41 col1\" >Précipitations dans les 12 dernières heures</td>\n",
+       "                        <td id=\"T_b4003_row41_col2\" class=\"data row41 col2\" >10919</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
+       "                        <td id=\"T_b4003_row42_col0\" class=\"data row42 col0\" >rr24</td>\n",
+       "                        <td id=\"T_b4003_row42_col1\" class=\"data row42 col1\" >Précipitations dans les 24 dernières heures</td>\n",
+       "                        <td id=\"T_b4003_row42_col2\" class=\"data row42 col2\" >12730</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
+       "                        <td id=\"T_b4003_row43_col0\" class=\"data row43 col0\" >phenspe1</td>\n",
+       "                        <td id=\"T_b4003_row43_col1\" class=\"data row43 col1\" >Phénomène spécial 1</td>\n",
+       "                        <td id=\"T_b4003_row43_col2\" class=\"data row43 col2\" >14818</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
+       "                        <td id=\"T_b4003_row44_col0\" class=\"data row44 col0\" >phenspe2</td>\n",
+       "                        <td id=\"T_b4003_row44_col1\" class=\"data row44 col1\" >Phénomène spécial 2</td>\n",
+       "                        <td id=\"T_b4003_row44_col2\" class=\"data row44 col2\" >14826</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
+       "                        <td id=\"T_b4003_row45_col0\" class=\"data row45 col0\" >phenspe3</td>\n",
+       "                        <td id=\"T_b4003_row45_col1\" class=\"data row45 col1\" >Phénomène spécial 3</td>\n",
+       "                        <td id=\"T_b4003_row45_col2\" class=\"data row45 col2\" >15515</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
+       "                        <td id=\"T_b4003_row46_col0\" class=\"data row46 col0\" >phenspe4</td>\n",
+       "                        <td id=\"T_b4003_row46_col1\" class=\"data row46 col1\" >Phénomène spécial 4</td>\n",
+       "                        <td id=\"T_b4003_row46_col2\" class=\"data row46 col2\" >28869</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
+       "                        <td id=\"T_b4003_row47_col0\" class=\"data row47 col0\" >nnuage1</td>\n",
+       "                        <td id=\"T_b4003_row47_col1\" class=\"data row47 col1\" >Nébulosité couche nuageuse 1</td>\n",
+       "                        <td id=\"T_b4003_row47_col2\" class=\"data row47 col2\" >4753</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
+       "                        <td id=\"T_b4003_row48_col0\" class=\"data row48 col0\" >ctype1</td>\n",
+       "                        <td id=\"T_b4003_row48_col1\" class=\"data row48 col1\" >Type nuage 1</td>\n",
+       "                        <td id=\"T_b4003_row48_col2\" class=\"data row48 col2\" >5699</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
+       "                        <td id=\"T_b4003_row49_col0\" class=\"data row49 col0\" >hnuage1</td>\n",
+       "                        <td id=\"T_b4003_row49_col1\" class=\"data row49 col1\" >Hauteur de base 1</td>\n",
+       "                        <td id=\"T_b4003_row49_col2\" class=\"data row49 col2\" >5439</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
+       "                        <td id=\"T_b4003_row50_col0\" class=\"data row50 col0\" >nnuage2</td>\n",
+       "                        <td id=\"T_b4003_row50_col1\" class=\"data row50 col1\" >Nébulosité couche nuageuse 2</td>\n",
+       "                        <td id=\"T_b4003_row50_col2\" class=\"data row50 col2\" >16112</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
+       "                        <td id=\"T_b4003_row51_col0\" class=\"data row51 col0\" >ctype2</td>\n",
+       "                        <td id=\"T_b4003_row51_col1\" class=\"data row51 col1\" >Type nuage 2</td>\n",
+       "                        <td id=\"T_b4003_row51_col2\" class=\"data row51 col2\" >16643</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
+       "                        <td id=\"T_b4003_row52_col0\" class=\"data row52 col0\" >hnuage2</td>\n",
+       "                        <td id=\"T_b4003_row52_col1\" class=\"data row52 col1\" >Hauteur de base 2</td>\n",
+       "                        <td id=\"T_b4003_row52_col2\" class=\"data row52 col2\" >16317</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
+       "                        <td id=\"T_b4003_row53_col0\" class=\"data row53 col0\" >nnuage3</td>\n",
+       "                        <td id=\"T_b4003_row53_col1\" class=\"data row53 col1\" >Nébulosité couche nuageuse 3</td>\n",
+       "                        <td id=\"T_b4003_row53_col2\" class=\"data row53 col2\" >25387</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
+       "                        <td id=\"T_b4003_row54_col0\" class=\"data row54 col0\" >ctype3</td>\n",
+       "                        <td id=\"T_b4003_row54_col1\" class=\"data row54 col1\" >Type nuage 3</td>\n",
+       "                        <td id=\"T_b4003_row54_col2\" class=\"data row54 col2\" >25642</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
+       "                        <td id=\"T_b4003_row55_col0\" class=\"data row55 col0\" >hnuage3</td>\n",
+       "                        <td id=\"T_b4003_row55_col1\" class=\"data row55 col1\" >Hauteur de base 3</td>\n",
+       "                        <td id=\"T_b4003_row55_col2\" class=\"data row55 col2\" >25431</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
+       "                        <td id=\"T_b4003_row56_col0\" class=\"data row56 col0\" >nnuage4</td>\n",
+       "                        <td id=\"T_b4003_row56_col1\" class=\"data row56 col1\" >Nébulosité couche nuageuse 4</td>\n",
+       "                        <td id=\"T_b4003_row56_col2\" class=\"data row56 col2\" >28850</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
+       "                        <td id=\"T_b4003_row57_col0\" class=\"data row57 col0\" >ctype4</td>\n",
+       "                        <td id=\"T_b4003_row57_col1\" class=\"data row57 col1\" >Type nuage 4</td>\n",
+       "                        <td id=\"T_b4003_row57_col2\" class=\"data row57 col2\" >28780</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row58\" class=\"row_heading level0 row58\" >58</th>\n",
+       "                        <td id=\"T_b4003_row58_col0\" class=\"data row58 col0\" >hnuage4</td>\n",
+       "                        <td id=\"T_b4003_row58_col1\" class=\"data row58 col1\" >Hauteur de base 4</td>\n",
+       "                        <td id=\"T_b4003_row58_col2\" class=\"data row58 col2\" >28850</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row59\" class=\"row_heading level0 row59\" >59</th>\n",
+       "                        <td id=\"T_b4003_row59_col0\" class=\"data row59 col0\" >coordonnees</td>\n",
+       "                        <td id=\"T_b4003_row59_col1\" class=\"data row59 col1\" >Coordonnees</td>\n",
+       "                        <td id=\"T_b4003_row59_col2\" class=\"data row59 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row60\" class=\"row_heading level0 row60\" >60</th>\n",
+       "                        <td id=\"T_b4003_row60_col0\" class=\"data row60 col0\" >nom</td>\n",
+       "                        <td id=\"T_b4003_row60_col1\" class=\"data row60 col1\" >Nom</td>\n",
+       "                        <td id=\"T_b4003_row60_col2\" class=\"data row60 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row61\" class=\"row_heading level0 row61\" >61</th>\n",
+       "                        <td id=\"T_b4003_row61_col0\" class=\"data row61 col0\" >type_de_tendance_barometrique</td>\n",
+       "                        <td id=\"T_b4003_row61_col1\" class=\"data row61 col1\" >Type de tendance barométrique.1</td>\n",
+       "                        <td id=\"T_b4003_row61_col2\" class=\"data row61 col2\" >2</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row62\" class=\"row_heading level0 row62\" >62</th>\n",
+       "                        <td id=\"T_b4003_row62_col0\" class=\"data row62 col0\" >temps_passe_1</td>\n",
+       "                        <td id=\"T_b4003_row62_col1\" class=\"data row62 col1\" >Temps passé 1.1</td>\n",
+       "                        <td id=\"T_b4003_row62_col2\" class=\"data row62 col2\" >542</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row63\" class=\"row_heading level0 row63\" >63</th>\n",
+       "                        <td id=\"T_b4003_row63_col0\" class=\"data row63 col0\" >temps_present</td>\n",
+       "                        <td id=\"T_b4003_row63_col1\" class=\"data row63 col1\" >Temps présent.1</td>\n",
+       "                        <td id=\"T_b4003_row63_col2\" class=\"data row63 col2\" >1</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row64\" class=\"row_heading level0 row64\" >64</th>\n",
+       "                        <td id=\"T_b4003_row64_col0\" class=\"data row64 col0\" >tc</td>\n",
+       "                        <td id=\"T_b4003_row64_col1\" class=\"data row64 col1\" >Température (°C)</td>\n",
+       "                        <td id=\"T_b4003_row64_col2\" class=\"data row64 col2\" >14</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row65\" class=\"row_heading level0 row65\" >65</th>\n",
+       "                        <td id=\"T_b4003_row65_col0\" class=\"data row65 col0\" >tn12c</td>\n",
+       "                        <td id=\"T_b4003_row65_col1\" class=\"data row65 col1\" >Température minimale sur 12 heures (°C)</td>\n",
+       "                        <td id=\"T_b4003_row65_col2\" class=\"data row65 col2\" >21883</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row66\" class=\"row_heading level0 row66\" >66</th>\n",
+       "                        <td id=\"T_b4003_row66_col0\" class=\"data row66 col0\" >tn24c</td>\n",
+       "                        <td id=\"T_b4003_row66_col1\" class=\"data row66 col1\" >Température minimale sur 24 heures (°C)</td>\n",
+       "                        <td id=\"T_b4003_row66_col2\" class=\"data row66 col2\" >29165</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row67\" class=\"row_heading level0 row67\" >67</th>\n",
+       "                        <td id=\"T_b4003_row67_col0\" class=\"data row67 col0\" >tx12c</td>\n",
+       "                        <td id=\"T_b4003_row67_col1\" class=\"data row67 col1\" >Température maximale sur 12 heures (°C)</td>\n",
+       "                        <td id=\"T_b4003_row67_col2\" class=\"data row67 col2\" >21883</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row68\" class=\"row_heading level0 row68\" >68</th>\n",
+       "                        <td id=\"T_b4003_row68_col0\" class=\"data row68 col0\" >tx24c</td>\n",
+       "                        <td id=\"T_b4003_row68_col1\" class=\"data row68 col1\" >Température maximale sur 24 heures (°C)</td>\n",
+       "                        <td id=\"T_b4003_row68_col2\" class=\"data row68 col2\" >29165</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row69\" class=\"row_heading level0 row69\" >69</th>\n",
+       "                        <td id=\"T_b4003_row69_col0\" class=\"data row69 col0\" >tminsolc</td>\n",
+       "                        <td id=\"T_b4003_row69_col1\" class=\"data row69 col1\" >Température minimale du sol sur 12 heures (en °C)</td>\n",
+       "                        <td id=\"T_b4003_row69_col2\" class=\"data row69 col2\" >27364</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row70\" class=\"row_heading level0 row70\" >70</th>\n",
+       "                        <td id=\"T_b4003_row70_col0\" class=\"data row70 col0\" >altitude</td>\n",
+       "                        <td id=\"T_b4003_row70_col1\" class=\"data row70 col1\" >Altitude</td>\n",
+       "                        <td id=\"T_b4003_row70_col2\" class=\"data row70 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row71\" class=\"row_heading level0 row71\" >71</th>\n",
+       "                        <td id=\"T_b4003_row71_col0\" class=\"data row71 col0\" >longitude</td>\n",
+       "                        <td id=\"T_b4003_row71_col1\" class=\"data row71 col1\" >Longitude</td>\n",
+       "                        <td id=\"T_b4003_row71_col2\" class=\"data row71 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row72\" class=\"row_heading level0 row72\" >72</th>\n",
+       "                        <td id=\"T_b4003_row72_col0\" class=\"data row72 col0\" >latitude</td>\n",
+       "                        <td id=\"T_b4003_row72_col1\" class=\"data row72 col1\" >Latitude</td>\n",
+       "                        <td id=\"T_b4003_row72_col2\" class=\"data row72 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row73\" class=\"row_heading level0 row73\" >73</th>\n",
+       "                        <td id=\"T_b4003_row73_col0\" class=\"data row73 col0\" >libgeo</td>\n",
+       "                        <td id=\"T_b4003_row73_col1\" class=\"data row73 col1\" >communes (name)</td>\n",
+       "                        <td id=\"T_b4003_row73_col2\" class=\"data row73 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row74\" class=\"row_heading level0 row74\" >74</th>\n",
+       "                        <td id=\"T_b4003_row74_col0\" class=\"data row74 col0\" >codegeo</td>\n",
+       "                        <td id=\"T_b4003_row74_col1\" class=\"data row74 col1\" >communes (code)</td>\n",
+       "                        <td id=\"T_b4003_row74_col2\" class=\"data row74 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row75\" class=\"row_heading level0 row75\" >75</th>\n",
+       "                        <td id=\"T_b4003_row75_col0\" class=\"data row75 col0\" >nom_epci</td>\n",
+       "                        <td id=\"T_b4003_row75_col1\" class=\"data row75 col1\" >EPCI (name)</td>\n",
+       "                        <td id=\"T_b4003_row75_col2\" class=\"data row75 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row76\" class=\"row_heading level0 row76\" >76</th>\n",
+       "                        <td id=\"T_b4003_row76_col0\" class=\"data row76 col0\" >code_epci</td>\n",
+       "                        <td id=\"T_b4003_row76_col1\" class=\"data row76 col1\" >EPCI (code)</td>\n",
+       "                        <td id=\"T_b4003_row76_col2\" class=\"data row76 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row77\" class=\"row_heading level0 row77\" >77</th>\n",
+       "                        <td id=\"T_b4003_row77_col0\" class=\"data row77 col0\" >nom_dept</td>\n",
+       "                        <td id=\"T_b4003_row77_col1\" class=\"data row77 col1\" >department (name)</td>\n",
+       "                        <td id=\"T_b4003_row77_col2\" class=\"data row77 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row78\" class=\"row_heading level0 row78\" >78</th>\n",
+       "                        <td id=\"T_b4003_row78_col0\" class=\"data row78 col0\" >code_dep</td>\n",
+       "                        <td id=\"T_b4003_row78_col1\" class=\"data row78 col1\" >department (code)</td>\n",
+       "                        <td id=\"T_b4003_row78_col2\" class=\"data row78 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row79\" class=\"row_heading level0 row79\" >79</th>\n",
+       "                        <td id=\"T_b4003_row79_col0\" class=\"data row79 col0\" >nom_reg</td>\n",
+       "                        <td id=\"T_b4003_row79_col1\" class=\"data row79 col1\" >region (name)</td>\n",
+       "                        <td id=\"T_b4003_row79_col2\" class=\"data row79 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b4003_level0_row80\" class=\"row_heading level0 row80\" >80</th>\n",
+       "                        <td id=\"T_b4003_row80_col0\" class=\"data row80 col0\" >code_reg</td>\n",
+       "                        <td id=\"T_b4003_row80_col1\" class=\"data row80 col1\" >region (code)</td>\n",
+       "                        <td id=\"T_b4003_row80_col2\" class=\"data row80 col2\" >0</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f55cf62dd90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape is :  (29165, 81)\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(f'{datasets_dir}/SYNOP/{data_filename}', header=0, sep=';')\n",
+    "pwk.subtitle('Raw data :')\n",
+    "display(df.tail(10))\n",
+    "\n",
+    "# ---- Get the columns name as descriptions\n",
+    "synop_desc = list(df.columns)\n",
+    "\n",
+    "# ---- Set Codes as columns name\n",
+    "df.columns   = synop_codes\n",
+    "code2desc    = dict(zip(synop_codes, synop_desc))\n",
+    "\n",
+    "# ---- Count the na values by columns\n",
+    "columns_na = df.isna().sum().tolist()\n",
+    "\n",
+    "# ---- Show all of that\n",
+    "df_desc=pd.DataFrame({'Code':synop_codes, 'Description':synop_desc, 'Na':columns_na})\n",
+    "\n",
+    "pwk.subtitle('List of columns :')\n",
+    "display(df_desc.style.set_properties(**{'text-align': 'left'}))\n",
+    "\n",
+    "print('Shape is : ', df.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Prepare dataset\n",
+    "### 4.1 - Keep only certain columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:15:58.283702Z",
+     "iopub.status.busy": "2021-03-07T20:15:58.283359Z",
+     "iopub.status.idle": "2021-03-07T20:15:58.347468Z",
+     "shell.execute_reply": "2021-03-07T20:15:58.347145Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "<br>**Our selected columns :**"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>pmer</th>\n",
+       "      <th>tend</th>\n",
+       "      <th>cod_tend</th>\n",
+       "      <th>dd</th>\n",
+       "      <th>ff</th>\n",
+       "      <th>td</th>\n",
+       "      <th>u</th>\n",
+       "      <th>ww</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>rafper</th>\n",
+       "      <th>per</th>\n",
+       "      <th>rr1</th>\n",
+       "      <th>rr3</th>\n",
+       "      <th>tc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2015-06-12T17:00:00+02:00</td>\n",
+       "      <td>101050.0</td>\n",
+       "      <td>-230.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>286.25</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98330.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.1</td>\n",
+       "      <td>24.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2015-06-05T17:00:00+02:00</td>\n",
+       "      <td>101590.0</td>\n",
+       "      <td>-220.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>286.95</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>98930.0</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>32.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2015-06-15T11:00:00+02:00</td>\n",
+       "      <td>101420.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>286.85</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>98660.0</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2015-06-15T14:00:00+02:00</td>\n",
+       "      <td>101430.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>286.45</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98680.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>22.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2015-06-20T05:00:00+02:00</td>\n",
+       "      <td>102030.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>282.95</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>99170.0</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2015-06-22T05:00:00+02:00</td>\n",
+       "      <td>101680.0</td>\n",
+       "      <td>-120.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>180.0</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>286.15</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98870.0</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.1</td>\n",
+       "      <td>16.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2015-06-23T02:00:00+02:00</td>\n",
+       "      <td>101270.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>282.95</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98490.0</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>19.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2015-06-25T14:00:00+02:00</td>\n",
+       "      <td>102180.0</td>\n",
+       "      <td>-40.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>283.25</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>99430.0</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2015-07-05T20:00:00+02:00</td>\n",
+       "      <td>101410.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>288.05</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>98760.0</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>33.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2015-05-14T17:00:00+02:00</td>\n",
+       "      <td>101070.0</td>\n",
+       "      <td>-150.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>284.95</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>98300.0</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>19.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2015-03-16T22:00:00+01:00</td>\n",
+       "      <td>102150.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>275.05</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>99240.0</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2015-03-26T01:00:00+01:00</td>\n",
+       "      <td>101140.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>330.0</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>275.45</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98220.0</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2015-04-03T17:00:00+02:00</td>\n",
+       "      <td>101690.0</td>\n",
+       "      <td>-250.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>340.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>278.15</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98850.0</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2015-04-05T20:00:00+02:00</td>\n",
+       "      <td>101850.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>268.45</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98950.0</td>\n",
+       "      <td>13.5</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2014-10-22T17:00:00+02:00</td>\n",
+       "      <td>102670.0</td>\n",
+       "      <td>-70.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>275.35</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>99770.0</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>2015-02-08T16:00:00+01:00</td>\n",
+       "      <td>102570.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>271.55</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>99590.0</td>\n",
+       "      <td>19.9</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2015-02-11T07:00:00+01:00</td>\n",
+       "      <td>102670.0</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>267.55</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>99610.0</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-3.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>2015-02-07T04:00:00+01:00</td>\n",
+       "      <td>101900.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>310.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>268.65</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98900.0</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>2015-02-13T04:00:00+01:00</td>\n",
+       "      <td>102140.0</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>2.1</td>\n",
+       "      <td>273.55</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>99190.0</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>2015-02-16T19:00:00+01:00</td>\n",
+       "      <td>102060.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>275.65</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>99110.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         date      pmer   tend  cod_tend     dd    ff      td  \\\n",
+       "0   2015-06-12T17:00:00+02:00  101050.0 -230.0       6.0  140.0   3.6  286.25   \n",
+       "1   2015-06-05T17:00:00+02:00  101590.0 -220.0       8.0  190.0   3.9  286.95   \n",
+       "2   2015-06-15T11:00:00+02:00  101420.0   90.0       1.0  270.0   1.5  286.85   \n",
+       "3   2015-06-15T14:00:00+02:00  101430.0   20.0       1.0   10.0   2.5  286.45   \n",
+       "4   2015-06-20T05:00:00+02:00  102030.0    0.0       4.0   50.0   0.7  282.95   \n",
+       "5   2015-06-22T05:00:00+02:00  101680.0 -120.0       6.0  180.0   0.7  286.15   \n",
+       "6   2015-06-23T02:00:00+02:00  101270.0  150.0       2.0   20.0   4.5  282.95   \n",
+       "7   2015-06-25T14:00:00+02:00  102180.0  -40.0       8.0   10.0   2.3  283.25   \n",
+       "8   2015-07-05T20:00:00+02:00  101410.0   50.0       3.0  190.0   8.3  288.05   \n",
+       "9   2015-05-14T17:00:00+02:00  101070.0 -150.0       6.0   20.0   6.2  284.95   \n",
+       "10  2015-03-16T22:00:00+01:00  102150.0   40.0       1.0   50.0   1.7  275.05   \n",
+       "11  2015-03-26T01:00:00+01:00  101140.0  100.0       1.0  330.0   5.9  275.45   \n",
+       "12  2015-04-03T17:00:00+02:00  101690.0 -250.0       7.0  340.0   3.5  278.15   \n",
+       "13  2015-04-05T20:00:00+02:00  101850.0  140.0       3.0   10.0   7.8  268.45   \n",
+       "14  2014-10-22T17:00:00+02:00  102670.0  -70.0       8.0   20.0   4.6  275.35   \n",
+       "15  2015-02-08T16:00:00+01:00  102570.0   20.0       3.0  350.0  12.3  271.55   \n",
+       "16  2015-02-11T07:00:00+01:00  102670.0  -10.0       7.0  290.0   2.0  267.55   \n",
+       "17  2015-02-07T04:00:00+01:00  101900.0  160.0       1.0  310.0   2.0  268.65   \n",
+       "18  2015-02-13T04:00:00+01:00  102140.0  -50.0       8.0  140.0   2.1  273.55   \n",
+       "19  2015-02-16T19:00:00+01:00  102060.0  100.0       3.0   20.0   3.2  275.65   \n",
+       "\n",
+       "       u    ww     pres  rafper   per  rr1  rr3    tc  \n",
+       "0   50.0   2.0  98330.0     5.1 -10.0  0.0 -0.1  24.2  \n",
+       "1   32.0   3.0  98930.0     9.9 -10.0  0.0  0.0  32.6  \n",
+       "2   64.0   3.0  98660.0     4.5 -10.0  0.0  0.0  20.8  \n",
+       "3   55.0   1.0  98680.0     5.1 -10.0  0.0  0.0  22.8  \n",
+       "4   82.0   2.0  99170.0     2.4 -10.0  0.0  0.0  12.8  \n",
+       "5   80.0   1.0  98870.0     4.7 -10.0  0.0 -0.1  16.5  \n",
+       "6   54.0   0.0  98490.0    10.2 -10.0  0.0  0.0  19.3  \n",
+       "7   38.0   1.0  99430.0     7.5 -10.0  0.0  0.0  25.5  \n",
+       "8   33.0   3.0  98760.0    13.4 -10.0  0.0  0.0  33.4  \n",
+       "9   60.0   3.0  98300.0    11.1 -10.0  0.0  0.0  19.8  \n",
+       "10  62.0   1.0  99240.0     4.6 -10.0  0.0  0.0   8.8  \n",
+       "11  82.0   1.0  98220.0     8.1 -10.0  0.0  0.0   5.1  \n",
+       "12  55.0   1.0  98850.0     6.4 -10.0  0.0  0.0  13.9  \n",
+       "13  38.0   1.0  98950.0    13.5 -10.0  0.0  0.0   8.9  \n",
+       "14  55.0   1.0  99770.0     7.2 -10.0  0.0  0.0  10.9  \n",
+       "15  68.0   0.0  99590.0    19.9 -10.0  0.0  0.0   3.8  \n",
+       "16  88.0  10.0  99610.0     3.3 -10.0  0.0  0.0  -3.9  \n",
+       "17  74.0   2.0  98900.0     3.2 -10.0  0.0  0.0  -0.4  \n",
+       "18  74.0   0.0  99190.0     4.9 -10.0  0.0  0.0   4.6  \n",
+       "19  88.0   1.0  99110.0     5.1 -10.0  0.0  0.0   4.3  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "<br>**Few statistics :**"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style><table id=\"T_0b262_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >pmer</th>        <th class=\"col_heading level0 col1\" >tend</th>        <th class=\"col_heading level0 col2\" >cod_tend</th>        <th class=\"col_heading level0 col3\" >dd</th>        <th class=\"col_heading level0 col4\" >ff</th>        <th class=\"col_heading level0 col5\" >td</th>        <th class=\"col_heading level0 col6\" >u</th>        <th class=\"col_heading level0 col7\" >ww</th>        <th class=\"col_heading level0 col8\" >pres</th>        <th class=\"col_heading level0 col9\" >rafper</th>        <th class=\"col_heading level0 col10\" >per</th>        <th class=\"col_heading level0 col11\" >rr1</th>        <th class=\"col_heading level0 col12\" >rr3</th>        <th class=\"col_heading level0 col13\" >tc</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_0b262_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+       "                        <td id=\"T_0b262_row0_col0\" class=\"data row0 col0\" >29148.00</td>\n",
+       "                        <td id=\"T_0b262_row0_col1\" class=\"data row0 col1\" >29163.00</td>\n",
+       "                        <td id=\"T_0b262_row0_col2\" class=\"data row0 col2\" >29163.00</td>\n",
+       "                        <td id=\"T_0b262_row0_col3\" class=\"data row0 col3\" >29162.00</td>\n",
+       "                        <td id=\"T_0b262_row0_col4\" class=\"data row0 col4\" >29163.00</td>\n",
+       "                        <td id=\"T_0b262_row0_col5\" class=\"data row0 col5\" >29148.00</td>\n",
+       "                        <td id=\"T_0b262_row0_col6\" class=\"data row0 col6\" >29148.00</td>\n",
+       "                        <td id=\"T_0b262_row0_col7\" class=\"data row0 col7\" >29164.00</td>\n",
+       "                        <td id=\"T_0b262_row0_col8\" class=\"data row0 col8\" >29165.00</td>\n",
+       "                        <td id=\"T_0b262_row0_col9\" class=\"data row0 col9\" >29156.00</td>\n",
+       "                        <td id=\"T_0b262_row0_col10\" class=\"data row0 col10\" >29157.00</td>\n",
+       "                        <td id=\"T_0b262_row0_col11\" class=\"data row0 col11\" >29070.00</td>\n",
+       "                        <td id=\"T_0b262_row0_col12\" class=\"data row0 col12\" >29092.00</td>\n",
+       "                        <td id=\"T_0b262_row0_col13\" class=\"data row0 col13\" >29151.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_0b262_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+       "                        <td id=\"T_0b262_row1_col0\" class=\"data row1 col0\" >101753.55</td>\n",
+       "                        <td id=\"T_0b262_row1_col1\" class=\"data row1 col1\" >0.26</td>\n",
+       "                        <td id=\"T_0b262_row1_col2\" class=\"data row1 col2\" >4.31</td>\n",
+       "                        <td id=\"T_0b262_row1_col3\" class=\"data row1 col3\" >204.09</td>\n",
+       "                        <td id=\"T_0b262_row1_col4\" class=\"data row1 col4\" >3.40</td>\n",
+       "                        <td id=\"T_0b262_row1_col5\" class=\"data row1 col5\" >280.03</td>\n",
+       "                        <td id=\"T_0b262_row1_col6\" class=\"data row1 col6\" >71.02</td>\n",
+       "                        <td id=\"T_0b262_row1_col7\" class=\"data row1 col7\" >10.11</td>\n",
+       "                        <td id=\"T_0b262_row1_col8\" class=\"data row1 col8\" >98894.60</td>\n",
+       "                        <td id=\"T_0b262_row1_col9\" class=\"data row1 col9\" >6.30</td>\n",
+       "                        <td id=\"T_0b262_row1_col10\" class=\"data row1 col10\" >-10.00</td>\n",
+       "                        <td id=\"T_0b262_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
+       "                        <td id=\"T_0b262_row1_col12\" class=\"data row1 col12\" >0.28</td>\n",
+       "                        <td id=\"T_0b262_row1_col13\" class=\"data row1 col13\" >12.69</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_0b262_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+       "                        <td id=\"T_0b262_row2_col0\" class=\"data row2 col0\" >798.09</td>\n",
+       "                        <td id=\"T_0b262_row2_col1\" class=\"data row2 col1\" >111.44</td>\n",
+       "                        <td id=\"T_0b262_row2_col2\" class=\"data row2 col2\" >2.72</td>\n",
+       "                        <td id=\"T_0b262_row2_col3\" class=\"data row2 col3\" >115.42</td>\n",
+       "                        <td id=\"T_0b262_row2_col4\" class=\"data row2 col4\" >2.47</td>\n",
+       "                        <td id=\"T_0b262_row2_col5\" class=\"data row2 col5\" >5.86</td>\n",
+       "                        <td id=\"T_0b262_row2_col6\" class=\"data row2 col6\" >18.28</td>\n",
+       "                        <td id=\"T_0b262_row2_col7\" class=\"data row2 col7\" >19.40</td>\n",
+       "                        <td id=\"T_0b262_row2_col8\" class=\"data row2 col8\" >761.59</td>\n",
+       "                        <td id=\"T_0b262_row2_col9\" class=\"data row2 col9\" >3.85</td>\n",
+       "                        <td id=\"T_0b262_row2_col10\" class=\"data row2 col10\" >0.00</td>\n",
+       "                        <td id=\"T_0b262_row2_col11\" class=\"data row2 col11\" >0.61</td>\n",
+       "                        <td id=\"T_0b262_row2_col12\" class=\"data row2 col12\" >1.41</td>\n",
+       "                        <td id=\"T_0b262_row2_col13\" class=\"data row2 col13\" >8.15</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_0b262_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+       "                        <td id=\"T_0b262_row3_col0\" class=\"data row3 col0\" >97960.00</td>\n",
+       "                        <td id=\"T_0b262_row3_col1\" class=\"data row3 col1\" >-750.00</td>\n",
+       "                        <td id=\"T_0b262_row3_col2\" class=\"data row3 col2\" >0.00</td>\n",
+       "                        <td id=\"T_0b262_row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+       "                        <td id=\"T_0b262_row3_col4\" class=\"data row3 col4\" >0.00</td>\n",
+       "                        <td id=\"T_0b262_row3_col5\" class=\"data row3 col5\" >249.25</td>\n",
+       "                        <td id=\"T_0b262_row3_col6\" class=\"data row3 col6\" >2.00</td>\n",
+       "                        <td id=\"T_0b262_row3_col7\" class=\"data row3 col7\" >0.00</td>\n",
+       "                        <td id=\"T_0b262_row3_col8\" class=\"data row3 col8\" >95170.00</td>\n",
+       "                        <td id=\"T_0b262_row3_col9\" class=\"data row3 col9\" >0.00</td>\n",
+       "                        <td id=\"T_0b262_row3_col10\" class=\"data row3 col10\" >-10.00</td>\n",
+       "                        <td id=\"T_0b262_row3_col11\" class=\"data row3 col11\" >-0.10</td>\n",
+       "                        <td id=\"T_0b262_row3_col12\" class=\"data row3 col12\" >-0.10</td>\n",
+       "                        <td id=\"T_0b262_row3_col13\" class=\"data row3 col13\" >-12.10</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_0b262_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+       "                        <td id=\"T_0b262_row4_col0\" class=\"data row4 col0\" >101300.00</td>\n",
+       "                        <td id=\"T_0b262_row4_col1\" class=\"data row4 col1\" >-70.00</td>\n",
+       "                        <td id=\"T_0b262_row4_col2\" class=\"data row4 col2\" >2.00</td>\n",
+       "                        <td id=\"T_0b262_row4_col3\" class=\"data row4 col3\" >130.00</td>\n",
+       "                        <td id=\"T_0b262_row4_col4\" class=\"data row4 col4\" >1.50</td>\n",
+       "                        <td id=\"T_0b262_row4_col5\" class=\"data row4 col5\" >275.83</td>\n",
+       "                        <td id=\"T_0b262_row4_col6\" class=\"data row4 col6\" >58.00</td>\n",
+       "                        <td id=\"T_0b262_row4_col7\" class=\"data row4 col7\" >2.00</td>\n",
+       "                        <td id=\"T_0b262_row4_col8\" class=\"data row4 col8\" >98480.00</td>\n",
+       "                        <td id=\"T_0b262_row4_col9\" class=\"data row4 col9\" >3.60</td>\n",
+       "                        <td id=\"T_0b262_row4_col10\" class=\"data row4 col10\" >-10.00</td>\n",
+       "                        <td id=\"T_0b262_row4_col11\" class=\"data row4 col11\" >0.00</td>\n",
+       "                        <td id=\"T_0b262_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
+       "                        <td id=\"T_0b262_row4_col13\" class=\"data row4 col13\" >6.60</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_0b262_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+       "                        <td id=\"T_0b262_row5_col0\" class=\"data row5 col0\" >101740.00</td>\n",
+       "                        <td id=\"T_0b262_row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
+       "                        <td id=\"T_0b262_row5_col2\" class=\"data row5 col2\" >3.00</td>\n",
+       "                        <td id=\"T_0b262_row5_col3\" class=\"data row5 col3\" >190.00</td>\n",
+       "                        <td id=\"T_0b262_row5_col4\" class=\"data row5 col4\" >2.90</td>\n",
+       "                        <td id=\"T_0b262_row5_col5\" class=\"data row5 col5\" >280.25</td>\n",
+       "                        <td id=\"T_0b262_row5_col6\" class=\"data row5 col6\" >74.00</td>\n",
+       "                        <td id=\"T_0b262_row5_col7\" class=\"data row5 col7\" >2.00</td>\n",
+       "                        <td id=\"T_0b262_row5_col8\" class=\"data row5 col8\" >98920.00</td>\n",
+       "                        <td id=\"T_0b262_row5_col9\" class=\"data row5 col9\" >5.30</td>\n",
+       "                        <td id=\"T_0b262_row5_col10\" class=\"data row5 col10\" >-10.00</td>\n",
+       "                        <td id=\"T_0b262_row5_col11\" class=\"data row5 col11\" >0.00</td>\n",
+       "                        <td id=\"T_0b262_row5_col12\" class=\"data row5 col12\" >0.00</td>\n",
+       "                        <td id=\"T_0b262_row5_col13\" class=\"data row5 col13\" >12.50</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_0b262_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+       "                        <td id=\"T_0b262_row6_col0\" class=\"data row6 col0\" >102240.00</td>\n",
+       "                        <td id=\"T_0b262_row6_col1\" class=\"data row6 col1\" >70.00</td>\n",
+       "                        <td id=\"T_0b262_row6_col2\" class=\"data row6 col2\" >7.00</td>\n",
+       "                        <td id=\"T_0b262_row6_col3\" class=\"data row6 col3\" >330.00</td>\n",
+       "                        <td id=\"T_0b262_row6_col4\" class=\"data row6 col4\" >4.60</td>\n",
+       "                        <td id=\"T_0b262_row6_col5\" class=\"data row6 col5\" >284.55</td>\n",
+       "                        <td id=\"T_0b262_row6_col6\" class=\"data row6 col6\" >86.00</td>\n",
+       "                        <td id=\"T_0b262_row6_col7\" class=\"data row6 col7\" >3.00</td>\n",
+       "                        <td id=\"T_0b262_row6_col8\" class=\"data row6 col8\" >99360.00</td>\n",
+       "                        <td id=\"T_0b262_row6_col9\" class=\"data row6 col9\" >8.20</td>\n",
+       "                        <td id=\"T_0b262_row6_col10\" class=\"data row6 col10\" >-10.00</td>\n",
+       "                        <td id=\"T_0b262_row6_col11\" class=\"data row6 col11\" >0.00</td>\n",
+       "                        <td id=\"T_0b262_row6_col12\" class=\"data row6 col12\" >0.00</td>\n",
+       "                        <td id=\"T_0b262_row6_col13\" class=\"data row6 col13\" >18.50</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_0b262_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+       "                        <td id=\"T_0b262_row7_col0\" class=\"data row7 col0\" >104280.00</td>\n",
+       "                        <td id=\"T_0b262_row7_col1\" class=\"data row7 col1\" >810.00</td>\n",
+       "                        <td id=\"T_0b262_row7_col2\" class=\"data row7 col2\" >8.00</td>\n",
+       "                        <td id=\"T_0b262_row7_col3\" class=\"data row7 col3\" >360.00</td>\n",
+       "                        <td id=\"T_0b262_row7_col4\" class=\"data row7 col4\" >18.80</td>\n",
+       "                        <td id=\"T_0b262_row7_col5\" class=\"data row7 col5\" >295.95</td>\n",
+       "                        <td id=\"T_0b262_row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
+       "                        <td id=\"T_0b262_row7_col7\" class=\"data row7 col7\" >97.00</td>\n",
+       "                        <td id=\"T_0b262_row7_col8\" class=\"data row7 col8\" >101210.00</td>\n",
+       "                        <td id=\"T_0b262_row7_col9\" class=\"data row7 col9\" >30.20</td>\n",
+       "                        <td id=\"T_0b262_row7_col10\" class=\"data row7 col10\" >-10.00</td>\n",
+       "                        <td id=\"T_0b262_row7_col11\" class=\"data row7 col11\" >19.00</td>\n",
+       "                        <td id=\"T_0b262_row7_col12\" class=\"data row7 col12\" >45.00</td>\n",
+       "                        <td id=\"T_0b262_row7_col13\" class=\"data row7 col13\" >38.90</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f564cd2e880>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "columns_used=['date','pmer','tend','cod_tend','dd','ff','td','u','ww','pres','rafper','per','rr1','rr3','tc']\n",
+    "\n",
+    "# ---- Drop unused columns\n",
+    "\n",
+    "to_drop = df.columns.difference(columns_used)\n",
+    "df.drop( to_drop, axis=1, inplace=True)\n",
+    "\n",
+    "# ---- Show all of that\n",
+    "\n",
+    "pwk.subtitle('Our selected columns :')\n",
+    "display(df.head(20))\n",
+    "\n",
+    "pwk.subtitle('Few statistics :')\n",
+    "display(df.describe().style.format('{:.2f}'))\n",
+    "\n",
+    "# ---- 'per' column is constant, we can drop it\n",
+    "\n",
+    "df.drop(['per'],axis=1,inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2 - Cleanup dataset\n",
+    "Let's sort it and cook up some NaN values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:15:58.352209Z",
+     "iopub.status.busy": "2021-03-07T20:15:58.351895Z",
+     "iopub.status.idle": "2021-03-07T20:15:58.405593Z",
+     "shell.execute_reply": "2021-03-07T20:15:58.405253Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "<br>**Before :**"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>pmer</th>\n",
+       "      <th>tend</th>\n",
+       "      <th>cod_tend</th>\n",
+       "      <th>dd</th>\n",
+       "      <th>ff</th>\n",
+       "      <th>td</th>\n",
+       "      <th>u</th>\n",
+       "      <th>ww</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>rafper</th>\n",
+       "      <th>rr1</th>\n",
+       "      <th>rr3</th>\n",
+       "      <th>tc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>2010-02-19T16:00:00+01:00</td>\n",
+       "      <td>99760.0</td>\n",
+       "      <td>180.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>330.0</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>275.85</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>96890.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>2010-02-24T10:00:00+01:00</td>\n",
+       "      <td>100310.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>279.25</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>97470.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>9.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>477</th>\n",
+       "      <td>2010-03-01T19:00:00+01:00</td>\n",
+       "      <td>101400.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>340.0</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>275.45</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98520.0</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>734</th>\n",
+       "      <td>2010-04-03T02:00:00+02:00</td>\n",
+       "      <td>101550.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>277.55</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98680.0</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1061</th>\n",
+       "      <td>2010-05-13T23:00:00+02:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>330.0</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98220.0</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1063</th>\n",
+       "      <td>2010-05-14T05:00:00+02:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98110.0</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1064</th>\n",
+       "      <td>2010-05-14T08:00:00+02:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98110.0</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2268</th>\n",
+       "      <td>2010-10-11T20:00:00+02:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98060.0</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2269</th>\n",
+       "      <td>2010-10-11T23:00:00+02:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98190.0</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2270</th>\n",
+       "      <td>2010-10-12T02:00:00+02:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98260.0</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           date      pmer   tend  cod_tend     dd   ff  \\\n",
+       "396   2010-02-19T16:00:00+01:00   99760.0  180.0       3.0  330.0  4.6   \n",
+       "434   2010-02-24T10:00:00+01:00  100310.0   60.0       1.0    NaN  NaN   \n",
+       "477   2010-03-01T19:00:00+01:00  101400.0    NaN       NaN  340.0  2.6   \n",
+       "734   2010-04-03T02:00:00+02:00  101550.0   50.0       0.0  190.0  7.7   \n",
+       "1061  2010-05-13T23:00:00+02:00       NaN   60.0       2.0  330.0  4.6   \n",
+       "1063  2010-05-14T05:00:00+02:00       NaN  -50.0       5.0  350.0  4.1   \n",
+       "1064  2010-05-14T08:00:00+02:00       NaN    0.0       5.0  350.0  4.6   \n",
+       "2268  2010-10-11T20:00:00+02:00       NaN  150.0       2.0   10.0  1.0   \n",
+       "2269  2010-10-11T23:00:00+02:00       NaN  130.0       3.0   80.0  1.0   \n",
+       "2270  2010-10-12T02:00:00+02:00       NaN   70.0       1.0    0.0  0.0   \n",
+       "\n",
+       "          td     u    ww     pres  rafper  rr1  rr3    tc  \n",
+       "396   275.85  79.0  21.0  96890.0     NaN  0.0  1.0   6.1  \n",
+       "434   279.25  77.0   2.0  97470.0     NaN  0.2  0.2   9.9  \n",
+       "477   275.45  61.0   2.0  98520.0     5.7  0.0  NaN   9.4  \n",
+       "734   277.55  64.0   2.0  98680.0    12.3  NaN  NaN  10.9  \n",
+       "1061     NaN   NaN   2.0  98220.0     7.7  0.0  0.0   9.9  \n",
+       "1063     NaN   NaN   2.0  98110.0     7.2  0.0  0.0   8.1  \n",
+       "1064     NaN   NaN   2.0  98110.0     6.7  0.0  0.0   8.1  \n",
+       "2268     NaN   NaN   2.0  98060.0     3.1  NaN  NaN   NaN  \n",
+       "2269     NaN   NaN   2.0  98190.0     2.6  NaN  NaN   NaN  \n",
+       "2270     NaN   NaN   2.0  98260.0     1.5  NaN  NaN   NaN  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "<br>**After :**"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>pmer</th>\n",
+       "      <th>tend</th>\n",
+       "      <th>cod_tend</th>\n",
+       "      <th>dd</th>\n",
+       "      <th>ff</th>\n",
+       "      <th>td</th>\n",
+       "      <th>u</th>\n",
+       "      <th>ww</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>rafper</th>\n",
+       "      <th>rr1</th>\n",
+       "      <th>rr3</th>\n",
+       "      <th>tc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>2010-02-19T16:00:00+01:00</td>\n",
+       "      <td>99760.000000</td>\n",
+       "      <td>180.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>330.0</td>\n",
+       "      <td>4.60</td>\n",
+       "      <td>275.85</td>\n",
+       "      <td>79.000000</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>96890.0</td>\n",
+       "      <td>8.25</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>2010-02-24T10:00:00+01:00</td>\n",
+       "      <td>100310.000000</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>170.0</td>\n",
+       "      <td>4.15</td>\n",
+       "      <td>279.25</td>\n",
+       "      <td>77.000000</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>97470.0</td>\n",
+       "      <td>6.65</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>9.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>477</th>\n",
+       "      <td>2010-03-01T19:00:00+01:00</td>\n",
+       "      <td>101400.000000</td>\n",
+       "      <td>195.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>340.0</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>275.45</td>\n",
+       "      <td>61.000000</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98520.0</td>\n",
+       "      <td>5.70</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>9.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>734</th>\n",
+       "      <td>2010-04-03T02:00:00+02:00</td>\n",
+       "      <td>101550.000000</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>7.70</td>\n",
+       "      <td>277.55</td>\n",
+       "      <td>64.000000</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98680.0</td>\n",
+       "      <td>12.30</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1061</th>\n",
+       "      <td>2010-05-13T23:00:00+02:00</td>\n",
+       "      <td>101020.000000</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>330.0</td>\n",
+       "      <td>4.60</td>\n",
+       "      <td>281.25</td>\n",
+       "      <td>86.500000</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98220.0</td>\n",
+       "      <td>7.70</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1063</th>\n",
+       "      <td>2010-05-14T05:00:00+02:00</td>\n",
+       "      <td>101040.000000</td>\n",
+       "      <td>-50.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>279.15</td>\n",
+       "      <td>80.666667</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98110.0</td>\n",
+       "      <td>7.20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1064</th>\n",
+       "      <td>2010-05-14T08:00:00+02:00</td>\n",
+       "      <td>101040.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>4.60</td>\n",
+       "      <td>279.35</td>\n",
+       "      <td>79.333333</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98110.0</td>\n",
+       "      <td>6.70</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2268</th>\n",
+       "      <td>2010-10-11T20:00:00+02:00</td>\n",
+       "      <td>100786.666667</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>284.75</td>\n",
+       "      <td>83.333333</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98060.0</td>\n",
+       "      <td>3.10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2269</th>\n",
+       "      <td>2010-10-11T23:00:00+02:00</td>\n",
+       "      <td>100863.333333</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>284.45</td>\n",
+       "      <td>84.666667</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98190.0</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2270</th>\n",
+       "      <td>2010-10-12T02:00:00+02:00</td>\n",
+       "      <td>100940.000000</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>284.15</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98260.0</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.35</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           date           pmer   tend  cod_tend     dd    ff  \\\n",
+       "396   2010-02-19T16:00:00+01:00   99760.000000  180.0       3.0  330.0  4.60   \n",
+       "434   2010-02-24T10:00:00+01:00  100310.000000   60.0       1.0  170.0  4.15   \n",
+       "477   2010-03-01T19:00:00+01:00  101400.000000  195.0       4.0  340.0  2.60   \n",
+       "734   2010-04-03T02:00:00+02:00  101550.000000   50.0       0.0  190.0  7.70   \n",
+       "1061  2010-05-13T23:00:00+02:00  101020.000000   60.0       2.0  330.0  4.60   \n",
+       "1063  2010-05-14T05:00:00+02:00  101040.000000  -50.0       5.0  350.0  4.10   \n",
+       "1064  2010-05-14T08:00:00+02:00  101040.000000    0.0       5.0  350.0  4.60   \n",
+       "2268  2010-10-11T20:00:00+02:00  100786.666667  150.0       2.0   10.0  1.00   \n",
+       "2269  2010-10-11T23:00:00+02:00  100863.333333  130.0       3.0   80.0  1.00   \n",
+       "2270  2010-10-12T02:00:00+02:00  100940.000000   70.0       1.0    0.0  0.00   \n",
+       "\n",
+       "          td          u    ww     pres  rafper  rr1  rr3     tc  \n",
+       "396   275.85  79.000000  21.0  96890.0    8.25  0.0  1.0   6.10  \n",
+       "434   279.25  77.000000   2.0  97470.0    6.65  0.2  0.2   9.90  \n",
+       "477   275.45  61.000000   2.0  98520.0    5.70  0.0  0.5   9.40  \n",
+       "734   277.55  64.000000   2.0  98680.0   12.30  0.0  0.0  10.90  \n",
+       "1061  281.25  86.500000   2.0  98220.0    7.70  0.0  0.0   9.90  \n",
+       "1063  279.15  80.666667   2.0  98110.0    7.20  0.0  0.0   8.10  \n",
+       "1064  279.35  79.333333   2.0  98110.0    6.70  0.0  0.0   8.10  \n",
+       "2268  284.75  83.333333   2.0  98060.0    3.10  0.0  0.0  14.45  \n",
+       "2269  284.45  84.666667   2.0  98190.0    2.60  0.0  0.0  13.90  \n",
+       "2270  284.15  86.000000   2.0  98260.0    1.50  0.0  0.0  13.35  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ---- First of all, we have to sort on the date\n",
+    "\n",
+    "df.sort_values(['date'],  inplace=True)\n",
+    "df.reset_index(drop=True, inplace=True)\n",
+    "\n",
+    "# ---- Before : Lines with NaN\n",
+    "\n",
+    "na_rows=df.isna().any(axis=1)\n",
+    "pwk.subtitle('Before :')\n",
+    "display( df[na_rows].head(10) )\n",
+    "\n",
+    "# ---- Nice interpolation for plugging holes\n",
+    "\n",
+    "df.interpolate(inplace=True)\n",
+    "\n",
+    "# ---- After\n",
+    "\n",
+    "pwk.subtitle('After :')\n",
+    "display(df[na_rows].head(10))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5 - About our enhanced dataset\n",
+    "### 5.1 - Summarize it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:15:58.410210Z",
+     "iopub.status.busy": "2021-03-07T20:15:58.409905Z",
+     "iopub.status.idle": "2021-03-07T20:15:58.443655Z",
+     "shell.execute_reply": "2021-03-07T20:15:58.443278Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "<br>**Dataset columns :**"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "#T_ff133_row0_col0,#T_ff133_row0_col1,#T_ff133_row0_col2,#T_ff133_row1_col0,#T_ff133_row1_col1,#T_ff133_row1_col2,#T_ff133_row2_col0,#T_ff133_row2_col1,#T_ff133_row2_col2,#T_ff133_row3_col0,#T_ff133_row3_col1,#T_ff133_row3_col2,#T_ff133_row4_col0,#T_ff133_row4_col1,#T_ff133_row4_col2,#T_ff133_row5_col0,#T_ff133_row5_col1,#T_ff133_row5_col2,#T_ff133_row6_col0,#T_ff133_row6_col1,#T_ff133_row6_col2,#T_ff133_row7_col0,#T_ff133_row7_col1,#T_ff133_row7_col2,#T_ff133_row8_col0,#T_ff133_row8_col1,#T_ff133_row8_col2,#T_ff133_row9_col0,#T_ff133_row9_col1,#T_ff133_row9_col2,#T_ff133_row10_col0,#T_ff133_row10_col1,#T_ff133_row10_col2,#T_ff133_row11_col0,#T_ff133_row11_col1,#T_ff133_row11_col2,#T_ff133_row12_col0,#T_ff133_row12_col1,#T_ff133_row12_col2,#T_ff133_row13_col0,#T_ff133_row13_col1,#T_ff133_row13_col2{\n",
+       "            text-align:  left;\n",
+       "        }</style><table id=\"T_ff133_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Columns</th>        <th class=\"col_heading level0 col1\" >Description</th>        <th class=\"col_heading level0 col2\" >Na</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_ff133_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "                        <td id=\"T_ff133_row0_col0\" class=\"data row0 col0\" >date</td>\n",
+       "                        <td id=\"T_ff133_row0_col1\" class=\"data row0 col1\" >Date</td>\n",
+       "                        <td id=\"T_ff133_row0_col2\" class=\"data row0 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ff133_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "                        <td id=\"T_ff133_row1_col0\" class=\"data row1 col0\" >pmer</td>\n",
+       "                        <td id=\"T_ff133_row1_col1\" class=\"data row1 col1\" >Pression au niveau mer</td>\n",
+       "                        <td id=\"T_ff133_row1_col2\" class=\"data row1 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ff133_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "                        <td id=\"T_ff133_row2_col0\" class=\"data row2 col0\" >tend</td>\n",
+       "                        <td id=\"T_ff133_row2_col1\" class=\"data row2 col1\" >Variation de pression en 3 heures</td>\n",
+       "                        <td id=\"T_ff133_row2_col2\" class=\"data row2 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ff133_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "                        <td id=\"T_ff133_row3_col0\" class=\"data row3 col0\" >cod_tend</td>\n",
+       "                        <td id=\"T_ff133_row3_col1\" class=\"data row3 col1\" >Type de tendance barométrique</td>\n",
+       "                        <td id=\"T_ff133_row3_col2\" class=\"data row3 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ff133_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "                        <td id=\"T_ff133_row4_col0\" class=\"data row4 col0\" >dd</td>\n",
+       "                        <td id=\"T_ff133_row4_col1\" class=\"data row4 col1\" >Direction du vent moyen 10 mn</td>\n",
+       "                        <td id=\"T_ff133_row4_col2\" class=\"data row4 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ff133_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "                        <td id=\"T_ff133_row5_col0\" class=\"data row5 col0\" >ff</td>\n",
+       "                        <td id=\"T_ff133_row5_col1\" class=\"data row5 col1\" >Vitesse du vent moyen 10 mn</td>\n",
+       "                        <td id=\"T_ff133_row5_col2\" class=\"data row5 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ff133_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "                        <td id=\"T_ff133_row6_col0\" class=\"data row6 col0\" >td</td>\n",
+       "                        <td id=\"T_ff133_row6_col1\" class=\"data row6 col1\" >Point de rosée</td>\n",
+       "                        <td id=\"T_ff133_row6_col2\" class=\"data row6 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ff133_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "                        <td id=\"T_ff133_row7_col0\" class=\"data row7 col0\" >u</td>\n",
+       "                        <td id=\"T_ff133_row7_col1\" class=\"data row7 col1\" >Humidité</td>\n",
+       "                        <td id=\"T_ff133_row7_col2\" class=\"data row7 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ff133_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "                        <td id=\"T_ff133_row8_col0\" class=\"data row8 col0\" >ww</td>\n",
+       "                        <td id=\"T_ff133_row8_col1\" class=\"data row8 col1\" >Temps présent</td>\n",
+       "                        <td id=\"T_ff133_row8_col2\" class=\"data row8 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ff133_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "                        <td id=\"T_ff133_row9_col0\" class=\"data row9 col0\" >pres</td>\n",
+       "                        <td id=\"T_ff133_row9_col1\" class=\"data row9 col1\" >Pression station</td>\n",
+       "                        <td id=\"T_ff133_row9_col2\" class=\"data row9 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ff133_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+       "                        <td id=\"T_ff133_row10_col0\" class=\"data row10 col0\" >rafper</td>\n",
+       "                        <td id=\"T_ff133_row10_col1\" class=\"data row10 col1\" >Rafales sur une période</td>\n",
+       "                        <td id=\"T_ff133_row10_col2\" class=\"data row10 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ff133_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+       "                        <td id=\"T_ff133_row11_col0\" class=\"data row11 col0\" >rr1</td>\n",
+       "                        <td id=\"T_ff133_row11_col1\" class=\"data row11 col1\" >Précipitations dans la dernière heure</td>\n",
+       "                        <td id=\"T_ff133_row11_col2\" class=\"data row11 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ff133_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+       "                        <td id=\"T_ff133_row12_col0\" class=\"data row12 col0\" >rr3</td>\n",
+       "                        <td id=\"T_ff133_row12_col1\" class=\"data row12 col1\" >Précipitations dans les 3 dernières heures</td>\n",
+       "                        <td id=\"T_ff133_row12_col2\" class=\"data row12 col2\" >0</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ff133_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+       "                        <td id=\"T_ff133_row13_col0\" class=\"data row13 col0\" >tc</td>\n",
+       "                        <td id=\"T_ff133_row13_col1\" class=\"data row13 col1\" >Température (°C)</td>\n",
+       "                        <td id=\"T_ff133_row13_col2\" class=\"data row13 col2\" >0</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f564cd339a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "<br>**Have a look :**"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>pmer</th>\n",
+       "      <th>tend</th>\n",
+       "      <th>cod_tend</th>\n",
+       "      <th>dd</th>\n",
+       "      <th>ff</th>\n",
+       "      <th>td</th>\n",
+       "      <th>u</th>\n",
+       "      <th>ww</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>rafper</th>\n",
+       "      <th>rr1</th>\n",
+       "      <th>rr3</th>\n",
+       "      <th>tc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>29145</th>\n",
+       "      <td>2020-02-24T13:00:00+01:00</td>\n",
+       "      <td>102380.0</td>\n",
+       "      <td>-220.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>281.15</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>99540.0</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29146</th>\n",
+       "      <td>2020-02-24T16:00:00+01:00</td>\n",
+       "      <td>101990.0</td>\n",
+       "      <td>-350.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>281.55</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>99190.0</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>19.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29147</th>\n",
+       "      <td>2020-02-24T19:00:00+01:00</td>\n",
+       "      <td>101800.0</td>\n",
+       "      <td>-220.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>280.05</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>98970.0</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29148</th>\n",
+       "      <td>2020-02-24T22:00:00+01:00</td>\n",
+       "      <td>101740.0</td>\n",
+       "      <td>-80.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>170.0</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>280.35</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98890.0</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29149</th>\n",
+       "      <td>2020-02-25T01:00:00+01:00</td>\n",
+       "      <td>101640.0</td>\n",
+       "      <td>-150.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>170.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>278.85</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98740.0</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29150</th>\n",
+       "      <td>2020-02-25T04:00:00+01:00</td>\n",
+       "      <td>101450.0</td>\n",
+       "      <td>-200.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>277.75</td>\n",
+       "      <td>87.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98540.0</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29151</th>\n",
+       "      <td>2020-02-25T07:00:00+01:00</td>\n",
+       "      <td>101530.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>276.95</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>98600.0</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29152</th>\n",
+       "      <td>2020-02-25T10:00:00+01:00</td>\n",
+       "      <td>101490.0</td>\n",
+       "      <td>-20.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>277.55</td>\n",
+       "      <td>87.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>98580.0</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29153</th>\n",
+       "      <td>2020-02-25T13:00:00+01:00</td>\n",
+       "      <td>101330.0</td>\n",
+       "      <td>-140.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>278.95</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>98440.0</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29154</th>\n",
+       "      <td>2020-02-25T16:00:00+01:00</td>\n",
+       "      <td>100990.0</td>\n",
+       "      <td>-290.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>279.55</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>98150.0</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29155</th>\n",
+       "      <td>2020-02-25T19:00:00+01:00</td>\n",
+       "      <td>100910.0</td>\n",
+       "      <td>-90.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>278.95</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>98060.0</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>-0.1</td>\n",
+       "      <td>-0.1</td>\n",
+       "      <td>11.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29156</th>\n",
+       "      <td>2020-02-25T22:00:00+01:00</td>\n",
+       "      <td>100980.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>273.65</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98120.0</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29157</th>\n",
+       "      <td>2020-02-26T01:00:00+01:00</td>\n",
+       "      <td>101040.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>230.0</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>275.65</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>98150.0</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>-0.1</td>\n",
+       "      <td>-0.1</td>\n",
+       "      <td>7.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29158</th>\n",
+       "      <td>2020-02-26T04:00:00+01:00</td>\n",
+       "      <td>101060.0</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>230.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>275.85</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>98140.0</td>\n",
+       "      <td>13.6</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>4.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29159</th>\n",
+       "      <td>2020-02-26T07:00:00+01:00</td>\n",
+       "      <td>100940.0</td>\n",
+       "      <td>-110.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>210.0</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>274.85</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>98030.0</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>-0.1</td>\n",
+       "      <td>-0.1</td>\n",
+       "      <td>5.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29160</th>\n",
+       "      <td>2020-02-26T10:00:00+01:00</td>\n",
+       "      <td>101100.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>230.0</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>274.45</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98190.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29161</th>\n",
+       "      <td>2020-02-26T13:00:00+01:00</td>\n",
+       "      <td>101200.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>310.0</td>\n",
+       "      <td>10.3</td>\n",
+       "      <td>270.55</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98290.0</td>\n",
+       "      <td>19.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.1</td>\n",
+       "      <td>6.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29162</th>\n",
+       "      <td>2020-02-26T16:00:00+01:00</td>\n",
+       "      <td>101290.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>310.0</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>270.55</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98390.0</td>\n",
+       "      <td>14.3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29163</th>\n",
+       "      <td>2020-02-26T19:00:00+01:00</td>\n",
+       "      <td>101550.0</td>\n",
+       "      <td>230.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>272.05</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98620.0</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29164</th>\n",
+       "      <td>2020-02-26T22:00:00+01:00</td>\n",
+       "      <td>101780.0</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>274.05</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98820.0</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            date      pmer   tend  cod_tend     dd    ff  \\\n",
+       "29145  2020-02-24T13:00:00+01:00  102380.0 -220.0       8.0  120.0   1.6   \n",
+       "29146  2020-02-24T16:00:00+01:00  101990.0 -350.0       6.0  110.0   1.6   \n",
+       "29147  2020-02-24T19:00:00+01:00  101800.0 -220.0       6.0  150.0   2.9   \n",
+       "29148  2020-02-24T22:00:00+01:00  101740.0  -80.0       6.0  170.0   1.8   \n",
+       "29149  2020-02-25T01:00:00+01:00  101640.0 -150.0       8.0  170.0   2.5   \n",
+       "29150  2020-02-25T04:00:00+01:00  101450.0 -200.0       6.0  150.0   3.7   \n",
+       "29151  2020-02-25T07:00:00+01:00  101530.0   60.0       3.0   30.0   4.0   \n",
+       "29152  2020-02-25T10:00:00+01:00  101490.0  -20.0       8.0  200.0   1.8   \n",
+       "29153  2020-02-25T13:00:00+01:00  101330.0 -140.0       8.0  150.0   3.8   \n",
+       "29154  2020-02-25T16:00:00+01:00  100990.0 -290.0       6.0  140.0   4.4   \n",
+       "29155  2020-02-25T19:00:00+01:00  100910.0  -90.0       5.0  260.0   4.3   \n",
+       "29156  2020-02-25T22:00:00+01:00  100980.0   60.0       3.0  280.0   8.0   \n",
+       "29157  2020-02-26T01:00:00+01:00  101040.0   30.0       2.0  230.0   2.8   \n",
+       "29158  2020-02-26T04:00:00+01:00  101060.0  -10.0       8.0  230.0   3.0   \n",
+       "29159  2020-02-26T07:00:00+01:00  100940.0 -110.0       6.0  210.0   3.3   \n",
+       "29160  2020-02-26T10:00:00+01:00  101100.0  160.0       3.0  230.0   6.8   \n",
+       "29161  2020-02-26T13:00:00+01:00  101200.0  100.0       3.0  310.0  10.3   \n",
+       "29162  2020-02-26T16:00:00+01:00  101290.0  100.0       3.0  310.0   8.9   \n",
+       "29163  2020-02-26T19:00:00+01:00  101550.0  230.0       2.0  300.0   2.8   \n",
+       "29164  2020-02-26T22:00:00+01:00  101780.0  200.0       2.0   50.0   3.2   \n",
+       "\n",
+       "           td     u    ww     pres  rafper  rr1  rr3    tc  \n",
+       "29145  281.15  59.0   0.0  99540.0     3.7  0.0  0.0  16.0  \n",
+       "29146  281.55  50.0   3.0  99190.0     3.3  0.0  0.0  19.1  \n",
+       "29147  280.05  55.0   3.0  98970.0     4.1  0.0  0.0  15.9  \n",
+       "29148  280.35  67.0   2.0  98890.0     4.3  0.0  0.0  13.2  \n",
+       "29149  278.85  83.0   2.0  98740.0     4.7  0.0  0.0   8.4  \n",
+       "29150  277.75  87.0   2.0  98540.0     4.8  0.0  0.0   6.6  \n",
+       "29151  276.95  92.0   3.0  98600.0     6.1  0.0  0.0   5.0  \n",
+       "29152  277.55  87.0   3.0  98580.0     5.5  0.0  0.0   6.4  \n",
+       "29153  278.95  85.0  21.0  98440.0     7.1  0.6  2.0   8.2  \n",
+       "29154  279.55  69.0   3.0  98150.0     7.2  0.0  0.0  11.9  \n",
+       "29155  278.95  69.0  25.0  98060.0     8.4 -0.1 -0.1  11.3  \n",
+       "29156  273.65  51.0   1.0  98120.0    11.3  0.0  0.0  10.2  \n",
+       "29157  275.65  69.0  25.0  98150.0    10.7 -0.1 -0.1   7.8  \n",
+       "29158  275.85  86.0  25.0  98140.0    13.6  0.4  1.8   4.8  \n",
+       "29159  274.85  78.0  21.0  98030.0     7.4 -0.1 -0.1   5.2  \n",
+       "29160  274.45  74.0   1.0  98190.0    10.0  0.0  0.0   5.6  \n",
+       "29161  270.55  52.0   1.0  98290.0    19.5  0.0 -0.1   6.6  \n",
+       "29162  270.55  47.0   1.0  98390.0    14.3  0.0  0.0   8.0  \n",
+       "29163  272.05  64.0   1.0  98620.0     7.4  0.0  0.0   5.2  \n",
+       "29164  274.05  84.0   1.0  98820.0     8.2  0.0  0.0   3.3  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape is :  (29165, 14)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ---- Count the na values by columns\n",
+    "dataset_na    = df.isna().sum().tolist()\n",
+    "dataset_cols  = df.columns.tolist()\n",
+    "dataset_desc  = [ code2desc[c] for c in dataset_cols ]\n",
+    "\n",
+    "# ---- Show all of that\n",
+    "df_desc=pd.DataFrame({'Columns':dataset_cols, 'Description':dataset_desc, 'Na':dataset_na})\n",
+    "pwk.subtitle('Dataset columns :')\n",
+    "display(df_desc.style.set_properties(**{'text-align': 'left'}))\n",
+    "\n",
+    "pwk.subtitle('Have a look :')\n",
+    "display(df.tail(20))\n",
+    "print('Shape is : ', df.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.2 - Have a look (1 month)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:15:58.447026Z",
+     "iopub.status.busy": "2021-03-07T20:15:58.446698Z",
+     "iopub.status.idle": "2021-03-07T20:16:01.203752Z",
+     "shell.execute_reply": "2021-03-07T20:16:01.204074Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"comment\">Saved: ./run/figs/SYNOP1-01-one-month</div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x1440 with 13 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "i=random.randint(0,len(df)-240)\n",
+    "df.iloc[i:i+240].plot(subplots=True, fontsize=12, figsize=(16,20))\n",
+    "pwk.save_fig('01-one-month')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6 - Save it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:16:01.208503Z",
+     "iopub.status.busy": "2021-03-07T20:16:01.208182Z",
+     "iopub.status.idle": "2021-03-07T20:16:01.397439Z",
+     "shell.execute_reply": "2021-03-07T20:16:01.397095Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset saved. (3.0 Mo)\n",
+      "Synop description saved.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ---- Save it\n",
+    "#\n",
+    "pwk.mkdir(output_dir)\n",
+    "\n",
+    "filedata = f'{output_dir}/{dataset_filename}'\n",
+    "filedesc = f'{output_dir}/{description_filename}'\n",
+    "\n",
+    "df.to_csv(filedata, sep=';', index=False)\n",
+    "size=os.path.getsize(filedata)/(1024*1024)\n",
+    "print(f'Dataset saved. ({size:0.1f} Mo)')\n",
+    "\n",
+    "with open(filedesc, 'w', encoding='utf-8') as f:\n",
+    "    json.dump(code2desc, f, indent=4)\n",
+    "print('Synop description saved.')\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:16:01.400549Z",
+     "iopub.status.busy": "2021-03-07T20:16:01.400158Z",
+     "iopub.status.idle": "2021-03-07T20:16:01.403552Z",
+     "shell.execute_reply": "2021-03-07T20:16:01.403236Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "End time is : Sunday 07 March 2021, 21:16:01\n",
+      "Duration is : 00:00:03 478ms\n",
+      "This notebook ends here\n"
+     ]
+    }
+   ],
+   "source": [
+    "pwk.end()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/SYNOP/02-First-predictions.ipynb b/SYNOP/SYNOP2-First-predictions.ipynb
similarity index 99%
rename from SYNOP/02-First-predictions.ipynb
rename to SYNOP/SYNOP2-First-predictions.ipynb
index 904b93e4331dd3f9f12a188ec9250a53db20fcfa..fa3bf98c666bbbdf9652c4b5a5688deb8ac86084 100644
--- a/SYNOP/02-First-predictions.ipynb
+++ b/SYNOP/SYNOP2-First-predictions.ipynb
@@ -410,7 +410,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/SYNOP/02-First-predictions==done==.ipynb b/SYNOP/SYNOP2-First-predictions==done==.ipynb
similarity index 55%
rename from SYNOP/02-First-predictions==done==.ipynb
rename to SYNOP/SYNOP2-First-predictions==done==.ipynb
index f3eb7ed52c33ac36d4ff3587de993a69d29328a5..57dadcaa7cd34285149832c95a7638407f9b503a 100644
--- a/SYNOP/02-First-predictions==done==.ipynb
+++ b/SYNOP/SYNOP2-First-predictions==done==.ipynb
@@ -33,10 +33,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2021-03-01T19:30:06.066516Z",
-     "iopub.status.busy": "2021-03-01T19:30:06.066035Z",
-     "iopub.status.idle": "2021-03-01T19:30:08.796064Z",
-     "shell.execute_reply": "2021-03-01T19:30:08.796554Z"
+     "iopub.execute_input": "2021-03-07T20:16:02.493135Z",
+     "iopub.status.busy": "2021-03-07T20:16:02.492753Z",
+     "iopub.status.idle": "2021-03-07T20:16:03.834042Z",
+     "shell.execute_reply": "2021-03-07T20:16:03.833655Z"
     }
    },
    "outputs": [
@@ -115,12 +115,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Version              : 2.0.17\n",
+      "Version              : 2.0.18\n",
       "Notebook id          : SYNOP2\n",
-      "Run time             : Monday 01 March 2021, 20:30:08\n",
-      "TensorFlow version   : 2.4.0\n",
-      "Keras version        : 2.4.0\n",
-      "Datasets dir         : /gpfswork/rech/mlh/uja62cb/datasets\n",
+      "Run time             : Sunday 07 March 2021, 21:16:03\n",
+      "TensorFlow version   : 2.2.0\n",
+      "Keras version        : 2.3.0-tf\n",
+      "Datasets dir         : /home/pjluc/datasets/fidle\n",
       "Run dir              : ./run\n",
       "Update keras cache   : False\n",
       "Save figs            : True\n",
@@ -162,10 +162,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2021-03-01T19:30:08.801188Z",
-     "iopub.status.busy": "2021-03-01T19:30:08.800707Z",
-     "iopub.status.idle": "2021-03-01T19:30:08.802376Z",
-     "shell.execute_reply": "2021-03-01T19:30:08.802848Z"
+     "iopub.execute_input": "2021-03-07T20:16:03.837603Z",
+     "iopub.status.busy": "2021-03-07T20:16:03.837240Z",
+     "iopub.status.idle": "2021-03-07T20:16:03.839816Z",
+     "shell.execute_reply": "2021-03-07T20:16:03.839480Z"
     }
    },
    "outputs": [],
@@ -199,37 +199,13 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2021-03-01T19:30:08.806112Z",
-     "iopub.status.busy": "2021-03-01T19:30:08.805636Z",
-     "iopub.status.idle": "2021-03-01T19:30:08.809560Z",
-     "shell.execute_reply": "2021-03-01T19:30:08.809071Z"
+     "iopub.execute_input": "2021-03-07T20:16:03.842622Z",
+     "iopub.status.busy": "2021-03-07T20:16:03.842323Z",
+     "iopub.status.idle": "2021-03-07T20:16:03.845202Z",
+     "shell.execute_reply": "2021-03-07T20:16:03.844524Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "**\\*\\* Overrided parameters : \\*\\***"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "scale                : 1\n",
-      "train_prop           : 0.8\n",
-      "sequence_len         : 16\n",
-      "batch_size           : 32\n",
-      "epochs               : 10\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "pwk.override('scale', 'train_prop', 'sequence_len', 'batch_size', 'epochs')"
    ]
@@ -247,10 +223,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2021-03-01T19:30:08.816673Z",
-     "iopub.status.busy": "2021-03-01T19:30:08.816196Z",
-     "iopub.status.idle": "2021-03-01T19:30:09.041365Z",
-     "shell.execute_reply": "2021-03-01T19:30:09.041858Z"
+     "iopub.execute_input": "2021-03-07T20:16:03.850604Z",
+     "iopub.status.busy": "2021-03-07T20:16:03.850280Z",
+     "iopub.status.idle": "2021-03-07T20:16:04.013808Z",
+     "shell.execute_reply": "2021-03-07T20:16:04.014060Z"
     }
    },
    "outputs": [
@@ -586,131 +562,131 @@
      "data": {
       "text/html": [
        "<style  type=\"text/css\" >\n",
-       "</style><table id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44f\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >tend</th>        <th class=\"col_heading level0 col1\" >cod_tend</th>        <th class=\"col_heading level0 col2\" >dd</th>        <th class=\"col_heading level0 col3\" >ff</th>        <th class=\"col_heading level0 col4\" >td</th>        <th class=\"col_heading level0 col5\" >u</th>        <th class=\"col_heading level0 col6\" >ww</th>        <th class=\"col_heading level0 col7\" >pres</th>        <th class=\"col_heading level0 col8\" >rafper</th>        <th class=\"col_heading level0 col9\" >rr1</th>        <th class=\"col_heading level0 col10\" >rr3</th>        <th class=\"col_heading level0 col11\" >tc</th>    </tr></thead><tbody>\n",
+       "</style><table id=\"T_26344_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >tend</th>        <th class=\"col_heading level0 col1\" >cod_tend</th>        <th class=\"col_heading level0 col2\" >dd</th>        <th class=\"col_heading level0 col3\" >ff</th>        <th class=\"col_heading level0 col4\" >td</th>        <th class=\"col_heading level0 col5\" >u</th>        <th class=\"col_heading level0 col6\" >ww</th>        <th class=\"col_heading level0 col7\" >pres</th>        <th class=\"col_heading level0 col8\" >rafper</th>        <th class=\"col_heading level0 col9\" >rr1</th>        <th class=\"col_heading level0 col10\" >rr3</th>        <th class=\"col_heading level0 col11\" >tc</th>    </tr></thead><tbody>\n",
        "                <tr>\n",
-       "                        <th id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44flevel0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow0_col0\" class=\"data row0 col0\" >23332.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow0_col1\" class=\"data row0 col1\" >23332.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow0_col2\" class=\"data row0 col2\" >23332.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow0_col3\" class=\"data row0 col3\" >23332.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow0_col4\" class=\"data row0 col4\" >23332.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow0_col5\" class=\"data row0 col5\" >23332.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow0_col6\" class=\"data row0 col6\" >23332.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow0_col7\" class=\"data row0 col7\" >23332.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow0_col8\" class=\"data row0 col8\" >23332.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow0_col9\" class=\"data row0 col9\" >23332.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow0_col10\" class=\"data row0 col10\" >23332.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow0_col11\" class=\"data row0 col11\" >23332.00</td>\n",
+       "                        <th id=\"T_26344_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+       "                        <td id=\"T_26344_row0_col0\" class=\"data row0 col0\" >23332.00</td>\n",
+       "                        <td id=\"T_26344_row0_col1\" class=\"data row0 col1\" >23332.00</td>\n",
+       "                        <td id=\"T_26344_row0_col2\" class=\"data row0 col2\" >23332.00</td>\n",
+       "                        <td id=\"T_26344_row0_col3\" class=\"data row0 col3\" >23332.00</td>\n",
+       "                        <td id=\"T_26344_row0_col4\" class=\"data row0 col4\" >23332.00</td>\n",
+       "                        <td id=\"T_26344_row0_col5\" class=\"data row0 col5\" >23332.00</td>\n",
+       "                        <td id=\"T_26344_row0_col6\" class=\"data row0 col6\" >23332.00</td>\n",
+       "                        <td id=\"T_26344_row0_col7\" class=\"data row0 col7\" >23332.00</td>\n",
+       "                        <td id=\"T_26344_row0_col8\" class=\"data row0 col8\" >23332.00</td>\n",
+       "                        <td id=\"T_26344_row0_col9\" class=\"data row0 col9\" >23332.00</td>\n",
+       "                        <td id=\"T_26344_row0_col10\" class=\"data row0 col10\" >23332.00</td>\n",
+       "                        <td id=\"T_26344_row0_col11\" class=\"data row0 col11\" >23332.00</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44flevel0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow1_col0\" class=\"data row1 col0\" >0.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow1_col1\" class=\"data row1 col1\" >0.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow1_col2\" class=\"data row1 col2\" >-0.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow1_col3\" class=\"data row1 col3\" >0.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow1_col4\" class=\"data row1 col4\" >0.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow1_col5\" class=\"data row1 col5\" >0.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow1_col6\" class=\"data row1 col6\" >0.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow1_col7\" class=\"data row1 col7\" >-0.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow1_col8\" class=\"data row1 col8\" >-0.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow1_col9\" class=\"data row1 col9\" >-0.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow1_col10\" class=\"data row1 col10\" >0.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
+       "                        <th id=\"T_26344_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+       "                        <td id=\"T_26344_row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
+       "                        <td id=\"T_26344_row1_col1\" class=\"data row1 col1\" >-0.00</td>\n",
+       "                        <td id=\"T_26344_row1_col2\" class=\"data row1 col2\" >-0.00</td>\n",
+       "                        <td id=\"T_26344_row1_col3\" class=\"data row1 col3\" >-0.00</td>\n",
+       "                        <td id=\"T_26344_row1_col4\" class=\"data row1 col4\" >0.00</td>\n",
+       "                        <td id=\"T_26344_row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
+       "                        <td id=\"T_26344_row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
+       "                        <td id=\"T_26344_row1_col7\" class=\"data row1 col7\" >-0.00</td>\n",
+       "                        <td id=\"T_26344_row1_col8\" class=\"data row1 col8\" >0.00</td>\n",
+       "                        <td id=\"T_26344_row1_col9\" class=\"data row1 col9\" >-0.00</td>\n",
+       "                        <td id=\"T_26344_row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
+       "                        <td id=\"T_26344_row1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44flevel0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow2_col0\" class=\"data row2 col0\" >1.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow2_col1\" class=\"data row2 col1\" >1.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow2_col2\" class=\"data row2 col2\" >1.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow2_col3\" class=\"data row2 col3\" >1.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow2_col4\" class=\"data row2 col4\" >1.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow2_col5\" class=\"data row2 col5\" >1.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow2_col6\" class=\"data row2 col6\" >1.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow2_col7\" class=\"data row2 col7\" >1.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow2_col8\" class=\"data row2 col8\" >1.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow2_col9\" class=\"data row2 col9\" >1.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow2_col10\" class=\"data row2 col10\" >1.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow2_col11\" class=\"data row2 col11\" >1.00</td>\n",
+       "                        <th id=\"T_26344_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+       "                        <td id=\"T_26344_row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
+       "                        <td id=\"T_26344_row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
+       "                        <td id=\"T_26344_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+       "                        <td id=\"T_26344_row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
+       "                        <td id=\"T_26344_row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
+       "                        <td id=\"T_26344_row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
+       "                        <td id=\"T_26344_row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
+       "                        <td id=\"T_26344_row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
+       "                        <td id=\"T_26344_row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
+       "                        <td id=\"T_26344_row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
+       "                        <td id=\"T_26344_row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
+       "                        <td id=\"T_26344_row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44flevel0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow3_col0\" class=\"data row3 col0\" >-6.79</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow3_col1\" class=\"data row3 col1\" >-1.59</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow3_col2\" class=\"data row3 col2\" >-1.74</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow3_col3\" class=\"data row3 col3\" >-1.36</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow3_col4\" class=\"data row3 col4\" >-5.22</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow3_col5\" class=\"data row3 col5\" >-3.85</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow3_col6\" class=\"data row3 col6\" >-0.53</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow3_col7\" class=\"data row3 col7\" >-4.97</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow3_col8\" class=\"data row3 col8\" >-1.62</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow3_col9\" class=\"data row3 col9\" >-0.32</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow3_col10\" class=\"data row3 col10\" >-0.27</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow3_col11\" class=\"data row3 col11\" >-3.04</td>\n",
+       "                        <th id=\"T_26344_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+       "                        <td id=\"T_26344_row3_col0\" class=\"data row3 col0\" >-6.79</td>\n",
+       "                        <td id=\"T_26344_row3_col1\" class=\"data row3 col1\" >-1.59</td>\n",
+       "                        <td id=\"T_26344_row3_col2\" class=\"data row3 col2\" >-1.74</td>\n",
+       "                        <td id=\"T_26344_row3_col3\" class=\"data row3 col3\" >-1.36</td>\n",
+       "                        <td id=\"T_26344_row3_col4\" class=\"data row3 col4\" >-5.22</td>\n",
+       "                        <td id=\"T_26344_row3_col5\" class=\"data row3 col5\" >-3.85</td>\n",
+       "                        <td id=\"T_26344_row3_col6\" class=\"data row3 col6\" >-0.53</td>\n",
+       "                        <td id=\"T_26344_row3_col7\" class=\"data row3 col7\" >-4.97</td>\n",
+       "                        <td id=\"T_26344_row3_col8\" class=\"data row3 col8\" >-1.62</td>\n",
+       "                        <td id=\"T_26344_row3_col9\" class=\"data row3 col9\" >-0.32</td>\n",
+       "                        <td id=\"T_26344_row3_col10\" class=\"data row3 col10\" >-0.27</td>\n",
+       "                        <td id=\"T_26344_row3_col11\" class=\"data row3 col11\" >-3.04</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44flevel0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow4_col0\" class=\"data row4 col0\" >-0.63</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow4_col1\" class=\"data row4 col1\" >-0.85</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow4_col2\" class=\"data row4 col2\" >-0.62</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow4_col3\" class=\"data row4 col3\" >-0.75</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow4_col4\" class=\"data row4 col4\" >-0.72</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow4_col5\" class=\"data row4 col5\" >-0.68</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow4_col6\" class=\"data row4 col6\" >-0.42</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow4_col7\" class=\"data row4 col7\" >-0.55</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow4_col8\" class=\"data row4 col8\" >-0.69</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow4_col9\" class=\"data row4 col9\" >-0.16</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow4_col10\" class=\"data row4 col10\" >-0.20</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow4_col11\" class=\"data row4 col11\" >-0.75</td>\n",
+       "                        <th id=\"T_26344_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+       "                        <td id=\"T_26344_row4_col0\" class=\"data row4 col0\" >-0.63</td>\n",
+       "                        <td id=\"T_26344_row4_col1\" class=\"data row4 col1\" >-0.85</td>\n",
+       "                        <td id=\"T_26344_row4_col2\" class=\"data row4 col2\" >-0.62</td>\n",
+       "                        <td id=\"T_26344_row4_col3\" class=\"data row4 col3\" >-0.75</td>\n",
+       "                        <td id=\"T_26344_row4_col4\" class=\"data row4 col4\" >-0.72</td>\n",
+       "                        <td id=\"T_26344_row4_col5\" class=\"data row4 col5\" >-0.68</td>\n",
+       "                        <td id=\"T_26344_row4_col6\" class=\"data row4 col6\" >-0.42</td>\n",
+       "                        <td id=\"T_26344_row4_col7\" class=\"data row4 col7\" >-0.55</td>\n",
+       "                        <td id=\"T_26344_row4_col8\" class=\"data row4 col8\" >-0.69</td>\n",
+       "                        <td id=\"T_26344_row4_col9\" class=\"data row4 col9\" >-0.16</td>\n",
+       "                        <td id=\"T_26344_row4_col10\" class=\"data row4 col10\" >-0.20</td>\n",
+       "                        <td id=\"T_26344_row4_col11\" class=\"data row4 col11\" >-0.75</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44flevel0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow5_col0\" class=\"data row5 col0\" >-0.00</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow5_col1\" class=\"data row5 col1\" >-0.48</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow5_col2\" class=\"data row5 col2\" >-0.11</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow5_col3\" class=\"data row5 col3\" >-0.19</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow5_col4\" class=\"data row5 col4\" >0.04</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow5_col5\" class=\"data row5 col5\" >0.21</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow5_col6\" class=\"data row5 col6\" >-0.42</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow5_col7\" class=\"data row5 col7\" >0.04</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow5_col8\" class=\"data row5 col8\" >-0.29</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow5_col9\" class=\"data row5 col9\" >-0.16</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow5_col10\" class=\"data row5 col10\" >-0.20</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow5_col11\" class=\"data row5 col11\" >-0.01</td>\n",
+       "                        <th id=\"T_26344_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+       "                        <td id=\"T_26344_row5_col0\" class=\"data row5 col0\" >-0.00</td>\n",
+       "                        <td id=\"T_26344_row5_col1\" class=\"data row5 col1\" >-0.48</td>\n",
+       "                        <td id=\"T_26344_row5_col2\" class=\"data row5 col2\" >-0.11</td>\n",
+       "                        <td id=\"T_26344_row5_col3\" class=\"data row5 col3\" >-0.19</td>\n",
+       "                        <td id=\"T_26344_row5_col4\" class=\"data row5 col4\" >0.04</td>\n",
+       "                        <td id=\"T_26344_row5_col5\" class=\"data row5 col5\" >0.21</td>\n",
+       "                        <td id=\"T_26344_row5_col6\" class=\"data row5 col6\" >-0.42</td>\n",
+       "                        <td id=\"T_26344_row5_col7\" class=\"data row5 col7\" >0.04</td>\n",
+       "                        <td id=\"T_26344_row5_col8\" class=\"data row5 col8\" >-0.29</td>\n",
+       "                        <td id=\"T_26344_row5_col9\" class=\"data row5 col9\" >-0.16</td>\n",
+       "                        <td id=\"T_26344_row5_col10\" class=\"data row5 col10\" >-0.20</td>\n",
+       "                        <td id=\"T_26344_row5_col11\" class=\"data row5 col11\" >-0.01</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44flevel0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow6_col0\" class=\"data row6 col0\" >0.63</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow6_col1\" class=\"data row6 col1\" >0.99</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow6_col2\" class=\"data row6 col2\" >1.10</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow6_col3\" class=\"data row6 col3\" >0.50</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow6_col4\" class=\"data row6 col4\" >0.77</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow6_col5\" class=\"data row6 col5\" >0.82</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow6_col6\" class=\"data row6 col6\" >-0.37</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow6_col7\" class=\"data row6 col7\" >0.62</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow6_col8\" class=\"data row6 col8\" >0.51</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow6_col9\" class=\"data row6 col9\" >-0.16</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow6_col10\" class=\"data row6 col10\" >-0.20</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow6_col11\" class=\"data row6 col11\" >0.71</td>\n",
+       "                        <th id=\"T_26344_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+       "                        <td id=\"T_26344_row6_col0\" class=\"data row6 col0\" >0.63</td>\n",
+       "                        <td id=\"T_26344_row6_col1\" class=\"data row6 col1\" >0.99</td>\n",
+       "                        <td id=\"T_26344_row6_col2\" class=\"data row6 col2\" >1.10</td>\n",
+       "                        <td id=\"T_26344_row6_col3\" class=\"data row6 col3\" >0.50</td>\n",
+       "                        <td id=\"T_26344_row6_col4\" class=\"data row6 col4\" >0.77</td>\n",
+       "                        <td id=\"T_26344_row6_col5\" class=\"data row6 col5\" >0.82</td>\n",
+       "                        <td id=\"T_26344_row6_col6\" class=\"data row6 col6\" >-0.37</td>\n",
+       "                        <td id=\"T_26344_row6_col7\" class=\"data row6 col7\" >0.62</td>\n",
+       "                        <td id=\"T_26344_row6_col8\" class=\"data row6 col8\" >0.51</td>\n",
+       "                        <td id=\"T_26344_row6_col9\" class=\"data row6 col9\" >-0.16</td>\n",
+       "                        <td id=\"T_26344_row6_col10\" class=\"data row6 col10\" >-0.20</td>\n",
+       "                        <td id=\"T_26344_row6_col11\" class=\"data row6 col11\" >0.71</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44flevel0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow7_col0\" class=\"data row7 col0\" >7.14</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow7_col1\" class=\"data row7 col1\" >1.36</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow7_col2\" class=\"data row7 col2\" >1.35</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow7_col3\" class=\"data row7 col3\" >6.24</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow7_col4\" class=\"data row7 col4\" >2.44</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow7_col5\" class=\"data row7 col5\" >1.59</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow7_col6\" class=\"data row7 col6\" >4.45</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow7_col7\" class=\"data row7 col7\" >3.08</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow7_col8\" class=\"data row7 col8\" >6.25</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow7_col9\" class=\"data row7 col9\" >29.82</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow7_col10\" class=\"data row7 col10\" >31.17</td>\n",
-       "                        <td id=\"T_88ac6fa8_7ac4_11eb_a31d_0cc47af5a44frow7_col11\" class=\"data row7 col11\" >3.07</td>\n",
+       "                        <th id=\"T_26344_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+       "                        <td id=\"T_26344_row7_col0\" class=\"data row7 col0\" >7.14</td>\n",
+       "                        <td id=\"T_26344_row7_col1\" class=\"data row7 col1\" >1.36</td>\n",
+       "                        <td id=\"T_26344_row7_col2\" class=\"data row7 col2\" >1.35</td>\n",
+       "                        <td id=\"T_26344_row7_col3\" class=\"data row7 col3\" >6.24</td>\n",
+       "                        <td id=\"T_26344_row7_col4\" class=\"data row7 col4\" >2.44</td>\n",
+       "                        <td id=\"T_26344_row7_col5\" class=\"data row7 col5\" >1.59</td>\n",
+       "                        <td id=\"T_26344_row7_col6\" class=\"data row7 col6\" >4.45</td>\n",
+       "                        <td id=\"T_26344_row7_col7\" class=\"data row7 col7\" >3.08</td>\n",
+       "                        <td id=\"T_26344_row7_col8\" class=\"data row7 col8\" >6.25</td>\n",
+       "                        <td id=\"T_26344_row7_col9\" class=\"data row7 col9\" >29.82</td>\n",
+       "                        <td id=\"T_26344_row7_col10\" class=\"data row7 col10\" >31.17</td>\n",
+       "                        <td id=\"T_26344_row7_col11\" class=\"data row7 col11\" >3.07</td>\n",
        "            </tr>\n",
        "    </tbody></table>"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x1501812d6550>"
+       "<pandas.io.formats.style.Styler at 0x7f1f39ee96d0>"
       ]
      },
      "metadata": {},
@@ -785,10 +761,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2021-03-01T19:30:09.047672Z",
-     "iopub.status.busy": "2021-03-01T19:30:09.047197Z",
-     "iopub.status.idle": "2021-03-01T19:30:09.056150Z",
-     "shell.execute_reply": "2021-03-01T19:30:09.056626Z"
+     "iopub.execute_input": "2021-03-07T20:16:04.018266Z",
+     "iopub.status.busy": "2021-03-07T20:16:04.017704Z",
+     "iopub.status.idle": "2021-03-07T20:16:04.027923Z",
+     "shell.execute_reply": "2021-03-07T20:16:04.027533Z"
     }
    },
    "outputs": [
@@ -900,20 +876,13 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2021-03-01T19:30:09.061015Z",
-     "iopub.status.busy": "2021-03-01T19:30:09.060537Z",
-     "iopub.status.idle": "2021-03-01T19:30:10.282534Z",
-     "shell.execute_reply": "2021-03-01T19:30:10.283036Z"
+     "iopub.execute_input": "2021-03-07T20:16:04.034556Z",
+     "iopub.status.busy": "2021-03-07T20:16:04.033889Z",
+     "iopub.status.idle": "2021-03-07T20:16:04.122854Z",
+     "shell.execute_reply": "2021-03-07T20:16:04.122514Z"
     }
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -964,10 +933,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2021-03-01T19:30:10.286639Z",
-     "iopub.status.busy": "2021-03-01T19:30:10.286170Z",
-     "iopub.status.idle": "2021-03-01T19:30:10.288573Z",
-     "shell.execute_reply": "2021-03-01T19:30:10.289055Z"
+     "iopub.execute_input": "2021-03-07T20:16:04.125779Z",
+     "iopub.status.busy": "2021-03-07T20:16:04.125468Z",
+     "iopub.status.idle": "2021-03-07T20:16:04.127875Z",
+     "shell.execute_reply": "2021-03-07T20:16:04.127535Z"
     }
    },
    "outputs": [],
@@ -989,10 +958,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2021-03-01T19:30:10.298480Z",
-     "iopub.status.busy": "2021-03-01T19:30:10.298013Z",
-     "iopub.status.idle": "2021-03-01T19:30:10.302661Z",
-     "shell.execute_reply": "2021-03-01T19:30:10.303139Z"
+     "iopub.execute_input": "2021-03-07T20:16:04.134147Z",
+     "iopub.status.busy": "2021-03-07T20:16:04.133730Z",
+     "iopub.status.idle": "2021-03-07T20:16:04.139553Z",
+     "shell.execute_reply": "2021-03-07T20:16:04.139265Z"
     }
    },
    "outputs": [],
@@ -1016,10 +985,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2021-03-01T19:30:10.306817Z",
-     "iopub.status.busy": "2021-03-01T19:30:10.306350Z",
-     "iopub.status.idle": "2021-03-01T19:32:17.817984Z",
-     "shell.execute_reply": "2021-03-01T19:32:17.818481Z"
+     "iopub.execute_input": "2021-03-07T20:16:04.143029Z",
+     "iopub.status.busy": "2021-03-07T20:16:04.142703Z",
+     "iopub.status.idle": "2021-03-07T20:17:56.997381Z",
+     "shell.execute_reply": "2021-03-07T20:17:56.996955Z"
     }
    },
    "outputs": [
@@ -1035,23 +1004,23 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  1/729 [..............................] - ETA: 28:58 - loss: 0.8208 - mae: 0.6601"
+      "  1/729 [..............................] - ETA: 0s - loss: 0.7998 - mae: 0.7227"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  5/729 [..............................] - ETA: 10s - loss: 0.8258 - mae: 0.6826  "
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  4/729 [..............................] - ETA: 9s - loss: 0.8029 - mae: 0.6500"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  9/729 [..............................] - ETA: 10s - loss: 0.8387 - mae: 0.6873"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  7/729 [..............................] - ETA: 10s - loss: 0.8472 - mae: 0.6846"
      ]
     },
     {
@@ -1059,7 +1028,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 13/729 [..............................] - ETA: 10s - loss: 0.8423 - mae: 0.6884"
+      " 11/729 [..............................] - ETA: 10s - loss: 1.0554 - mae: 0.7089"
      ]
     },
     {
@@ -1067,7 +1036,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 17/729 [..............................] - ETA: 10s - loss: 0.8445 - mae: 0.6905"
+      " 15/729 [..............................] - ETA: 10s - loss: 1.1500 - mae: 0.7243"
      ]
     },
     {
@@ -1075,7 +1044,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 21/729 [..............................] - ETA: 10s - loss: 0.8436 - mae: 0.6909"
+      " 19/729 [..............................] - ETA: 10s - loss: 1.1198 - mae: 0.7231"
      ]
     },
     {
@@ -1083,7 +1052,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 25/729 [>.............................] - ETA: 10s - loss: 0.8470 - mae: 0.6911"
+      " 23/729 [..............................] - ETA: 10s - loss: 1.0680 - mae: 0.7143"
      ]
     },
     {
@@ -1091,7 +1060,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 29/729 [>.............................] - ETA: 10s - loss: 0.8500 - mae: 0.6902"
+      " 27/729 [>.............................] - ETA: 10s - loss: 1.0398 - mae: 0.7143"
      ]
     },
     {
@@ -1099,7 +1068,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 33/729 [>.............................] - ETA: 10s - loss: 0.8579 - mae: 0.6897"
+      " 31/729 [>.............................] - ETA: 10s - loss: 1.0116 - mae: 0.7119"
      ]
     },
     {
@@ -1107,7 +1076,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 37/729 [>.............................] - ETA: 10s - loss: 0.8638 - mae: 0.6888"
+      " 35/729 [>.............................] - ETA: 10s - loss: 0.9665 - mae: 0.6979"
      ]
     },
     {
@@ -1115,7 +1084,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 41/729 [>.............................] - ETA: 10s - loss: 0.8772 - mae: 0.6889"
+      " 39/729 [>.............................] - ETA: 10s - loss: 0.9391 - mae: 0.6891"
      ]
     },
     {
@@ -1123,7 +1092,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 45/729 [>.............................] - ETA: 10s - loss: 0.8860 - mae: 0.6884"
+      " 43/729 [>.............................] - ETA: 10s - loss: 0.9188 - mae: 0.6822"
      ]
     },
     {
@@ -1131,7 +1100,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 49/729 [=>............................] - ETA: 10s - loss: 0.8912 - mae: 0.6874"
+      " 47/729 [>.............................] - ETA: 10s - loss: 0.9072 - mae: 0.6766"
      ]
     },
     {
@@ -1139,7 +1108,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 52/729 [=>............................] - ETA: 10s - loss: 0.8932 - mae: 0.6863"
+      " 51/729 [=>............................] - ETA: 10s - loss: 0.8839 - mae: 0.6696"
      ]
     },
     {
@@ -1147,159 +1116,159 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 55/729 [=>............................] - ETA: 10s - loss: 0.8946 - mae: 0.6853"
+      " 55/729 [=>............................] - ETA: 9s - loss: 0.8574 - mae: 0.6589 "
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 58/729 [=>............................] - ETA: 10s - loss: 0.8965 - mae: 0.6845"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 60/729 [=>............................] - ETA: 9s - loss: 0.8542 - mae: 0.6539"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 61/729 [=>............................] - ETA: 10s - loss: 0.8983 - mae: 0.6837"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 64/729 [=>............................] - ETA: 9s - loss: 0.8286 - mae: 0.6441"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 65/729 [=>............................] - ETA: 10s - loss: 0.8996 - mae: 0.6825"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 68/729 [=>............................] - ETA: 9s - loss: 0.8356 - mae: 0.6413"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 68/729 [=>............................] - ETA: 10s - loss: 0.8997 - mae: 0.6814"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 72/729 [=>............................] - ETA: 9s - loss: 0.8383 - mae: 0.6386"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 71/729 [=>............................] - ETA: 10s - loss: 0.8992 - mae: 0.6802"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 77/729 [==>...........................] - ETA: 9s - loss: 0.8205 - mae: 0.6326"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 74/729 [==>...........................] - ETA: 10s - loss: 0.8987 - mae: 0.6790"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 82/729 [==>...........................] - ETA: 9s - loss: 0.8110 - mae: 0.6289"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 77/729 [==>...........................] - ETA: 10s - loss: 0.8979 - mae: 0.6779"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 87/729 [==>...........................] - ETA: 8s - loss: 0.7991 - mae: 0.6260"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 80/729 [==>...........................] - ETA: 10s - loss: 0.8968 - mae: 0.6768"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 92/729 [==>...........................] - ETA: 8s - loss: 0.7875 - mae: 0.6214"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 84/729 [==>...........................] - ETA: 10s - loss: 0.8951 - mae: 0.6753"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 97/729 [==>...........................] - ETA: 8s - loss: 0.7775 - mae: 0.6179"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 87/729 [==>...........................] - ETA: 10s - loss: 0.8936 - mae: 0.6743"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "102/729 [===>..........................] - ETA: 8s - loss: 0.8083 - mae: 0.6139"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 90/729 [==>...........................] - ETA: 10s - loss: 0.8920 - mae: 0.6732"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "107/729 [===>..........................] - ETA: 8s - loss: 0.8059 - mae: 0.6131"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 93/729 [==>...........................] - ETA: 10s - loss: 0.8901 - mae: 0.6721"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "112/729 [===>..........................] - ETA: 8s - loss: 0.8065 - mae: 0.6131"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 96/729 [==>...........................] - ETA: 10s - loss: 0.8882 - mae: 0.6709"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "117/729 [===>..........................] - ETA: 8s - loss: 0.8056 - mae: 0.6124"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 99/729 [===>..........................] - ETA: 10s - loss: 0.8867 - mae: 0.6698"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "122/729 [====>.........................] - ETA: 8s - loss: 0.7986 - mae: 0.6110"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "102/729 [===>..........................] - ETA: 10s - loss: 0.8850 - mae: 0.6688"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "127/729 [====>.........................] - ETA: 8s - loss: 0.7878 - mae: 0.6072"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "105/729 [===>..........................] - ETA: 10s - loss: 0.8832 - mae: 0.6676"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "132/729 [====>.........................] - ETA: 7s - loss: 0.7781 - mae: 0.6037"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "109/729 [===>..........................] - ETA: 10s - loss: 0.8807 - mae: 0.6661"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "137/729 [====>.........................] - ETA: 7s - loss: 0.7737 - mae: 0.6025"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "112/729 [===>..........................] - ETA: 10s - loss: 0.8788 - mae: 0.6650"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "142/729 [====>.........................] - ETA: 7s - loss: 0.7690 - mae: 0.6005"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "115/729 [===>..........................] - ETA: 9s - loss: 0.8773 - mae: 0.6639 "
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "147/729 [=====>........................] - ETA: 7s - loss: 0.7703 - mae: 0.5986"
      ]
     },
     {
@@ -1307,7 +1276,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "118/729 [===>..........................] - ETA: 9s - loss: 0.8756 - mae: 0.6627"
+      "152/729 [=====>........................] - ETA: 7s - loss: 0.7671 - mae: 0.5982"
      ]
     },
     {
@@ -1315,7 +1284,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "121/729 [===>..........................] - ETA: 9s - loss: 0.8739 - mae: 0.6616"
+      "156/729 [=====>........................] - ETA: 7s - loss: 0.7650 - mae: 0.5978"
      ]
     },
     {
@@ -1323,7 +1292,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "125/729 [====>.........................] - ETA: 9s - loss: 0.8728 - mae: 0.6603"
+      "161/729 [=====>........................] - ETA: 7s - loss: 0.7563 - mae: 0.5950"
      ]
     },
     {
@@ -1331,7 +1300,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "128/729 [====>.........................] - ETA: 9s - loss: 0.8718 - mae: 0.6593"
+      "166/729 [=====>........................] - ETA: 7s - loss: 0.7517 - mae: 0.5935"
      ]
     },
     {
@@ -1339,7 +1308,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "131/729 [====>.........................] - ETA: 9s - loss: 0.8708 - mae: 0.6583"
+      "171/729 [======>.......................] - ETA: 7s - loss: 0.7587 - mae: 0.5954"
      ]
     },
     {
@@ -1347,7 +1316,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "134/729 [====>.........................] - ETA: 9s - loss: 0.8698 - mae: 0.6574"
+      "176/729 [======>.......................] - ETA: 7s - loss: 0.7680 - mae: 0.5947"
      ]
     },
     {
@@ -1355,7 +1324,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "137/729 [====>.........................] - ETA: 9s - loss: 0.8688 - mae: 0.6564"
+      "181/729 [======>.......................] - ETA: 7s - loss: 0.7653 - mae: 0.5947"
      ]
     },
     {
@@ -1363,7 +1332,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "140/729 [====>.........................] - ETA: 9s - loss: 0.8678 - mae: 0.6555"
+      "186/729 [======>.......................] - ETA: 7s - loss: 0.7627 - mae: 0.5941"
      ]
     },
     {
@@ -1371,7 +1340,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "143/729 [====>.........................] - ETA: 9s - loss: 0.8668 - mae: 0.6547"
+      "191/729 [======>.......................] - ETA: 6s - loss: 0.7553 - mae: 0.5920"
      ]
     },
     {
@@ -1379,7 +1348,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "146/729 [=====>........................] - ETA: 9s - loss: 0.8658 - mae: 0.6538"
+      "196/729 [=======>......................] - ETA: 6s - loss: 0.7499 - mae: 0.5894"
      ]
     },
     {
@@ -1387,7 +1356,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "149/729 [=====>........................] - ETA: 9s - loss: 0.8648 - mae: 0.6530"
+      "201/729 [=======>......................] - ETA: 6s - loss: 0.7425 - mae: 0.5867"
      ]
     },
     {
@@ -1395,7 +1364,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "153/729 [=====>........................] - ETA: 9s - loss: 0.8635 - mae: 0.6519"
+      "206/729 [=======>......................] - ETA: 6s - loss: 0.7377 - mae: 0.5852"
      ]
     },
     {
@@ -1403,7 +1372,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "157/729 [=====>........................] - ETA: 9s - loss: 0.8622 - mae: 0.6509"
+      "211/729 [=======>......................] - ETA: 6s - loss: 0.7319 - mae: 0.5824"
      ]
     },
     {
@@ -1411,7 +1380,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "160/729 [=====>........................] - ETA: 9s - loss: 0.8612 - mae: 0.6501"
+      "216/729 [=======>......................] - ETA: 6s - loss: 0.7291 - mae: 0.5805"
      ]
     },
     {
@@ -1419,7 +1388,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "163/729 [=====>........................] - ETA: 9s - loss: 0.8603 - mae: 0.6493"
+      "221/729 [========>.....................] - ETA: 6s - loss: 0.7251 - mae: 0.5794"
      ]
     },
     {
@@ -1427,7 +1396,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "166/729 [=====>........................] - ETA: 9s - loss: 0.8593 - mae: 0.6485"
+      "226/729 [========>.....................] - ETA: 6s - loss: 0.7183 - mae: 0.5767"
      ]
     },
     {
@@ -1435,7 +1404,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "169/729 [=====>........................] - ETA: 9s - loss: 0.8582 - mae: 0.6477"
+      "231/729 [========>.....................] - ETA: 6s - loss: 0.7165 - mae: 0.5763"
      ]
     },
     {
@@ -1443,7 +1412,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "172/729 [======>.......................] - ETA: 9s - loss: 0.8571 - mae: 0.6470"
+      "236/729 [========>.....................] - ETA: 6s - loss: 0.7118 - mae: 0.5748"
      ]
     },
     {
@@ -1451,7 +1420,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "175/729 [======>.......................] - ETA: 9s - loss: 0.8561 - mae: 0.6462"
+      "241/729 [========>.....................] - ETA: 6s - loss: 0.7072 - mae: 0.5732"
      ]
     },
     {
@@ -1459,7 +1428,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "178/729 [======>.......................] - ETA: 9s - loss: 0.8550 - mae: 0.6455"
+      "246/729 [=========>....................] - ETA: 6s - loss: 0.7022 - mae: 0.5713"
      ]
     },
     {
@@ -1467,7 +1436,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "181/729 [======>.......................] - ETA: 9s - loss: 0.8538 - mae: 0.6447"
+      "251/729 [=========>....................] - ETA: 6s - loss: 0.6989 - mae: 0.5694"
      ]
     },
     {
@@ -1475,7 +1444,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "184/729 [======>.......................] - ETA: 9s - loss: 0.8526 - mae: 0.6440"
+      "256/729 [=========>....................] - ETA: 6s - loss: 0.6995 - mae: 0.5687"
      ]
     },
     {
@@ -1483,7 +1452,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "187/729 [======>.......................] - ETA: 9s - loss: 0.8514 - mae: 0.6432"
+      "260/729 [=========>....................] - ETA: 5s - loss: 0.6954 - mae: 0.5673"
      ]
     },
     {
@@ -1491,7 +1460,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "190/729 [======>.......................] - ETA: 8s - loss: 0.8501 - mae: 0.6425"
+      "265/729 [=========>....................] - ETA: 5s - loss: 0.6952 - mae: 0.5668"
      ]
     },
     {
@@ -1499,7 +1468,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "193/729 [======>.......................] - ETA: 8s - loss: 0.8488 - mae: 0.6418"
+      "269/729 [==========>...................] - ETA: 5s - loss: 0.6932 - mae: 0.5661"
      ]
     },
     {
@@ -1507,7 +1476,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "197/729 [=======>......................] - ETA: 8s - loss: 0.8471 - mae: 0.6408"
+      "273/729 [==========>...................] - ETA: 5s - loss: 0.6963 - mae: 0.5662"
      ]
     },
     {
@@ -1515,7 +1484,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "200/729 [=======>......................] - ETA: 8s - loss: 0.8457 - mae: 0.6401"
+      "277/729 [==========>...................] - ETA: 5s - loss: 0.6943 - mae: 0.5658"
      ]
     },
     {
@@ -1523,7 +1492,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "203/729 [=======>......................] - ETA: 8s - loss: 0.8444 - mae: 0.6393"
+      "281/729 [==========>...................] - ETA: 5s - loss: 0.6918 - mae: 0.5650"
      ]
     },
     {
@@ -1531,7 +1500,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "206/729 [=======>......................] - ETA: 8s - loss: 0.8431 - mae: 0.6386"
+      "285/729 [==========>...................] - ETA: 5s - loss: 0.6901 - mae: 0.5643"
      ]
     },
     {
@@ -1539,7 +1508,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "210/729 [=======>......................] - ETA: 8s - loss: 0.8415 - mae: 0.6377"
+      "289/729 [==========>...................] - ETA: 5s - loss: 0.6899 - mae: 0.5642"
      ]
     },
     {
@@ -1547,7 +1516,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "214/729 [=======>......................] - ETA: 8s - loss: 0.8400 - mae: 0.6368"
+      "293/729 [===========>..................] - ETA: 5s - loss: 0.6867 - mae: 0.5630"
      ]
     },
     {
@@ -1555,7 +1524,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "218/729 [=======>......................] - ETA: 8s - loss: 0.8384 - mae: 0.6359"
+      "297/729 [===========>..................] - ETA: 5s - loss: 0.6894 - mae: 0.5634"
      ]
     },
     {
@@ -1563,7 +1532,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "222/729 [========>.....................] - ETA: 8s - loss: 0.8368 - mae: 0.6350"
+      "301/729 [===========>..................] - ETA: 5s - loss: 0.6962 - mae: 0.5627"
      ]
     },
     {
@@ -1571,7 +1540,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "226/729 [========>.....................] - ETA: 8s - loss: 0.8353 - mae: 0.6341"
+      "305/729 [===========>..................] - ETA: 5s - loss: 0.6944 - mae: 0.5618"
      ]
     },
     {
@@ -1579,7 +1548,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "230/729 [========>.....................] - ETA: 8s - loss: 0.8339 - mae: 0.6333"
+      "309/729 [===========>..................] - ETA: 5s - loss: 0.6922 - mae: 0.5612"
      ]
     },
     {
@@ -1587,7 +1556,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "234/729 [========>.....................] - ETA: 8s - loss: 0.8324 - mae: 0.6324"
+      "313/729 [===========>..................] - ETA: 5s - loss: 0.6922 - mae: 0.5606"
      ]
     },
     {
@@ -1595,7 +1564,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "238/729 [========>.....................] - ETA: 8s - loss: 0.8310 - mae: 0.6316"
+      "317/729 [============>.................] - ETA: 5s - loss: 0.6884 - mae: 0.5592"
      ]
     },
     {
@@ -1603,7 +1572,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "242/729 [========>.....................] - ETA: 8s - loss: 0.8295 - mae: 0.6307"
+      "321/729 [============>.................] - ETA: 5s - loss: 0.6882 - mae: 0.5584"
      ]
     },
     {
@@ -1611,7 +1580,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "246/729 [=========>....................] - ETA: 7s - loss: 0.8280 - mae: 0.6299"
+      "325/729 [============>.................] - ETA: 5s - loss: 0.6860 - mae: 0.5575"
      ]
     },
     {
@@ -1619,7 +1588,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "250/729 [=========>....................] - ETA: 7s - loss: 0.8265 - mae: 0.6291"
+      "329/729 [============>.................] - ETA: 5s - loss: 0.6852 - mae: 0.5574"
      ]
     },
     {
@@ -1627,7 +1596,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "254/729 [=========>....................] - ETA: 7s - loss: 0.8251 - mae: 0.6282"
+      "333/729 [============>.................] - ETA: 5s - loss: 0.6941 - mae: 0.5579"
      ]
     },
     {
@@ -1635,7 +1604,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "258/729 [=========>....................] - ETA: 7s - loss: 0.8236 - mae: 0.6274"
+      "337/729 [============>.................] - ETA: 5s - loss: 0.6913 - mae: 0.5570"
      ]
     },
     {
@@ -1643,7 +1612,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "262/729 [=========>....................] - ETA: 7s - loss: 0.8221 - mae: 0.6266"
+      "341/729 [=============>................] - ETA: 5s - loss: 0.6885 - mae: 0.5560"
      ]
     },
     {
@@ -1651,7 +1620,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "266/729 [=========>....................] - ETA: 7s - loss: 0.8206 - mae: 0.6258"
+      "345/729 [=============>................] - ETA: 4s - loss: 0.6855 - mae: 0.5550"
      ]
     },
     {
@@ -1659,7 +1628,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "270/729 [==========>...................] - ETA: 7s - loss: 0.8190 - mae: 0.6250"
+      "349/729 [=============>................] - ETA: 4s - loss: 0.6827 - mae: 0.5541"
      ]
     },
     {
@@ -1667,7 +1636,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "274/729 [==========>...................] - ETA: 7s - loss: 0.8175 - mae: 0.6242"
+      "353/729 [=============>................] - ETA: 4s - loss: 0.6867 - mae: 0.5543"
      ]
     },
     {
@@ -1675,7 +1644,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "278/729 [==========>...................] - ETA: 7s - loss: 0.8159 - mae: 0.6234"
+      "357/729 [=============>................] - ETA: 4s - loss: 0.6874 - mae: 0.5544"
      ]
     },
     {
@@ -1683,7 +1652,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "282/729 [==========>...................] - ETA: 7s - loss: 0.8144 - mae: 0.6226"
+      "361/729 [=============>................] - ETA: 4s - loss: 0.6845 - mae: 0.5535"
      ]
     },
     {
@@ -1691,7 +1660,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "286/729 [==========>...................] - ETA: 7s - loss: 0.8128 - mae: 0.6218"
+      "365/729 [==============>...............] - ETA: 4s - loss: 0.6815 - mae: 0.5523"
      ]
     },
     {
@@ -1699,7 +1668,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "290/729 [==========>...................] - ETA: 7s - loss: 0.8112 - mae: 0.6210"
+      "369/729 [==============>...............] - ETA: 4s - loss: 0.6790 - mae: 0.5513"
      ]
     },
     {
@@ -1707,7 +1676,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "294/729 [===========>..................] - ETA: 7s - loss: 0.8097 - mae: 0.6202"
+      "373/729 [==============>...............] - ETA: 4s - loss: 0.6766 - mae: 0.5503"
      ]
     },
     {
@@ -1715,7 +1684,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "298/729 [===========>..................] - ETA: 6s - loss: 0.8081 - mae: 0.6194"
+      "377/729 [==============>...............] - ETA: 4s - loss: 0.6753 - mae: 0.5498"
      ]
     },
     {
@@ -1723,7 +1692,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "302/729 [===========>..................] - ETA: 6s - loss: 0.8065 - mae: 0.6186"
+      "381/729 [==============>...............] - ETA: 4s - loss: 0.6762 - mae: 0.5495"
      ]
     },
     {
@@ -1731,7 +1700,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "306/729 [===========>..................] - ETA: 6s - loss: 0.8050 - mae: 0.6179"
+      "385/729 [==============>...............] - ETA: 4s - loss: 0.6737 - mae: 0.5487"
      ]
     },
     {
@@ -1739,7 +1708,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "310/729 [===========>..................] - ETA: 6s - loss: 0.8034 - mae: 0.6171"
+      "389/729 [===============>..............] - ETA: 4s - loss: 0.6776 - mae: 0.5486"
      ]
     },
     {
@@ -1747,7 +1716,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "314/729 [===========>..................] - ETA: 6s - loss: 0.8019 - mae: 0.6164"
+      "393/729 [===============>..............] - ETA: 4s - loss: 0.6751 - mae: 0.5477"
      ]
     },
     {
@@ -1755,7 +1724,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "318/729 [============>.................] - ETA: 6s - loss: 0.8004 - mae: 0.6156"
+      "397/729 [===============>..............] - ETA: 4s - loss: 0.6729 - mae: 0.5469"
      ]
     },
     {
@@ -1763,7 +1732,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "322/729 [============>.................] - ETA: 6s - loss: 0.7989 - mae: 0.6149"
+      "401/729 [===============>..............] - ETA: 4s - loss: 0.6719 - mae: 0.5465"
      ]
     },
     {
@@ -1771,7 +1740,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "326/729 [============>.................] - ETA: 6s - loss: 0.7975 - mae: 0.6142"
+      "405/729 [===============>..............] - ETA: 4s - loss: 0.6703 - mae: 0.5458"
      ]
     },
     {
@@ -1779,7 +1748,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "330/729 [============>.................] - ETA: 6s - loss: 0.7960 - mae: 0.6135"
+      "409/729 [===============>..............] - ETA: 4s - loss: 0.6677 - mae: 0.5450"
      ]
     },
     {
@@ -1787,7 +1756,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "334/729 [============>.................] - ETA: 6s - loss: 0.7945 - mae: 0.6127"
+      "413/729 [===============>..............] - ETA: 4s - loss: 0.6718 - mae: 0.5451"
      ]
     },
     {
@@ -1795,7 +1764,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "338/729 [============>.................] - ETA: 6s - loss: 0.7931 - mae: 0.6120"
+      "417/729 [================>.............] - ETA: 4s - loss: 0.6701 - mae: 0.5446"
      ]
     },
     {
@@ -1803,7 +1772,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "342/729 [=============>................] - ETA: 6s - loss: 0.7916 - mae: 0.6113"
+      "421/729 [================>.............] - ETA: 4s - loss: 0.6675 - mae: 0.5436"
      ]
     },
     {
@@ -1811,7 +1780,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "346/729 [=============>................] - ETA: 6s - loss: 0.7901 - mae: 0.6106"
+      "425/729 [================>.............] - ETA: 4s - loss: 0.6648 - mae: 0.5428"
      ]
     },
     {
@@ -1819,7 +1788,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "350/729 [=============>................] - ETA: 6s - loss: 0.7887 - mae: 0.6099"
+      "429/729 [================>.............] - ETA: 3s - loss: 0.6635 - mae: 0.5424"
      ]
     },
     {
@@ -1827,7 +1796,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "354/729 [=============>................] - ETA: 5s - loss: 0.7872 - mae: 0.6092"
+      "433/729 [================>.............] - ETA: 3s - loss: 0.6631 - mae: 0.5421"
      ]
     },
     {
@@ -1835,7 +1804,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "358/729 [=============>................] - ETA: 5s - loss: 0.7859 - mae: 0.6085"
+      "437/729 [================>.............] - ETA: 3s - loss: 0.6609 - mae: 0.5413"
      ]
     },
     {
@@ -1843,7 +1812,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "362/729 [=============>................] - ETA: 5s - loss: 0.7845 - mae: 0.6079"
+      "441/729 [=================>............] - ETA: 3s - loss: 0.6586 - mae: 0.5403"
      ]
     },
     {
@@ -1851,7 +1820,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "366/729 [==============>...............] - ETA: 5s - loss: 0.7832 - mae: 0.6072"
+      "445/729 [=================>............] - ETA: 3s - loss: 0.6569 - mae: 0.5398"
      ]
     },
     {
@@ -1859,7 +1828,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "370/729 [==============>...............] - ETA: 5s - loss: 0.7818 - mae: 0.6065"
+      "449/729 [=================>............] - ETA: 3s - loss: 0.6564 - mae: 0.5394"
      ]
     },
     {
@@ -1867,7 +1836,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "374/729 [==============>...............] - ETA: 5s - loss: 0.7805 - mae: 0.6059"
+      "453/729 [=================>............] - ETA: 3s - loss: 0.6543 - mae: 0.5387"
      ]
     },
     {
@@ -1875,7 +1844,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "378/729 [==============>...............] - ETA: 5s - loss: 0.7792 - mae: 0.6052"
+      "457/729 [=================>............] - ETA: 3s - loss: 0.6521 - mae: 0.5380"
      ]
     },
     {
@@ -1883,7 +1852,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "382/729 [==============>...............] - ETA: 5s - loss: 0.7779 - mae: 0.6046"
+      "461/729 [=================>............] - ETA: 3s - loss: 0.6511 - mae: 0.5375"
      ]
     },
     {
@@ -1891,7 +1860,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "386/729 [==============>...............] - ETA: 5s - loss: 0.7767 - mae: 0.6039"
+      "465/729 [==================>...........] - ETA: 3s - loss: 0.6499 - mae: 0.5371"
      ]
     },
     {
@@ -1899,7 +1868,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "390/729 [===============>..............] - ETA: 5s - loss: 0.7754 - mae: 0.6033"
+      "469/729 [==================>...........] - ETA: 3s - loss: 0.6486 - mae: 0.5364"
      ]
     },
     {
@@ -1907,7 +1876,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "394/729 [===============>..............] - ETA: 5s - loss: 0.7742 - mae: 0.6027"
+      "473/729 [==================>...........] - ETA: 3s - loss: 0.6478 - mae: 0.5362"
      ]
     },
     {
@@ -1915,7 +1884,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "398/729 [===============>..............] - ETA: 5s - loss: 0.7730 - mae: 0.6021"
+      "477/729 [==================>...........] - ETA: 3s - loss: 0.6465 - mae: 0.5357"
      ]
     },
     {
@@ -1923,7 +1892,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "402/729 [===============>..............] - ETA: 5s - loss: 0.7718 - mae: 0.6015"
+      "481/729 [==================>...........] - ETA: 3s - loss: 0.6454 - mae: 0.5351"
      ]
     },
     {
@@ -1931,7 +1900,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "406/729 [===============>..............] - ETA: 5s - loss: 0.7706 - mae: 0.6009"
+      "485/729 [==================>...........] - ETA: 3s - loss: 0.6441 - mae: 0.5344"
      ]
     },
     {
@@ -1939,7 +1908,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "410/729 [===============>..............] - ETA: 5s - loss: 0.7694 - mae: 0.6003"
+      "489/729 [===================>..........] - ETA: 3s - loss: 0.6439 - mae: 0.5340"
      ]
     },
     {
@@ -1947,7 +1916,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "414/729 [================>.............] - ETA: 4s - loss: 0.7683 - mae: 0.5997"
+      "493/729 [===================>..........] - ETA: 3s - loss: 0.6442 - mae: 0.5337"
      ]
     },
     {
@@ -1955,7 +1924,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "418/729 [================>.............] - ETA: 4s - loss: 0.7671 - mae: 0.5991"
+      "497/729 [===================>..........] - ETA: 3s - loss: 0.6436 - mae: 0.5333"
      ]
     },
     {
@@ -1963,7 +1932,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "422/729 [================>.............] - ETA: 4s - loss: 0.7659 - mae: 0.5985"
+      "501/729 [===================>..........] - ETA: 3s - loss: 0.6418 - mae: 0.5327"
      ]
     },
     {
@@ -1971,7 +1940,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "426/729 [================>.............] - ETA: 4s - loss: 0.7648 - mae: 0.5979"
+      "505/729 [===================>..........] - ETA: 2s - loss: 0.6397 - mae: 0.5319"
      ]
     },
     {
@@ -1979,7 +1948,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "430/729 [================>.............] - ETA: 4s - loss: 0.7637 - mae: 0.5974"
+      "509/729 [===================>..........] - ETA: 2s - loss: 0.6421 - mae: 0.5318"
      ]
     },
     {
@@ -1987,7 +1956,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "434/729 [================>.............] - ETA: 4s - loss: 0.7626 - mae: 0.5968"
+      "513/729 [====================>.........] - ETA: 2s - loss: 0.6421 - mae: 0.5314"
      ]
     },
     {
@@ -1995,7 +1964,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "438/729 [=================>............] - ETA: 4s - loss: 0.7615 - mae: 0.5963"
+      "517/729 [====================>.........] - ETA: 2s - loss: 0.6407 - mae: 0.5310"
      ]
     },
     {
@@ -2003,7 +1972,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "442/729 [=================>............] - ETA: 4s - loss: 0.7604 - mae: 0.5957"
+      "521/729 [====================>.........] - ETA: 2s - loss: 0.6397 - mae: 0.5305"
      ]
     },
     {
@@ -2011,7 +1980,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "446/729 [=================>............] - ETA: 4s - loss: 0.7594 - mae: 0.5952"
+      "525/729 [====================>.........] - ETA: 2s - loss: 0.6387 - mae: 0.5303"
      ]
     },
     {
@@ -2019,7 +1988,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "450/729 [=================>............] - ETA: 4s - loss: 0.7584 - mae: 0.5946"
+      "529/729 [====================>.........] - ETA: 2s - loss: 0.6410 - mae: 0.5301"
      ]
     },
     {
@@ -2027,7 +1996,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "454/729 [=================>............] - ETA: 4s - loss: 0.7575 - mae: 0.5941"
+      "533/729 [====================>.........] - ETA: 2s - loss: 0.6398 - mae: 0.5298"
      ]
     },
     {
@@ -2035,7 +2004,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "458/729 [=================>............] - ETA: 4s - loss: 0.7565 - mae: 0.5936"
+      "537/729 [=====================>........] - ETA: 2s - loss: 0.6387 - mae: 0.5294"
      ]
     },
     {
@@ -2043,7 +2012,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "462/729 [==================>...........] - ETA: 4s - loss: 0.7556 - mae: 0.5931"
+      "541/729 [=====================>........] - ETA: 2s - loss: 0.6372 - mae: 0.5289"
      ]
     },
     {
@@ -2051,7 +2020,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "466/729 [==================>...........] - ETA: 4s - loss: 0.7547 - mae: 0.5926"
+      "545/729 [=====================>........] - ETA: 2s - loss: 0.6354 - mae: 0.5282"
      ]
     },
     {
@@ -2059,7 +2028,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "470/729 [==================>...........] - ETA: 4s - loss: 0.7537 - mae: 0.5921"
+      "549/729 [=====================>........] - ETA: 2s - loss: 0.6350 - mae: 0.5281"
      ]
     },
     {
@@ -2067,7 +2036,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "474/729 [==================>...........] - ETA: 4s - loss: 0.7528 - mae: 0.5916"
+      "553/729 [=====================>........] - ETA: 2s - loss: 0.6337 - mae: 0.5277"
      ]
     },
     {
@@ -2075,7 +2044,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "478/729 [==================>...........] - ETA: 3s - loss: 0.7519 - mae: 0.5911"
+      "557/729 [=====================>........] - ETA: 2s - loss: 0.6361 - mae: 0.5276"
      ]
     },
     {
@@ -2083,7 +2052,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "482/729 [==================>...........] - ETA: 3s - loss: 0.7510 - mae: 0.5906"
+      "561/729 [======================>.......] - ETA: 2s - loss: 0.6356 - mae: 0.5275"
      ]
     },
     {
@@ -2091,7 +2060,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "486/729 [===================>..........] - ETA: 3s - loss: 0.7501 - mae: 0.5901"
+      "565/729 [======================>.......] - ETA: 2s - loss: 0.6344 - mae: 0.5270"
      ]
     },
     {
@@ -2099,7 +2068,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "490/729 [===================>..........] - ETA: 3s - loss: 0.7492 - mae: 0.5896"
+      "569/729 [======================>.......] - ETA: 2s - loss: 0.6368 - mae: 0.5270"
      ]
     },
     {
@@ -2107,7 +2076,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "494/729 [===================>..........] - ETA: 3s - loss: 0.7483 - mae: 0.5891"
+      "573/729 [======================>.......] - ETA: 2s - loss: 0.6364 - mae: 0.5269"
      ]
     },
     {
@@ -2115,7 +2084,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "498/729 [===================>..........] - ETA: 3s - loss: 0.7474 - mae: 0.5886"
+      "577/729 [======================>.......] - ETA: 2s - loss: 0.6355 - mae: 0.5265"
      ]
     },
     {
@@ -2123,7 +2092,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "502/729 [===================>..........] - ETA: 3s - loss: 0.7465 - mae: 0.5881"
+      "581/729 [======================>.......] - ETA: 1s - loss: 0.6370 - mae: 0.5267"
      ]
     },
     {
@@ -2131,7 +2100,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "506/729 [===================>..........] - ETA: 3s - loss: 0.7456 - mae: 0.5876"
+      "585/729 [=======================>......] - ETA: 1s - loss: 0.6349 - mae: 0.5259"
      ]
     },
     {
@@ -2139,7 +2108,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "510/729 [===================>..........] - ETA: 3s - loss: 0.7448 - mae: 0.5872"
+      "589/729 [=======================>......] - ETA: 1s - loss: 0.6334 - mae: 0.5253"
      ]
     },
     {
@@ -2147,7 +2116,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "514/729 [====================>.........] - ETA: 3s - loss: 0.7439 - mae: 0.5867"
+      "593/729 [=======================>......] - ETA: 1s - loss: 0.6329 - mae: 0.5252"
      ]
     },
     {
@@ -2155,7 +2124,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "518/729 [====================>.........] - ETA: 3s - loss: 0.7431 - mae: 0.5862"
+      "597/729 [=======================>......] - ETA: 1s - loss: 0.6321 - mae: 0.5248"
      ]
     },
     {
@@ -2163,7 +2132,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "522/729 [====================>.........] - ETA: 3s - loss: 0.7422 - mae: 0.5858"
+      "601/729 [=======================>......] - ETA: 1s - loss: 0.6304 - mae: 0.5242"
      ]
     },
     {
@@ -2171,7 +2140,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "526/729 [====================>.........] - ETA: 3s - loss: 0.7414 - mae: 0.5853"
+      "605/729 [=======================>......] - ETA: 1s - loss: 0.6293 - mae: 0.5239"
      ]
     },
     {
@@ -2179,7 +2148,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "530/729 [====================>.........] - ETA: 3s - loss: 0.7406 - mae: 0.5849"
+      "609/729 [========================>.....] - ETA: 1s - loss: 0.6281 - mae: 0.5235"
      ]
     },
     {
@@ -2187,7 +2156,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "534/729 [====================>.........] - ETA: 3s - loss: 0.7398 - mae: 0.5844"
+      "613/729 [========================>.....] - ETA: 1s - loss: 0.6275 - mae: 0.5231"
      ]
     },
     {
@@ -2195,7 +2164,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "538/729 [=====================>........] - ETA: 2s - loss: 0.7389 - mae: 0.5840"
+      "617/729 [========================>.....] - ETA: 1s - loss: 0.6260 - mae: 0.5225"
      ]
     },
     {
@@ -2203,7 +2172,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "542/729 [=====================>........] - ETA: 2s - loss: 0.7381 - mae: 0.5835"
+      "621/729 [========================>.....] - ETA: 1s - loss: 0.6267 - mae: 0.5223"
      ]
     },
     {
@@ -2211,7 +2180,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "546/729 [=====================>........] - ETA: 2s - loss: 0.7373 - mae: 0.5831"
+      "625/729 [========================>.....] - ETA: 1s - loss: 0.6257 - mae: 0.5219"
      ]
     },
     {
@@ -2219,7 +2188,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "550/729 [=====================>........] - ETA: 2s - loss: 0.7365 - mae: 0.5827"
+      "629/729 [========================>.....] - ETA: 1s - loss: 0.6248 - mae: 0.5214"
      ]
     },
     {
@@ -2227,7 +2196,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "554/729 [=====================>........] - ETA: 2s - loss: 0.7357 - mae: 0.5822"
+      "633/729 [=========================>....] - ETA: 1s - loss: 0.6240 - mae: 0.5211"
      ]
     },
     {
@@ -2235,7 +2204,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "558/729 [=====================>........] - ETA: 2s - loss: 0.7349 - mae: 0.5818"
+      "637/729 [=========================>....] - ETA: 1s - loss: 0.6275 - mae: 0.5211"
      ]
     },
     {
@@ -2243,7 +2212,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "562/729 [======================>.......] - ETA: 2s - loss: 0.7341 - mae: 0.5814"
+      "641/729 [=========================>....] - ETA: 1s - loss: 0.6263 - mae: 0.5206"
      ]
     },
     {
@@ -2251,7 +2220,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "566/729 [======================>.......] - ETA: 2s - loss: 0.7333 - mae: 0.5810"
+      "645/729 [=========================>....] - ETA: 1s - loss: 0.6254 - mae: 0.5202"
      ]
     },
     {
@@ -2259,7 +2228,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "570/729 [======================>.......] - ETA: 2s - loss: 0.7325 - mae: 0.5805"
+      "649/729 [=========================>....] - ETA: 1s - loss: 0.6240 - mae: 0.5197"
      ]
     },
     {
@@ -2267,7 +2236,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "574/729 [======================>.......] - ETA: 2s - loss: 0.7317 - mae: 0.5801"
+      "653/729 [=========================>....] - ETA: 1s - loss: 0.6231 - mae: 0.5195"
      ]
     },
     {
@@ -2275,7 +2244,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "578/729 [======================>.......] - ETA: 2s - loss: 0.7309 - mae: 0.5797"
+      "657/729 [==========================>...] - ETA: 0s - loss: 0.6220 - mae: 0.5191"
      ]
     },
     {
@@ -2283,7 +2252,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "582/729 [======================>.......] - ETA: 2s - loss: 0.7302 - mae: 0.5793"
+      "661/729 [==========================>...] - ETA: 0s - loss: 0.6212 - mae: 0.5190"
      ]
     },
     {
@@ -2291,7 +2260,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "586/729 [=======================>......] - ETA: 2s - loss: 0.7295 - mae: 0.5789"
+      "665/729 [==========================>...] - ETA: 0s - loss: 0.6210 - mae: 0.5188"
      ]
     },
     {
@@ -2299,7 +2268,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "590/729 [=======================>......] - ETA: 2s - loss: 0.7288 - mae: 0.5785"
+      "669/729 [==========================>...] - ETA: 0s - loss: 0.6209 - mae: 0.5187"
      ]
     },
     {
@@ -2307,7 +2276,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "594/729 [=======================>......] - ETA: 2s - loss: 0.7281 - mae: 0.5781"
+      "673/729 [==========================>...] - ETA: 0s - loss: 0.6199 - mae: 0.5183"
      ]
     },
     {
@@ -2315,7 +2284,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "598/729 [=======================>......] - ETA: 2s - loss: 0.7274 - mae: 0.5777"
+      "677/729 [==========================>...] - ETA: 0s - loss: 0.6184 - mae: 0.5178"
      ]
     },
     {
@@ -2323,7 +2292,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "602/729 [=======================>......] - ETA: 1s - loss: 0.7267 - mae: 0.5773"
+      "681/729 [===========================>..] - ETA: 0s - loss: 0.6174 - mae: 0.5175"
      ]
     },
     {
@@ -2331,7 +2300,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "606/729 [=======================>......] - ETA: 1s - loss: 0.7261 - mae: 0.5769"
+      "685/729 [===========================>..] - ETA: 0s - loss: 0.6174 - mae: 0.5172"
      ]
     },
     {
@@ -2339,7 +2308,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "610/729 [========================>.....] - ETA: 1s - loss: 0.7254 - mae: 0.5766"
+      "689/729 [===========================>..] - ETA: 0s - loss: 0.6165 - mae: 0.5168"
      ]
     },
     {
@@ -2347,7 +2316,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "614/729 [========================>.....] - ETA: 1s - loss: 0.7248 - mae: 0.5762"
+      "693/729 [===========================>..] - ETA: 0s - loss: 0.6150 - mae: 0.5163"
      ]
     },
     {
@@ -2355,7 +2324,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "618/729 [========================>.....] - ETA: 1s - loss: 0.7242 - mae: 0.5758"
+      "697/729 [===========================>..] - ETA: 0s - loss: 0.6144 - mae: 0.5161"
      ]
     },
     {
@@ -2363,7 +2332,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "622/729 [========================>.....] - ETA: 1s - loss: 0.7235 - mae: 0.5755"
+      "701/729 [===========================>..] - ETA: 0s - loss: 0.6151 - mae: 0.5162"
      ]
     },
     {
@@ -2371,7 +2340,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "626/729 [========================>.....] - ETA: 1s - loss: 0.7229 - mae: 0.5751"
+      "705/729 [============================>.] - ETA: 0s - loss: 0.6137 - mae: 0.5157"
      ]
     },
     {
@@ -2379,7 +2348,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "630/729 [========================>.....] - ETA: 1s - loss: 0.7223 - mae: 0.5747"
+      "709/729 [============================>.] - ETA: 0s - loss: 0.6125 - mae: 0.5153"
      ]
     },
     {
@@ -2387,7 +2356,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "634/729 [=========================>....] - ETA: 1s - loss: 0.7216 - mae: 0.5744"
+      "713/729 [============================>.] - ETA: 0s - loss: 0.6117 - mae: 0.5150"
      ]
     },
     {
@@ -2395,7 +2364,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "638/729 [=========================>....] - ETA: 1s - loss: 0.7210 - mae: 0.5740"
+      "717/729 [============================>.] - ETA: 0s - loss: 0.6152 - mae: 0.5147"
      ]
     },
     {
@@ -2403,7 +2372,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "642/729 [=========================>....] - ETA: 1s - loss: 0.7204 - mae: 0.5736"
+      "721/729 [============================>.] - ETA: 0s - loss: 0.6168 - mae: 0.5148"
      ]
     },
     {
@@ -2411,7 +2380,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "646/729 [=========================>....] - ETA: 1s - loss: 0.7198 - mae: 0.5733"
+      "725/729 [============================>.] - ETA: 0s - loss: 0.6160 - mae: 0.5146"
      ]
     },
     {
@@ -2419,7 +2388,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "650/729 [=========================>....] - ETA: 1s - loss: 0.7192 - mae: 0.5729"
+      "729/729 [==============================] - ETA: 0s - loss: 0.6152 - mae: 0.5144"
      ]
     },
     {
@@ -2427,15 +2396,16 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "654/729 [=========================>....] - ETA: 1s - loss: 0.7185 - mae: 0.5726"
+      "729/729 [==============================] - 11s 15ms/step - loss: 0.6152 - mae: 0.5144 - val_loss: 0.4935 - val_mae: 0.4329\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "658/729 [==========================>...] - ETA: 1s - loss: 0.7180 - mae: 0.5723"
+      "Epoch 2/10\n",
+      "\r",
+      "  1/729 [..............................] - ETA: 0s - loss: 0.5714 - mae: 0.5356"
      ]
     },
     {
@@ -2443,7 +2413,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "662/729 [==========================>...] - ETA: 1s - loss: 0.7174 - mae: 0.5719"
+      "  5/729 [..............................] - ETA: 8s - loss: 0.6197 - mae: 0.5102"
      ]
     },
     {
@@ -2451,7 +2421,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "666/729 [==========================>...] - ETA: 0s - loss: 0.7168 - mae: 0.5716"
+      "  9/729 [..............................] - ETA: 9s - loss: 0.5084 - mae: 0.4746"
      ]
     },
     {
@@ -2459,7 +2429,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "670/729 [==========================>...] - ETA: 0s - loss: 0.7162 - mae: 0.5712"
+      " 13/729 [..............................] - ETA: 9s - loss: 0.4564 - mae: 0.4545"
      ]
     },
     {
@@ -2467,7 +2437,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "674/729 [==========================>...] - ETA: 0s - loss: 0.7156 - mae: 0.5709"
+      " 17/729 [..............................] - ETA: 9s - loss: 0.4512 - mae: 0.4521"
      ]
     },
     {
@@ -2475,7 +2445,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "678/729 [==========================>...] - ETA: 0s - loss: 0.7150 - mae: 0.5706"
+      " 21/729 [..............................] - ETA: 9s - loss: 0.4350 - mae: 0.4444"
      ]
     },
     {
@@ -2483,7 +2453,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "682/729 [===========================>..] - ETA: 0s - loss: 0.7145 - mae: 0.5703"
+      " 25/729 [>.............................] - ETA: 9s - loss: 0.4412 - mae: 0.4472"
      ]
     },
     {
@@ -2491,7 +2461,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "686/729 [===========================>..] - ETA: 0s - loss: 0.7139 - mae: 0.5699"
+      " 29/729 [>.............................] - ETA: 9s - loss: 0.4286 - mae: 0.4422"
      ]
     },
     {
@@ -2499,7 +2469,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "690/729 [===========================>..] - ETA: 0s - loss: 0.7133 - mae: 0.5696"
+      " 33/729 [>.............................] - ETA: 9s - loss: 0.4459 - mae: 0.4453"
      ]
     },
     {
@@ -2507,7 +2477,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "693/729 [===========================>..] - ETA: 0s - loss: 0.7129 - mae: 0.5694"
+      " 37/729 [>.............................] - ETA: 9s - loss: 0.4419 - mae: 0.4464"
      ]
     },
     {
@@ -2515,7 +2485,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "696/729 [===========================>..] - ETA: 0s - loss: 0.7125 - mae: 0.5691"
+      " 41/729 [>.............................] - ETA: 9s - loss: 0.4732 - mae: 0.4476"
      ]
     },
     {
@@ -2523,7 +2493,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "699/729 [===========================>..] - ETA: 0s - loss: 0.7121 - mae: 0.5689"
+      " 45/729 [>.............................] - ETA: 9s - loss: 0.4644 - mae: 0.4460"
      ]
     },
     {
@@ -2531,7 +2501,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "702/729 [===========================>..] - ETA: 0s - loss: 0.7116 - mae: 0.5686"
+      " 49/729 [=>............................] - ETA: 9s - loss: 0.4664 - mae: 0.4480"
      ]
     },
     {
@@ -2539,7 +2509,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "705/729 [============================>.] - ETA: 0s - loss: 0.7112 - mae: 0.5684"
+      " 53/729 [=>............................] - ETA: 9s - loss: 0.4884 - mae: 0.4511"
      ]
     },
     {
@@ -2547,7 +2517,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "708/729 [============================>.] - ETA: 0s - loss: 0.7108 - mae: 0.5682"
+      " 57/729 [=>............................] - ETA: 9s - loss: 0.4904 - mae: 0.4512"
      ]
     },
     {
@@ -2555,7 +2525,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "711/729 [============================>.] - ETA: 0s - loss: 0.7103 - mae: 0.5679"
+      " 61/729 [=>............................] - ETA: 9s - loss: 0.5238 - mae: 0.4534"
      ]
     },
     {
@@ -2563,7 +2533,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "714/729 [============================>.] - ETA: 0s - loss: 0.7099 - mae: 0.5677"
+      " 65/729 [=>............................] - ETA: 9s - loss: 0.5220 - mae: 0.4527"
      ]
     },
     {
@@ -2571,7 +2541,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "717/729 [============================>.] - ETA: 0s - loss: 0.7095 - mae: 0.5675"
+      " 69/729 [=>............................] - ETA: 9s - loss: 0.5184 - mae: 0.4543"
      ]
     },
     {
@@ -2579,7 +2549,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "720/729 [============================>.] - ETA: 0s - loss: 0.7091 - mae: 0.5672"
+      " 73/729 [==>...........................] - ETA: 9s - loss: 0.5085 - mae: 0.4516"
      ]
     },
     {
@@ -2587,7 +2557,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "723/729 [============================>.] - ETA: 0s - loss: 0.7087 - mae: 0.5670"
+      " 77/729 [==>...........................] - ETA: 9s - loss: 0.5058 - mae: 0.4526"
      ]
     },
     {
@@ -2595,7 +2565,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "726/729 [============================>.] - ETA: 0s - loss: 0.7083 - mae: 0.5668"
+      " 81/729 [==>...........................] - ETA: 9s - loss: 0.5059 - mae: 0.4534"
      ]
     },
     {
@@ -2603,7 +2573,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "729/729 [==============================] - ETA: 0s - loss: 0.7079 - mae: 0.5665"
+      " 85/729 [==>...........................] - ETA: 8s - loss: 0.5048 - mae: 0.4543"
      ]
     },
     {
@@ -2611,216 +2581,215 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "729/729 [==============================] - 15s 18ms/step - loss: 0.7077 - mae: 0.5664 - val_loss: 0.4901 - val_mae: 0.4331\n"
+      " 89/729 [==>...........................] - ETA: 8s - loss: 0.5388 - mae: 0.4564"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 2/10\n",
-      "\r",
-      "  1/729 [..............................] - ETA: 31s - loss: 0.5878 - mae: 0.5049"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 93/729 [==>...........................] - ETA: 8s - loss: 0.5322 - mae: 0.4545"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  4/729 [..............................] - ETA: 13s - loss: 0.7851 - mae: 0.5189"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 97/729 [==>...........................] - ETA: 8s - loss: 0.5273 - mae: 0.4545"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  7/729 [..............................] - ETA: 12s - loss: 0.7589 - mae: 0.5200"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "101/729 [===>..........................] - ETA: 8s - loss: 0.5356 - mae: 0.4548"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 10/729 [..............................] - ETA: 12s - loss: 0.7220 - mae: 0.5125"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "105/729 [===>..........................] - ETA: 8s - loss: 0.5393 - mae: 0.4559"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 13/729 [..............................] - ETA: 12s - loss: 0.6949 - mae: 0.5073"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "109/729 [===>..........................] - ETA: 8s - loss: 0.5415 - mae: 0.4561"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 16/729 [..............................] - ETA: 12s - loss: 0.6817 - mae: 0.5054"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "113/729 [===>..........................] - ETA: 8s - loss: 0.5514 - mae: 0.4566"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 19/729 [..............................] - ETA: 12s - loss: 0.6713 - mae: 0.5047"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "117/729 [===>..........................] - ETA: 8s - loss: 0.5625 - mae: 0.4597"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 22/729 [..............................] - ETA: 12s - loss: 0.6636 - mae: 0.5044"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "121/729 [===>..........................] - ETA: 8s - loss: 0.5785 - mae: 0.4613"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 26/729 [>.............................] - ETA: 12s - loss: 0.6547 - mae: 0.5039"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "125/729 [====>.........................] - ETA: 8s - loss: 0.5708 - mae: 0.4597"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 30/729 [>.............................] - ETA: 12s - loss: 0.6454 - mae: 0.5027"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "129/729 [====>.........................] - ETA: 8s - loss: 0.5642 - mae: 0.4585"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 34/729 [>.............................] - ETA: 11s - loss: 0.6477 - mae: 0.5024"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "133/729 [====>.........................] - ETA: 8s - loss: 0.5621 - mae: 0.4594"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 38/729 [>.............................] - ETA: 11s - loss: 0.6489 - mae: 0.5019"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "137/729 [====>.........................] - ETA: 8s - loss: 0.5582 - mae: 0.4590"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 42/729 [>.............................] - ETA: 11s - loss: 0.6474 - mae: 0.5009"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "141/729 [====>.........................] - ETA: 8s - loss: 0.5571 - mae: 0.4592"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 46/729 [>.............................] - ETA: 11s - loss: 0.6463 - mae: 0.5000"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "145/729 [====>.........................] - ETA: 8s - loss: 0.5561 - mae: 0.4595"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 50/729 [=>............................] - ETA: 11s - loss: 0.6440 - mae: 0.4988"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "149/729 [=====>........................] - ETA: 8s - loss: 0.5511 - mae: 0.4582"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 54/729 [=>............................] - ETA: 10s - loss: 0.6415 - mae: 0.4978"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "153/729 [=====>........................] - ETA: 8s - loss: 0.5474 - mae: 0.4574"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 58/729 [=>............................] - ETA: 10s - loss: 0.6388 - mae: 0.4969"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "157/729 [=====>........................] - ETA: 7s - loss: 0.5443 - mae: 0.4570"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 62/729 [=>............................] - ETA: 10s - loss: 0.6371 - mae: 0.4961"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "161/729 [=====>........................] - ETA: 7s - loss: 0.5424 - mae: 0.4564"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 66/729 [=>............................] - ETA: 10s - loss: 0.6366 - mae: 0.4956"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "165/729 [=====>........................] - ETA: 7s - loss: 0.5412 - mae: 0.4568"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 70/729 [=>............................] - ETA: 10s - loss: 0.6354 - mae: 0.4950"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "169/729 [=====>........................] - ETA: 7s - loss: 0.5383 - mae: 0.4566"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 74/729 [==>...........................] - ETA: 10s - loss: 0.6341 - mae: 0.4945"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "173/729 [======>.......................] - ETA: 7s - loss: 0.5363 - mae: 0.4567"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 78/729 [==>...........................] - ETA: 10s - loss: 0.6324 - mae: 0.4939"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "177/729 [======>.......................] - ETA: 7s - loss: 0.5493 - mae: 0.4586"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 82/729 [==>...........................] - ETA: 10s - loss: 0.6305 - mae: 0.4933"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "181/729 [======>.......................] - ETA: 7s - loss: 0.5444 - mae: 0.4574"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 86/729 [==>...........................] - ETA: 10s - loss: 0.6285 - mae: 0.4927"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "185/729 [======>.......................] - ETA: 7s - loss: 0.5410 - mae: 0.4565"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 90/729 [==>...........................] - ETA: 10s - loss: 0.6264 - mae: 0.4921"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "189/729 [======>.......................] - ETA: 7s - loss: 0.5374 - mae: 0.4561"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 94/729 [==>...........................] - ETA: 9s - loss: 0.6242 - mae: 0.4915 "
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "193/729 [======>.......................] - ETA: 7s - loss: 0.5351 - mae: 0.4556"
      ]
     },
     {
@@ -2828,7 +2797,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 98/729 [===>..........................] - ETA: 9s - loss: 0.6228 - mae: 0.4909"
+      "197/729 [=======>......................] - ETA: 7s - loss: 0.5319 - mae: 0.4548"
      ]
     },
     {
@@ -2836,7 +2805,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "102/729 [===>..........................] - ETA: 9s - loss: 0.6220 - mae: 0.4904"
+      "201/729 [=======>......................] - ETA: 7s - loss: 0.5318 - mae: 0.4550"
      ]
     },
     {
@@ -2844,7 +2813,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "106/729 [===>..........................] - ETA: 9s - loss: 0.6210 - mae: 0.4898"
+      "205/729 [=======>......................] - ETA: 7s - loss: 0.5306 - mae: 0.4555"
      ]
     },
     {
@@ -2852,7 +2821,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "109/729 [===>..........................] - ETA: 9s - loss: 0.6200 - mae: 0.4894"
+      "209/729 [=======>......................] - ETA: 7s - loss: 0.5264 - mae: 0.4542"
      ]
     },
     {
@@ -2860,7 +2829,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "112/729 [===>..........................] - ETA: 9s - loss: 0.6189 - mae: 0.4889"
+      "213/729 [=======>......................] - ETA: 7s - loss: 0.5331 - mae: 0.4537"
      ]
     },
     {
@@ -2868,7 +2837,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "115/729 [===>..........................] - ETA: 9s - loss: 0.6178 - mae: 0.4884"
+      "217/729 [=======>......................] - ETA: 7s - loss: 0.5299 - mae: 0.4529"
      ]
     },
     {
@@ -2876,7 +2845,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "118/729 [===>..........................] - ETA: 9s - loss: 0.6167 - mae: 0.4879"
+      "221/729 [========>.....................] - ETA: 7s - loss: 0.5293 - mae: 0.4525"
      ]
     },
     {
@@ -2884,7 +2853,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "121/729 [===>..........................] - ETA: 9s - loss: 0.6158 - mae: 0.4875"
+      "225/729 [========>.....................] - ETA: 7s - loss: 0.5290 - mae: 0.4524"
      ]
     },
     {
@@ -2892,7 +2861,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "124/729 [====>.........................] - ETA: 9s - loss: 0.6148 - mae: 0.4871"
+      "229/729 [========>.....................] - ETA: 7s - loss: 0.5293 - mae: 0.4530"
      ]
     },
     {
@@ -2900,7 +2869,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "127/729 [====>.........................] - ETA: 9s - loss: 0.6137 - mae: 0.4866"
+      "233/729 [========>.....................] - ETA: 6s - loss: 0.5277 - mae: 0.4529"
      ]
     },
     {
@@ -2908,7 +2877,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "130/729 [====>.........................] - ETA: 9s - loss: 0.6126 - mae: 0.4862"
+      "237/729 [========>.....................] - ETA: 6s - loss: 0.5261 - mae: 0.4526"
      ]
     },
     {
@@ -2916,7 +2885,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "134/729 [====>.........................] - ETA: 9s - loss: 0.6111 - mae: 0.4857"
+      "241/729 [========>.....................] - ETA: 6s - loss: 0.5246 - mae: 0.4520"
      ]
     },
     {
@@ -2924,7 +2893,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "137/729 [====>.........................] - ETA: 9s - loss: 0.6100 - mae: 0.4853"
+      "245/729 [=========>....................] - ETA: 6s - loss: 0.5224 - mae: 0.4518"
      ]
     },
     {
@@ -2932,7 +2901,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "140/729 [====>.........................] - ETA: 9s - loss: 0.6090 - mae: 0.4849"
+      "249/729 [=========>....................] - ETA: 6s - loss: 0.5199 - mae: 0.4509"
      ]
     },
     {
@@ -2940,7 +2909,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "143/729 [====>.........................] - ETA: 9s - loss: 0.6079 - mae: 0.4845"
+      "253/729 [=========>....................] - ETA: 6s - loss: 0.5191 - mae: 0.4502"
      ]
     },
     {
@@ -2948,7 +2917,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "146/729 [=====>........................] - ETA: 9s - loss: 0.6067 - mae: 0.4841"
+      "257/729 [=========>....................] - ETA: 6s - loss: 0.5178 - mae: 0.4499"
      ]
     },
     {
@@ -2956,7 +2925,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "150/729 [=====>........................] - ETA: 9s - loss: 0.6052 - mae: 0.4836"
+      "261/729 [=========>....................] - ETA: 6s - loss: 0.5178 - mae: 0.4499"
      ]
     },
     {
@@ -2964,7 +2933,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "153/729 [=====>........................] - ETA: 9s - loss: 0.6040 - mae: 0.4832"
+      "265/729 [=========>....................] - ETA: 6s - loss: 0.5152 - mae: 0.4489"
      ]
     },
     {
@@ -2972,7 +2941,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "156/729 [=====>........................] - ETA: 9s - loss: 0.6029 - mae: 0.4829"
+      "269/729 [==========>...................] - ETA: 6s - loss: 0.5175 - mae: 0.4492"
      ]
     },
     {
@@ -2980,7 +2949,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "159/729 [=====>........................] - ETA: 9s - loss: 0.6017 - mae: 0.4825"
+      "273/729 [==========>...................] - ETA: 6s - loss: 0.5171 - mae: 0.4495"
      ]
     },
     {
@@ -2988,7 +2957,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "162/729 [=====>........................] - ETA: 9s - loss: 0.6006 - mae: 0.4821"
+      "277/729 [==========>...................] - ETA: 6s - loss: 0.5169 - mae: 0.4496"
      ]
     },
     {
@@ -2996,7 +2965,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "165/729 [=====>........................] - ETA: 9s - loss: 0.5996 - mae: 0.4818"
+      "281/729 [==========>...................] - ETA: 6s - loss: 0.5155 - mae: 0.4492"
      ]
     },
     {
@@ -3004,7 +2973,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "168/729 [=====>........................] - ETA: 9s - loss: 0.5987 - mae: 0.4815"
+      "285/729 [==========>...................] - ETA: 6s - loss: 0.5129 - mae: 0.4483"
      ]
     },
     {
@@ -3012,7 +2981,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "171/729 [======>.......................] - ETA: 9s - loss: 0.5981 - mae: 0.4812"
+      "289/729 [==========>...................] - ETA: 6s - loss: 0.5111 - mae: 0.4480"
      ]
     },
     {
@@ -3020,7 +2989,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "174/729 [======>.......................] - ETA: 9s - loss: 0.5976 - mae: 0.4809"
+      "293/729 [===========>..................] - ETA: 6s - loss: 0.5178 - mae: 0.4494"
      ]
     },
     {
@@ -3028,7 +2997,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "177/729 [======>.......................] - ETA: 9s - loss: 0.5970 - mae: 0.4806"
+      "297/729 [===========>..................] - ETA: 6s - loss: 0.5167 - mae: 0.4492"
      ]
     },
     {
@@ -3036,7 +3005,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "180/729 [======>.......................] - ETA: 9s - loss: 0.5964 - mae: 0.4803"
+      "301/729 [===========>..................] - ETA: 6s - loss: 0.5180 - mae: 0.4501"
      ]
     },
     {
@@ -3044,7 +3013,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "183/729 [======>.......................] - ETA: 9s - loss: 0.5958 - mae: 0.4800"
+      "305/729 [===========>..................] - ETA: 5s - loss: 0.5165 - mae: 0.4499"
      ]
     },
     {
@@ -3052,7 +3021,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "186/729 [======>.......................] - ETA: 8s - loss: 0.5952 - mae: 0.4797"
+      "309/729 [===========>..................] - ETA: 5s - loss: 0.5161 - mae: 0.4501"
      ]
     },
     {
@@ -3060,7 +3029,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "189/729 [======>.......................] - ETA: 8s - loss: 0.5946 - mae: 0.4794"
+      "313/729 [===========>..................] - ETA: 5s - loss: 0.5159 - mae: 0.4502"
      ]
     },
     {
@@ -3068,7 +3037,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "192/729 [======>.......................] - ETA: 8s - loss: 0.5940 - mae: 0.4791"
+      "317/729 [============>.................] - ETA: 5s - loss: 0.5145 - mae: 0.4499"
      ]
     },
     {
@@ -3076,7 +3045,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "195/729 [=======>......................] - ETA: 8s - loss: 0.5933 - mae: 0.4788"
+      "321/729 [============>.................] - ETA: 5s - loss: 0.5152 - mae: 0.4495"
      ]
     },
     {
@@ -3084,7 +3053,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "198/729 [=======>......................] - ETA: 8s - loss: 0.5926 - mae: 0.4786"
+      "325/729 [============>.................] - ETA: 5s - loss: 0.5143 - mae: 0.4496"
      ]
     },
     {
@@ -3092,7 +3061,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "201/729 [=======>......................] - ETA: 8s - loss: 0.5920 - mae: 0.4783"
+      "329/729 [============>.................] - ETA: 5s - loss: 0.5190 - mae: 0.4508"
      ]
     },
     {
@@ -3100,7 +3069,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "204/729 [=======>......................] - ETA: 8s - loss: 0.5913 - mae: 0.4780"
+      "333/729 [============>.................] - ETA: 5s - loss: 0.5175 - mae: 0.4504"
      ]
     },
     {
@@ -3108,7 +3077,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "207/729 [=======>......................] - ETA: 8s - loss: 0.5906 - mae: 0.4777"
+      "337/729 [============>.................] - ETA: 5s - loss: 0.5163 - mae: 0.4502"
      ]
     },
     {
@@ -3116,7 +3085,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "210/729 [=======>......................] - ETA: 8s - loss: 0.5899 - mae: 0.4775"
+      "341/729 [=============>................] - ETA: 5s - loss: 0.5144 - mae: 0.4497"
      ]
     },
     {
@@ -3124,7 +3093,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "213/729 [=======>......................] - ETA: 8s - loss: 0.5893 - mae: 0.4772"
+      "345/729 [=============>................] - ETA: 5s - loss: 0.5128 - mae: 0.4492"
      ]
     },
     {
@@ -3132,7 +3101,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "217/729 [=======>......................] - ETA: 8s - loss: 0.5885 - mae: 0.4769"
+      "349/729 [=============>................] - ETA: 5s - loss: 0.5126 - mae: 0.4489"
      ]
     },
     {
@@ -3140,7 +3109,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "220/729 [========>.....................] - ETA: 8s - loss: 0.5878 - mae: 0.4766"
+      "353/729 [=============>................] - ETA: 5s - loss: 0.5188 - mae: 0.4497"
      ]
     },
     {
@@ -3148,7 +3117,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "223/729 [========>.....................] - ETA: 8s - loss: 0.5872 - mae: 0.4764"
+      "357/729 [=============>................] - ETA: 5s - loss: 0.5165 - mae: 0.4490"
      ]
     },
     {
@@ -3156,7 +3125,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "226/729 [========>.....................] - ETA: 8s - loss: 0.5866 - mae: 0.4761"
+      "361/729 [=============>................] - ETA: 5s - loss: 0.5161 - mae: 0.4492"
      ]
     },
     {
@@ -3164,7 +3133,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "229/729 [========>.....................] - ETA: 8s - loss: 0.5859 - mae: 0.4759"
+      "365/729 [==============>...............] - ETA: 5s - loss: 0.5155 - mae: 0.4493"
      ]
     },
     {
@@ -3172,7 +3141,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "232/729 [========>.....................] - ETA: 8s - loss: 0.5853 - mae: 0.4756"
+      "369/729 [==============>...............] - ETA: 5s - loss: 0.5143 - mae: 0.4491"
      ]
     },
     {
@@ -3180,7 +3149,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "235/729 [========>.....................] - ETA: 8s - loss: 0.5847 - mae: 0.4754"
+      "373/729 [==============>...............] - ETA: 5s - loss: 0.5134 - mae: 0.4490"
      ]
     },
     {
@@ -3188,7 +3157,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "238/729 [========>.....................] - ETA: 8s - loss: 0.5841 - mae: 0.4752"
+      "377/729 [==============>...............] - ETA: 4s - loss: 0.5127 - mae: 0.4489"
      ]
     },
     {
@@ -3196,7 +3165,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "241/729 [========>.....................] - ETA: 8s - loss: 0.5835 - mae: 0.4750"
+      "381/729 [==============>...............] - ETA: 4s - loss: 0.5114 - mae: 0.4486"
      ]
     },
     {
@@ -3204,7 +3173,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "244/729 [=========>....................] - ETA: 8s - loss: 0.5831 - mae: 0.4748"
+      "385/729 [==============>...............] - ETA: 4s - loss: 0.5116 - mae: 0.4486"
      ]
     },
     {
@@ -3212,7 +3181,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "247/729 [=========>....................] - ETA: 8s - loss: 0.5826 - mae: 0.4746"
+      "389/729 [===============>..............] - ETA: 4s - loss: 0.5105 - mae: 0.4483"
      ]
     },
     {
@@ -3220,7 +3189,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "250/729 [=========>....................] - ETA: 8s - loss: 0.5822 - mae: 0.4744"
+      "393/729 [===============>..............] - ETA: 4s - loss: 0.5084 - mae: 0.4477"
      ]
     },
     {
@@ -3228,7 +3197,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "253/729 [=========>....................] - ETA: 8s - loss: 0.5819 - mae: 0.4742"
+      "397/729 [===============>..............] - ETA: 4s - loss: 0.5075 - mae: 0.4475"
      ]
     },
     {
@@ -3236,7 +3205,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "256/729 [=========>....................] - ETA: 7s - loss: 0.5815 - mae: 0.4740"
+      "401/729 [===============>..............] - ETA: 4s - loss: 0.5066 - mae: 0.4475"
      ]
     },
     {
@@ -3244,7 +3213,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "259/729 [=========>....................] - ETA: 7s - loss: 0.5812 - mae: 0.4738"
+      "405/729 [===============>..............] - ETA: 4s - loss: 0.5049 - mae: 0.4470"
      ]
     },
     {
@@ -3252,7 +3221,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "262/729 [=========>....................] - ETA: 7s - loss: 0.5808 - mae: 0.4736"
+      "409/729 [===============>..............] - ETA: 4s - loss: 0.5045 - mae: 0.4470"
      ]
     },
     {
@@ -3260,7 +3229,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "265/729 [=========>....................] - ETA: 7s - loss: 0.5804 - mae: 0.4735"
+      "413/729 [===============>..............] - ETA: 4s - loss: 0.5029 - mae: 0.4465"
      ]
     },
     {
@@ -3268,7 +3237,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "268/729 [==========>...................] - ETA: 7s - loss: 0.5801 - mae: 0.4733"
+      "417/729 [================>.............] - ETA: 4s - loss: 0.5011 - mae: 0.4460"
      ]
     },
     {
@@ -3276,7 +3245,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "272/729 [==========>...................] - ETA: 7s - loss: 0.5796 - mae: 0.4731"
+      "421/729 [================>.............] - ETA: 4s - loss: 0.5012 - mae: 0.4461"
      ]
     },
     {
@@ -3284,7 +3253,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "276/729 [==========>...................] - ETA: 7s - loss: 0.5792 - mae: 0.4728"
+      "425/729 [================>.............] - ETA: 4s - loss: 0.5000 - mae: 0.4458"
      ]
     },
     {
@@ -3292,7 +3261,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "280/729 [==========>...................] - ETA: 7s - loss: 0.5787 - mae: 0.4726"
+      "429/729 [================>.............] - ETA: 4s - loss: 0.5005 - mae: 0.4462"
      ]
     },
     {
@@ -3300,7 +3269,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "284/729 [==========>...................] - ETA: 7s - loss: 0.5783 - mae: 0.4724"
+      "433/729 [================>.............] - ETA: 4s - loss: 0.5085 - mae: 0.4464"
      ]
     },
     {
@@ -3308,7 +3277,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "288/729 [==========>...................] - ETA: 7s - loss: 0.5778 - mae: 0.4722"
+      "437/729 [================>.............] - ETA: 4s - loss: 0.5093 - mae: 0.4470"
      ]
     },
     {
@@ -3316,7 +3285,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "292/729 [===========>..................] - ETA: 7s - loss: 0.5774 - mae: 0.4720"
+      "441/729 [=================>............] - ETA: 4s - loss: 0.5087 - mae: 0.4467"
      ]
     },
     {
@@ -3324,7 +3293,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "296/729 [===========>..................] - ETA: 7s - loss: 0.5769 - mae: 0.4718"
+      "445/729 [=================>............] - ETA: 3s - loss: 0.5076 - mae: 0.4466"
      ]
     },
     {
@@ -3332,7 +3301,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "300/729 [===========>..................] - ETA: 7s - loss: 0.5765 - mae: 0.4716"
+      "449/729 [=================>............] - ETA: 3s - loss: 0.5088 - mae: 0.4469"
      ]
     },
     {
@@ -3340,7 +3309,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "304/729 [===========>..................] - ETA: 7s - loss: 0.5761 - mae: 0.4714"
+      "453/729 [=================>............] - ETA: 3s - loss: 0.5089 - mae: 0.4469"
      ]
     },
     {
@@ -3348,7 +3317,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "308/729 [===========>..................] - ETA: 7s - loss: 0.5758 - mae: 0.4712"
+      "457/729 [=================>............] - ETA: 3s - loss: 0.5090 - mae: 0.4470"
      ]
     },
     {
@@ -3356,7 +3325,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "312/729 [===========>..................] - ETA: 6s - loss: 0.5754 - mae: 0.4710"
+      "461/729 [=================>............] - ETA: 3s - loss: 0.5088 - mae: 0.4468"
      ]
     },
     {
@@ -3364,7 +3333,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "316/729 [============>.................] - ETA: 6s - loss: 0.5751 - mae: 0.4709"
+      "465/729 [==================>...........] - ETA: 3s - loss: 0.5074 - mae: 0.4464"
      ]
     },
     {
@@ -3372,7 +3341,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "320/729 [============>.................] - ETA: 6s - loss: 0.5748 - mae: 0.4707"
+      "469/729 [==================>...........] - ETA: 3s - loss: 0.5100 - mae: 0.4471"
      ]
     },
     {
@@ -3380,7 +3349,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "324/729 [============>.................] - ETA: 6s - loss: 0.5745 - mae: 0.4706"
+      "473/729 [==================>...........] - ETA: 3s - loss: 0.5087 - mae: 0.4467"
      ]
     },
     {
@@ -3388,7 +3357,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "328/729 [============>.................] - ETA: 6s - loss: 0.5743 - mae: 0.4704"
+      "477/729 [==================>...........] - ETA: 3s - loss: 0.5089 - mae: 0.4468"
      ]
     },
     {
@@ -3396,7 +3365,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "332/729 [============>.................] - ETA: 6s - loss: 0.5740 - mae: 0.4702"
+      "481/729 [==================>...........] - ETA: 3s - loss: 0.5103 - mae: 0.4470"
      ]
     },
     {
@@ -3404,7 +3373,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "336/729 [============>.................] - ETA: 6s - loss: 0.5737 - mae: 0.4701"
+      "485/729 [==================>...........] - ETA: 3s - loss: 0.5089 - mae: 0.4467"
      ]
     },
     {
@@ -3412,7 +3381,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "340/729 [============>.................] - ETA: 6s - loss: 0.5734 - mae: 0.4699"
+      "489/729 [===================>..........] - ETA: 3s - loss: 0.5089 - mae: 0.4466"
      ]
     },
     {
@@ -3420,7 +3389,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "344/729 [=============>................] - ETA: 6s - loss: 0.5731 - mae: 0.4698"
+      "493/729 [===================>..........] - ETA: 3s - loss: 0.5084 - mae: 0.4466"
      ]
     },
     {
@@ -3428,7 +3397,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "348/729 [=============>................] - ETA: 6s - loss: 0.5728 - mae: 0.4696"
+      "497/729 [===================>..........] - ETA: 3s - loss: 0.5069 - mae: 0.4461"
      ]
     },
     {
@@ -3436,7 +3405,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "352/729 [=============>................] - ETA: 6s - loss: 0.5725 - mae: 0.4695"
+      "501/729 [===================>..........] - ETA: 3s - loss: 0.5060 - mae: 0.4460"
      ]
     },
     {
@@ -3444,7 +3413,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "356/729 [=============>................] - ETA: 6s - loss: 0.5721 - mae: 0.4693"
+      "505/729 [===================>..........] - ETA: 3s - loss: 0.5057 - mae: 0.4459"
      ]
     },
     {
@@ -3452,7 +3421,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "360/729 [=============>................] - ETA: 6s - loss: 0.5718 - mae: 0.4691"
+      "509/729 [===================>..........] - ETA: 3s - loss: 0.5117 - mae: 0.4463"
      ]
     },
     {
@@ -3460,7 +3429,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "364/729 [=============>................] - ETA: 5s - loss: 0.5714 - mae: 0.4690"
+      "513/729 [====================>.........] - ETA: 3s - loss: 0.5106 - mae: 0.4459"
      ]
     },
     {
@@ -3468,7 +3437,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "368/729 [==============>...............] - ETA: 5s - loss: 0.5710 - mae: 0.4688"
+      "517/729 [====================>.........] - ETA: 2s - loss: 0.5095 - mae: 0.4456"
      ]
     },
     {
@@ -3476,7 +3445,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "372/729 [==============>...............] - ETA: 5s - loss: 0.5706 - mae: 0.4686"
+      "521/729 [====================>.........] - ETA: 2s - loss: 0.5076 - mae: 0.4449"
      ]
     },
     {
@@ -3484,7 +3453,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "376/729 [==============>...............] - ETA: 5s - loss: 0.5702 - mae: 0.4685"
+      "525/729 [====================>.........] - ETA: 2s - loss: 0.5062 - mae: 0.4444"
      ]
     },
     {
@@ -3492,7 +3461,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "380/729 [==============>...............] - ETA: 5s - loss: 0.5699 - mae: 0.4683"
+      "529/729 [====================>.........] - ETA: 2s - loss: 0.5053 - mae: 0.4441"
      ]
     },
     {
@@ -3500,7 +3469,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "384/729 [==============>...............] - ETA: 5s - loss: 0.5695 - mae: 0.4682"
+      "533/729 [====================>.........] - ETA: 2s - loss: 0.5040 - mae: 0.4438"
      ]
     },
     {
@@ -3508,7 +3477,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "388/729 [==============>...............] - ETA: 5s - loss: 0.5691 - mae: 0.4680"
+      "537/729 [=====================>........] - ETA: 2s - loss: 0.5029 - mae: 0.4436"
      ]
     },
     {
@@ -3516,7 +3485,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "392/729 [===============>..............] - ETA: 5s - loss: 0.5688 - mae: 0.4679"
+      "541/729 [=====================>........] - ETA: 2s - loss: 0.5042 - mae: 0.4438"
      ]
     },
     {
@@ -3524,7 +3493,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "396/729 [===============>..............] - ETA: 5s - loss: 0.5684 - mae: 0.4677"
+      "545/729 [=====================>........] - ETA: 2s - loss: 0.5031 - mae: 0.4435"
      ]
     },
     {
@@ -3532,7 +3501,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "400/729 [===============>..............] - ETA: 5s - loss: 0.5681 - mae: 0.4676"
+      "549/729 [=====================>........] - ETA: 2s - loss: 0.5023 - mae: 0.4432"
      ]
     },
     {
@@ -3540,7 +3509,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "404/729 [===============>..............] - ETA: 5s - loss: 0.5677 - mae: 0.4674"
+      "553/729 [=====================>........] - ETA: 2s - loss: 0.5062 - mae: 0.4430"
      ]
     },
     {
@@ -3548,7 +3517,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "408/729 [===============>..............] - ETA: 5s - loss: 0.5673 - mae: 0.4673"
+      "557/729 [=====================>........] - ETA: 2s - loss: 0.5056 - mae: 0.4429"
      ]
     },
     {
@@ -3556,7 +3525,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "412/729 [===============>..............] - ETA: 5s - loss: 0.5670 - mae: 0.4671"
+      "561/729 [======================>.......] - ETA: 2s - loss: 0.5052 - mae: 0.4428"
      ]
     },
     {
@@ -3564,7 +3533,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "416/729 [================>.............] - ETA: 5s - loss: 0.5666 - mae: 0.4670"
+      "565/729 [======================>.......] - ETA: 2s - loss: 0.5047 - mae: 0.4426"
      ]
     },
     {
@@ -3572,7 +3541,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "420/729 [================>.............] - ETA: 5s - loss: 0.5663 - mae: 0.4668"
+      "569/729 [======================>.......] - ETA: 2s - loss: 0.5051 - mae: 0.4429"
      ]
     },
     {
@@ -3580,7 +3549,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "424/729 [================>.............] - ETA: 4s - loss: 0.5659 - mae: 0.4667"
+      "573/729 [======================>.......] - ETA: 2s - loss: 0.5051 - mae: 0.4429"
      ]
     },
     {
@@ -3588,7 +3557,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "428/729 [================>.............] - ETA: 4s - loss: 0.5656 - mae: 0.4665"
+      "577/729 [======================>.......] - ETA: 2s - loss: 0.5067 - mae: 0.4430"
      ]
     },
     {
@@ -3596,7 +3565,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "432/729 [================>.............] - ETA: 4s - loss: 0.5652 - mae: 0.4664"
+      "581/729 [======================>.......] - ETA: 2s - loss: 0.5058 - mae: 0.4428"
      ]
     },
     {
@@ -3604,7 +3573,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "436/729 [================>.............] - ETA: 4s - loss: 0.5649 - mae: 0.4663"
+      "585/729 [=======================>......] - ETA: 2s - loss: 0.5050 - mae: 0.4425"
      ]
     },
     {
@@ -3612,7 +3581,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "440/729 [=================>............] - ETA: 4s - loss: 0.5645 - mae: 0.4662"
+      "589/729 [=======================>......] - ETA: 1s - loss: 0.5038 - mae: 0.4421"
      ]
     },
     {
@@ -3620,7 +3589,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "444/729 [=================>............] - ETA: 4s - loss: 0.5642 - mae: 0.4660"
+      "593/729 [=======================>......] - ETA: 1s - loss: 0.5033 - mae: 0.4420"
      ]
     },
     {
@@ -3628,7 +3597,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "448/729 [=================>............] - ETA: 4s - loss: 0.5638 - mae: 0.4659"
+      "597/729 [=======================>......] - ETA: 1s - loss: 0.5031 - mae: 0.4419"
      ]
     },
     {
@@ -3636,7 +3605,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "452/729 [=================>............] - ETA: 4s - loss: 0.5635 - mae: 0.4658"
+      "601/729 [=======================>......] - ETA: 1s - loss: 0.5038 - mae: 0.4421"
      ]
     },
     {
@@ -3644,7 +3613,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "456/729 [=================>............] - ETA: 4s - loss: 0.5631 - mae: 0.4656"
+      "605/729 [=======================>......] - ETA: 1s - loss: 0.5034 - mae: 0.4420"
      ]
     },
     {
@@ -3652,7 +3621,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "460/729 [=================>............] - ETA: 4s - loss: 0.5628 - mae: 0.4655"
+      "609/729 [========================>.....] - ETA: 1s - loss: 0.5073 - mae: 0.4421"
      ]
     },
     {
@@ -3660,7 +3629,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "464/729 [==================>...........] - ETA: 4s - loss: 0.5624 - mae: 0.4654"
+      "613/729 [========================>.....] - ETA: 1s - loss: 0.5073 - mae: 0.4421"
      ]
     },
     {
@@ -3668,7 +3637,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "468/729 [==================>...........] - ETA: 4s - loss: 0.5621 - mae: 0.4653"
+      "617/729 [========================>.....] - ETA: 1s - loss: 0.5060 - mae: 0.4417"
      ]
     },
     {
@@ -3676,7 +3645,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "472/729 [==================>...........] - ETA: 4s - loss: 0.5617 - mae: 0.4651"
+      "621/729 [========================>.....] - ETA: 1s - loss: 0.5051 - mae: 0.4415"
      ]
     },
     {
@@ -3684,7 +3653,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "476/729 [==================>...........] - ETA: 4s - loss: 0.5614 - mae: 0.4650"
+      "625/729 [========================>.....] - ETA: 1s - loss: 0.5035 - mae: 0.4410"
      ]
     },
     {
@@ -3692,7 +3661,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "480/729 [==================>...........] - ETA: 3s - loss: 0.5610 - mae: 0.4649"
+      "629/729 [========================>.....] - ETA: 1s - loss: 0.5028 - mae: 0.4408"
      ]
     },
     {
@@ -3700,7 +3669,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "484/729 [==================>...........] - ETA: 3s - loss: 0.5606 - mae: 0.4647"
+      "633/729 [=========================>....] - ETA: 1s - loss: 0.5026 - mae: 0.4408"
      ]
     },
     {
@@ -3708,7 +3677,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "488/729 [===================>..........] - ETA: 3s - loss: 0.5603 - mae: 0.4646"
+      "637/729 [=========================>....] - ETA: 1s - loss: 0.5021 - mae: 0.4406"
      ]
     },
     {
@@ -3716,7 +3685,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "492/729 [===================>..........] - ETA: 3s - loss: 0.5599 - mae: 0.4645"
+      "641/729 [=========================>....] - ETA: 1s - loss: 0.5017 - mae: 0.4406"
      ]
     },
     {
@@ -3724,7 +3693,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "496/729 [===================>..........] - ETA: 3s - loss: 0.5595 - mae: 0.4643"
+      "645/729 [=========================>....] - ETA: 1s - loss: 0.5027 - mae: 0.4407"
      ]
     },
     {
@@ -3732,7 +3701,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "500/729 [===================>..........] - ETA: 3s - loss: 0.5592 - mae: 0.4642"
+      "649/729 [=========================>....] - ETA: 1s - loss: 0.5022 - mae: 0.4406"
      ]
     },
     {
@@ -3740,7 +3709,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "504/729 [===================>..........] - ETA: 3s - loss: 0.5589 - mae: 0.4641"
+      "653/729 [=========================>....] - ETA: 1s - loss: 0.5037 - mae: 0.4408"
      ]
     },
     {
@@ -3748,7 +3717,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "508/729 [===================>..........] - ETA: 3s - loss: 0.5585 - mae: 0.4640"
+      "657/729 [==========================>...] - ETA: 1s - loss: 0.5045 - mae: 0.4411"
      ]
     },
     {
@@ -3756,7 +3725,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "512/729 [====================>.........] - ETA: 3s - loss: 0.5582 - mae: 0.4638"
+      "661/729 [==========================>...] - ETA: 0s - loss: 0.5044 - mae: 0.4410"
      ]
     },
     {
@@ -3764,7 +3733,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "516/729 [====================>.........] - ETA: 3s - loss: 0.5578 - mae: 0.4637"
+      "665/729 [==========================>...] - ETA: 0s - loss: 0.5071 - mae: 0.4415"
      ]
     },
     {
@@ -3772,7 +3741,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "520/729 [====================>.........] - ETA: 3s - loss: 0.5575 - mae: 0.4636"
+      "669/729 [==========================>...] - ETA: 0s - loss: 0.5060 - mae: 0.4412"
      ]
     },
     {
@@ -3780,7 +3749,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "524/729 [====================>.........] - ETA: 3s - loss: 0.5572 - mae: 0.4635"
+      "673/729 [==========================>...] - ETA: 0s - loss: 0.5055 - mae: 0.4413"
      ]
     },
     {
@@ -3788,7 +3757,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "528/729 [====================>.........] - ETA: 3s - loss: 0.5568 - mae: 0.4633"
+      "677/729 [==========================>...] - ETA: 0s - loss: 0.5056 - mae: 0.4415"
      ]
     },
     {
@@ -3796,7 +3765,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "532/729 [====================>.........] - ETA: 3s - loss: 0.5565 - mae: 0.4632"
+      "681/729 [===========================>..] - ETA: 0s - loss: 0.5051 - mae: 0.4414"
      ]
     },
     {
@@ -3804,7 +3773,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "536/729 [=====================>........] - ETA: 3s - loss: 0.5561 - mae: 0.4631"
+      "685/729 [===========================>..] - ETA: 0s - loss: 0.5073 - mae: 0.4416"
      ]
     },
     {
@@ -3812,7 +3781,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "540/729 [=====================>........] - ETA: 3s - loss: 0.5558 - mae: 0.4630"
+      "689/729 [===========================>..] - ETA: 0s - loss: 0.5066 - mae: 0.4413"
      ]
     },
     {
@@ -3820,7 +3789,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "544/729 [=====================>........] - ETA: 2s - loss: 0.5554 - mae: 0.4629"
+      "693/729 [===========================>..] - ETA: 0s - loss: 0.5055 - mae: 0.4410"
      ]
     },
     {
@@ -3828,7 +3797,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "548/729 [=====================>........] - ETA: 2s - loss: 0.5551 - mae: 0.4627"
+      "697/729 [===========================>..] - ETA: 0s - loss: 0.5048 - mae: 0.4408"
      ]
     },
     {
@@ -3836,7 +3805,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "552/729 [=====================>........] - ETA: 2s - loss: 0.5547 - mae: 0.4626"
+      "701/729 [===========================>..] - ETA: 0s - loss: 0.5046 - mae: 0.4408"
      ]
     },
     {
@@ -3844,7 +3813,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "556/729 [=====================>........] - ETA: 2s - loss: 0.5544 - mae: 0.4625"
+      "705/729 [============================>.] - ETA: 0s - loss: 0.5038 - mae: 0.4406"
      ]
     },
     {
@@ -3852,7 +3821,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "560/729 [======================>.......] - ETA: 2s - loss: 0.5540 - mae: 0.4624"
+      "709/729 [============================>.] - ETA: 0s - loss: 0.5046 - mae: 0.4409"
      ]
     },
     {
@@ -3860,7 +3829,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "564/729 [======================>.......] - ETA: 2s - loss: 0.5536 - mae: 0.4622"
+      "713/729 [============================>.] - ETA: 0s - loss: 0.5039 - mae: 0.4408"
      ]
     },
     {
@@ -3868,7 +3837,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "568/729 [======================>.......] - ETA: 2s - loss: 0.5533 - mae: 0.4621"
+      "717/729 [============================>.] - ETA: 0s - loss: 0.5040 - mae: 0.4409"
      ]
     },
     {
@@ -3876,7 +3845,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "572/729 [======================>.......] - ETA: 2s - loss: 0.5530 - mae: 0.4620"
+      "721/729 [============================>.] - ETA: 0s - loss: 0.5035 - mae: 0.4409"
      ]
     },
     {
@@ -3884,7 +3853,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "576/729 [======================>.......] - ETA: 2s - loss: 0.5527 - mae: 0.4619"
+      "725/729 [============================>.] - ETA: 0s - loss: 0.5066 - mae: 0.4413"
      ]
     },
     {
@@ -3892,7 +3861,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "580/729 [======================>.......] - ETA: 2s - loss: 0.5524 - mae: 0.4618"
+      "729/729 [==============================] - ETA: 0s - loss: 0.5064 - mae: 0.4412"
      ]
     },
     {
@@ -3900,15 +3869,16 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "584/729 [=======================>......] - ETA: 2s - loss: 0.5521 - mae: 0.4616"
+      "729/729 [==============================] - 11s 15ms/step - loss: 0.5064 - mae: 0.4412 - val_loss: 0.4618 - val_mae: 0.4092\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "588/729 [=======================>......] - ETA: 2s - loss: 0.5518 - mae: 0.4615"
+      "Epoch 3/10\n",
+      "\r",
+      "  1/729 [..............................] - ETA: 0s - loss: 0.3005 - mae: 0.3752"
      ]
     },
     {
@@ -3916,7 +3886,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "592/729 [=======================>......] - ETA: 2s - loss: 0.5515 - mae: 0.4614"
+      "  5/729 [..............................] - ETA: 7s - loss: 0.3743 - mae: 0.4228"
      ]
     },
     {
@@ -3924,7 +3894,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "596/729 [=======================>......] - ETA: 2s - loss: 0.5512 - mae: 0.4613"
+      "  9/729 [..............................] - ETA: 8s - loss: 0.3697 - mae: 0.4135"
      ]
     },
     {
@@ -3932,7 +3902,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "600/729 [=======================>......] - ETA: 2s - loss: 0.5509 - mae: 0.4612"
+      " 13/729 [..............................] - ETA: 8s - loss: 0.4249 - mae: 0.4289"
      ]
     },
     {
@@ -3940,7 +3910,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "604/729 [=======================>......] - ETA: 1s - loss: 0.5506 - mae: 0.4611"
+      " 17/729 [..............................] - ETA: 8s - loss: 0.4300 - mae: 0.4244"
      ]
     },
     {
@@ -3948,7 +3918,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "608/729 [========================>.....] - ETA: 1s - loss: 0.5504 - mae: 0.4610"
+      " 21/729 [..............................] - ETA: 9s - loss: 0.5392 - mae: 0.4288"
      ]
     },
     {
@@ -3956,7 +3926,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "612/729 [========================>.....] - ETA: 1s - loss: 0.5501 - mae: 0.4609"
+      " 25/729 [>.............................] - ETA: 9s - loss: 0.5310 - mae: 0.4320"
      ]
     },
     {
@@ -3964,7 +3934,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "616/729 [========================>.....] - ETA: 1s - loss: 0.5498 - mae: 0.4608"
+      " 29/729 [>.............................] - ETA: 9s - loss: 0.5189 - mae: 0.4319"
      ]
     },
     {
@@ -3972,7 +3942,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "620/729 [========================>.....] - ETA: 1s - loss: 0.5495 - mae: 0.4607"
+      " 33/729 [>.............................] - ETA: 9s - loss: 0.4904 - mae: 0.4225"
      ]
     },
     {
@@ -3980,7 +3950,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "624/729 [========================>.....] - ETA: 1s - loss: 0.5493 - mae: 0.4606"
+      " 37/729 [>.............................] - ETA: 9s - loss: 0.4704 - mae: 0.4178"
      ]
     },
     {
@@ -3988,7 +3958,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "628/729 [========================>.....] - ETA: 1s - loss: 0.5490 - mae: 0.4605"
+      " 41/729 [>.............................] - ETA: 9s - loss: 0.4552 - mae: 0.4145"
      ]
     },
     {
@@ -3996,7 +3966,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "632/729 [=========================>....] - ETA: 1s - loss: 0.5487 - mae: 0.4604"
+      " 45/729 [>.............................] - ETA: 9s - loss: 0.4412 - mae: 0.4108"
      ]
     },
     {
@@ -4004,7 +3974,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "636/729 [=========================>....] - ETA: 1s - loss: 0.5485 - mae: 0.4602"
+      " 49/729 [=>............................] - ETA: 9s - loss: 0.4418 - mae: 0.4123"
      ]
     },
     {
@@ -4012,7 +3982,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "640/729 [=========================>....] - ETA: 1s - loss: 0.5483 - mae: 0.4601"
+      " 53/729 [=>............................] - ETA: 9s - loss: 0.4666 - mae: 0.4165"
      ]
     },
     {
@@ -4020,7 +3990,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "644/729 [=========================>....] - ETA: 1s - loss: 0.5480 - mae: 0.4600"
+      " 57/729 [=>............................] - ETA: 9s - loss: 0.4616 - mae: 0.4154"
      ]
     },
     {
@@ -4028,7 +3998,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "648/729 [=========================>....] - ETA: 1s - loss: 0.5478 - mae: 0.4599"
+      " 61/729 [=>............................] - ETA: 9s - loss: 0.4592 - mae: 0.4165"
      ]
     },
     {
@@ -4036,7 +4006,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "652/729 [=========================>....] - ETA: 1s - loss: 0.5475 - mae: 0.4598"
+      " 65/729 [=>............................] - ETA: 9s - loss: 0.4582 - mae: 0.4154"
      ]
     },
     {
@@ -4044,7 +4014,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "656/729 [=========================>....] - ETA: 1s - loss: 0.5473 - mae: 0.4597"
+      " 69/729 [=>............................] - ETA: 9s - loss: 0.4581 - mae: 0.4161"
      ]
     },
     {
@@ -4052,7 +4022,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "660/729 [==========================>...] - ETA: 1s - loss: 0.5471 - mae: 0.4596"
+      " 73/729 [==>...........................] - ETA: 8s - loss: 0.4502 - mae: 0.4138"
      ]
     },
     {
@@ -4060,7 +4030,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "664/729 [==========================>...] - ETA: 1s - loss: 0.5468 - mae: 0.4595"
+      " 77/729 [==>...........................] - ETA: 8s - loss: 0.4635 - mae: 0.4192"
      ]
     },
     {
@@ -4068,7 +4038,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "668/729 [==========================>...] - ETA: 0s - loss: 0.5466 - mae: 0.4594"
+      " 81/729 [==>...........................] - ETA: 8s - loss: 0.4682 - mae: 0.4193"
      ]
     },
     {
@@ -4076,7 +4046,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "672/729 [==========================>...] - ETA: 0s - loss: 0.5463 - mae: 0.4593"
+      " 85/729 [==>...........................] - ETA: 8s - loss: 0.4610 - mae: 0.4178"
      ]
     },
     {
@@ -4084,7 +4054,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "676/729 [==========================>...] - ETA: 0s - loss: 0.5461 - mae: 0.4592"
+      " 89/729 [==>...........................] - ETA: 8s - loss: 0.4860 - mae: 0.4175"
      ]
     },
     {
@@ -4092,7 +4062,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "680/729 [==========================>...] - ETA: 0s - loss: 0.5458 - mae: 0.4591"
+      " 93/729 [==>...........................] - ETA: 8s - loss: 0.4893 - mae: 0.4192"
      ]
     },
     {
@@ -4100,7 +4070,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "684/729 [===========================>..] - ETA: 0s - loss: 0.5456 - mae: 0.4590"
+      " 97/729 [==>...........................] - ETA: 8s - loss: 0.5008 - mae: 0.4202"
      ]
     },
     {
@@ -4108,7 +4078,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "688/729 [===========================>..] - ETA: 0s - loss: 0.5454 - mae: 0.4589"
+      "101/729 [===>..........................] - ETA: 8s - loss: 0.4973 - mae: 0.4206"
      ]
     },
     {
@@ -4116,7 +4086,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "692/729 [===========================>..] - ETA: 0s - loss: 0.5451 - mae: 0.4588"
+      "105/729 [===>..........................] - ETA: 8s - loss: 0.4882 - mae: 0.4179"
      ]
     },
     {
@@ -4124,7 +4094,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "696/729 [===========================>..] - ETA: 0s - loss: 0.5449 - mae: 0.4587"
+      "109/729 [===>..........................] - ETA: 8s - loss: 0.4856 - mae: 0.4176"
      ]
     },
     {
@@ -4132,7 +4102,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "700/729 [===========================>..] - ETA: 0s - loss: 0.5447 - mae: 0.4586"
+      "113/729 [===>..........................] - ETA: 8s - loss: 0.4799 - mae: 0.4169"
      ]
     },
     {
@@ -4140,7 +4110,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "704/729 [===========================>..] - ETA: 0s - loss: 0.5445 - mae: 0.4585"
+      "117/729 [===>..........................] - ETA: 8s - loss: 0.4767 - mae: 0.4169"
      ]
     },
     {
@@ -4148,7 +4118,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "708/729 [============================>.] - ETA: 0s - loss: 0.5443 - mae: 0.4584"
+      "121/729 [===>..........................] - ETA: 8s - loss: 0.4754 - mae: 0.4181"
      ]
     },
     {
@@ -4156,7 +4126,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "712/729 [============================>.] - ETA: 0s - loss: 0.5441 - mae: 0.4583"
+      "125/729 [====>.........................] - ETA: 8s - loss: 0.4763 - mae: 0.4190"
      ]
     },
     {
@@ -4164,7 +4134,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "716/729 [============================>.] - ETA: 0s - loss: 0.5439 - mae: 0.4583"
+      "129/729 [====>.........................] - ETA: 8s - loss: 0.4852 - mae: 0.4203"
      ]
     },
     {
@@ -4172,7 +4142,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "720/729 [============================>.] - ETA: 0s - loss: 0.5437 - mae: 0.4582"
+      "133/729 [====>.........................] - ETA: 8s - loss: 0.4860 - mae: 0.4210"
      ]
     },
     {
@@ -4180,7 +4150,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "724/729 [============================>.] - ETA: 0s - loss: 0.5435 - mae: 0.4581"
+      "137/729 [====>.........................] - ETA: 8s - loss: 0.4845 - mae: 0.4210"
      ]
     },
     {
@@ -4188,7 +4158,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "728/729 [============================>.] - ETA: 0s - loss: 0.5433 - mae: 0.4580"
+      "141/729 [====>.........................] - ETA: 8s - loss: 0.4868 - mae: 0.4216"
      ]
     },
     {
@@ -4196,208 +4166,207 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "729/729 [==============================] - 12s 17ms/step - loss: 0.5432 - mae: 0.4579 - val_loss: 0.4635 - val_mae: 0.4052\n"
+      "145/729 [====>.........................] - ETA: 8s - loss: 0.4828 - mae: 0.4210"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 3/10\n",
-      "\r",
-      "  1/729 [..............................] - ETA: 26s - loss: 0.6168 - mae: 0.4961"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "149/729 [=====>........................] - ETA: 8s - loss: 0.4785 - mae: 0.4201"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  5/729 [..............................] - ETA: 10s - loss: 0.4479 - mae: 0.4277"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "153/729 [=====>........................] - ETA: 8s - loss: 0.4748 - mae: 0.4189"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  9/729 [..............................] - ETA: 10s - loss: 0.4115 - mae: 0.4146"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "157/729 [=====>........................] - ETA: 7s - loss: 0.4849 - mae: 0.4209"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 13/729 [..............................] - ETA: 10s - loss: 0.3955 - mae: 0.4100"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "161/729 [=====>........................] - ETA: 7s - loss: 0.4848 - mae: 0.4216"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 17/729 [..............................] - ETA: 10s - loss: 0.4023 - mae: 0.4116"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "165/729 [=====>........................] - ETA: 7s - loss: 0.4861 - mae: 0.4216"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 21/729 [..............................] - ETA: 10s - loss: 0.4183 - mae: 0.4128"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "169/729 [=====>........................] - ETA: 7s - loss: 0.4826 - mae: 0.4211"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 25/729 [>.............................] - ETA: 10s - loss: 0.4253 - mae: 0.4128"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "173/729 [======>.......................] - ETA: 7s - loss: 0.4836 - mae: 0.4215"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 29/729 [>.............................] - ETA: 10s - loss: 0.4303 - mae: 0.4132"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "177/729 [======>.......................] - ETA: 7s - loss: 0.4805 - mae: 0.4210"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 33/729 [>.............................] - ETA: 10s - loss: 0.4322 - mae: 0.4131"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "181/729 [======>.......................] - ETA: 7s - loss: 0.4869 - mae: 0.4227"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 37/729 [>.............................] - ETA: 10s - loss: 0.4348 - mae: 0.4136"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "185/729 [======>.......................] - ETA: 7s - loss: 0.4822 - mae: 0.4213"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 41/729 [>.............................] - ETA: 10s - loss: 0.4379 - mae: 0.4145"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "189/729 [======>.......................] - ETA: 7s - loss: 0.4837 - mae: 0.4228"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 45/729 [>.............................] - ETA: 10s - loss: 0.4421 - mae: 0.4154"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "193/729 [======>.......................] - ETA: 7s - loss: 0.4825 - mae: 0.4224"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 49/729 [=>............................] - ETA: 10s - loss: 0.4448 - mae: 0.4161"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "197/729 [=======>......................] - ETA: 7s - loss: 0.4863 - mae: 0.4238"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 53/729 [=>............................] - ETA: 10s - loss: 0.4471 - mae: 0.4168"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "201/729 [=======>......................] - ETA: 7s - loss: 0.4830 - mae: 0.4228"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 57/729 [=>............................] - ETA: 10s - loss: 0.4508 - mae: 0.4177"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "205/729 [=======>......................] - ETA: 7s - loss: 0.4799 - mae: 0.4220"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 61/729 [=>............................] - ETA: 10s - loss: 0.4538 - mae: 0.4185"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "209/729 [=======>......................] - ETA: 7s - loss: 0.4764 - mae: 0.4209"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 65/729 [=>............................] - ETA: 10s - loss: 0.4566 - mae: 0.4191"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "213/729 [=======>......................] - ETA: 7s - loss: 0.4750 - mae: 0.4210"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 69/729 [=>............................] - ETA: 10s - loss: 0.4597 - mae: 0.4197"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "217/729 [=======>......................] - ETA: 7s - loss: 0.4732 - mae: 0.4208"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 73/729 [==>...........................] - ETA: 10s - loss: 0.4619 - mae: 0.4202"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "221/729 [========>.....................] - ETA: 7s - loss: 0.4824 - mae: 0.4228"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 77/729 [==>...........................] - ETA: 10s - loss: 0.4636 - mae: 0.4205"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "225/729 [========>.....................] - ETA: 7s - loss: 0.4814 - mae: 0.4232"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 81/729 [==>...........................] - ETA: 10s - loss: 0.4647 - mae: 0.4207"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "229/729 [========>.....................] - ETA: 6s - loss: 0.4806 - mae: 0.4233"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 85/729 [==>...........................] - ETA: 10s - loss: 0.4660 - mae: 0.4210"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "233/729 [========>.....................] - ETA: 6s - loss: 0.4801 - mae: 0.4237"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 89/729 [==>...........................] - ETA: 10s - loss: 0.4682 - mae: 0.4214"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "237/729 [========>.....................] - ETA: 6s - loss: 0.4905 - mae: 0.4243"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 93/729 [==>...........................] - ETA: 10s - loss: 0.4713 - mae: 0.4218"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "241/729 [========>.....................] - ETA: 6s - loss: 0.4907 - mae: 0.4245"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 97/729 [==>...........................] - ETA: 9s - loss: 0.4745 - mae: 0.4222 "
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "245/729 [=========>....................] - ETA: 6s - loss: 0.4935 - mae: 0.4248"
      ]
     },
     {
@@ -4405,7 +4374,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "101/729 [===>..........................] - ETA: 9s - loss: 0.4771 - mae: 0.4225"
+      "249/729 [=========>....................] - ETA: 6s - loss: 0.4914 - mae: 0.4247"
      ]
     },
     {
@@ -4413,7 +4382,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "105/729 [===>..........................] - ETA: 9s - loss: 0.4793 - mae: 0.4227"
+      "253/729 [=========>....................] - ETA: 6s - loss: 0.4942 - mae: 0.4252"
      ]
     },
     {
@@ -4421,7 +4390,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "109/729 [===>..........................] - ETA: 9s - loss: 0.4812 - mae: 0.4229"
+      "257/729 [=========>....................] - ETA: 6s - loss: 0.4994 - mae: 0.4256"
      ]
     },
     {
@@ -4429,7 +4398,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "113/729 [===>..........................] - ETA: 9s - loss: 0.4827 - mae: 0.4231"
+      "261/729 [=========>....................] - ETA: 6s - loss: 0.4968 - mae: 0.4250"
      ]
     },
     {
@@ -4437,7 +4406,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "117/729 [===>..........................] - ETA: 9s - loss: 0.4839 - mae: 0.4232"
+      "265/729 [=========>....................] - ETA: 6s - loss: 0.4951 - mae: 0.4247"
      ]
     },
     {
@@ -4445,7 +4414,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "121/729 [===>..........................] - ETA: 9s - loss: 0.4848 - mae: 0.4232"
+      "269/729 [==========>...................] - ETA: 6s - loss: 0.4929 - mae: 0.4245"
      ]
     },
     {
@@ -4453,7 +4422,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "125/729 [====>.........................] - ETA: 9s - loss: 0.4855 - mae: 0.4232"
+      "273/729 [==========>...................] - ETA: 6s - loss: 0.4972 - mae: 0.4253"
      ]
     },
     {
@@ -4461,7 +4430,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "129/729 [====>.........................] - ETA: 9s - loss: 0.4860 - mae: 0.4232"
+      "277/729 [==========>...................] - ETA: 6s - loss: 0.4966 - mae: 0.4254"
      ]
     },
     {
@@ -4469,7 +4438,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "133/729 [====>.........................] - ETA: 9s - loss: 0.4865 - mae: 0.4231"
+      "281/729 [==========>...................] - ETA: 6s - loss: 0.4947 - mae: 0.4248"
      ]
     },
     {
@@ -4477,7 +4446,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "137/729 [====>.........................] - ETA: 9s - loss: 0.4868 - mae: 0.4231"
+      "285/729 [==========>...................] - ETA: 6s - loss: 0.4941 - mae: 0.4249"
      ]
     },
     {
@@ -4485,7 +4454,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "141/729 [====>.........................] - ETA: 9s - loss: 0.4869 - mae: 0.4230"
+      "289/729 [==========>...................] - ETA: 6s - loss: 0.4937 - mae: 0.4251"
      ]
     },
     {
@@ -4493,7 +4462,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "145/729 [====>.........................] - ETA: 9s - loss: 0.4872 - mae: 0.4229"
+      "293/729 [===========>..................] - ETA: 6s - loss: 0.4924 - mae: 0.4249"
      ]
     },
     {
@@ -4501,7 +4470,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "149/729 [=====>........................] - ETA: 8s - loss: 0.4874 - mae: 0.4229"
+      "297/729 [===========>..................] - ETA: 6s - loss: 0.4942 - mae: 0.4258"
      ]
     },
     {
@@ -4509,7 +4478,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "153/729 [=====>........................] - ETA: 8s - loss: 0.4876 - mae: 0.4229"
+      "301/729 [===========>..................] - ETA: 5s - loss: 0.4920 - mae: 0.4252"
      ]
     },
     {
@@ -4517,7 +4486,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "157/729 [=====>........................] - ETA: 8s - loss: 0.4877 - mae: 0.4229"
+      "305/729 [===========>..................] - ETA: 5s - loss: 0.4909 - mae: 0.4249"
      ]
     },
     {
@@ -4525,7 +4494,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "161/729 [=====>........................] - ETA: 8s - loss: 0.4877 - mae: 0.4228"
+      "309/729 [===========>..................] - ETA: 5s - loss: 0.4896 - mae: 0.4245"
      ]
     },
     {
@@ -4533,7 +4502,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "165/729 [=====>........................] - ETA: 8s - loss: 0.4879 - mae: 0.4228"
+      "313/729 [===========>..................] - ETA: 5s - loss: 0.4881 - mae: 0.4241"
      ]
     },
     {
@@ -4541,7 +4510,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "169/729 [=====>........................] - ETA: 8s - loss: 0.4879 - mae: 0.4228"
+      "317/729 [============>.................] - ETA: 5s - loss: 0.4862 - mae: 0.4235"
      ]
     },
     {
@@ -4549,7 +4518,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "173/729 [======>.......................] - ETA: 8s - loss: 0.4880 - mae: 0.4228"
+      "321/729 [============>.................] - ETA: 5s - loss: 0.4865 - mae: 0.4236"
      ]
     },
     {
@@ -4557,7 +4526,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "177/729 [======>.......................] - ETA: 8s - loss: 0.4881 - mae: 0.4228"
+      "325/729 [============>.................] - ETA: 5s - loss: 0.4861 - mae: 0.4236"
      ]
     },
     {
@@ -4565,7 +4534,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "181/729 [======>.......................] - ETA: 8s - loss: 0.4883 - mae: 0.4229"
+      "329/729 [============>.................] - ETA: 5s - loss: 0.4856 - mae: 0.4237"
      ]
     },
     {
@@ -4573,7 +4542,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "185/729 [======>.......................] - ETA: 8s - loss: 0.4887 - mae: 0.4230"
+      "333/729 [============>.................] - ETA: 5s - loss: 0.4836 - mae: 0.4232"
      ]
     },
     {
@@ -4581,7 +4550,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "189/729 [======>.......................] - ETA: 8s - loss: 0.4892 - mae: 0.4230"
+      "337/729 [============>.................] - ETA: 5s - loss: 0.4830 - mae: 0.4234"
      ]
     },
     {
@@ -4589,7 +4558,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "193/729 [======>.......................] - ETA: 8s - loss: 0.4896 - mae: 0.4231"
+      "341/729 [=============>................] - ETA: 5s - loss: 0.4808 - mae: 0.4227"
      ]
     },
     {
@@ -4597,7 +4566,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "197/729 [=======>......................] - ETA: 8s - loss: 0.4899 - mae: 0.4232"
+      "345/729 [=============>................] - ETA: 5s - loss: 0.4805 - mae: 0.4225"
      ]
     },
     {
@@ -4605,7 +4574,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "201/729 [=======>......................] - ETA: 8s - loss: 0.4902 - mae: 0.4232"
+      "349/729 [=============>................] - ETA: 5s - loss: 0.4794 - mae: 0.4223"
      ]
     },
     {
@@ -4613,7 +4582,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "205/729 [=======>......................] - ETA: 8s - loss: 0.4903 - mae: 0.4232"
+      "353/729 [=============>................] - ETA: 5s - loss: 0.4771 - mae: 0.4216"
      ]
     },
     {
@@ -4621,7 +4590,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "209/729 [=======>......................] - ETA: 7s - loss: 0.4906 - mae: 0.4232"
+      "357/729 [=============>................] - ETA: 5s - loss: 0.4756 - mae: 0.4213"
      ]
     },
     {
@@ -4629,7 +4598,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "213/729 [=======>......................] - ETA: 7s - loss: 0.4909 - mae: 0.4233"
+      "361/729 [=============>................] - ETA: 5s - loss: 0.4747 - mae: 0.4209"
      ]
     },
     {
@@ -4637,7 +4606,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "217/729 [=======>......................] - ETA: 7s - loss: 0.4916 - mae: 0.4233"
+      "365/729 [==============>...............] - ETA: 5s - loss: 0.4813 - mae: 0.4221"
      ]
     },
     {
@@ -4645,7 +4614,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "221/729 [========>.....................] - ETA: 7s - loss: 0.4921 - mae: 0.4234"
+      "369/729 [==============>...............] - ETA: 5s - loss: 0.4796 - mae: 0.4217"
      ]
     },
     {
@@ -4653,7 +4622,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "225/729 [========>.....................] - ETA: 7s - loss: 0.4925 - mae: 0.4235"
+      "373/729 [==============>...............] - ETA: 4s - loss: 0.4784 - mae: 0.4213"
      ]
     },
     {
@@ -4661,7 +4630,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "229/729 [========>.....................] - ETA: 7s - loss: 0.4930 - mae: 0.4235"
+      "377/729 [==============>...............] - ETA: 4s - loss: 0.4778 - mae: 0.4211"
      ]
     },
     {
@@ -4669,7 +4638,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "233/729 [========>.....................] - ETA: 7s - loss: 0.4935 - mae: 0.4236"
+      "381/729 [==============>...............] - ETA: 4s - loss: 0.4796 - mae: 0.4214"
      ]
     },
     {
@@ -4677,7 +4646,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "237/729 [========>.....................] - ETA: 7s - loss: 0.4939 - mae: 0.4236"
+      "385/729 [==============>...............] - ETA: 4s - loss: 0.4801 - mae: 0.4217"
      ]
     },
     {
@@ -4685,7 +4654,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "241/729 [========>.....................] - ETA: 7s - loss: 0.4943 - mae: 0.4236"
+      "389/729 [===============>..............] - ETA: 4s - loss: 0.4791 - mae: 0.4216"
      ]
     },
     {
@@ -4693,7 +4662,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "245/729 [=========>....................] - ETA: 7s - loss: 0.4947 - mae: 0.4237"
+      "393/729 [===============>..............] - ETA: 4s - loss: 0.4780 - mae: 0.4216"
      ]
     },
     {
@@ -4701,7 +4670,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "249/729 [=========>....................] - ETA: 7s - loss: 0.4951 - mae: 0.4238"
+      "397/729 [===============>..............] - ETA: 4s - loss: 0.4794 - mae: 0.4218"
      ]
     },
     {
@@ -4709,7 +4678,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "253/729 [=========>....................] - ETA: 7s - loss: 0.4955 - mae: 0.4238"
+      "401/729 [===============>..............] - ETA: 4s - loss: 0.4786 - mae: 0.4218"
      ]
     },
     {
@@ -4717,7 +4686,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "257/729 [=========>....................] - ETA: 7s - loss: 0.4960 - mae: 0.4239"
+      "405/729 [===============>..............] - ETA: 4s - loss: 0.4788 - mae: 0.4222"
      ]
     },
     {
@@ -4725,7 +4694,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "261/729 [=========>....................] - ETA: 7s - loss: 0.4965 - mae: 0.4240"
+      "409/729 [===============>..............] - ETA: 4s - loss: 0.4808 - mae: 0.4225"
      ]
     },
     {
@@ -4733,7 +4702,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "265/729 [=========>....................] - ETA: 7s - loss: 0.4969 - mae: 0.4241"
+      "413/729 [===============>..............] - ETA: 4s - loss: 0.4790 - mae: 0.4219"
      ]
     },
     {
@@ -4741,7 +4710,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "269/729 [==========>...................] - ETA: 7s - loss: 0.4973 - mae: 0.4241"
+      "417/729 [================>.............] - ETA: 4s - loss: 0.4779 - mae: 0.4216"
      ]
     },
     {
@@ -4749,7 +4718,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "273/729 [==========>...................] - ETA: 6s - loss: 0.4976 - mae: 0.4242"
+      "421/729 [================>.............] - ETA: 4s - loss: 0.4760 - mae: 0.4210"
      ]
     },
     {
@@ -4757,7 +4726,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "277/729 [==========>...................] - ETA: 6s - loss: 0.4979 - mae: 0.4242"
+      "425/729 [================>.............] - ETA: 4s - loss: 0.4750 - mae: 0.4209"
      ]
     },
     {
@@ -4765,7 +4734,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "281/729 [==========>...................] - ETA: 6s - loss: 0.4982 - mae: 0.4243"
+      "429/729 [================>.............] - ETA: 4s - loss: 0.4747 - mae: 0.4210"
      ]
     },
     {
@@ -4773,7 +4742,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "285/729 [==========>...................] - ETA: 6s - loss: 0.4984 - mae: 0.4243"
+      "433/729 [================>.............] - ETA: 4s - loss: 0.4742 - mae: 0.4210"
      ]
     },
     {
@@ -4781,7 +4750,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "289/729 [==========>...................] - ETA: 6s - loss: 0.4987 - mae: 0.4244"
+      "437/729 [================>.............] - ETA: 4s - loss: 0.4740 - mae: 0.4211"
      ]
     },
     {
@@ -4789,7 +4758,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "293/729 [===========>..................] - ETA: 6s - loss: 0.4990 - mae: 0.4244"
+      "441/729 [=================>............] - ETA: 4s - loss: 0.4761 - mae: 0.4214"
      ]
     },
     {
@@ -4797,7 +4766,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "297/729 [===========>..................] - ETA: 6s - loss: 0.4992 - mae: 0.4245"
+      "445/729 [=================>............] - ETA: 3s - loss: 0.4767 - mae: 0.4215"
      ]
     },
     {
@@ -4805,7 +4774,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "301/729 [===========>..................] - ETA: 6s - loss: 0.4995 - mae: 0.4245"
+      "449/729 [=================>............] - ETA: 3s - loss: 0.4761 - mae: 0.4215"
      ]
     },
     {
@@ -4813,7 +4782,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "305/729 [===========>..................] - ETA: 6s - loss: 0.4996 - mae: 0.4246"
+      "453/729 [=================>............] - ETA: 3s - loss: 0.4762 - mae: 0.4217"
      ]
     },
     {
@@ -4821,7 +4790,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "309/729 [===========>..................] - ETA: 6s - loss: 0.4998 - mae: 0.4246"
+      "457/729 [=================>............] - ETA: 3s - loss: 0.4758 - mae: 0.4217"
      ]
     },
     {
@@ -4829,7 +4798,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "313/729 [===========>..................] - ETA: 6s - loss: 0.5000 - mae: 0.4246"
+      "461/729 [=================>............] - ETA: 3s - loss: 0.4747 - mae: 0.4215"
      ]
     },
     {
@@ -4837,7 +4806,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "317/729 [============>.................] - ETA: 6s - loss: 0.5001 - mae: 0.4247"
+      "465/729 [==================>...........] - ETA: 3s - loss: 0.4736 - mae: 0.4212"
      ]
     },
     {
@@ -4845,7 +4814,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "321/729 [============>.................] - ETA: 6s - loss: 0.5002 - mae: 0.4247"
+      "469/729 [==================>...........] - ETA: 3s - loss: 0.4797 - mae: 0.4217"
      ]
     },
     {
@@ -4853,7 +4822,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "325/729 [============>.................] - ETA: 6s - loss: 0.5003 - mae: 0.4247"
+      "473/729 [==================>...........] - ETA: 3s - loss: 0.4853 - mae: 0.4220"
      ]
     },
     {
@@ -4861,7 +4830,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "329/729 [============>.................] - ETA: 6s - loss: 0.5004 - mae: 0.4247"
+      "477/729 [==================>...........] - ETA: 3s - loss: 0.4843 - mae: 0.4220"
      ]
     },
     {
@@ -4869,7 +4838,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "333/729 [============>.................] - ETA: 6s - loss: 0.5004 - mae: 0.4247"
+      "481/729 [==================>...........] - ETA: 3s - loss: 0.4842 - mae: 0.4221"
      ]
     },
     {
@@ -4877,7 +4846,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "337/729 [============>.................] - ETA: 5s - loss: 0.5005 - mae: 0.4247"
+      "485/729 [==================>...........] - ETA: 3s - loss: 0.4830 - mae: 0.4219"
      ]
     },
     {
@@ -4885,7 +4854,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "341/729 [=============>................] - ETA: 5s - loss: 0.5005 - mae: 0.4247"
+      "489/729 [===================>..........] - ETA: 3s - loss: 0.4825 - mae: 0.4221"
      ]
     },
     {
@@ -4893,7 +4862,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "345/729 [=============>................] - ETA: 5s - loss: 0.5005 - mae: 0.4247"
+      "493/729 [===================>..........] - ETA: 3s - loss: 0.4844 - mae: 0.4224"
      ]
     },
     {
@@ -4901,7 +4870,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "349/729 [=============>................] - ETA: 5s - loss: 0.5005 - mae: 0.4247"
+      "497/729 [===================>..........] - ETA: 3s - loss: 0.4834 - mae: 0.4223"
      ]
     },
     {
@@ -4909,7 +4878,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "353/729 [=============>................] - ETA: 5s - loss: 0.5005 - mae: 0.4247"
+      "501/729 [===================>..........] - ETA: 3s - loss: 0.4910 - mae: 0.4230"
      ]
     },
     {
@@ -4917,7 +4886,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "357/729 [=============>................] - ETA: 5s - loss: 0.5005 - mae: 0.4247"
+      "505/729 [===================>..........] - ETA: 3s - loss: 0.4901 - mae: 0.4229"
      ]
     },
     {
@@ -4925,7 +4894,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "361/729 [=============>................] - ETA: 5s - loss: 0.5005 - mae: 0.4248"
+      "509/729 [===================>..........] - ETA: 3s - loss: 0.4903 - mae: 0.4233"
      ]
     },
     {
@@ -4933,7 +4902,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "365/729 [==============>...............] - ETA: 5s - loss: 0.5004 - mae: 0.4248"
+      "513/729 [====================>.........] - ETA: 3s - loss: 0.4904 - mae: 0.4234"
      ]
     },
     {
@@ -4941,7 +4910,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "369/729 [==============>...............] - ETA: 5s - loss: 0.5004 - mae: 0.4247"
+      "517/729 [====================>.........] - ETA: 2s - loss: 0.4939 - mae: 0.4238"
      ]
     },
     {
@@ -4949,7 +4918,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "373/729 [==============>...............] - ETA: 5s - loss: 0.5003 - mae: 0.4247"
+      "521/729 [====================>.........] - ETA: 2s - loss: 0.4934 - mae: 0.4237"
      ]
     },
     {
@@ -4957,7 +4926,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "377/729 [==============>...............] - ETA: 5s - loss: 0.5003 - mae: 0.4247"
+      "525/729 [====================>.........] - ETA: 2s - loss: 0.4925 - mae: 0.4236"
      ]
     },
     {
@@ -4965,7 +4934,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "381/729 [==============>...............] - ETA: 5s - loss: 0.5004 - mae: 0.4247"
+      "529/729 [====================>.........] - ETA: 2s - loss: 0.4920 - mae: 0.4236"
      ]
     },
     {
@@ -4973,7 +4942,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "385/729 [==============>...............] - ETA: 5s - loss: 0.5005 - mae: 0.4247"
+      "533/729 [====================>.........] - ETA: 2s - loss: 0.4914 - mae: 0.4236"
      ]
     },
     {
@@ -4981,7 +4950,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "389/729 [===============>..............] - ETA: 5s - loss: 0.5006 - mae: 0.4247"
+      "537/729 [=====================>........] - ETA: 2s - loss: 0.4912 - mae: 0.4238"
      ]
     },
     {
@@ -4989,7 +4958,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "393/729 [===============>..............] - ETA: 5s - loss: 0.5007 - mae: 0.4247"
+      "541/729 [=====================>........] - ETA: 2s - loss: 0.4895 - mae: 0.4232"
      ]
     },
     {
@@ -4997,7 +4966,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "397/729 [===============>..............] - ETA: 5s - loss: 0.5008 - mae: 0.4248"
+      "545/729 [=====================>........] - ETA: 2s - loss: 0.4888 - mae: 0.4232"
      ]
     },
     {
@@ -5005,7 +4974,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "401/729 [===============>..............] - ETA: 4s - loss: 0.5009 - mae: 0.4248"
+      "549/729 [=====================>........] - ETA: 2s - loss: 0.4884 - mae: 0.4231"
      ]
     },
     {
@@ -5013,7 +4982,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "405/729 [===============>..............] - ETA: 4s - loss: 0.5009 - mae: 0.4248"
+      "553/729 [=====================>........] - ETA: 2s - loss: 0.4884 - mae: 0.4230"
      ]
     },
     {
@@ -5021,7 +4990,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "409/729 [===============>..............] - ETA: 4s - loss: 0.5009 - mae: 0.4247"
+      "557/729 [=====================>........] - ETA: 2s - loss: 0.4896 - mae: 0.4235"
      ]
     },
     {
@@ -5029,7 +4998,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "413/729 [===============>..............] - ETA: 4s - loss: 0.5010 - mae: 0.4247"
+      "561/729 [======================>.......] - ETA: 2s - loss: 0.4914 - mae: 0.4238"
      ]
     },
     {
@@ -5037,7 +5006,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "417/729 [================>.............] - ETA: 4s - loss: 0.5010 - mae: 0.4247"
+      "565/729 [======================>.......] - ETA: 2s - loss: 0.4906 - mae: 0.4237"
      ]
     },
     {
@@ -5045,7 +5014,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "421/729 [================>.............] - ETA: 4s - loss: 0.5010 - mae: 0.4247"
+      "569/729 [======================>.......] - ETA: 2s - loss: 0.4905 - mae: 0.4237"
      ]
     },
     {
@@ -5053,7 +5022,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "425/729 [================>.............] - ETA: 4s - loss: 0.5011 - mae: 0.4247"
+      "573/729 [======================>.......] - ETA: 2s - loss: 0.4892 - mae: 0.4233"
      ]
     },
     {
@@ -5061,7 +5030,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "429/729 [================>.............] - ETA: 4s - loss: 0.5011 - mae: 0.4247"
+      "577/729 [======================>.......] - ETA: 2s - loss: 0.4891 - mae: 0.4235"
      ]
     },
     {
@@ -5069,7 +5038,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "433/729 [================>.............] - ETA: 4s - loss: 0.5011 - mae: 0.4247"
+      "581/729 [======================>.......] - ETA: 2s - loss: 0.4917 - mae: 0.4236"
      ]
     },
     {
@@ -5077,7 +5046,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "437/729 [================>.............] - ETA: 4s - loss: 0.5012 - mae: 0.4247"
+      "585/729 [=======================>......] - ETA: 2s - loss: 0.4911 - mae: 0.4238"
      ]
     },
     {
@@ -5085,7 +5054,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "441/729 [=================>............] - ETA: 4s - loss: 0.5012 - mae: 0.4247"
+      "589/729 [=======================>......] - ETA: 1s - loss: 0.4927 - mae: 0.4242"
      ]
     },
     {
@@ -5093,7 +5062,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "445/729 [=================>............] - ETA: 4s - loss: 0.5013 - mae: 0.4247"
+      "593/729 [=======================>......] - ETA: 1s - loss: 0.4923 - mae: 0.4241"
      ]
     },
     {
@@ -5101,7 +5070,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "449/729 [=================>............] - ETA: 4s - loss: 0.5013 - mae: 0.4247"
+      "597/729 [=======================>......] - ETA: 1s - loss: 0.4917 - mae: 0.4241"
      ]
     },
     {
@@ -5109,7 +5078,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "453/729 [=================>............] - ETA: 4s - loss: 0.5013 - mae: 0.4248"
+      "601/729 [=======================>......] - ETA: 1s - loss: 0.4909 - mae: 0.4238"
      ]
     },
     {
@@ -5117,7 +5086,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "457/729 [=================>............] - ETA: 4s - loss: 0.5013 - mae: 0.4248"
+      "605/729 [=======================>......] - ETA: 1s - loss: 0.4901 - mae: 0.4237"
      ]
     },
     {
@@ -5125,7 +5094,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "461/729 [=================>............] - ETA: 4s - loss: 0.5013 - mae: 0.4248"
+      "609/729 [========================>.....] - ETA: 1s - loss: 0.4908 - mae: 0.4242"
      ]
     },
     {
@@ -5133,7 +5102,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "465/729 [==================>...........] - ETA: 3s - loss: 0.5013 - mae: 0.4248"
+      "613/729 [========================>.....] - ETA: 1s - loss: 0.4907 - mae: 0.4245"
      ]
     },
     {
@@ -5141,7 +5110,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "469/729 [==================>...........] - ETA: 3s - loss: 0.5013 - mae: 0.4248"
+      "617/729 [========================>.....] - ETA: 1s - loss: 0.4899 - mae: 0.4243"
      ]
     },
     {
@@ -5149,7 +5118,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "473/729 [==================>...........] - ETA: 3s - loss: 0.5013 - mae: 0.4248"
+      "621/729 [========================>.....] - ETA: 1s - loss: 0.4901 - mae: 0.4243"
      ]
     },
     {
@@ -5157,7 +5126,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "477/729 [==================>...........] - ETA: 3s - loss: 0.5013 - mae: 0.4248"
+      "625/729 [========================>.....] - ETA: 1s - loss: 0.4892 - mae: 0.4240"
      ]
     },
     {
@@ -5165,7 +5134,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "481/729 [==================>...........] - ETA: 3s - loss: 0.5013 - mae: 0.4248"
+      "629/729 [========================>.....] - ETA: 1s - loss: 0.4895 - mae: 0.4239"
      ]
     },
     {
@@ -5173,7 +5142,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "485/729 [==================>...........] - ETA: 3s - loss: 0.5013 - mae: 0.4248"
+      "633/729 [=========================>....] - ETA: 1s - loss: 0.4895 - mae: 0.4241"
      ]
     },
     {
@@ -5181,7 +5150,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "489/729 [===================>..........] - ETA: 3s - loss: 0.5012 - mae: 0.4248"
+      "637/729 [=========================>....] - ETA: 1s - loss: 0.4889 - mae: 0.4239"
      ]
     },
     {
@@ -5189,7 +5158,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "493/729 [===================>..........] - ETA: 3s - loss: 0.5012 - mae: 0.4248"
+      "641/729 [=========================>....] - ETA: 1s - loss: 0.4878 - mae: 0.4236"
      ]
     },
     {
@@ -5197,7 +5166,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "497/729 [===================>..........] - ETA: 3s - loss: 0.5011 - mae: 0.4248"
+      "645/729 [=========================>....] - ETA: 1s - loss: 0.4869 - mae: 0.4235"
      ]
     },
     {
@@ -5205,7 +5174,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "501/729 [===================>..........] - ETA: 3s - loss: 0.5011 - mae: 0.4248"
+      "649/729 [=========================>....] - ETA: 1s - loss: 0.4867 - mae: 0.4235"
      ]
     },
     {
@@ -5213,7 +5182,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "505/729 [===================>..........] - ETA: 3s - loss: 0.5010 - mae: 0.4248"
+      "653/729 [=========================>....] - ETA: 1s - loss: 0.4871 - mae: 0.4238"
      ]
     },
     {
@@ -5221,7 +5190,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "509/729 [===================>..........] - ETA: 3s - loss: 0.5010 - mae: 0.4248"
+      "657/729 [==========================>...] - ETA: 1s - loss: 0.4857 - mae: 0.4233"
      ]
     },
     {
@@ -5229,7 +5198,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "513/729 [====================>.........] - ETA: 3s - loss: 0.5010 - mae: 0.4248"
+      "661/729 [==========================>...] - ETA: 0s - loss: 0.4882 - mae: 0.4236"
      ]
     },
     {
@@ -5237,7 +5206,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "517/729 [====================>.........] - ETA: 3s - loss: 0.5009 - mae: 0.4248"
+      "665/729 [==========================>...] - ETA: 0s - loss: 0.4876 - mae: 0.4236"
      ]
     },
     {
@@ -5245,7 +5214,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "521/729 [====================>.........] - ETA: 3s - loss: 0.5009 - mae: 0.4248"
+      "669/729 [==========================>...] - ETA: 0s - loss: 0.4876 - mae: 0.4237"
      ]
     },
     {
@@ -5253,7 +5222,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "525/729 [====================>.........] - ETA: 3s - loss: 0.5008 - mae: 0.4248"
+      "673/729 [==========================>...] - ETA: 0s - loss: 0.4866 - mae: 0.4234"
      ]
     },
     {
@@ -5261,7 +5230,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "529/729 [====================>.........] - ETA: 3s - loss: 0.5008 - mae: 0.4248"
+      "677/729 [==========================>...] - ETA: 0s - loss: 0.4859 - mae: 0.4233"
      ]
     },
     {
@@ -5269,7 +5238,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "533/729 [====================>.........] - ETA: 2s - loss: 0.5007 - mae: 0.4248"
+      "681/729 [===========================>..] - ETA: 0s - loss: 0.4860 - mae: 0.4235"
      ]
     },
     {
@@ -5277,7 +5246,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "537/729 [=====================>........] - ETA: 2s - loss: 0.5007 - mae: 0.4248"
+      "685/729 [===========================>..] - ETA: 0s - loss: 0.4849 - mae: 0.4230"
      ]
     },
     {
@@ -5285,7 +5254,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "541/729 [=====================>........] - ETA: 2s - loss: 0.5007 - mae: 0.4248"
+      "689/729 [===========================>..] - ETA: 0s - loss: 0.4862 - mae: 0.4237"
      ]
     },
     {
@@ -5293,7 +5262,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "545/729 [=====================>........] - ETA: 2s - loss: 0.5006 - mae: 0.4248"
+      "693/729 [===========================>..] - ETA: 0s - loss: 0.4860 - mae: 0.4236"
      ]
     },
     {
@@ -5301,7 +5270,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "549/729 [=====================>........] - ETA: 2s - loss: 0.5006 - mae: 0.4248"
+      "697/729 [===========================>..] - ETA: 0s - loss: 0.4853 - mae: 0.4235"
      ]
     },
     {
@@ -5309,7 +5278,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "553/729 [=====================>........] - ETA: 2s - loss: 0.5006 - mae: 0.4248"
+      "701/729 [===========================>..] - ETA: 0s - loss: 0.4847 - mae: 0.4233"
      ]
     },
     {
@@ -5317,7 +5286,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "557/729 [=====================>........] - ETA: 2s - loss: 0.5005 - mae: 0.4247"
+      "705/729 [============================>.] - ETA: 0s - loss: 0.4839 - mae: 0.4232"
      ]
     },
     {
@@ -5325,7 +5294,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "561/729 [======================>.......] - ETA: 2s - loss: 0.5005 - mae: 0.4247"
+      "709/729 [============================>.] - ETA: 0s - loss: 0.4845 - mae: 0.4232"
      ]
     },
     {
@@ -5333,7 +5302,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "565/729 [======================>.......] - ETA: 2s - loss: 0.5005 - mae: 0.4247"
+      "713/729 [============================>.] - ETA: 0s - loss: 0.4860 - mae: 0.4230"
      ]
     },
     {
@@ -5341,7 +5310,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "569/729 [======================>.......] - ETA: 2s - loss: 0.5004 - mae: 0.4247"
+      "717/729 [============================>.] - ETA: 0s - loss: 0.4848 - mae: 0.4226"
      ]
     },
     {
@@ -5349,7 +5318,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "573/729 [======================>.......] - ETA: 2s - loss: 0.5004 - mae: 0.4247"
+      "721/729 [============================>.] - ETA: 0s - loss: 0.4840 - mae: 0.4222"
      ]
     },
     {
@@ -5357,7 +5326,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "577/729 [======================>.......] - ETA: 2s - loss: 0.5003 - mae: 0.4247"
+      "725/729 [============================>.] - ETA: 0s - loss: 0.4834 - mae: 0.4221"
      ]
     },
     {
@@ -5365,7 +5334,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "581/729 [======================>.......] - ETA: 2s - loss: 0.5003 - mae: 0.4247"
+      "729/729 [==============================] - ETA: 0s - loss: 0.4836 - mae: 0.4222"
      ]
     },
     {
@@ -5373,15 +5342,16 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "585/729 [=======================>......] - ETA: 2s - loss: 0.5002 - mae: 0.4247"
+      "729/729 [==============================] - 11s 15ms/step - loss: 0.4836 - mae: 0.4222 - val_loss: 0.4484 - val_mae: 0.3869\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "589/729 [=======================>......] - ETA: 2s - loss: 0.5001 - mae: 0.4247"
+      "Epoch 4/10\n",
+      "\r",
+      "  1/729 [..............................] - ETA: 0s - loss: 0.5692 - mae: 0.4610"
      ]
     },
     {
@@ -5389,7 +5359,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "593/729 [=======================>......] - ETA: 2s - loss: 0.5001 - mae: 0.4247"
+      "  5/729 [..............................] - ETA: 8s - loss: 0.4579 - mae: 0.4453"
      ]
     },
     {
@@ -5397,7 +5367,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "597/729 [=======================>......] - ETA: 1s - loss: 0.5000 - mae: 0.4247"
+      "  9/729 [..............................] - ETA: 9s - loss: 0.4278 - mae: 0.4341"
      ]
     },
     {
@@ -5405,7 +5375,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "601/729 [=======================>......] - ETA: 1s - loss: 0.4999 - mae: 0.4246"
+      " 13/729 [..............................] - ETA: 9s - loss: 0.4535 - mae: 0.4448"
      ]
     },
     {
@@ -5413,7 +5383,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "605/729 [=======================>......] - ETA: 1s - loss: 0.4999 - mae: 0.4246"
+      " 17/729 [..............................] - ETA: 9s - loss: 0.4260 - mae: 0.4319"
      ]
     },
     {
@@ -5421,7 +5391,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "609/729 [========================>.....] - ETA: 1s - loss: 0.4998 - mae: 0.4246"
+      " 21/729 [..............................] - ETA: 9s - loss: 0.4114 - mae: 0.4250"
      ]
     },
     {
@@ -5429,7 +5399,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "613/729 [========================>.....] - ETA: 1s - loss: 0.4997 - mae: 0.4246"
+      " 25/729 [>.............................] - ETA: 9s - loss: 0.4086 - mae: 0.4221"
      ]
     },
     {
@@ -5437,7 +5407,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "617/729 [========================>.....] - ETA: 1s - loss: 0.4996 - mae: 0.4246"
+      " 29/729 [>.............................] - ETA: 9s - loss: 0.4075 - mae: 0.4189"
      ]
     },
     {
@@ -5445,7 +5415,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "621/729 [========================>.....] - ETA: 1s - loss: 0.4995 - mae: 0.4246"
+      " 33/729 [>.............................] - ETA: 9s - loss: 0.4010 - mae: 0.4165"
      ]
     },
     {
@@ -5453,7 +5423,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "625/729 [========================>.....] - ETA: 1s - loss: 0.4994 - mae: 0.4246"
+      " 37/729 [>.............................] - ETA: 9s - loss: 0.4275 - mae: 0.4224"
      ]
     },
     {
@@ -5461,7 +5431,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "629/729 [========================>.....] - ETA: 1s - loss: 0.4993 - mae: 0.4245"
+      " 41/729 [>.............................] - ETA: 9s - loss: 0.4211 - mae: 0.4190"
      ]
     },
     {
@@ -5469,7 +5439,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "633/729 [=========================>....] - ETA: 1s - loss: 0.4992 - mae: 0.4245"
+      " 45/729 [>.............................] - ETA: 9s - loss: 0.4089 - mae: 0.4131"
      ]
     },
     {
@@ -5477,7 +5447,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "637/729 [=========================>....] - ETA: 1s - loss: 0.4992 - mae: 0.4245"
+      " 49/729 [=>............................] - ETA: 9s - loss: 0.4160 - mae: 0.4192"
      ]
     },
     {
@@ -5485,7 +5455,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "641/729 [=========================>....] - ETA: 1s - loss: 0.4991 - mae: 0.4245"
+      " 53/729 [=>............................] - ETA: 9s - loss: 0.4210 - mae: 0.4205"
      ]
     },
     {
@@ -5493,7 +5463,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "645/729 [=========================>....] - ETA: 1s - loss: 0.4990 - mae: 0.4245"
+      " 57/729 [=>............................] - ETA: 9s - loss: 0.4230 - mae: 0.4212"
      ]
     },
     {
@@ -5501,7 +5471,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "649/729 [=========================>....] - ETA: 1s - loss: 0.4989 - mae: 0.4245"
+      " 61/729 [=>............................] - ETA: 9s - loss: 0.4152 - mae: 0.4169"
      ]
     },
     {
@@ -5509,7 +5479,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "653/729 [=========================>....] - ETA: 1s - loss: 0.4988 - mae: 0.4245"
+      " 65/729 [=>............................] - ETA: 9s - loss: 0.4429 - mae: 0.4224"
      ]
     },
     {
@@ -5517,7 +5487,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "657/729 [==========================>...] - ETA: 1s - loss: 0.4987 - mae: 0.4244"
+      " 69/729 [=>............................] - ETA: 9s - loss: 0.4410 - mae: 0.4219"
      ]
     },
     {
@@ -5525,7 +5495,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "661/729 [==========================>...] - ETA: 1s - loss: 0.4986 - mae: 0.4244"
+      " 73/729 [==>...........................] - ETA: 9s - loss: 0.4370 - mae: 0.4206"
      ]
     },
     {
@@ -5533,7 +5503,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "665/729 [==========================>...] - ETA: 0s - loss: 0.4985 - mae: 0.4244"
+      " 77/729 [==>...........................] - ETA: 9s - loss: 0.4308 - mae: 0.4175"
      ]
     },
     {
@@ -5541,7 +5511,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "669/729 [==========================>...] - ETA: 0s - loss: 0.4984 - mae: 0.4244"
+      " 81/729 [==>...........................] - ETA: 9s - loss: 0.4366 - mae: 0.4207"
      ]
     },
     {
@@ -5549,7 +5519,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "673/729 [==========================>...] - ETA: 0s - loss: 0.4983 - mae: 0.4244"
+      " 85/729 [==>...........................] - ETA: 9s - loss: 0.4313 - mae: 0.4186"
      ]
     },
     {
@@ -5557,7 +5527,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "677/729 [==========================>...] - ETA: 0s - loss: 0.4982 - mae: 0.4244"
+      " 89/729 [==>...........................] - ETA: 9s - loss: 0.4306 - mae: 0.4181"
      ]
     },
     {
@@ -5565,7 +5535,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "681/729 [===========================>..] - ETA: 0s - loss: 0.4981 - mae: 0.4244"
+      " 93/729 [==>...........................] - ETA: 8s - loss: 0.4256 - mae: 0.4157"
      ]
     },
     {
@@ -5573,7 +5543,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "685/729 [===========================>..] - ETA: 0s - loss: 0.4980 - mae: 0.4243"
+      " 97/729 [==>...........................] - ETA: 8s - loss: 0.4191 - mae: 0.4130"
      ]
     },
     {
@@ -5581,7 +5551,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "689/729 [===========================>..] - ETA: 0s - loss: 0.4980 - mae: 0.4243"
+      "101/729 [===>..........................] - ETA: 8s - loss: 0.4240 - mae: 0.4135"
      ]
     },
     {
@@ -5589,7 +5559,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "693/729 [===========================>..] - ETA: 0s - loss: 0.4979 - mae: 0.4243"
+      "105/729 [===>..........................] - ETA: 8s - loss: 0.4188 - mae: 0.4114"
      ]
     },
     {
@@ -5597,7 +5567,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "697/729 [===========================>..] - ETA: 0s - loss: 0.4978 - mae: 0.4243"
+      "109/729 [===>..........................] - ETA: 8s - loss: 0.4183 - mae: 0.4116"
      ]
     },
     {
@@ -5605,7 +5575,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "701/729 [===========================>..] - ETA: 0s - loss: 0.4978 - mae: 0.4243"
+      "113/729 [===>..........................] - ETA: 8s - loss: 0.4185 - mae: 0.4114"
      ]
     },
     {
@@ -5613,7 +5583,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "705/729 [============================>.] - ETA: 0s - loss: 0.4977 - mae: 0.4243"
+      "117/729 [===>..........................] - ETA: 8s - loss: 0.4230 - mae: 0.4118"
      ]
     },
     {
@@ -5621,7 +5591,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "709/729 [============================>.] - ETA: 0s - loss: 0.4976 - mae: 0.4243"
+      "121/729 [===>..........................] - ETA: 8s - loss: 0.4301 - mae: 0.4146"
      ]
     },
     {
@@ -5629,7 +5599,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "713/729 [============================>.] - ETA: 0s - loss: 0.4975 - mae: 0.4243"
+      "125/729 [====>.........................] - ETA: 8s - loss: 0.4474 - mae: 0.4166"
      ]
     },
     {
@@ -5637,7 +5607,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "717/729 [============================>.] - ETA: 0s - loss: 0.4975 - mae: 0.4243"
+      "129/729 [====>.........................] - ETA: 8s - loss: 0.4448 - mae: 0.4159"
      ]
     },
     {
@@ -5645,7 +5615,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "721/729 [============================>.] - ETA: 0s - loss: 0.4974 - mae: 0.4242"
+      "133/729 [====>.........................] - ETA: 8s - loss: 0.4424 - mae: 0.4152"
      ]
     },
     {
@@ -5653,7 +5623,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "725/729 [============================>.] - ETA: 0s - loss: 0.4973 - mae: 0.4242"
+      "137/729 [====>.........................] - ETA: 8s - loss: 0.4427 - mae: 0.4158"
      ]
     },
     {
@@ -5661,7 +5631,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "729/729 [==============================] - ETA: 0s - loss: 0.4972 - mae: 0.4242"
+      "141/729 [====>.........................] - ETA: 8s - loss: 0.4405 - mae: 0.4152"
      ]
     },
     {
@@ -5669,144 +5639,143 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "729/729 [==============================] - 12s 16ms/step - loss: 0.4972 - mae: 0.4242 - val_loss: 0.4483 - val_mae: 0.3943\n"
+      "145/729 [====>.........................] - ETA: 8s - loss: 0.4380 - mae: 0.4144"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 4/10\n",
-      "\r",
-      "  1/729 [..............................] - ETA: 29s - loss: 0.2783 - mae: 0.3676"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "149/729 [=====>........................] - ETA: 8s - loss: 0.4414 - mae: 0.4169"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  5/729 [..............................] - ETA: 10s - loss: 0.3972 - mae: 0.3893"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "153/729 [=====>........................] - ETA: 8s - loss: 0.4537 - mae: 0.4180"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  9/729 [..............................] - ETA: 10s - loss: 0.3861 - mae: 0.3922"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "157/729 [=====>........................] - ETA: 8s - loss: 0.4504 - mae: 0.4172"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 13/729 [..............................] - ETA: 10s - loss: 0.3925 - mae: 0.3978"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "161/729 [=====>........................] - ETA: 7s - loss: 0.4474 - mae: 0.4158"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 17/729 [..............................] - ETA: 10s - loss: 0.3950 - mae: 0.3989"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "165/729 [=====>........................] - ETA: 7s - loss: 0.4487 - mae: 0.4153"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 21/729 [..............................] - ETA: 10s - loss: 0.3988 - mae: 0.4007"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "169/729 [=====>........................] - ETA: 7s - loss: 0.4455 - mae: 0.4145"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 25/729 [>.............................] - ETA: 10s - loss: 0.4045 - mae: 0.4022"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "173/729 [======>.......................] - ETA: 7s - loss: 0.4534 - mae: 0.4165"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 29/729 [>.............................] - ETA: 10s - loss: 0.4130 - mae: 0.4040"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "177/729 [======>.......................] - ETA: 7s - loss: 0.4501 - mae: 0.4155"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 33/729 [>.............................] - ETA: 10s - loss: 0.4222 - mae: 0.4058"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "181/729 [======>.......................] - ETA: 7s - loss: 0.4526 - mae: 0.4152"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 37/729 [>.............................] - ETA: 10s - loss: 0.4326 - mae: 0.4075"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "185/729 [======>.......................] - ETA: 7s - loss: 0.4500 - mae: 0.4145"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 41/729 [>.............................] - ETA: 10s - loss: 0.4401 - mae: 0.4088"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "189/729 [======>.......................] - ETA: 7s - loss: 0.4518 - mae: 0.4156"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 45/729 [>.............................] - ETA: 10s - loss: 0.4471 - mae: 0.4102"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "193/729 [======>.......................] - ETA: 7s - loss: 0.4504 - mae: 0.4153"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 49/729 [=>............................] - ETA: 10s - loss: 0.4521 - mae: 0.4112"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "197/729 [=======>......................] - ETA: 7s - loss: 0.4561 - mae: 0.4156"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 53/729 [=>............................] - ETA: 10s - loss: 0.4558 - mae: 0.4120"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "201/729 [=======>......................] - ETA: 7s - loss: 0.4555 - mae: 0.4160"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 57/729 [=>............................] - ETA: 10s - loss: 0.4583 - mae: 0.4126"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "205/729 [=======>......................] - ETA: 7s - loss: 0.4552 - mae: 0.4167"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 61/729 [=>............................] - ETA: 10s - loss: 0.4597 - mae: 0.4129"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "209/729 [=======>......................] - ETA: 7s - loss: 0.4530 - mae: 0.4161"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 65/729 [=>............................] - ETA: 9s - loss: 0.4602 - mae: 0.4129 "
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "213/729 [=======>......................] - ETA: 7s - loss: 0.4502 - mae: 0.4151"
      ]
     },
     {
@@ -5814,7 +5783,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 69/729 [=>............................] - ETA: 9s - loss: 0.4607 - mae: 0.4130"
+      "217/729 [=======>......................] - ETA: 7s - loss: 0.4479 - mae: 0.4144"
      ]
     },
     {
@@ -5822,7 +5791,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 73/729 [==>...........................] - ETA: 9s - loss: 0.4627 - mae: 0.4133"
+      "221/729 [========>.....................] - ETA: 7s - loss: 0.4512 - mae: 0.4151"
      ]
     },
     {
@@ -5830,7 +5799,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 77/729 [==>...........................] - ETA: 9s - loss: 0.4641 - mae: 0.4134"
+      "225/729 [========>.....................] - ETA: 7s - loss: 0.4487 - mae: 0.4141"
      ]
     },
     {
@@ -5838,7 +5807,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 81/729 [==>...........................] - ETA: 9s - loss: 0.4650 - mae: 0.4135"
+      "229/729 [========>.....................] - ETA: 7s - loss: 0.4498 - mae: 0.4141"
      ]
     },
     {
@@ -5846,7 +5815,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 85/729 [==>...........................] - ETA: 9s - loss: 0.4654 - mae: 0.4135"
+      "233/729 [========>.....................] - ETA: 6s - loss: 0.4479 - mae: 0.4135"
      ]
     },
     {
@@ -5854,7 +5823,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 89/729 [==>...........................] - ETA: 9s - loss: 0.4664 - mae: 0.4135"
+      "237/729 [========>.....................] - ETA: 6s - loss: 0.4458 - mae: 0.4129"
      ]
     },
     {
@@ -5862,7 +5831,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 93/729 [==>...........................] - ETA: 9s - loss: 0.4673 - mae: 0.4134"
+      "241/729 [========>.....................] - ETA: 6s - loss: 0.4647 - mae: 0.4143"
      ]
     },
     {
@@ -5870,7 +5839,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 97/729 [==>...........................] - ETA: 9s - loss: 0.4688 - mae: 0.4136"
+      "245/729 [=========>....................] - ETA: 6s - loss: 0.4676 - mae: 0.4147"
      ]
     },
     {
@@ -5878,7 +5847,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "101/729 [===>..........................] - ETA: 9s - loss: 0.4703 - mae: 0.4139"
+      "249/729 [=========>....................] - ETA: 6s - loss: 0.4657 - mae: 0.4142"
      ]
     },
     {
@@ -5886,7 +5855,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "105/729 [===>..........................] - ETA: 9s - loss: 0.4715 - mae: 0.4140"
+      "253/729 [=========>....................] - ETA: 6s - loss: 0.4666 - mae: 0.4153"
      ]
     },
     {
@@ -5894,7 +5863,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "109/729 [===>..........................] - ETA: 9s - loss: 0.4724 - mae: 0.4142"
+      "257/729 [=========>....................] - ETA: 6s - loss: 0.4723 - mae: 0.4157"
      ]
     },
     {
@@ -5902,7 +5871,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "113/729 [===>..........................] - ETA: 9s - loss: 0.4732 - mae: 0.4143"
+      "261/729 [=========>....................] - ETA: 6s - loss: 0.4698 - mae: 0.4152"
      ]
     },
     {
@@ -5910,7 +5879,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "117/729 [===>..........................] - ETA: 9s - loss: 0.4737 - mae: 0.4144"
+      "265/729 [=========>....................] - ETA: 6s - loss: 0.4671 - mae: 0.4142"
      ]
     },
     {
@@ -5918,7 +5887,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "121/729 [===>..........................] - ETA: 9s - loss: 0.4741 - mae: 0.4145"
+      "269/729 [==========>...................] - ETA: 6s - loss: 0.4682 - mae: 0.4148"
      ]
     },
     {
@@ -5926,7 +5895,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "125/729 [====>.........................] - ETA: 9s - loss: 0.4745 - mae: 0.4146"
+      "273/729 [==========>...................] - ETA: 6s - loss: 0.4669 - mae: 0.4142"
      ]
     },
     {
@@ -5934,7 +5903,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "129/729 [====>.........................] - ETA: 8s - loss: 0.4748 - mae: 0.4147"
+      "277/729 [==========>...................] - ETA: 6s - loss: 0.4670 - mae: 0.4147"
      ]
     },
     {
@@ -5942,7 +5911,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "133/729 [====>.........................] - ETA: 8s - loss: 0.4751 - mae: 0.4148"
+      "281/729 [==========>...................] - ETA: 6s - loss: 0.4693 - mae: 0.4152"
      ]
     },
     {
@@ -5950,7 +5919,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "137/729 [====>.........................] - ETA: 8s - loss: 0.4752 - mae: 0.4148"
+      "285/729 [==========>...................] - ETA: 6s - loss: 0.4717 - mae: 0.4155"
      ]
     },
     {
@@ -5958,7 +5927,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "141/729 [====>.........................] - ETA: 8s - loss: 0.4754 - mae: 0.4149"
+      "289/729 [==========>...................] - ETA: 6s - loss: 0.4701 - mae: 0.4153"
      ]
     },
     {
@@ -5966,7 +5935,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "145/729 [====>.........................] - ETA: 8s - loss: 0.4756 - mae: 0.4150"
+      "293/729 [===========>..................] - ETA: 6s - loss: 0.4760 - mae: 0.4160"
      ]
     },
     {
@@ -5974,7 +5943,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "149/729 [=====>........................] - ETA: 8s - loss: 0.4758 - mae: 0.4151"
+      "297/729 [===========>..................] - ETA: 6s - loss: 0.4760 - mae: 0.4164"
      ]
     },
     {
@@ -5982,7 +5951,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "153/729 [=====>........................] - ETA: 8s - loss: 0.4759 - mae: 0.4152"
+      "301/729 [===========>..................] - ETA: 6s - loss: 0.4759 - mae: 0.4170"
      ]
     },
     {
@@ -5990,7 +5959,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "157/729 [=====>........................] - ETA: 8s - loss: 0.4760 - mae: 0.4153"
+      "305/729 [===========>..................] - ETA: 5s - loss: 0.4752 - mae: 0.4171"
      ]
     },
     {
@@ -5998,7 +5967,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "161/729 [=====>........................] - ETA: 8s - loss: 0.4760 - mae: 0.4153"
+      "309/729 [===========>..................] - ETA: 5s - loss: 0.4727 - mae: 0.4164"
      ]
     },
     {
@@ -6006,7 +5975,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "165/729 [=====>........................] - ETA: 8s - loss: 0.4760 - mae: 0.4154"
+      "313/729 [===========>..................] - ETA: 5s - loss: 0.4755 - mae: 0.4167"
      ]
     },
     {
@@ -6014,7 +5983,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "169/729 [=====>........................] - ETA: 8s - loss: 0.4759 - mae: 0.4155"
+      "317/729 [============>.................] - ETA: 5s - loss: 0.4750 - mae: 0.4169"
      ]
     },
     {
@@ -6022,7 +5991,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "173/729 [======>.......................] - ETA: 8s - loss: 0.4759 - mae: 0.4156"
+      "321/729 [============>.................] - ETA: 5s - loss: 0.4747 - mae: 0.4175"
      ]
     },
     {
@@ -6030,7 +5999,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "177/729 [======>.......................] - ETA: 8s - loss: 0.4760 - mae: 0.4157"
+      "325/729 [============>.................] - ETA: 5s - loss: 0.4729 - mae: 0.4170"
      ]
     },
     {
@@ -6038,7 +6007,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "181/729 [======>.......................] - ETA: 8s - loss: 0.4766 - mae: 0.4158"
+      "329/729 [============>.................] - ETA: 5s - loss: 0.4721 - mae: 0.4166"
      ]
     },
     {
@@ -6046,7 +6015,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "185/729 [======>.......................] - ETA: 8s - loss: 0.4773 - mae: 0.4159"
+      "333/729 [============>.................] - ETA: 5s - loss: 0.4699 - mae: 0.4158"
      ]
     },
     {
@@ -6054,7 +6023,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "189/729 [======>.......................] - ETA: 8s - loss: 0.4779 - mae: 0.4161"
+      "337/729 [============>.................] - ETA: 5s - loss: 0.4675 - mae: 0.4150"
      ]
     },
     {
@@ -6062,7 +6031,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "193/729 [======>.......................] - ETA: 8s - loss: 0.4786 - mae: 0.4162"
+      "341/729 [=============>................] - ETA: 5s - loss: 0.4651 - mae: 0.4141"
      ]
     },
     {
@@ -6070,7 +6039,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "197/729 [=======>......................] - ETA: 7s - loss: 0.4792 - mae: 0.4163"
+      "345/729 [=============>................] - ETA: 5s - loss: 0.4628 - mae: 0.4133"
      ]
     },
     {
@@ -6078,7 +6047,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "201/729 [=======>......................] - ETA: 7s - loss: 0.4798 - mae: 0.4164"
+      "349/729 [=============>................] - ETA: 5s - loss: 0.4617 - mae: 0.4127"
      ]
     },
     {
@@ -6086,7 +6055,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "205/729 [=======>......................] - ETA: 7s - loss: 0.4802 - mae: 0.4165"
+      "353/729 [=============>................] - ETA: 5s - loss: 0.4620 - mae: 0.4128"
      ]
     },
     {
@@ -6094,7 +6063,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "209/729 [=======>......................] - ETA: 7s - loss: 0.4806 - mae: 0.4165"
+      "357/729 [=============>................] - ETA: 5s - loss: 0.4657 - mae: 0.4138"
      ]
     },
     {
@@ -6102,7 +6071,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "213/729 [=======>......................] - ETA: 7s - loss: 0.4809 - mae: 0.4166"
+      "361/729 [=============>................] - ETA: 5s - loss: 0.4642 - mae: 0.4136"
      ]
     },
     {
@@ -6110,7 +6079,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "217/729 [=======>......................] - ETA: 7s - loss: 0.4812 - mae: 0.4166"
+      "365/729 [==============>...............] - ETA: 5s - loss: 0.4633 - mae: 0.4132"
      ]
     },
     {
@@ -6118,7 +6087,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "221/729 [========>.....................] - ETA: 7s - loss: 0.4814 - mae: 0.4167"
+      "369/729 [==============>...............] - ETA: 5s - loss: 0.4632 - mae: 0.4133"
      ]
     },
     {
@@ -6126,7 +6095,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "225/729 [========>.....................] - ETA: 7s - loss: 0.4816 - mae: 0.4167"
+      "373/729 [==============>...............] - ETA: 5s - loss: 0.4623 - mae: 0.4131"
      ]
     },
     {
@@ -6134,7 +6103,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "229/729 [========>.....................] - ETA: 7s - loss: 0.4818 - mae: 0.4168"
+      "377/729 [==============>...............] - ETA: 4s - loss: 0.4628 - mae: 0.4133"
      ]
     },
     {
@@ -6142,7 +6111,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "233/729 [========>.....................] - ETA: 7s - loss: 0.4819 - mae: 0.4168"
+      "381/729 [==============>...............] - ETA: 4s - loss: 0.4685 - mae: 0.4141"
      ]
     },
     {
@@ -6150,7 +6119,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "237/729 [========>.....................] - ETA: 7s - loss: 0.4821 - mae: 0.4168"
+      "385/729 [==============>...............] - ETA: 4s - loss: 0.4670 - mae: 0.4136"
      ]
     },
     {
@@ -6158,7 +6127,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "241/729 [========>.....................] - ETA: 7s - loss: 0.4822 - mae: 0.4168"
+      "389/729 [===============>..............] - ETA: 4s - loss: 0.4664 - mae: 0.4135"
      ]
     },
     {
@@ -6166,7 +6135,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "245/729 [=========>....................] - ETA: 7s - loss: 0.4822 - mae: 0.4168"
+      "393/729 [===============>..............] - ETA: 4s - loss: 0.4676 - mae: 0.4140"
      ]
     },
     {
@@ -6174,7 +6143,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "249/729 [=========>....................] - ETA: 7s - loss: 0.4823 - mae: 0.4169"
+      "397/729 [===============>..............] - ETA: 4s - loss: 0.4667 - mae: 0.4136"
      ]
     },
     {
@@ -6182,7 +6151,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "253/729 [=========>....................] - ETA: 7s - loss: 0.4823 - mae: 0.4169"
+      "401/729 [===============>..............] - ETA: 4s - loss: 0.4657 - mae: 0.4134"
      ]
     },
     {
@@ -6190,7 +6159,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "257/729 [=========>....................] - ETA: 7s - loss: 0.4823 - mae: 0.4169"
+      "405/729 [===============>..............] - ETA: 4s - loss: 0.4635 - mae: 0.4125"
      ]
     },
     {
@@ -6198,7 +6167,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "261/729 [=========>....................] - ETA: 7s - loss: 0.4823 - mae: 0.4169"
+      "409/729 [===============>..............] - ETA: 4s - loss: 0.4623 - mae: 0.4122"
      ]
     },
     {
@@ -6206,7 +6175,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "265/729 [=========>....................] - ETA: 6s - loss: 0.4822 - mae: 0.4168"
+      "413/729 [===============>..............] - ETA: 4s - loss: 0.4656 - mae: 0.4122"
      ]
     },
     {
@@ -6214,7 +6183,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "269/729 [==========>...................] - ETA: 6s - loss: 0.4821 - mae: 0.4168"
+      "417/729 [================>.............] - ETA: 4s - loss: 0.4637 - mae: 0.4115"
      ]
     },
     {
@@ -6222,7 +6191,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "273/729 [==========>...................] - ETA: 6s - loss: 0.4820 - mae: 0.4168"
+      "421/729 [================>.............] - ETA: 4s - loss: 0.4616 - mae: 0.4107"
      ]
     },
     {
@@ -6230,7 +6199,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "277/729 [==========>...................] - ETA: 6s - loss: 0.4818 - mae: 0.4167"
+      "425/729 [================>.............] - ETA: 4s - loss: 0.4629 - mae: 0.4109"
      ]
     },
     {
@@ -6238,7 +6207,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "281/729 [==========>...................] - ETA: 6s - loss: 0.4817 - mae: 0.4167"
+      "429/729 [================>.............] - ETA: 4s - loss: 0.4625 - mae: 0.4106"
      ]
     },
     {
@@ -6246,7 +6215,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "285/729 [==========>...................] - ETA: 6s - loss: 0.4815 - mae: 0.4167"
+      "433/729 [================>.............] - ETA: 4s - loss: 0.4620 - mae: 0.4107"
      ]
     },
     {
@@ -6254,7 +6223,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "289/729 [==========>...................] - ETA: 6s - loss: 0.4814 - mae: 0.4166"
+      "437/729 [================>.............] - ETA: 4s - loss: 0.4609 - mae: 0.4104"
      ]
     },
     {
@@ -6262,7 +6231,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "293/729 [===========>..................] - ETA: 6s - loss: 0.4814 - mae: 0.4166"
+      "441/729 [=================>............] - ETA: 4s - loss: 0.4621 - mae: 0.4103"
      ]
     },
     {
@@ -6270,7 +6239,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "297/729 [===========>..................] - ETA: 6s - loss: 0.4814 - mae: 0.4166"
+      "445/729 [=================>............] - ETA: 4s - loss: 0.4712 - mae: 0.4107"
      ]
     },
     {
@@ -6278,7 +6247,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "301/729 [===========>..................] - ETA: 6s - loss: 0.4813 - mae: 0.4166"
+      "449/729 [=================>............] - ETA: 3s - loss: 0.4700 - mae: 0.4105"
      ]
     },
     {
@@ -6286,7 +6255,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "305/729 [===========>..................] - ETA: 6s - loss: 0.4813 - mae: 0.4166"
+      "453/729 [=================>............] - ETA: 3s - loss: 0.4691 - mae: 0.4104"
      ]
     },
     {
@@ -6294,7 +6263,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "309/729 [===========>..................] - ETA: 6s - loss: 0.4813 - mae: 0.4165"
+      "457/729 [=================>............] - ETA: 3s - loss: 0.4692 - mae: 0.4104"
      ]
     },
     {
@@ -6302,7 +6271,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "313/729 [===========>..................] - ETA: 6s - loss: 0.4812 - mae: 0.4165"
+      "461/729 [=================>............] - ETA: 3s - loss: 0.4696 - mae: 0.4108"
      ]
     },
     {
@@ -6310,7 +6279,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "317/729 [============>.................] - ETA: 6s - loss: 0.4812 - mae: 0.4165"
+      "465/729 [==================>...........] - ETA: 3s - loss: 0.4704 - mae: 0.4112"
      ]
     },
     {
@@ -6318,7 +6287,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "321/729 [============>.................] - ETA: 6s - loss: 0.4813 - mae: 0.4165"
+      "469/729 [==================>...........] - ETA: 3s - loss: 0.4691 - mae: 0.4108"
      ]
     },
     {
@@ -6326,7 +6295,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "325/729 [============>.................] - ETA: 6s - loss: 0.4813 - mae: 0.4165"
+      "473/729 [==================>...........] - ETA: 3s - loss: 0.4683 - mae: 0.4106"
      ]
     },
     {
@@ -6334,7 +6303,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "329/729 [============>.................] - ETA: 5s - loss: 0.4814 - mae: 0.4165"
+      "477/729 [==================>...........] - ETA: 3s - loss: 0.4669 - mae: 0.4100"
      ]
     },
     {
@@ -6342,7 +6311,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "333/729 [============>.................] - ETA: 5s - loss: 0.4814 - mae: 0.4164"
+      "481/729 [==================>...........] - ETA: 3s - loss: 0.4661 - mae: 0.4101"
      ]
     },
     {
@@ -6350,7 +6319,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "337/729 [============>.................] - ETA: 5s - loss: 0.4813 - mae: 0.4164"
+      "485/729 [==================>...........] - ETA: 3s - loss: 0.4664 - mae: 0.4101"
      ]
     },
     {
@@ -6358,7 +6327,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "341/729 [=============>................] - ETA: 5s - loss: 0.4814 - mae: 0.4164"
+      "489/729 [===================>..........] - ETA: 3s - loss: 0.4670 - mae: 0.4104"
      ]
     },
     {
@@ -6366,7 +6335,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "345/729 [=============>................] - ETA: 5s - loss: 0.4813 - mae: 0.4164"
+      "493/729 [===================>..........] - ETA: 3s - loss: 0.4659 - mae: 0.4101"
      ]
     },
     {
@@ -6374,7 +6343,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "349/729 [=============>................] - ETA: 5s - loss: 0.4813 - mae: 0.4164"
+      "497/729 [===================>..........] - ETA: 3s - loss: 0.4645 - mae: 0.4097"
      ]
     },
     {
@@ -6382,7 +6351,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "353/729 [=============>................] - ETA: 5s - loss: 0.4813 - mae: 0.4164"
+      "501/729 [===================>..........] - ETA: 3s - loss: 0.4637 - mae: 0.4095"
      ]
     },
     {
@@ -6390,7 +6359,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "357/729 [=============>................] - ETA: 5s - loss: 0.4812 - mae: 0.4163"
+      "505/729 [===================>..........] - ETA: 3s - loss: 0.4630 - mae: 0.4095"
      ]
     },
     {
@@ -6398,7 +6367,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "361/729 [=============>................] - ETA: 5s - loss: 0.4812 - mae: 0.4163"
+      "509/729 [===================>..........] - ETA: 3s - loss: 0.4620 - mae: 0.4092"
      ]
     },
     {
@@ -6406,7 +6375,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "365/729 [==============>...............] - ETA: 5s - loss: 0.4811 - mae: 0.4163"
+      "513/729 [====================>.........] - ETA: 3s - loss: 0.4625 - mae: 0.4095"
      ]
     },
     {
@@ -6414,7 +6383,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "369/729 [==============>...............] - ETA: 5s - loss: 0.4810 - mae: 0.4163"
+      "517/729 [====================>.........] - ETA: 2s - loss: 0.4620 - mae: 0.4096"
      ]
     },
     {
@@ -6422,7 +6391,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "373/729 [==============>...............] - ETA: 5s - loss: 0.4809 - mae: 0.4162"
+      "521/729 [====================>.........] - ETA: 2s - loss: 0.4612 - mae: 0.4094"
      ]
     },
     {
@@ -6430,7 +6399,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "377/729 [==============>...............] - ETA: 5s - loss: 0.4808 - mae: 0.4162"
+      "525/729 [====================>.........] - ETA: 2s - loss: 0.4667 - mae: 0.4100"
      ]
     },
     {
@@ -6438,7 +6407,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "381/729 [==============>...............] - ETA: 5s - loss: 0.4808 - mae: 0.4162"
+      "529/729 [====================>.........] - ETA: 2s - loss: 0.4661 - mae: 0.4098"
      ]
     },
     {
@@ -6446,7 +6415,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "385/729 [==============>...............] - ETA: 5s - loss: 0.4807 - mae: 0.4162"
+      "533/729 [====================>.........] - ETA: 2s - loss: 0.4653 - mae: 0.4096"
      ]
     },
     {
@@ -6454,7 +6423,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "389/729 [===============>..............] - ETA: 5s - loss: 0.4806 - mae: 0.4162"
+      "537/729 [=====================>........] - ETA: 2s - loss: 0.4649 - mae: 0.4094"
      ]
     },
     {
@@ -6462,7 +6431,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "393/729 [===============>..............] - ETA: 5s - loss: 0.4805 - mae: 0.4161"
+      "541/729 [=====================>........] - ETA: 2s - loss: 0.4639 - mae: 0.4092"
      ]
     },
     {
@@ -6470,7 +6439,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "396/729 [===============>..............] - ETA: 4s - loss: 0.4805 - mae: 0.4161"
+      "545/729 [=====================>........] - ETA: 2s - loss: 0.4631 - mae: 0.4092"
      ]
     },
     {
@@ -6478,7 +6447,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "399/729 [===============>..............] - ETA: 4s - loss: 0.4804 - mae: 0.4161"
+      "549/729 [=====================>........] - ETA: 2s - loss: 0.4631 - mae: 0.4093"
      ]
     },
     {
@@ -6486,7 +6455,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "402/729 [===============>..............] - ETA: 4s - loss: 0.4803 - mae: 0.4161"
+      "553/729 [=====================>........] - ETA: 2s - loss: 0.4624 - mae: 0.4093"
      ]
     },
     {
@@ -6494,7 +6463,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "405/729 [===============>..............] - ETA: 4s - loss: 0.4803 - mae: 0.4161"
+      "557/729 [=====================>........] - ETA: 2s - loss: 0.4631 - mae: 0.4095"
      ]
     },
     {
@@ -6502,7 +6471,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "408/729 [===============>..............] - ETA: 4s - loss: 0.4802 - mae: 0.4161"
+      "561/729 [======================>.......] - ETA: 2s - loss: 0.4630 - mae: 0.4095"
      ]
     },
     {
@@ -6510,7 +6479,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "411/729 [===============>..............] - ETA: 4s - loss: 0.4801 - mae: 0.4160"
+      "565/729 [======================>.......] - ETA: 2s - loss: 0.4624 - mae: 0.4095"
      ]
     },
     {
@@ -6518,7 +6487,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "414/729 [================>.............] - ETA: 4s - loss: 0.4800 - mae: 0.4160"
+      "569/729 [======================>.......] - ETA: 2s - loss: 0.4623 - mae: 0.4095"
      ]
     },
     {
@@ -6526,7 +6495,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "417/729 [================>.............] - ETA: 4s - loss: 0.4799 - mae: 0.4160"
+      "573/729 [======================>.......] - ETA: 2s - loss: 0.4614 - mae: 0.4092"
      ]
     },
     {
@@ -6534,7 +6503,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "420/729 [================>.............] - ETA: 4s - loss: 0.4799 - mae: 0.4160"
+      "577/729 [======================>.......] - ETA: 2s - loss: 0.4609 - mae: 0.4091"
      ]
     },
     {
@@ -6542,7 +6511,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "423/729 [================>.............] - ETA: 4s - loss: 0.4798 - mae: 0.4160"
+      "581/729 [======================>.......] - ETA: 2s - loss: 0.4596 - mae: 0.4087"
      ]
     },
     {
@@ -6550,7 +6519,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "426/729 [================>.............] - ETA: 4s - loss: 0.4798 - mae: 0.4159"
+      "585/729 [=======================>......] - ETA: 2s - loss: 0.4593 - mae: 0.4086"
      ]
     },
     {
@@ -6558,7 +6527,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "429/729 [================>.............] - ETA: 4s - loss: 0.4797 - mae: 0.4159"
+      "589/729 [=======================>......] - ETA: 1s - loss: 0.4589 - mae: 0.4083"
      ]
     },
     {
@@ -6566,7 +6535,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "432/729 [================>.............] - ETA: 4s - loss: 0.4797 - mae: 0.4159"
+      "593/729 [=======================>......] - ETA: 1s - loss: 0.4601 - mae: 0.4078"
      ]
     },
     {
@@ -6574,7 +6543,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "435/729 [================>.............] - ETA: 4s - loss: 0.4797 - mae: 0.4159"
+      "597/729 [=======================>......] - ETA: 1s - loss: 0.4613 - mae: 0.4082"
      ]
     },
     {
@@ -6582,7 +6551,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "438/729 [=================>............] - ETA: 4s - loss: 0.4796 - mae: 0.4159"
+      "601/729 [=======================>......] - ETA: 1s - loss: 0.4606 - mae: 0.4081"
      ]
     },
     {
@@ -6590,7 +6559,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "441/729 [=================>............] - ETA: 4s - loss: 0.4796 - mae: 0.4158"
+      "605/729 [=======================>......] - ETA: 1s - loss: 0.4646 - mae: 0.4083"
      ]
     },
     {
@@ -6598,7 +6567,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "444/729 [=================>............] - ETA: 4s - loss: 0.4795 - mae: 0.4158"
+      "609/729 [========================>.....] - ETA: 1s - loss: 0.4648 - mae: 0.4085"
      ]
     },
     {
@@ -6606,7 +6575,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "447/729 [=================>............] - ETA: 4s - loss: 0.4795 - mae: 0.4158"
+      "613/729 [========================>.....] - ETA: 1s - loss: 0.4650 - mae: 0.4087"
      ]
     },
     {
@@ -6614,7 +6583,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "450/729 [=================>............] - ETA: 4s - loss: 0.4794 - mae: 0.4157"
+      "617/729 [========================>.....] - ETA: 1s - loss: 0.4654 - mae: 0.4090"
      ]
     },
     {
@@ -6622,7 +6591,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "453/729 [=================>............] - ETA: 4s - loss: 0.4794 - mae: 0.4157"
+      "621/729 [========================>.....] - ETA: 1s - loss: 0.4661 - mae: 0.4093"
      ]
     },
     {
@@ -6630,7 +6599,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "456/729 [=================>............] - ETA: 4s - loss: 0.4793 - mae: 0.4157"
+      "625/729 [========================>.....] - ETA: 1s - loss: 0.4659 - mae: 0.4095"
      ]
     },
     {
@@ -6638,7 +6607,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "459/729 [=================>............] - ETA: 4s - loss: 0.4792 - mae: 0.4157"
+      "629/729 [========================>.....] - ETA: 1s - loss: 0.4663 - mae: 0.4097"
      ]
     },
     {
@@ -6646,7 +6615,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "462/729 [==================>...........] - ETA: 4s - loss: 0.4792 - mae: 0.4156"
+      "633/729 [=========================>....] - ETA: 1s - loss: 0.4669 - mae: 0.4099"
      ]
     },
     {
@@ -6654,7 +6623,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "465/729 [==================>...........] - ETA: 4s - loss: 0.4791 - mae: 0.4156"
+      "637/729 [=========================>....] - ETA: 1s - loss: 0.4661 - mae: 0.4096"
      ]
     },
     {
@@ -6662,7 +6631,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "468/729 [==================>...........] - ETA: 4s - loss: 0.4790 - mae: 0.4156"
+      "641/729 [=========================>....] - ETA: 1s - loss: 0.4658 - mae: 0.4096"
      ]
     },
     {
@@ -6670,7 +6639,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "471/729 [==================>...........] - ETA: 3s - loss: 0.4790 - mae: 0.4155"
+      "645/729 [=========================>....] - ETA: 1s - loss: 0.4655 - mae: 0.4096"
      ]
     },
     {
@@ -6678,7 +6647,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "474/729 [==================>...........] - ETA: 3s - loss: 0.4789 - mae: 0.4155"
+      "649/729 [=========================>....] - ETA: 1s - loss: 0.4646 - mae: 0.4094"
      ]
     },
     {
@@ -6686,7 +6655,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "477/729 [==================>...........] - ETA: 3s - loss: 0.4788 - mae: 0.4155"
+      "653/729 [=========================>....] - ETA: 1s - loss: 0.4645 - mae: 0.4095"
      ]
     },
     {
@@ -6694,7 +6663,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "480/729 [==================>...........] - ETA: 3s - loss: 0.4788 - mae: 0.4155"
+      "657/729 [==========================>...] - ETA: 1s - loss: 0.4636 - mae: 0.4092"
      ]
     },
     {
@@ -6702,7 +6671,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "483/729 [==================>...........] - ETA: 3s - loss: 0.4787 - mae: 0.4154"
+      "661/729 [==========================>...] - ETA: 0s - loss: 0.4637 - mae: 0.4095"
      ]
     },
     {
@@ -6710,7 +6679,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "486/729 [===================>..........] - ETA: 3s - loss: 0.4787 - mae: 0.4154"
+      "665/729 [==========================>...] - ETA: 0s - loss: 0.4679 - mae: 0.4100"
      ]
     },
     {
@@ -6718,7 +6687,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "489/729 [===================>..........] - ETA: 3s - loss: 0.4786 - mae: 0.4154"
+      "669/729 [==========================>...] - ETA: 0s - loss: 0.4671 - mae: 0.4098"
      ]
     },
     {
@@ -6726,7 +6695,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "492/729 [===================>..........] - ETA: 3s - loss: 0.4785 - mae: 0.4154"
+      "673/729 [==========================>...] - ETA: 0s - loss: 0.4667 - mae: 0.4100"
      ]
     },
     {
@@ -6734,7 +6703,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "495/729 [===================>..........] - ETA: 3s - loss: 0.4784 - mae: 0.4153"
+      "677/729 [==========================>...] - ETA: 0s - loss: 0.4667 - mae: 0.4102"
      ]
     },
     {
@@ -6742,7 +6711,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "498/729 [===================>..........] - ETA: 3s - loss: 0.4784 - mae: 0.4153"
+      "681/729 [===========================>..] - ETA: 0s - loss: 0.4658 - mae: 0.4099"
      ]
     },
     {
@@ -6750,7 +6719,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "501/729 [===================>..........] - ETA: 3s - loss: 0.4783 - mae: 0.4153"
+      "685/729 [===========================>..] - ETA: 0s - loss: 0.4670 - mae: 0.4102"
      ]
     },
     {
@@ -6758,7 +6727,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "504/729 [===================>..........] - ETA: 3s - loss: 0.4782 - mae: 0.4153"
+      "689/729 [===========================>..] - ETA: 0s - loss: 0.4674 - mae: 0.4105"
      ]
     },
     {
@@ -6766,7 +6735,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "507/729 [===================>..........] - ETA: 3s - loss: 0.4781 - mae: 0.4152"
+      "693/729 [===========================>..] - ETA: 0s - loss: 0.4666 - mae: 0.4103"
      ]
     },
     {
@@ -6774,7 +6743,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "510/729 [===================>..........] - ETA: 3s - loss: 0.4780 - mae: 0.4152"
+      "697/729 [===========================>..] - ETA: 0s - loss: 0.4674 - mae: 0.4108"
      ]
     },
     {
@@ -6782,7 +6751,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "513/729 [====================>.........] - ETA: 3s - loss: 0.4780 - mae: 0.4152"
+      "701/729 [===========================>..] - ETA: 0s - loss: 0.4672 - mae: 0.4108"
      ]
     },
     {
@@ -6790,7 +6759,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "516/729 [====================>.........] - ETA: 3s - loss: 0.4779 - mae: 0.4151"
+      "705/729 [============================>.] - ETA: 0s - loss: 0.4674 - mae: 0.4110"
      ]
     },
     {
@@ -6798,7 +6767,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "519/729 [====================>.........] - ETA: 3s - loss: 0.4778 - mae: 0.4151"
+      "709/729 [============================>.] - ETA: 0s - loss: 0.4670 - mae: 0.4111"
      ]
     },
     {
@@ -6806,7 +6775,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "522/729 [====================>.........] - ETA: 3s - loss: 0.4777 - mae: 0.4151"
+      "713/729 [============================>.] - ETA: 0s - loss: 0.4678 - mae: 0.4112"
      ]
     },
     {
@@ -6814,7 +6783,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "525/729 [====================>.........] - ETA: 3s - loss: 0.4777 - mae: 0.4151"
+      "717/729 [============================>.] - ETA: 0s - loss: 0.4673 - mae: 0.4111"
      ]
     },
     {
@@ -6822,7 +6791,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "528/729 [====================>.........] - ETA: 3s - loss: 0.4776 - mae: 0.4150"
+      "721/729 [============================>.] - ETA: 0s - loss: 0.4675 - mae: 0.4111"
      ]
     },
     {
@@ -6830,7 +6799,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "531/729 [====================>.........] - ETA: 3s - loss: 0.4776 - mae: 0.4150"
+      "725/729 [============================>.] - ETA: 0s - loss: 0.4663 - mae: 0.4107"
      ]
     },
     {
@@ -6838,7 +6807,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "534/729 [====================>.........] - ETA: 3s - loss: 0.4775 - mae: 0.4150"
+      "729/729 [==============================] - ETA: 0s - loss: 0.4693 - mae: 0.4113"
      ]
     },
     {
@@ -6846,15 +6815,16 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "537/729 [=====================>........] - ETA: 3s - loss: 0.4775 - mae: 0.4149"
+      "729/729 [==============================] - 11s 15ms/step - loss: 0.4693 - mae: 0.4113 - val_loss: 0.4393 - val_mae: 0.3825\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "540/729 [=====================>........] - ETA: 2s - loss: 0.4774 - mae: 0.4149"
+      "Epoch 5/10\n",
+      "\r",
+      "  1/729 [..............................] - ETA: 0s - loss: 0.5621 - mae: 0.4813"
      ]
     },
     {
@@ -6862,7 +6832,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "543/729 [=====================>........] - ETA: 2s - loss: 0.4773 - mae: 0.4149"
+      "  5/729 [..............................] - ETA: 8s - loss: 0.3803 - mae: 0.3866"
      ]
     },
     {
@@ -6870,7 +6840,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "546/729 [=====================>........] - ETA: 2s - loss: 0.4773 - mae: 0.4149"
+      "  9/729 [..............................] - ETA: 9s - loss: 0.7031 - mae: 0.4062"
      ]
     },
     {
@@ -6878,7 +6848,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "549/729 [=====================>........] - ETA: 2s - loss: 0.4772 - mae: 0.4148"
+      " 13/729 [..............................] - ETA: 9s - loss: 0.5849 - mae: 0.3974"
      ]
     },
     {
@@ -6886,7 +6856,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "553/729 [=====================>........] - ETA: 2s - loss: 0.4771 - mae: 0.4148"
+      " 17/729 [..............................] - ETA: 9s - loss: 0.5725 - mae: 0.4030"
      ]
     },
     {
@@ -6894,7 +6864,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "556/729 [=====================>........] - ETA: 2s - loss: 0.4771 - mae: 0.4148"
+      " 21/729 [..............................] - ETA: 9s - loss: 0.5411 - mae: 0.4078"
      ]
     },
     {
@@ -6902,7 +6872,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "559/729 [======================>.......] - ETA: 2s - loss: 0.4770 - mae: 0.4148"
+      " 25/729 [>.............................] - ETA: 9s - loss: 0.5010 - mae: 0.3990"
      ]
     },
     {
@@ -6910,7 +6880,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "562/729 [======================>.......] - ETA: 2s - loss: 0.4770 - mae: 0.4147"
+      " 29/729 [>.............................] - ETA: 9s - loss: 0.4773 - mae: 0.3957"
      ]
     },
     {
@@ -6918,7 +6888,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "565/729 [======================>.......] - ETA: 2s - loss: 0.4769 - mae: 0.4147"
+      " 33/729 [>.............................] - ETA: 9s - loss: 0.4541 - mae: 0.3913"
      ]
     },
     {
@@ -6926,7 +6896,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "568/729 [======================>.......] - ETA: 2s - loss: 0.4768 - mae: 0.4147"
+      " 37/729 [>.............................] - ETA: 9s - loss: 0.4471 - mae: 0.3917"
      ]
     },
     {
@@ -6934,7 +6904,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "571/729 [======================>.......] - ETA: 2s - loss: 0.4768 - mae: 0.4147"
+      " 41/729 [>.............................] - ETA: 9s - loss: 0.4446 - mae: 0.3923"
      ]
     },
     {
@@ -6942,7 +6912,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "574/729 [======================>.......] - ETA: 2s - loss: 0.4767 - mae: 0.4146"
+      " 45/729 [>.............................] - ETA: 9s - loss: 0.4334 - mae: 0.3902"
      ]
     },
     {
@@ -6950,7 +6920,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "577/729 [======================>.......] - ETA: 2s - loss: 0.4766 - mae: 0.4146"
+      " 49/729 [=>............................] - ETA: 9s - loss: 0.4251 - mae: 0.3888"
      ]
     },
     {
@@ -6958,7 +6928,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "580/729 [======================>.......] - ETA: 2s - loss: 0.4766 - mae: 0.4146"
+      " 53/729 [=>............................] - ETA: 9s - loss: 0.4209 - mae: 0.3890"
      ]
     },
     {
@@ -6966,7 +6936,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "583/729 [======================>.......] - ETA: 2s - loss: 0.4765 - mae: 0.4146"
+      " 57/729 [=>............................] - ETA: 9s - loss: 0.4187 - mae: 0.3900"
      ]
     },
     {
@@ -6974,7 +6944,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "586/729 [=======================>......] - ETA: 2s - loss: 0.4764 - mae: 0.4145"
+      " 61/729 [=>............................] - ETA: 9s - loss: 0.4079 - mae: 0.3860"
      ]
     },
     {
@@ -6982,7 +6952,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "589/729 [=======================>......] - ETA: 2s - loss: 0.4764 - mae: 0.4145"
+      " 65/729 [=>............................] - ETA: 9s - loss: 0.4051 - mae: 0.3869"
      ]
     },
     {
@@ -6990,7 +6960,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "592/729 [=======================>......] - ETA: 2s - loss: 0.4763 - mae: 0.4145"
+      " 69/729 [=>............................] - ETA: 9s - loss: 0.4056 - mae: 0.3880"
      ]
     },
     {
@@ -6998,7 +6968,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "595/729 [=======================>......] - ETA: 2s - loss: 0.4763 - mae: 0.4145"
+      " 73/729 [==>...........................] - ETA: 9s - loss: 0.4030 - mae: 0.3879"
      ]
     },
     {
@@ -7006,7 +6976,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "598/729 [=======================>......] - ETA: 2s - loss: 0.4762 - mae: 0.4144"
+      " 77/729 [==>...........................] - ETA: 9s - loss: 0.3975 - mae: 0.3860"
      ]
     },
     {
@@ -7014,7 +6984,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "602/729 [=======================>......] - ETA: 2s - loss: 0.4761 - mae: 0.4144"
+      " 81/729 [==>...........................] - ETA: 8s - loss: 0.4007 - mae: 0.3873"
      ]
     },
     {
@@ -7022,7 +6992,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "605/729 [=======================>......] - ETA: 1s - loss: 0.4760 - mae: 0.4144"
+      " 85/729 [==>...........................] - ETA: 8s - loss: 0.4040 - mae: 0.3888"
      ]
     },
     {
@@ -7030,7 +7000,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "608/729 [========================>.....] - ETA: 1s - loss: 0.4759 - mae: 0.4144"
+      " 89/729 [==>...........................] - ETA: 8s - loss: 0.4033 - mae: 0.3894"
      ]
     },
     {
@@ -7038,7 +7008,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "611/729 [========================>.....] - ETA: 1s - loss: 0.4759 - mae: 0.4143"
+      " 93/729 [==>...........................] - ETA: 8s - loss: 0.4119 - mae: 0.3938"
      ]
     },
     {
@@ -7046,7 +7016,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "614/729 [========================>.....] - ETA: 1s - loss: 0.4758 - mae: 0.4143"
+      " 97/729 [==>...........................] - ETA: 8s - loss: 0.4077 - mae: 0.3926"
      ]
     },
     {
@@ -7054,7 +7024,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "617/729 [========================>.....] - ETA: 1s - loss: 0.4758 - mae: 0.4143"
+      "101/729 [===>..........................] - ETA: 8s - loss: 0.4149 - mae: 0.3929"
      ]
     },
     {
@@ -7062,7 +7032,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "620/729 [========================>.....] - ETA: 1s - loss: 0.4757 - mae: 0.4143"
+      "105/729 [===>..........................] - ETA: 8s - loss: 0.4165 - mae: 0.3920"
      ]
     },
     {
@@ -7070,7 +7040,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "623/729 [========================>.....] - ETA: 1s - loss: 0.4756 - mae: 0.4143"
+      "109/729 [===>..........................] - ETA: 8s - loss: 0.4164 - mae: 0.3928"
      ]
     },
     {
@@ -7078,7 +7048,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "626/729 [========================>.....] - ETA: 1s - loss: 0.4756 - mae: 0.4142"
+      "113/729 [===>..........................] - ETA: 8s - loss: 0.4167 - mae: 0.3942"
      ]
     },
     {
@@ -7086,7 +7056,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "629/729 [========================>.....] - ETA: 1s - loss: 0.4755 - mae: 0.4142"
+      "117/729 [===>..........................] - ETA: 8s - loss: 0.4152 - mae: 0.3941"
      ]
     },
     {
@@ -7094,7 +7064,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "632/729 [=========================>....] - ETA: 1s - loss: 0.4754 - mae: 0.4142"
+      "121/729 [===>..........................] - ETA: 8s - loss: 0.4115 - mae: 0.3933"
      ]
     },
     {
@@ -7102,7 +7072,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "635/729 [=========================>....] - ETA: 1s - loss: 0.4754 - mae: 0.4142"
+      "125/729 [====>.........................] - ETA: 8s - loss: 0.4089 - mae: 0.3925"
      ]
     },
     {
@@ -7110,7 +7080,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "638/729 [=========================>....] - ETA: 1s - loss: 0.4753 - mae: 0.4142"
+      "129/729 [====>.........................] - ETA: 8s - loss: 0.4072 - mae: 0.3923"
      ]
     },
     {
@@ -7118,7 +7088,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "641/729 [=========================>....] - ETA: 1s - loss: 0.4753 - mae: 0.4141"
+      "133/729 [====>.........................] - ETA: 8s - loss: 0.4032 - mae: 0.3912"
      ]
     },
     {
@@ -7126,7 +7096,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "644/729 [=========================>....] - ETA: 1s - loss: 0.4752 - mae: 0.4141"
+      "137/729 [====>.........................] - ETA: 8s - loss: 0.3998 - mae: 0.3903"
      ]
     },
     {
@@ -7134,7 +7104,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "647/729 [=========================>....] - ETA: 1s - loss: 0.4751 - mae: 0.4141"
+      "141/729 [====>.........................] - ETA: 8s - loss: 0.4021 - mae: 0.3913"
      ]
     },
     {
@@ -7142,7 +7112,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "650/729 [=========================>....] - ETA: 1s - loss: 0.4751 - mae: 0.4141"
+      "145/729 [====>.........................] - ETA: 8s - loss: 0.3991 - mae: 0.3904"
      ]
     },
     {
@@ -7150,7 +7120,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "653/729 [=========================>....] - ETA: 1s - loss: 0.4750 - mae: 0.4141"
+      "149/729 [=====>........................] - ETA: 8s - loss: 0.4148 - mae: 0.3913"
      ]
     },
     {
@@ -7158,7 +7128,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "656/729 [=========================>....] - ETA: 1s - loss: 0.4750 - mae: 0.4140"
+      "153/729 [=====>........................] - ETA: 8s - loss: 0.4156 - mae: 0.3927"
      ]
     },
     {
@@ -7166,7 +7136,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "659/729 [==========================>...] - ETA: 1s - loss: 0.4749 - mae: 0.4140"
+      "157/729 [=====>........................] - ETA: 8s - loss: 0.4127 - mae: 0.3920"
      ]
     },
     {
@@ -7174,7 +7144,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "662/729 [==========================>...] - ETA: 1s - loss: 0.4749 - mae: 0.4140"
+      "161/729 [=====>........................] - ETA: 7s - loss: 0.4104 - mae: 0.3916"
      ]
     },
     {
@@ -7182,7 +7152,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "665/729 [==========================>...] - ETA: 1s - loss: 0.4748 - mae: 0.4140"
+      "165/729 [=====>........................] - ETA: 7s - loss: 0.4125 - mae: 0.3918"
      ]
     },
     {
@@ -7190,7 +7160,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "668/729 [==========================>...] - ETA: 0s - loss: 0.4748 - mae: 0.4140"
+      "169/729 [=====>........................] - ETA: 7s - loss: 0.4132 - mae: 0.3921"
      ]
     },
     {
@@ -7198,7 +7168,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "671/729 [==========================>...] - ETA: 0s - loss: 0.4748 - mae: 0.4140"
+      "173/729 [======>.......................] - ETA: 7s - loss: 0.4121 - mae: 0.3923"
      ]
     },
     {
@@ -7206,7 +7176,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "674/729 [==========================>...] - ETA: 0s - loss: 0.4747 - mae: 0.4140"
+      "177/729 [======>.......................] - ETA: 7s - loss: 0.4175 - mae: 0.3939"
      ]
     },
     {
@@ -7214,7 +7184,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "677/729 [==========================>...] - ETA: 0s - loss: 0.4747 - mae: 0.4140"
+      "181/729 [======>.......................] - ETA: 7s - loss: 0.4154 - mae: 0.3931"
      ]
     },
     {
@@ -7222,7 +7192,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "680/729 [==========================>...] - ETA: 0s - loss: 0.4747 - mae: 0.4139"
+      "185/729 [======>.......................] - ETA: 7s - loss: 0.4152 - mae: 0.3934"
      ]
     },
     {
@@ -7230,7 +7200,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "683/729 [===========================>..] - ETA: 0s - loss: 0.4747 - mae: 0.4139"
+      "189/729 [======>.......................] - ETA: 7s - loss: 0.4128 - mae: 0.3926"
      ]
     },
     {
@@ -7238,7 +7208,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "686/729 [===========================>..] - ETA: 0s - loss: 0.4746 - mae: 0.4139"
+      "193/729 [======>.......................] - ETA: 7s - loss: 0.4266 - mae: 0.3945"
      ]
     },
     {
@@ -7246,7 +7216,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "689/729 [===========================>..] - ETA: 0s - loss: 0.4746 - mae: 0.4139"
+      "197/729 [=======>......................] - ETA: 7s - loss: 0.4234 - mae: 0.3934"
      ]
     },
     {
@@ -7254,7 +7224,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "692/729 [===========================>..] - ETA: 0s - loss: 0.4746 - mae: 0.4139"
+      "201/729 [=======>......................] - ETA: 7s - loss: 0.4221 - mae: 0.3936"
      ]
     },
     {
@@ -7262,7 +7232,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "695/729 [===========================>..] - ETA: 0s - loss: 0.4746 - mae: 0.4139"
+      "205/729 [=======>......................] - ETA: 7s - loss: 0.4277 - mae: 0.3946"
      ]
     },
     {
@@ -7270,7 +7240,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "698/729 [===========================>..] - ETA: 0s - loss: 0.4745 - mae: 0.4139"
+      "209/729 [=======>......................] - ETA: 7s - loss: 0.4266 - mae: 0.3948"
      ]
     },
     {
@@ -7278,7 +7248,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "701/729 [===========================>..] - ETA: 0s - loss: 0.4745 - mae: 0.4139"
+      "213/729 [=======>......................] - ETA: 7s - loss: 0.4252 - mae: 0.3946"
      ]
     },
     {
@@ -7286,7 +7256,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "704/729 [===========================>..] - ETA: 0s - loss: 0.4745 - mae: 0.4139"
+      "217/729 [=======>......................] - ETA: 7s - loss: 0.4234 - mae: 0.3942"
      ]
     },
     {
@@ -7294,7 +7264,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "707/729 [============================>.] - ETA: 0s - loss: 0.4745 - mae: 0.4139"
+      "221/729 [========>.....................] - ETA: 7s - loss: 0.4352 - mae: 0.3951"
      ]
     },
     {
@@ -7302,7 +7272,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "710/729 [============================>.] - ETA: 0s - loss: 0.4745 - mae: 0.4139"
+      "225/729 [========>.....................] - ETA: 7s - loss: 0.4328 - mae: 0.3947"
      ]
     },
     {
@@ -7310,7 +7280,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "713/729 [============================>.] - ETA: 0s - loss: 0.4744 - mae: 0.4139"
+      "229/729 [========>.....................] - ETA: 7s - loss: 0.4307 - mae: 0.3943"
      ]
     },
     {
@@ -7318,7 +7288,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "716/729 [============================>.] - ETA: 0s - loss: 0.4744 - mae: 0.4138"
+      "233/729 [========>.....................] - ETA: 6s - loss: 0.4284 - mae: 0.3938"
      ]
     },
     {
@@ -7326,7 +7296,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "719/729 [============================>.] - ETA: 0s - loss: 0.4744 - mae: 0.4138"
+      "237/729 [========>.....................] - ETA: 6s - loss: 0.4349 - mae: 0.3959"
      ]
     },
     {
@@ -7334,7 +7304,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "722/729 [============================>.] - ETA: 0s - loss: 0.4744 - mae: 0.4138"
+      "241/729 [========>.....................] - ETA: 6s - loss: 0.4379 - mae: 0.3969"
      ]
     },
     {
@@ -7342,7 +7312,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "726/729 [============================>.] - ETA: 0s - loss: 0.4744 - mae: 0.4138"
+      "245/729 [=========>....................] - ETA: 6s - loss: 0.4409 - mae: 0.3982"
      ]
     },
     {
@@ -7350,7 +7320,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "729/729 [==============================] - ETA: 0s - loss: 0.4744 - mae: 0.4138"
+      "249/729 [=========>....................] - ETA: 6s - loss: 0.4484 - mae: 0.3995"
      ]
     },
     {
@@ -7358,1856 +7328,151 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "729/729 [==============================] - 13s 17ms/step - loss: 0.4744 - mae: 0.4138 - val_loss: 0.4412 - val_mae: 0.3823\n"
+      "253/729 [=========>....................] - ETA: 6s - loss: 0.4566 - mae: 0.4008"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 5/10\n",
-      "\r",
-      "  1/729 [..............................] - ETA: 30s - loss: 0.4745 - mae: 0.4475"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "257/729 [=========>....................] - ETA: 6s - loss: 0.4551 - mae: 0.4005"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  4/729 [..............................] - ETA: 12s - loss: 0.4920 - mae: 0.4515"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "261/729 [=========>....................] - ETA: 6s - loss: 0.4571 - mae: 0.4015"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  7/729 [..............................] - ETA: 12s - loss: 0.7076 - mae: 0.4698"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "265/729 [=========>....................] - ETA: 6s - loss: 0.4556 - mae: 0.4016"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 10/729 [..............................] - ETA: 12s - loss: 0.7640 - mae: 0.4753"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "269/729 [==========>...................] - ETA: 6s - loss: 0.4540 - mae: 0.4014"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 13/729 [..............................] - ETA: 12s - loss: 0.7822 - mae: 0.4774"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "273/729 [==========>...................] - ETA: 6s - loss: 0.4543 - mae: 0.4021"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 16/729 [..............................] - ETA: 12s - loss: 0.7779 - mae: 0.4757"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "277/729 [==========>...................] - ETA: 6s - loss: 0.4590 - mae: 0.4026"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 19/729 [..............................] - ETA: 12s - loss: 0.7699 - mae: 0.4744"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "281/729 [==========>...................] - ETA: 6s - loss: 0.4569 - mae: 0.4022"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 22/729 [..............................] - ETA: 12s - loss: 0.7586 - mae: 0.4722"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "285/729 [==========>...................] - ETA: 6s - loss: 0.4563 - mae: 0.4025"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 25/729 [>.............................] - ETA: 12s - loss: 0.7469 - mae: 0.4704"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "289/729 [==========>...................] - ETA: 6s - loss: 0.4598 - mae: 0.4040"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 28/729 [>.............................] - ETA: 12s - loss: 0.7355 - mae: 0.4689"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "293/729 [===========>..................] - ETA: 6s - loss: 0.4581 - mae: 0.4035"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 31/729 [>.............................] - ETA: 12s - loss: 0.7392 - mae: 0.4685"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "297/729 [===========>..................] - ETA: 6s - loss: 0.4566 - mae: 0.4031"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 34/729 [>.............................] - ETA: 12s - loss: 0.7426 - mae: 0.4685"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "301/729 [===========>..................] - ETA: 6s - loss: 0.4552 - mae: 0.4028"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 37/729 [>.............................] - ETA: 12s - loss: 0.7438 - mae: 0.4684"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "305/729 [===========>..................] - ETA: 5s - loss: 0.4541 - mae: 0.4028"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 40/729 [>.............................] - ETA: 12s - loss: 0.7435 - mae: 0.4683"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "309/729 [===========>..................] - ETA: 5s - loss: 0.4535 - mae: 0.4029"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 43/729 [>.............................] - ETA: 12s - loss: 0.7422 - mae: 0.4681"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "313/729 [===========>..................] - ETA: 5s - loss: 0.4528 - mae: 0.4028"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 46/729 [>.............................] - ETA: 12s - loss: 0.7395 - mae: 0.4675"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "317/729 [============>.................] - ETA: 5s - loss: 0.4521 - mae: 0.4024"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 49/729 [=>............................] - ETA: 11s - loss: 0.7359 - mae: 0.4667"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "321/729 [============>.................] - ETA: 5s - loss: 0.4574 - mae: 0.4040"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 52/729 [=>............................] - ETA: 11s - loss: 0.7317 - mae: 0.4659"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 55/729 [=>............................] - ETA: 11s - loss: 0.7270 - mae: 0.4648"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 58/729 [=>............................] - ETA: 11s - loss: 0.7217 - mae: 0.4636"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 61/729 [=>............................] - ETA: 11s - loss: 0.7164 - mae: 0.4625"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 64/729 [=>............................] - ETA: 11s - loss: 0.7112 - mae: 0.4614"
-     ]
-    },
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 67/729 [=>............................] - ETA: 11s - loss: 0.7059 - mae: 0.4602"
-     ]
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 70/729 [=>............................] - ETA: 11s - loss: 0.7007 - mae: 0.4590"
-     ]
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 73/729 [==>...........................] - ETA: 11s - loss: 0.6968 - mae: 0.4580"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 76/729 [==>...........................] - ETA: 11s - loss: 0.6929 - mae: 0.4571"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 79/729 [==>...........................] - ETA: 11s - loss: 0.6890 - mae: 0.4561"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 82/729 [==>...........................] - ETA: 11s - loss: 0.6851 - mae: 0.4552"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 86/729 [==>...........................] - ETA: 11s - loss: 0.6801 - mae: 0.4540"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 89/729 [==>...........................] - ETA: 11s - loss: 0.6764 - mae: 0.4531"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 92/729 [==>...........................] - ETA: 11s - loss: 0.6725 - mae: 0.4522"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 95/729 [==>...........................] - ETA: 11s - loss: 0.6687 - mae: 0.4512"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 98/729 [===>..........................] - ETA: 11s - loss: 0.6652 - mae: 0.4504"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "101/729 [===>..........................] - ETA: 11s - loss: 0.6620 - mae: 0.4497"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "104/729 [===>..........................] - ETA: 11s - loss: 0.6592 - mae: 0.4491"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "107/729 [===>..........................] - ETA: 10s - loss: 0.6564 - mae: 0.4485"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "110/729 [===>..........................] - ETA: 10s - loss: 0.6536 - mae: 0.4480"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "113/729 [===>..........................] - ETA: 10s - loss: 0.6508 - mae: 0.4474"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "116/729 [===>..........................] - ETA: 10s - loss: 0.6480 - mae: 0.4469"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "119/729 [===>..........................] - ETA: 10s - loss: 0.6453 - mae: 0.4464"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "122/729 [====>.........................] - ETA: 10s - loss: 0.6426 - mae: 0.4459"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "125/729 [====>.........................] - ETA: 10s - loss: 0.6399 - mae: 0.4453"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "128/729 [====>.........................] - ETA: 10s - loss: 0.6372 - mae: 0.4448"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "132/729 [====>.........................] - ETA: 10s - loss: 0.6335 - mae: 0.4441"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "135/729 [====>.........................] - ETA: 10s - loss: 0.6308 - mae: 0.4435"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "138/729 [====>.........................] - ETA: 10s - loss: 0.6281 - mae: 0.4429"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "141/729 [====>.........................] - ETA: 10s - loss: 0.6255 - mae: 0.4424"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "144/729 [====>.........................] - ETA: 10s - loss: 0.6229 - mae: 0.4418"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "147/729 [=====>........................] - ETA: 10s - loss: 0.6203 - mae: 0.4413"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "150/729 [=====>........................] - ETA: 10s - loss: 0.6179 - mae: 0.4408"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "153/729 [=====>........................] - ETA: 10s - loss: 0.6156 - mae: 0.4403"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "156/729 [=====>........................] - ETA: 10s - loss: 0.6133 - mae: 0.4399"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "159/729 [=====>........................] - ETA: 10s - loss: 0.6110 - mae: 0.4394"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "162/729 [=====>........................] - ETA: 9s - loss: 0.6089 - mae: 0.4390 "
-     ]
-    },
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "165/729 [=====>........................] - ETA: 9s - loss: 0.6068 - mae: 0.4386"
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-      "168/729 [=====>........................] - ETA: 9s - loss: 0.6048 - mae: 0.4382"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "171/729 [======>.......................] - ETA: 9s - loss: 0.6029 - mae: 0.4378"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "174/729 [======>.......................] - ETA: 9s - loss: 0.6011 - mae: 0.4374"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "177/729 [======>.......................] - ETA: 9s - loss: 0.5993 - mae: 0.4370"
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-      "180/729 [======>.......................] - ETA: 9s - loss: 0.5976 - mae: 0.4366"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "183/729 [======>.......................] - ETA: 9s - loss: 0.5959 - mae: 0.4363"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "186/729 [======>.......................] - ETA: 9s - loss: 0.5943 - mae: 0.4360"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "189/729 [======>.......................] - ETA: 9s - loss: 0.5927 - mae: 0.4357"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "192/729 [======>.......................] - ETA: 9s - loss: 0.5911 - mae: 0.4354"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "195/729 [=======>......................] - ETA: 9s - loss: 0.5896 - mae: 0.4351"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "198/729 [=======>......................] - ETA: 9s - loss: 0.5882 - mae: 0.4348"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "201/729 [=======>......................] - ETA: 9s - loss: 0.5868 - mae: 0.4345"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "204/729 [=======>......................] - ETA: 9s - loss: 0.5853 - mae: 0.4343"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "207/729 [=======>......................] - ETA: 9s - loss: 0.5840 - mae: 0.4340"
-     ]
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "211/729 [=======>......................] - ETA: 9s - loss: 0.5821 - mae: 0.4337"
-     ]
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "214/729 [=======>......................] - ETA: 9s - loss: 0.5808 - mae: 0.4334"
-     ]
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "217/729 [=======>......................] - ETA: 8s - loss: 0.5795 - mae: 0.4331"
-     ]
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "220/729 [========>.....................] - ETA: 8s - loss: 0.5781 - mae: 0.4329"
-     ]
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "223/729 [========>.....................] - ETA: 8s - loss: 0.5769 - mae: 0.4327"
-     ]
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "227/729 [========>.....................] - ETA: 8s - loss: 0.5752 - mae: 0.4323"
-     ]
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "230/729 [========>.....................] - ETA: 8s - loss: 0.5740 - mae: 0.4321"
-     ]
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "233/729 [========>.....................] - ETA: 8s - loss: 0.5729 - mae: 0.4319"
-     ]
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "236/729 [========>.....................] - ETA: 8s - loss: 0.5718 - mae: 0.4317"
-     ]
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "240/729 [========>.....................] - ETA: 8s - loss: 0.5703 - mae: 0.4314"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "243/729 [=========>....................] - ETA: 8s - loss: 0.5693 - mae: 0.4312"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "246/729 [=========>....................] - ETA: 8s - loss: 0.5683 - mae: 0.4310"
-     ]
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-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "249/729 [=========>....................] - ETA: 8s - loss: 0.5673 - mae: 0.4309"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "252/729 [=========>....................] - ETA: 8s - loss: 0.5663 - mae: 0.4307"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "255/729 [=========>....................] - ETA: 8s - loss: 0.5653 - mae: 0.4305"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "259/729 [=========>....................] - ETA: 8s - loss: 0.5639 - mae: 0.4302"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "262/729 [=========>....................] - ETA: 8s - loss: 0.5630 - mae: 0.4301"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "265/729 [=========>....................] - ETA: 8s - loss: 0.5621 - mae: 0.4299"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "268/729 [==========>...................] - ETA: 8s - loss: 0.5612 - mae: 0.4297"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "271/729 [==========>...................] - ETA: 8s - loss: 0.5603 - mae: 0.4296"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "274/729 [==========>...................] - ETA: 7s - loss: 0.5594 - mae: 0.4294"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "278/729 [==========>...................] - ETA: 7s - loss: 0.5582 - mae: 0.4292"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "281/729 [==========>...................] - ETA: 7s - loss: 0.5574 - mae: 0.4290"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "284/729 [==========>...................] - ETA: 7s - loss: 0.5566 - mae: 0.4289"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "288/729 [==========>...................] - ETA: 7s - loss: 0.5555 - mae: 0.4287"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "291/729 [==========>...................] - ETA: 7s - loss: 0.5547 - mae: 0.4285"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "294/729 [===========>..................] - ETA: 7s - loss: 0.5539 - mae: 0.4284"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "297/729 [===========>..................] - ETA: 7s - loss: 0.5531 - mae: 0.4282"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "300/729 [===========>..................] - ETA: 7s - loss: 0.5523 - mae: 0.4281"
-     ]
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "303/729 [===========>..................] - ETA: 7s - loss: 0.5516 - mae: 0.4279"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "307/729 [===========>..................] - ETA: 7s - loss: 0.5506 - mae: 0.4277"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "310/729 [===========>..................] - ETA: 7s - loss: 0.5499 - mae: 0.4276"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "313/729 [===========>..................] - ETA: 7s - loss: 0.5492 - mae: 0.4275"
-     ]
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-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "316/729 [============>.................] - ETA: 7s - loss: 0.5484 - mae: 0.4273"
-     ]
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-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "319/729 [============>.................] - ETA: 7s - loss: 0.5477 - mae: 0.4272"
-     ]
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "322/729 [============>.................] - ETA: 7s - loss: 0.5471 - mae: 0.4270"
-     ]
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "325/729 [============>.................] - ETA: 7s - loss: 0.5464 - mae: 0.4269"
-     ]
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "328/729 [============>.................] - ETA: 7s - loss: 0.5458 - mae: 0.4268"
-     ]
-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "331/729 [============>.................] - ETA: 6s - loss: 0.5452 - mae: 0.4266"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "335/729 [============>.................] - ETA: 6s - loss: 0.5444 - mae: 0.4264"
-     ]
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "338/729 [============>.................] - ETA: 6s - loss: 0.5438 - mae: 0.4263"
-     ]
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-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "342/729 [=============>................] - ETA: 6s - loss: 0.5431 - mae: 0.4261"
-     ]
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "345/729 [=============>................] - ETA: 6s - loss: 0.5426 - mae: 0.4260"
-     ]
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "348/729 [=============>................] - ETA: 6s - loss: 0.5421 - mae: 0.4259"
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "351/729 [=============>................] - ETA: 6s - loss: 0.5416 - mae: 0.4258"
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "354/729 [=============>................] - ETA: 6s - loss: 0.5410 - mae: 0.4257"
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "357/729 [=============>................] - ETA: 6s - loss: 0.5405 - mae: 0.4256"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "360/729 [=============>................] - ETA: 6s - loss: 0.5400 - mae: 0.4255"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "363/729 [=============>................] - ETA: 6s - loss: 0.5395 - mae: 0.4253"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "367/729 [==============>...............] - ETA: 6s - loss: 0.5389 - mae: 0.4252"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "371/729 [==============>...............] - ETA: 6s - loss: 0.5382 - mae: 0.4251"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "374/729 [==============>...............] - ETA: 6s - loss: 0.5378 - mae: 0.4249"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "393/729 [===============>..............] - ETA: 5s - loss: 0.5349 - mae: 0.4243"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "403/729 [===============>..............] - ETA: 5s - loss: 0.5335 - mae: 0.4240"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "406/729 [===============>..............] - ETA: 5s - loss: 0.5331 - mae: 0.4239"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "409/729 [===============>..............] - ETA: 5s - loss: 0.5327 - mae: 0.4238"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "412/729 [===============>..............] - ETA: 5s - loss: 0.5322 - mae: 0.4237"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "415/729 [================>.............] - ETA: 5s - loss: 0.5319 - mae: 0.4236"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "418/729 [================>.............] - ETA: 5s - loss: 0.5315 - mae: 0.4235"
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-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "421/729 [================>.............] - ETA: 5s - loss: 0.5311 - mae: 0.4234"
-     ]
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-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "425/729 [================>.............] - ETA: 5s - loss: 0.5306 - mae: 0.4232"
-     ]
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-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "428/729 [================>.............] - ETA: 5s - loss: 0.5302 - mae: 0.4232"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "431/729 [================>.............] - ETA: 5s - loss: 0.5299 - mae: 0.4231"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "434/729 [================>.............] - ETA: 5s - loss: 0.5295 - mae: 0.4230"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "437/729 [================>.............] - ETA: 5s - loss: 0.5291 - mae: 0.4229"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "440/729 [=================>............] - ETA: 5s - loss: 0.5288 - mae: 0.4228"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "444/729 [=================>............] - ETA: 4s - loss: 0.5283 - mae: 0.4227"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "447/729 [=================>............] - ETA: 4s - loss: 0.5280 - mae: 0.4226"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "450/729 [=================>............] - ETA: 4s - loss: 0.5277 - mae: 0.4225"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "453/729 [=================>............] - ETA: 4s - loss: 0.5275 - mae: 0.4225"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "456/729 [=================>............] - ETA: 4s - loss: 0.5272 - mae: 0.4224"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "459/729 [=================>............] - ETA: 4s - loss: 0.5269 - mae: 0.4223"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "462/729 [==================>...........] - ETA: 4s - loss: 0.5267 - mae: 0.4223"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "465/729 [==================>...........] - ETA: 4s - loss: 0.5264 - mae: 0.4222"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "468/729 [==================>...........] - ETA: 4s - loss: 0.5262 - mae: 0.4221"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "471/729 [==================>...........] - ETA: 4s - loss: 0.5259 - mae: 0.4220"
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-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "474/729 [==================>...........] - ETA: 4s - loss: 0.5257 - mae: 0.4220"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "477/729 [==================>...........] - ETA: 4s - loss: 0.5254 - mae: 0.4219"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "481/729 [==================>...........] - ETA: 4s - loss: 0.5251 - mae: 0.4218"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "484/729 [==================>...........] - ETA: 4s - loss: 0.5248 - mae: 0.4217"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "487/729 [===================>..........] - ETA: 4s - loss: 0.5245 - mae: 0.4217"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "490/729 [===================>..........] - ETA: 4s - loss: 0.5243 - mae: 0.4216"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "493/729 [===================>..........] - ETA: 4s - loss: 0.5240 - mae: 0.4215"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "496/729 [===================>..........] - ETA: 4s - loss: 0.5238 - mae: 0.4215"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "499/729 [===================>..........] - ETA: 4s - loss: 0.5235 - mae: 0.4214"
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-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "502/729 [===================>..........] - ETA: 3s - loss: 0.5232 - mae: 0.4213"
-     ]
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-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "505/729 [===================>..........] - ETA: 3s - loss: 0.5230 - mae: 0.4213"
-     ]
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-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "508/729 [===================>..........] - ETA: 3s - loss: 0.5227 - mae: 0.4212"
-     ]
-    },
-    {
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-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "511/729 [====================>.........] - ETA: 3s - loss: 0.5224 - mae: 0.4211"
-     ]
-    },
-    {
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-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "515/729 [====================>.........] - ETA: 3s - loss: 0.5221 - mae: 0.4210"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "518/729 [====================>.........] - ETA: 3s - loss: 0.5218 - mae: 0.4210"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "521/729 [====================>.........] - ETA: 3s - loss: 0.5216 - mae: 0.4209"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "524/729 [====================>.........] - ETA: 3s - loss: 0.5213 - mae: 0.4209"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "527/729 [====================>.........] - ETA: 3s - loss: 0.5211 - mae: 0.4208"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "530/729 [====================>.........] - ETA: 3s - loss: 0.5208 - mae: 0.4207"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "533/729 [====================>.........] - ETA: 3s - loss: 0.5206 - mae: 0.4207"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "536/729 [=====================>........] - ETA: 3s - loss: 0.5204 - mae: 0.4206"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "539/729 [=====================>........] - ETA: 3s - loss: 0.5201 - mae: 0.4206"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "542/729 [=====================>........] - ETA: 3s - loss: 0.5199 - mae: 0.4205"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "545/729 [=====================>........] - ETA: 3s - loss: 0.5196 - mae: 0.4204"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "548/729 [=====================>........] - ETA: 3s - loss: 0.5194 - mae: 0.4204"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "551/729 [=====================>........] - ETA: 3s - loss: 0.5192 - mae: 0.4203"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "554/729 [=====================>........] - ETA: 3s - loss: 0.5189 - mae: 0.4203"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "557/729 [=====================>........] - ETA: 2s - loss: 0.5187 - mae: 0.4202"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "560/729 [======================>.......] - ETA: 2s - loss: 0.5184 - mae: 0.4201"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "563/729 [======================>.......] - ETA: 2s - loss: 0.5182 - mae: 0.4201"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "566/729 [======================>.......] - ETA: 2s - loss: 0.5179 - mae: 0.4200"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "569/729 [======================>.......] - ETA: 2s - loss: 0.5177 - mae: 0.4199"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "572/729 [======================>.......] - ETA: 2s - loss: 0.5174 - mae: 0.4199"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "575/729 [======================>.......] - ETA: 2s - loss: 0.5172 - mae: 0.4198"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "578/729 [======================>.......] - ETA: 2s - loss: 0.5170 - mae: 0.4198"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "581/729 [======================>.......] - ETA: 2s - loss: 0.5168 - mae: 0.4197"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "593/729 [=======================>......] - ETA: 2s - loss: 0.5160 - mae: 0.4195"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "597/729 [=======================>......] - ETA: 2s - loss: 0.5157 - mae: 0.4194"
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "606/729 [=======================>......] - ETA: 2s - loss: 0.5151 - mae: 0.4193"
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-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "609/729 [========================>.....] - ETA: 2s - loss: 0.5149 - mae: 0.4192"
-     ]
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-    {
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-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "612/729 [========================>.....] - ETA: 2s - loss: 0.5147 - mae: 0.4191"
-     ]
-    },
-    {
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-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "615/729 [========================>.....] - ETA: 1s - loss: 0.5145 - mae: 0.4191"
-     ]
-    },
-    {
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-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "618/729 [========================>.....] - ETA: 1s - loss: 0.5143 - mae: 0.4190"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "621/729 [========================>.....] - ETA: 1s - loss: 0.5141 - mae: 0.4190"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "624/729 [========================>.....] - ETA: 1s - loss: 0.5139 - mae: 0.4189"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "627/729 [========================>.....] - ETA: 1s - loss: 0.5137 - mae: 0.4189"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "630/729 [========================>.....] - ETA: 1s - loss: 0.5135 - mae: 0.4188"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "633/729 [=========================>....] - ETA: 1s - loss: 0.5133 - mae: 0.4188"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "636/729 [=========================>....] - ETA: 1s - loss: 0.5131 - mae: 0.4187"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "640/729 [=========================>....] - ETA: 1s - loss: 0.5128 - mae: 0.4187"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "644/729 [=========================>....] - ETA: 1s - loss: 0.5125 - mae: 0.4186"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "647/729 [=========================>....] - ETA: 1s - loss: 0.5124 - mae: 0.4185"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "650/729 [=========================>....] - ETA: 1s - loss: 0.5122 - mae: 0.4185"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "653/729 [=========================>....] - ETA: 1s - loss: 0.5120 - mae: 0.4184"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "656/729 [=========================>....] - ETA: 1s - loss: 0.5118 - mae: 0.4184"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "659/729 [==========================>...] - ETA: 1s - loss: 0.5116 - mae: 0.4183"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "662/729 [==========================>...] - ETA: 1s - loss: 0.5114 - mae: 0.4183"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "665/729 [==========================>...] - ETA: 1s - loss: 0.5112 - mae: 0.4183"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "668/729 [==========================>...] - ETA: 1s - loss: 0.5110 - mae: 0.4182"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "671/729 [==========================>...] - ETA: 1s - loss: 0.5108 - mae: 0.4182"
-     ]
-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "674/729 [==========================>...] - ETA: 0s - loss: 0.5106 - mae: 0.4181"
-     ]
-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "680/729 [==========================>...] - ETA: 0s - loss: 0.5102 - mae: 0.4180"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "683/729 [===========================>..] - ETA: 0s - loss: 0.5100 - mae: 0.4180"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "686/729 [===========================>..] - ETA: 0s - loss: 0.5098 - mae: 0.4179"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "689/729 [===========================>..] - ETA: 0s - loss: 0.5096 - mae: 0.4179"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "692/729 [===========================>..] - ETA: 0s - loss: 0.5094 - mae: 0.4178"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "695/729 [===========================>..] - ETA: 0s - loss: 0.5093 - mae: 0.4178"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "698/729 [===========================>..] - ETA: 0s - loss: 0.5091 - mae: 0.4177"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "701/729 [===========================>..] - ETA: 0s - loss: 0.5089 - mae: 0.4177"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "704/729 [===========================>..] - ETA: 0s - loss: 0.5087 - mae: 0.4176"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "707/729 [============================>.] - ETA: 0s - loss: 0.5085 - mae: 0.4176"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "710/729 [============================>.] - ETA: 0s - loss: 0.5083 - mae: 0.4176"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "714/729 [============================>.] - ETA: 0s - loss: 0.5081 - mae: 0.4175"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "325/729 [============>.................] - ETA: 5s - loss: 0.4693 - mae: 0.4051"
      ]
     },
     {
@@ -9215,7 +7480,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "717/729 [============================>.] - ETA: 0s - loss: 0.5079 - mae: 0.4174"
+      "329/729 [============>.................] - ETA: 5s - loss: 0.4726 - mae: 0.4052"
      ]
     },
     {
@@ -9223,7 +7488,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "720/729 [============================>.] - ETA: 0s - loss: 0.5077 - mae: 0.4174"
+      "333/729 [============>.................] - ETA: 5s - loss: 0.4760 - mae: 0.4054"
      ]
     },
     {
@@ -9231,7 +7496,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "723/729 [============================>.] - ETA: 0s - loss: 0.5075 - mae: 0.4174"
+      "337/729 [============>.................] - ETA: 5s - loss: 0.4744 - mae: 0.4053"
      ]
     },
     {
@@ -9239,7 +7504,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "726/729 [============================>.] - ETA: 0s - loss: 0.5073 - mae: 0.4173"
+      "341/729 [=============>................] - ETA: 5s - loss: 0.4735 - mae: 0.4052"
      ]
     },
     {
@@ -9247,7 +7512,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "729/729 [==============================] - ETA: 0s - loss: 0.5071 - mae: 0.4173"
+      "345/729 [=============>................] - ETA: 5s - loss: 0.4727 - mae: 0.4054"
      ]
     },
     {
@@ -9255,432 +7520,231 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "729/729 [==============================] - 13s 18ms/step - loss: 0.5071 - mae: 0.4173 - val_loss: 0.4359 - val_mae: 0.3729\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 6/10\n",
-      "\r",
-      "  1/729 [..............................] - ETA: 30s - loss: 0.2362 - mae: 0.3376"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  4/729 [..............................] - ETA: 12s - loss: 0.4750 - mae: 0.3686"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  7/729 [..............................] - ETA: 12s - loss: 0.6398 - mae: 0.3875"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 10/729 [..............................] - ETA: 12s - loss: 0.6727 - mae: 0.4001"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 13/729 [..............................] - ETA: 12s - loss: 0.6721 - mae: 0.4058"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 16/729 [..............................] - ETA: 12s - loss: 0.6641 - mae: 0.4098"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 19/729 [..............................] - ETA: 12s - loss: 0.6542 - mae: 0.4129"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 22/729 [..............................] - ETA: 12s - loss: 0.6411 - mae: 0.4136"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 25/729 [>.............................] - ETA: 12s - loss: 0.6284 - mae: 0.4133"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 28/729 [>.............................] - ETA: 12s - loss: 0.6160 - mae: 0.4126"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 31/729 [>.............................] - ETA: 12s - loss: 0.6038 - mae: 0.4114"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 34/729 [>.............................] - ETA: 12s - loss: 0.5934 - mae: 0.4105"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 37/729 [>.............................] - ETA: 12s - loss: 0.5841 - mae: 0.4098"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 40/729 [>.............................] - ETA: 12s - loss: 0.5756 - mae: 0.4092"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 43/729 [>.............................] - ETA: 12s - loss: 0.5673 - mae: 0.4084"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 46/729 [>.............................] - ETA: 11s - loss: 0.5596 - mae: 0.4077"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 49/729 [=>............................] - ETA: 11s - loss: 0.5534 - mae: 0.4071"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 52/729 [=>............................] - ETA: 11s - loss: 0.5474 - mae: 0.4065"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 55/729 [=>............................] - ETA: 11s - loss: 0.5418 - mae: 0.4059"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 58/729 [=>............................] - ETA: 11s - loss: 0.5372 - mae: 0.4057"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 61/729 [=>............................] - ETA: 11s - loss: 0.5326 - mae: 0.4054"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 64/729 [=>............................] - ETA: 11s - loss: 0.5288 - mae: 0.4053"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 67/729 [=>............................] - ETA: 11s - loss: 0.5251 - mae: 0.4052"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 70/729 [=>............................] - ETA: 11s - loss: 0.5218 - mae: 0.4051"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 73/729 [==>...........................] - ETA: 11s - loss: 0.5192 - mae: 0.4050"
+      "349/729 [=============>................] - ETA: 5s - loss: 0.4716 - mae: 0.4051"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 76/729 [==>...........................] - ETA: 11s - loss: 0.5167 - mae: 0.4049"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "353/729 [=============>................] - ETA: 5s - loss: 0.4704 - mae: 0.4049"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 79/729 [==>...........................] - ETA: 11s - loss: 0.5145 - mae: 0.4048"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "357/729 [=============>................] - ETA: 5s - loss: 0.4696 - mae: 0.4049"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 82/729 [==>...........................] - ETA: 11s - loss: 0.5123 - mae: 0.4047"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "361/729 [=============>................] - ETA: 5s - loss: 0.4687 - mae: 0.4049"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 85/729 [==>...........................] - ETA: 11s - loss: 0.5103 - mae: 0.4047"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "365/729 [==============>...............] - ETA: 5s - loss: 0.4668 - mae: 0.4044"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 88/729 [==>...........................] - ETA: 11s - loss: 0.5089 - mae: 0.4047"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "369/729 [==============>...............] - ETA: 5s - loss: 0.4657 - mae: 0.4044"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 91/729 [==>...........................] - ETA: 11s - loss: 0.5077 - mae: 0.4048"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "373/729 [==============>...............] - ETA: 5s - loss: 0.4653 - mae: 0.4046"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 94/729 [==>...........................] - ETA: 11s - loss: 0.5064 - mae: 0.4049"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "377/729 [==============>...............] - ETA: 4s - loss: 0.4641 - mae: 0.4045"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 97/729 [==>...........................] - ETA: 11s - loss: 0.5052 - mae: 0.4050"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "381/729 [==============>...............] - ETA: 4s - loss: 0.4647 - mae: 0.4048"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "101/729 [===>..........................] - ETA: 10s - loss: 0.5037 - mae: 0.4051"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "385/729 [==============>...............] - ETA: 4s - loss: 0.4644 - mae: 0.4051"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "104/729 [===>..........................] - ETA: 10s - loss: 0.5025 - mae: 0.4052"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "389/729 [===============>..............] - ETA: 4s - loss: 0.4647 - mae: 0.4055"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "107/729 [===>..........................] - ETA: 10s - loss: 0.5012 - mae: 0.4052"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "393/729 [===============>..............] - ETA: 4s - loss: 0.4661 - mae: 0.4062"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "110/729 [===>..........................] - ETA: 10s - loss: 0.4999 - mae: 0.4052"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "397/729 [===============>..............] - ETA: 4s - loss: 0.4666 - mae: 0.4062"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "113/729 [===>..........................] - ETA: 10s - loss: 0.4986 - mae: 0.4051"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "401/729 [===============>..............] - ETA: 4s - loss: 0.4649 - mae: 0.4057"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "116/729 [===>..........................] - ETA: 10s - loss: 0.4973 - mae: 0.4051"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "405/729 [===============>..............] - ETA: 4s - loss: 0.4655 - mae: 0.4062"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "119/729 [===>..........................] - ETA: 10s - loss: 0.4961 - mae: 0.4050"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "409/729 [===============>..............] - ETA: 4s - loss: 0.4653 - mae: 0.4064"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "122/729 [====>.........................] - ETA: 10s - loss: 0.4948 - mae: 0.4050"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "413/729 [===============>..............] - ETA: 4s - loss: 0.4640 - mae: 0.4062"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "125/729 [====>.........................] - ETA: 10s - loss: 0.4935 - mae: 0.4048"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "417/729 [================>.............] - ETA: 4s - loss: 0.4629 - mae: 0.4059"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "128/729 [====>.........................] - ETA: 10s - loss: 0.4922 - mae: 0.4047"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "421/729 [================>.............] - ETA: 4s - loss: 0.4634 - mae: 0.4060"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "132/729 [====>.........................] - ETA: 10s - loss: 0.4905 - mae: 0.4046"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "425/729 [================>.............] - ETA: 4s - loss: 0.4640 - mae: 0.4063"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "135/729 [====>.........................] - ETA: 10s - loss: 0.4892 - mae: 0.4045"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "429/729 [================>.............] - ETA: 4s - loss: 0.4634 - mae: 0.4063"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "138/729 [====>.........................] - ETA: 10s - loss: 0.4881 - mae: 0.4044"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "433/729 [================>.............] - ETA: 4s - loss: 0.4621 - mae: 0.4060"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "141/729 [====>.........................] - ETA: 10s - loss: 0.4870 - mae: 0.4043"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "437/729 [================>.............] - ETA: 4s - loss: 0.4659 - mae: 0.4064"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "144/729 [====>.........................] - ETA: 10s - loss: 0.4859 - mae: 0.4042"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "441/729 [=================>............] - ETA: 4s - loss: 0.4660 - mae: 0.4068"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "147/729 [=====>........................] - ETA: 10s - loss: 0.4848 - mae: 0.4041"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "445/729 [=================>............] - ETA: 3s - loss: 0.4645 - mae: 0.4063"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "150/729 [=====>........................] - ETA: 10s - loss: 0.4837 - mae: 0.4040"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "449/729 [=================>............] - ETA: 3s - loss: 0.4641 - mae: 0.4062"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "153/729 [=====>........................] - ETA: 10s - loss: 0.4826 - mae: 0.4039"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "453/729 [=================>............] - ETA: 3s - loss: 0.4627 - mae: 0.4059"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "156/729 [=====>........................] - ETA: 10s - loss: 0.4815 - mae: 0.4038"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "457/729 [=================>............] - ETA: 3s - loss: 0.4612 - mae: 0.4055"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "159/729 [=====>........................] - ETA: 9s - loss: 0.4804 - mae: 0.4037 "
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "461/729 [=================>............] - ETA: 3s - loss: 0.4596 - mae: 0.4050"
      ]
     },
     {
@@ -9688,7 +7752,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "162/729 [=====>........................] - ETA: 9s - loss: 0.4794 - mae: 0.4035"
+      "465/729 [==================>...........] - ETA: 3s - loss: 0.4588 - mae: 0.4047"
      ]
     },
     {
@@ -9696,7 +7760,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "165/729 [=====>........................] - ETA: 9s - loss: 0.4784 - mae: 0.4034"
+      "469/729 [==================>...........] - ETA: 3s - loss: 0.4573 - mae: 0.4043"
      ]
     },
     {
@@ -9704,7 +7768,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "168/729 [=====>........................] - ETA: 9s - loss: 0.4774 - mae: 0.4033"
+      "473/729 [==================>...........] - ETA: 3s - loss: 0.4573 - mae: 0.4045"
      ]
     },
     {
@@ -9712,7 +7776,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "171/729 [======>.......................] - ETA: 9s - loss: 0.4764 - mae: 0.4032"
+      "477/729 [==================>...........] - ETA: 3s - loss: 0.4555 - mae: 0.4038"
      ]
     },
     {
@@ -9720,7 +7784,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "174/729 [======>.......................] - ETA: 9s - loss: 0.4755 - mae: 0.4031"
+      "481/729 [==================>...........] - ETA: 3s - loss: 0.4549 - mae: 0.4037"
      ]
     },
     {
@@ -9728,7 +7792,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "177/729 [======>.......................] - ETA: 9s - loss: 0.4746 - mae: 0.4030"
+      "485/729 [==================>...........] - ETA: 3s - loss: 0.4531 - mae: 0.4029"
      ]
     },
     {
@@ -9736,7 +7800,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "180/729 [======>.......................] - ETA: 9s - loss: 0.4737 - mae: 0.4029"
+      "489/729 [===================>..........] - ETA: 3s - loss: 0.4570 - mae: 0.4034"
      ]
     },
     {
@@ -9744,7 +7808,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "183/729 [======>.......................] - ETA: 9s - loss: 0.4730 - mae: 0.4028"
+      "493/729 [===================>..........] - ETA: 3s - loss: 0.4580 - mae: 0.4038"
      ]
     },
     {
@@ -9752,7 +7816,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "186/729 [======>.......................] - ETA: 9s - loss: 0.4724 - mae: 0.4027"
+      "497/729 [===================>..........] - ETA: 3s - loss: 0.4574 - mae: 0.4039"
      ]
     },
     {
@@ -9760,7 +7824,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "190/729 [======>.......................] - ETA: 9s - loss: 0.4716 - mae: 0.4026"
+      "501/729 [===================>..........] - ETA: 3s - loss: 0.4580 - mae: 0.4042"
      ]
     },
     {
@@ -9768,7 +7832,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "193/729 [======>.......................] - ETA: 9s - loss: 0.4709 - mae: 0.4026"
+      "505/729 [===================>..........] - ETA: 3s - loss: 0.4576 - mae: 0.4044"
      ]
     },
     {
@@ -9776,7 +7840,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "196/729 [=======>......................] - ETA: 9s - loss: 0.4703 - mae: 0.4025"
+      "509/729 [===================>..........] - ETA: 3s - loss: 0.4564 - mae: 0.4041"
      ]
     },
     {
@@ -9784,7 +7848,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "199/729 [=======>......................] - ETA: 9s - loss: 0.4697 - mae: 0.4024"
+      "513/729 [====================>.........] - ETA: 3s - loss: 0.4583 - mae: 0.4043"
      ]
     },
     {
@@ -9792,7 +7856,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "202/729 [=======>......................] - ETA: 9s - loss: 0.4692 - mae: 0.4024"
+      "517/729 [====================>.........] - ETA: 2s - loss: 0.4577 - mae: 0.4041"
      ]
     },
     {
@@ -9800,7 +7864,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "205/729 [=======>......................] - ETA: 9s - loss: 0.4686 - mae: 0.4023"
+      "521/729 [====================>.........] - ETA: 2s - loss: 0.4574 - mae: 0.4042"
      ]
     },
     {
@@ -9808,7 +7872,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "208/729 [=======>......................] - ETA: 9s - loss: 0.4680 - mae: 0.4023"
+      "525/729 [====================>.........] - ETA: 2s - loss: 0.4569 - mae: 0.4043"
      ]
     },
     {
@@ -9816,7 +7880,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "211/729 [=======>......................] - ETA: 9s - loss: 0.4675 - mae: 0.4022"
+      "529/729 [====================>.........] - ETA: 2s - loss: 0.4566 - mae: 0.4044"
      ]
     },
     {
@@ -9824,7 +7888,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "214/729 [=======>......................] - ETA: 9s - loss: 0.4669 - mae: 0.4022"
+      "533/729 [====================>.........] - ETA: 2s - loss: 0.4587 - mae: 0.4046"
      ]
     },
     {
@@ -9832,7 +7896,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "217/729 [=======>......................] - ETA: 8s - loss: 0.4665 - mae: 0.4021"
+      "537/729 [=====================>........] - ETA: 2s - loss: 0.4607 - mae: 0.4048"
      ]
     },
     {
@@ -9840,7 +7904,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "220/729 [========>.....................] - ETA: 8s - loss: 0.4660 - mae: 0.4021"
+      "541/729 [=====================>........] - ETA: 2s - loss: 0.4610 - mae: 0.4047"
      ]
     },
     {
@@ -9848,7 +7912,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "223/729 [========>.....................] - ETA: 8s - loss: 0.4656 - mae: 0.4021"
+      "545/729 [=====================>........] - ETA: 2s - loss: 0.4611 - mae: 0.4049"
      ]
     },
     {
@@ -9856,7 +7920,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "226/729 [========>.....................] - ETA: 8s - loss: 0.4651 - mae: 0.4020"
+      "549/729 [=====================>........] - ETA: 2s - loss: 0.4601 - mae: 0.4046"
      ]
     },
     {
@@ -9864,7 +7928,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "229/729 [========>.....................] - ETA: 8s - loss: 0.4647 - mae: 0.4020"
+      "553/729 [=====================>........] - ETA: 2s - loss: 0.4598 - mae: 0.4047"
      ]
     },
     {
@@ -9872,7 +7936,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "232/729 [========>.....................] - ETA: 8s - loss: 0.4642 - mae: 0.4020"
+      "557/729 [=====================>........] - ETA: 2s - loss: 0.4600 - mae: 0.4049"
      ]
     },
     {
@@ -9880,7 +7944,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "235/729 [========>.....................] - ETA: 8s - loss: 0.4638 - mae: 0.4019"
+      "561/729 [======================>.......] - ETA: 2s - loss: 0.4601 - mae: 0.4051"
      ]
     },
     {
@@ -9888,7 +7952,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "239/729 [========>.....................] - ETA: 8s - loss: 0.4632 - mae: 0.4019"
+      "565/729 [======================>.......] - ETA: 2s - loss: 0.4590 - mae: 0.4048"
      ]
     },
     {
@@ -9896,7 +7960,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "242/729 [========>.....................] - ETA: 8s - loss: 0.4628 - mae: 0.4018"
+      "569/729 [======================>.......] - ETA: 2s - loss: 0.4594 - mae: 0.4053"
      ]
     },
     {
@@ -9904,7 +7968,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "245/729 [=========>....................] - ETA: 8s - loss: 0.4624 - mae: 0.4018"
+      "573/729 [======================>.......] - ETA: 2s - loss: 0.4586 - mae: 0.4052"
      ]
     },
     {
@@ -9912,7 +7976,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "248/729 [=========>....................] - ETA: 8s - loss: 0.4619 - mae: 0.4017"
+      "577/729 [======================>.......] - ETA: 2s - loss: 0.4583 - mae: 0.4053"
      ]
     },
     {
@@ -9920,7 +7984,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "251/729 [=========>....................] - ETA: 8s - loss: 0.4615 - mae: 0.4017"
+      "581/729 [======================>.......] - ETA: 2s - loss: 0.4647 - mae: 0.4057"
      ]
     },
     {
@@ -9928,7 +7992,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "254/729 [=========>....................] - ETA: 8s - loss: 0.4610 - mae: 0.4016"
+      "585/729 [=======================>......] - ETA: 2s - loss: 0.4642 - mae: 0.4057"
      ]
     },
     {
@@ -9936,7 +8000,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "257/729 [=========>....................] - ETA: 8s - loss: 0.4606 - mae: 0.4016"
+      "589/729 [=======================>......] - ETA: 1s - loss: 0.4661 - mae: 0.4056"
      ]
     },
     {
@@ -9944,7 +8008,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "260/729 [=========>....................] - ETA: 8s - loss: 0.4602 - mae: 0.4015"
+      "593/729 [=======================>......] - ETA: 1s - loss: 0.4653 - mae: 0.4054"
      ]
     },
     {
@@ -9952,7 +8016,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "263/729 [=========>....................] - ETA: 8s - loss: 0.4597 - mae: 0.4015"
+      "597/729 [=======================>......] - ETA: 1s - loss: 0.4662 - mae: 0.4059"
      ]
     },
     {
@@ -9960,7 +8024,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "266/729 [=========>....................] - ETA: 8s - loss: 0.4592 - mae: 0.4014"
+      "601/729 [=======================>......] - ETA: 1s - loss: 0.4668 - mae: 0.4064"
      ]
     },
     {
@@ -9968,7 +8032,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "269/729 [==========>...................] - ETA: 8s - loss: 0.4590 - mae: 0.4013"
+      "605/729 [=======================>......] - ETA: 1s - loss: 0.4677 - mae: 0.4065"
      ]
     },
     {
@@ -9976,7 +8040,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "272/729 [==========>...................] - ETA: 7s - loss: 0.4587 - mae: 0.4013"
+      "609/729 [========================>.....] - ETA: 1s - loss: 0.4674 - mae: 0.4064"
      ]
     },
     {
@@ -9984,7 +8048,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "276/729 [==========>...................] - ETA: 7s - loss: 0.4582 - mae: 0.4012"
+      "613/729 [========================>.....] - ETA: 1s - loss: 0.4667 - mae: 0.4063"
      ]
     },
     {
@@ -9992,7 +8056,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "279/729 [==========>...................] - ETA: 7s - loss: 0.4580 - mae: 0.4011"
+      "617/729 [========================>.....] - ETA: 1s - loss: 0.4663 - mae: 0.4064"
      ]
     },
     {
@@ -10000,7 +8064,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "282/729 [==========>...................] - ETA: 7s - loss: 0.4577 - mae: 0.4011"
+      "621/729 [========================>.....] - ETA: 1s - loss: 0.4658 - mae: 0.4063"
      ]
     },
     {
@@ -10008,7 +8072,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "285/729 [==========>...................] - ETA: 7s - loss: 0.4575 - mae: 0.4010"
+      "625/729 [========================>.....] - ETA: 1s - loss: 0.4648 - mae: 0.4062"
      ]
     },
     {
@@ -10016,7 +8080,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "288/729 [==========>...................] - ETA: 7s - loss: 0.4573 - mae: 0.4010"
+      "629/729 [========================>.....] - ETA: 1s - loss: 0.4646 - mae: 0.4060"
      ]
     },
     {
@@ -10024,7 +8088,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "291/729 [==========>...................] - ETA: 7s - loss: 0.4570 - mae: 0.4009"
+      "633/729 [=========================>....] - ETA: 1s - loss: 0.4648 - mae: 0.4060"
      ]
     },
     {
@@ -10032,7 +8096,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "294/729 [===========>..................] - ETA: 7s - loss: 0.4568 - mae: 0.4008"
+      "637/729 [=========================>....] - ETA: 1s - loss: 0.4647 - mae: 0.4060"
      ]
     },
     {
@@ -10040,7 +8104,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "297/729 [===========>..................] - ETA: 7s - loss: 0.4566 - mae: 0.4008"
+      "641/729 [=========================>....] - ETA: 1s - loss: 0.4650 - mae: 0.4062"
      ]
     },
     {
@@ -10048,7 +8112,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "300/729 [===========>..................] - ETA: 7s - loss: 0.4564 - mae: 0.4007"
+      "645/729 [=========================>....] - ETA: 1s - loss: 0.4665 - mae: 0.4066"
      ]
     },
     {
@@ -10056,7 +8120,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "303/729 [===========>..................] - ETA: 7s - loss: 0.4562 - mae: 0.4007"
+      "649/729 [=========================>....] - ETA: 1s - loss: 0.4664 - mae: 0.4065"
      ]
     },
     {
@@ -10064,7 +8128,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "306/729 [===========>..................] - ETA: 7s - loss: 0.4560 - mae: 0.4006"
+      "653/729 [=========================>....] - ETA: 1s - loss: 0.4656 - mae: 0.4064"
      ]
     },
     {
@@ -10072,7 +8136,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "309/729 [===========>..................] - ETA: 7s - loss: 0.4558 - mae: 0.4006"
+      "657/729 [==========================>...] - ETA: 1s - loss: 0.4668 - mae: 0.4070"
      ]
     },
     {
@@ -10080,7 +8144,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "312/729 [===========>..................] - ETA: 7s - loss: 0.4557 - mae: 0.4006"
+      "661/729 [==========================>...] - ETA: 0s - loss: 0.4671 - mae: 0.4070"
      ]
     },
     {
@@ -10088,7 +8152,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "315/729 [===========>..................] - ETA: 7s - loss: 0.4555 - mae: 0.4005"
+      "665/729 [==========================>...] - ETA: 0s - loss: 0.4659 - mae: 0.4066"
      ]
     },
     {
@@ -10096,7 +8160,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "318/729 [============>.................] - ETA: 7s - loss: 0.4554 - mae: 0.4005"
+      "669/729 [==========================>...] - ETA: 0s - loss: 0.4648 - mae: 0.4063"
      ]
     },
     {
@@ -10104,7 +8168,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "321/729 [============>.................] - ETA: 7s - loss: 0.4553 - mae: 0.4005"
+      "673/729 [==========================>...] - ETA: 0s - loss: 0.4646 - mae: 0.4063"
      ]
     },
     {
@@ -10112,7 +8176,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "324/729 [============>.................] - ETA: 7s - loss: 0.4551 - mae: 0.4005"
+      "677/729 [==========================>...] - ETA: 0s - loss: 0.4637 - mae: 0.4061"
      ]
     },
     {
@@ -10120,7 +8184,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "327/729 [============>.................] - ETA: 7s - loss: 0.4550 - mae: 0.4004"
+      "681/729 [===========================>..] - ETA: 0s - loss: 0.4632 - mae: 0.4059"
      ]
     },
     {
@@ -10128,7 +8192,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "330/729 [============>.................] - ETA: 6s - loss: 0.4550 - mae: 0.4004"
+      "685/729 [===========================>..] - ETA: 0s - loss: 0.4652 - mae: 0.4063"
      ]
     },
     {
@@ -10136,7 +8200,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "333/729 [============>.................] - ETA: 6s - loss: 0.4549 - mae: 0.4004"
+      "689/729 [===========================>..] - ETA: 0s - loss: 0.4654 - mae: 0.4065"
      ]
     },
     {
@@ -10144,7 +8208,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "336/729 [============>.................] - ETA: 6s - loss: 0.4548 - mae: 0.4004"
+      "693/729 [===========================>..] - ETA: 0s - loss: 0.4650 - mae: 0.4066"
      ]
     },
     {
@@ -10152,7 +8216,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "339/729 [============>.................] - ETA: 6s - loss: 0.4547 - mae: 0.4004"
+      "697/729 [===========================>..] - ETA: 0s - loss: 0.4647 - mae: 0.4066"
      ]
     },
     {
@@ -10160,7 +8224,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "342/729 [=============>................] - ETA: 6s - loss: 0.4546 - mae: 0.4004"
+      "701/729 [===========================>..] - ETA: 0s - loss: 0.4636 - mae: 0.4062"
      ]
     },
     {
@@ -10168,7 +8232,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "345/729 [=============>................] - ETA: 6s - loss: 0.4545 - mae: 0.4003"
+      "705/729 [============================>.] - ETA: 0s - loss: 0.4633 - mae: 0.4061"
      ]
     },
     {
@@ -10176,7 +8240,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "348/729 [=============>................] - ETA: 6s - loss: 0.4544 - mae: 0.4003"
+      "709/729 [============================>.] - ETA: 0s - loss: 0.4625 - mae: 0.4058"
      ]
     },
     {
@@ -10184,7 +8248,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "352/729 [=============>................] - ETA: 6s - loss: 0.4543 - mae: 0.4003"
+      "713/729 [============================>.] - ETA: 0s - loss: 0.4623 - mae: 0.4058"
      ]
     },
     {
@@ -10192,7 +8256,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "355/729 [=============>................] - ETA: 6s - loss: 0.4543 - mae: 0.4003"
+      "717/729 [============================>.] - ETA: 0s - loss: 0.4611 - mae: 0.4053"
      ]
     },
     {
@@ -10200,7 +8264,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "358/729 [=============>................] - ETA: 6s - loss: 0.4542 - mae: 0.4003"
+      "721/729 [============================>.] - ETA: 0s - loss: 0.4610 - mae: 0.4052"
      ]
     },
     {
@@ -10208,7 +8272,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "361/729 [=============>................] - ETA: 6s - loss: 0.4541 - mae: 0.4003"
+      "725/729 [============================>.] - ETA: 0s - loss: 0.4606 - mae: 0.4050"
      ]
     },
     {
@@ -10216,7 +8280,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "364/729 [=============>................] - ETA: 6s - loss: 0.4541 - mae: 0.4003"
+      "729/729 [==============================] - ETA: 0s - loss: 0.4605 - mae: 0.4050"
      ]
     },
     {
@@ -10224,15 +8288,16 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "367/729 [==============>...............] - ETA: 6s - loss: 0.4541 - mae: 0.4003"
+      "729/729 [==============================] - 11s 15ms/step - loss: 0.4605 - mae: 0.4050 - val_loss: 0.4320 - val_mae: 0.3750\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "370/729 [==============>...............] - ETA: 6s - loss: 0.4540 - mae: 0.4003"
+      "Epoch 6/10\n",
+      "\r",
+      "  1/729 [..............................] - ETA: 0s - loss: 0.3291 - mae: 0.3396"
      ]
     },
     {
@@ -10240,7 +8305,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "373/729 [==============>...............] - ETA: 6s - loss: 0.4540 - mae: 0.4003"
+      "  5/729 [..............................] - ETA: 8s - loss: 0.6787 - mae: 0.4152"
      ]
     },
     {
@@ -10248,7 +8313,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "376/729 [==============>...............] - ETA: 6s - loss: 0.4539 - mae: 0.4003"
+      "  9/729 [..............................] - ETA: 9s - loss: 0.6767 - mae: 0.4053"
      ]
     },
     {
@@ -10256,7 +8321,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "379/729 [==============>...............] - ETA: 6s - loss: 0.4539 - mae: 0.4002"
+      " 13/729 [..............................] - ETA: 9s - loss: 0.7441 - mae: 0.4412"
      ]
     },
     {
@@ -10264,7 +8329,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "382/729 [==============>...............] - ETA: 6s - loss: 0.4538 - mae: 0.4002"
+      " 17/729 [..............................] - ETA: 9s - loss: 0.6379 - mae: 0.4228"
      ]
     },
     {
@@ -10272,7 +8337,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "385/729 [==============>...............] - ETA: 6s - loss: 0.4537 - mae: 0.4002"
+      " 21/729 [..............................] - ETA: 9s - loss: 0.5611 - mae: 0.4031"
      ]
     },
     {
@@ -10280,7 +8345,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "388/729 [==============>...............] - ETA: 5s - loss: 0.4536 - mae: 0.4002"
+      " 25/729 [>.............................] - ETA: 9s - loss: 0.5432 - mae: 0.4058"
      ]
     },
     {
@@ -10288,7 +8353,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "391/729 [===============>..............] - ETA: 5s - loss: 0.4536 - mae: 0.4002"
+      " 29/729 [>.............................] - ETA: 9s - loss: 0.5122 - mae: 0.4026"
      ]
     },
     {
@@ -10296,7 +8361,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "394/729 [===============>..............] - ETA: 5s - loss: 0.4535 - mae: 0.4002"
+      " 33/729 [>.............................] - ETA: 9s - loss: 0.4878 - mae: 0.4007"
      ]
     },
     {
@@ -10304,7 +8369,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "397/729 [===============>..............] - ETA: 5s - loss: 0.4535 - mae: 0.4002"
+      " 37/729 [>.............................] - ETA: 9s - loss: 0.4766 - mae: 0.3992"
      ]
     },
     {
@@ -10312,7 +8377,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "400/729 [===============>..............] - ETA: 5s - loss: 0.4535 - mae: 0.4002"
+      " 41/729 [>.............................] - ETA: 9s - loss: 0.4629 - mae: 0.3946"
      ]
     },
     {
@@ -10320,7 +8385,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "403/729 [===============>..............] - ETA: 5s - loss: 0.4534 - mae: 0.4002"
+      " 45/729 [>.............................] - ETA: 9s - loss: 0.4441 - mae: 0.3895"
      ]
     },
     {
@@ -10328,7 +8393,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "407/729 [===============>..............] - ETA: 5s - loss: 0.4534 - mae: 0.4002"
+      " 49/729 [=>............................] - ETA: 9s - loss: 0.4426 - mae: 0.3930"
      ]
     },
     {
@@ -10336,7 +8401,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "410/729 [===============>..............] - ETA: 5s - loss: 0.4534 - mae: 0.4002"
+      " 53/729 [=>............................] - ETA: 9s - loss: 0.4524 - mae: 0.3959"
      ]
     },
     {
@@ -10344,7 +8409,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "413/729 [===============>..............] - ETA: 5s - loss: 0.4534 - mae: 0.4002"
+      " 57/729 [=>............................] - ETA: 9s - loss: 0.4471 - mae: 0.3971"
      ]
     },
     {
@@ -10352,7 +8417,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "416/729 [================>.............] - ETA: 5s - loss: 0.4533 - mae: 0.4002"
+      " 61/729 [=>............................] - ETA: 9s - loss: 0.4379 - mae: 0.3948"
      ]
     },
     {
@@ -10360,7 +8425,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "419/729 [================>.............] - ETA: 5s - loss: 0.4533 - mae: 0.4002"
+      " 65/729 [=>............................] - ETA: 9s - loss: 0.4375 - mae: 0.3965"
      ]
     },
     {
@@ -10368,7 +8433,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "422/729 [================>.............] - ETA: 5s - loss: 0.4533 - mae: 0.4002"
+      " 69/729 [=>............................] - ETA: 9s - loss: 0.4297 - mae: 0.3934"
      ]
     },
     {
@@ -10376,7 +8441,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "426/729 [================>.............] - ETA: 5s - loss: 0.4532 - mae: 0.4002"
+      " 73/729 [==>...........................] - ETA: 9s - loss: 0.4312 - mae: 0.3957"
      ]
     },
     {
@@ -10384,7 +8449,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "429/729 [================>.............] - ETA: 5s - loss: 0.4532 - mae: 0.4002"
+      " 77/729 [==>...........................] - ETA: 9s - loss: 0.4305 - mae: 0.3961"
      ]
     },
     {
@@ -10392,7 +8457,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "432/729 [================>.............] - ETA: 5s - loss: 0.4532 - mae: 0.4002"
+      " 81/729 [==>...........................] - ETA: 9s - loss: 0.4228 - mae: 0.3933"
      ]
     },
     {
@@ -10400,7 +8465,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "435/729 [================>.............] - ETA: 5s - loss: 0.4532 - mae: 0.4002"
+      " 85/729 [==>...........................] - ETA: 8s - loss: 0.4358 - mae: 0.3957"
      ]
     },
     {
@@ -10408,7 +8473,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "438/729 [=================>............] - ETA: 5s - loss: 0.4532 - mae: 0.4002"
+      " 89/729 [==>...........................] - ETA: 8s - loss: 0.4298 - mae: 0.3940"
      ]
     },
     {
@@ -10416,7 +8481,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "441/729 [=================>............] - ETA: 5s - loss: 0.4531 - mae: 0.4003"
+      " 93/729 [==>...........................] - ETA: 8s - loss: 0.4316 - mae: 0.3960"
      ]
     },
     {
@@ -10424,7 +8489,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "444/729 [=================>............] - ETA: 4s - loss: 0.4531 - mae: 0.4003"
+      " 97/729 [==>...........................] - ETA: 8s - loss: 0.4566 - mae: 0.3968"
      ]
     },
     {
@@ -10432,7 +8497,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "447/729 [=================>............] - ETA: 4s - loss: 0.4531 - mae: 0.4003"
+      "101/729 [===>..........................] - ETA: 8s - loss: 0.4487 - mae: 0.3946"
      ]
     },
     {
@@ -10440,7 +8505,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "450/729 [=================>............] - ETA: 4s - loss: 0.4530 - mae: 0.4003"
+      "105/729 [===>..........................] - ETA: 8s - loss: 0.4468 - mae: 0.3948"
      ]
     },
     {
@@ -10448,7 +8513,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "453/729 [=================>............] - ETA: 4s - loss: 0.4530 - mae: 0.4003"
+      "109/729 [===>..........................] - ETA: 8s - loss: 0.4457 - mae: 0.3955"
      ]
     },
     {
@@ -10456,7 +8521,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "456/729 [=================>............] - ETA: 4s - loss: 0.4530 - mae: 0.4003"
+      "113/729 [===>..........................] - ETA: 8s - loss: 0.4425 - mae: 0.3951"
      ]
     },
     {
@@ -10464,7 +8529,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "459/729 [=================>............] - ETA: 4s - loss: 0.4530 - mae: 0.4003"
+      "117/729 [===>..........................] - ETA: 8s - loss: 0.4409 - mae: 0.3948"
      ]
     },
     {
@@ -10472,7 +8537,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "462/729 [==================>...........] - ETA: 4s - loss: 0.4530 - mae: 0.4003"
+      "121/729 [===>..........................] - ETA: 8s - loss: 0.4429 - mae: 0.3960"
      ]
     },
     {
@@ -10480,7 +8545,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "465/729 [==================>...........] - ETA: 4s - loss: 0.4530 - mae: 0.4003"
+      "125/729 [====>.........................] - ETA: 8s - loss: 0.4383 - mae: 0.3950"
      ]
     },
     {
@@ -10488,7 +8553,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "468/729 [==================>...........] - ETA: 4s - loss: 0.4530 - mae: 0.4003"
+      "129/729 [====>.........................] - ETA: 8s - loss: 0.4363 - mae: 0.3950"
      ]
     },
     {
@@ -10496,7 +8561,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "471/729 [==================>...........] - ETA: 4s - loss: 0.4530 - mae: 0.4004"
+      "133/729 [====>.........................] - ETA: 8s - loss: 0.4453 - mae: 0.3979"
      ]
     },
     {
@@ -10504,7 +8569,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "474/729 [==================>...........] - ETA: 4s - loss: 0.4530 - mae: 0.4004"
+      "137/729 [====>.........................] - ETA: 8s - loss: 0.4453 - mae: 0.3991"
      ]
     },
     {
@@ -10512,7 +8577,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "477/729 [==================>...........] - ETA: 4s - loss: 0.4529 - mae: 0.4004"
+      "141/729 [====>.........................] - ETA: 8s - loss: 0.4421 - mae: 0.3985"
      ]
     },
     {
@@ -10520,7 +8585,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "480/729 [==================>...........] - ETA: 4s - loss: 0.4529 - mae: 0.4004"
+      "145/729 [====>.........................] - ETA: 8s - loss: 0.4411 - mae: 0.3993"
      ]
     },
     {
@@ -10528,7 +8593,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "483/729 [==================>...........] - ETA: 4s - loss: 0.4529 - mae: 0.4004"
+      "149/729 [=====>........................] - ETA: 8s - loss: 0.4419 - mae: 0.3997"
      ]
     },
     {
@@ -10536,7 +8601,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "486/729 [===================>..........] - ETA: 4s - loss: 0.4529 - mae: 0.4004"
+      "153/729 [=====>........................] - ETA: 8s - loss: 0.4418 - mae: 0.3995"
      ]
     },
     {
@@ -10544,7 +8609,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "489/729 [===================>..........] - ETA: 4s - loss: 0.4529 - mae: 0.4004"
+      "157/729 [=====>........................] - ETA: 8s - loss: 0.4415 - mae: 0.3999"
      ]
     },
     {
@@ -10552,7 +8617,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "492/729 [===================>..........] - ETA: 4s - loss: 0.4528 - mae: 0.4004"
+      "161/729 [=====>........................] - ETA: 7s - loss: 0.4393 - mae: 0.3998"
      ]
     },
     {
@@ -10560,7 +8625,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "495/729 [===================>..........] - ETA: 4s - loss: 0.4528 - mae: 0.4004"
+      "165/729 [=====>........................] - ETA: 7s - loss: 0.4377 - mae: 0.3999"
      ]
     },
     {
@@ -10568,7 +8633,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "499/729 [===================>..........] - ETA: 4s - loss: 0.4528 - mae: 0.4004"
+      "169/729 [=====>........................] - ETA: 7s - loss: 0.4407 - mae: 0.4004"
      ]
     },
     {
@@ -10576,7 +8641,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "502/729 [===================>..........] - ETA: 3s - loss: 0.4527 - mae: 0.4004"
+      "173/729 [======>.......................] - ETA: 7s - loss: 0.4375 - mae: 0.3988"
      ]
     },
     {
@@ -10584,7 +8649,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "505/729 [===================>..........] - ETA: 3s - loss: 0.4527 - mae: 0.4004"
+      "177/729 [======>.......................] - ETA: 7s - loss: 0.4361 - mae: 0.3986"
      ]
     },
     {
@@ -10592,7 +8657,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "508/729 [===================>..........] - ETA: 3s - loss: 0.4526 - mae: 0.4004"
+      "181/729 [======>.......................] - ETA: 7s - loss: 0.4351 - mae: 0.3984"
      ]
     },
     {
@@ -10600,7 +8665,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "511/729 [====================>.........] - ETA: 3s - loss: 0.4526 - mae: 0.4004"
+      "185/729 [======>.......................] - ETA: 7s - loss: 0.4320 - mae: 0.3977"
      ]
     },
     {
@@ -10608,7 +8673,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "514/729 [====================>.........] - ETA: 3s - loss: 0.4526 - mae: 0.4004"
+      "189/729 [======>.......................] - ETA: 7s - loss: 0.4341 - mae: 0.3973"
      ]
     },
     {
@@ -10616,7 +8681,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "517/729 [====================>.........] - ETA: 3s - loss: 0.4525 - mae: 0.4004"
+      "193/729 [======>.......................] - ETA: 7s - loss: 0.4340 - mae: 0.3976"
      ]
     },
     {
@@ -10624,7 +8689,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "520/729 [====================>.........] - ETA: 3s - loss: 0.4525 - mae: 0.4004"
+      "197/729 [=======>......................] - ETA: 7s - loss: 0.4340 - mae: 0.3976"
      ]
     },
     {
@@ -10632,7 +8697,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "523/729 [====================>.........] - ETA: 3s - loss: 0.4524 - mae: 0.4004"
+      "201/729 [=======>......................] - ETA: 7s - loss: 0.4344 - mae: 0.3983"
      ]
     },
     {
@@ -10640,7 +8705,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "526/729 [====================>.........] - ETA: 3s - loss: 0.4524 - mae: 0.4004"
+      "205/729 [=======>......................] - ETA: 7s - loss: 0.4395 - mae: 0.3977"
      ]
     },
     {
@@ -10648,7 +8713,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "529/729 [====================>.........] - ETA: 3s - loss: 0.4523 - mae: 0.4004"
+      "209/729 [=======>......................] - ETA: 7s - loss: 0.4362 - mae: 0.3966"
      ]
     },
     {
@@ -10656,7 +8721,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "532/729 [====================>.........] - ETA: 3s - loss: 0.4522 - mae: 0.4004"
+      "213/729 [=======>......................] - ETA: 7s - loss: 0.4331 - mae: 0.3956"
      ]
     },
     {
@@ -10664,7 +8729,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "535/729 [=====================>........] - ETA: 3s - loss: 0.4522 - mae: 0.4003"
+      "217/729 [=======>......................] - ETA: 7s - loss: 0.4323 - mae: 0.3957"
      ]
     },
     {
@@ -10672,7 +8737,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "538/729 [=====================>........] - ETA: 3s - loss: 0.4521 - mae: 0.4003"
+      "221/729 [========>.....................] - ETA: 7s - loss: 0.4481 - mae: 0.3971"
      ]
     },
     {
@@ -10680,7 +8745,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "541/729 [=====================>........] - ETA: 3s - loss: 0.4521 - mae: 0.4003"
+      "225/729 [========>.....................] - ETA: 7s - loss: 0.4475 - mae: 0.3974"
      ]
     },
     {
@@ -10688,7 +8753,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "544/729 [=====================>........] - ETA: 3s - loss: 0.4520 - mae: 0.4003"
+      "229/729 [========>.....................] - ETA: 7s - loss: 0.4454 - mae: 0.3967"
      ]
     },
     {
@@ -10696,7 +8761,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "547/729 [=====================>........] - ETA: 3s - loss: 0.4519 - mae: 0.4003"
+      "233/729 [========>.....................] - ETA: 6s - loss: 0.4479 - mae: 0.3969"
      ]
     },
     {
@@ -10704,7 +8769,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "550/729 [=====================>........] - ETA: 3s - loss: 0.4519 - mae: 0.4003"
+      "237/729 [========>.....................] - ETA: 6s - loss: 0.4481 - mae: 0.3972"
      ]
     },
     {
@@ -10712,7 +8777,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "553/729 [=====================>........] - ETA: 3s - loss: 0.4518 - mae: 0.4003"
+      "241/729 [========>.....................] - ETA: 6s - loss: 0.4486 - mae: 0.3975"
      ]
     },
     {
@@ -10720,7 +8785,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "556/729 [=====================>........] - ETA: 3s - loss: 0.4518 - mae: 0.4003"
+      "245/729 [=========>....................] - ETA: 6s - loss: 0.4484 - mae: 0.3982"
      ]
     },
     {
@@ -10728,7 +8793,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "559/729 [======================>.......] - ETA: 2s - loss: 0.4517 - mae: 0.4003"
+      "249/729 [=========>....................] - ETA: 6s - loss: 0.4456 - mae: 0.3975"
      ]
     },
     {
@@ -10736,7 +8801,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "563/729 [======================>.......] - ETA: 2s - loss: 0.4516 - mae: 0.4002"
+      "253/729 [=========>....................] - ETA: 6s - loss: 0.4435 - mae: 0.3971"
      ]
     },
     {
@@ -10744,7 +8809,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "566/729 [======================>.......] - ETA: 2s - loss: 0.4515 - mae: 0.4002"
+      "257/729 [=========>....................] - ETA: 6s - loss: 0.4438 - mae: 0.3981"
      ]
     },
     {
@@ -10752,7 +8817,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "570/729 [======================>.......] - ETA: 2s - loss: 0.4515 - mae: 0.4002"
+      "261/729 [=========>....................] - ETA: 6s - loss: 0.4452 - mae: 0.3988"
      ]
     },
     {
@@ -10760,7 +8825,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "573/729 [======================>.......] - ETA: 2s - loss: 0.4514 - mae: 0.4002"
+      "265/729 [=========>....................] - ETA: 6s - loss: 0.4428 - mae: 0.3981"
      ]
     },
     {
@@ -10768,7 +8833,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "576/729 [======================>.......] - ETA: 2s - loss: 0.4514 - mae: 0.4002"
+      "269/729 [==========>...................] - ETA: 6s - loss: 0.4457 - mae: 0.3990"
      ]
     },
     {
@@ -10776,7 +8841,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "579/729 [======================>.......] - ETA: 2s - loss: 0.4513 - mae: 0.4002"
+      "273/729 [==========>...................] - ETA: 6s - loss: 0.4453 - mae: 0.3991"
      ]
     },
     {
@@ -10784,7 +8849,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "582/729 [======================>.......] - ETA: 2s - loss: 0.4512 - mae: 0.4002"
+      "277/729 [==========>...................] - ETA: 6s - loss: 0.4462 - mae: 0.3992"
      ]
     },
     {
@@ -10792,7 +8857,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "585/729 [=======================>......] - ETA: 2s - loss: 0.4512 - mae: 0.4001"
+      "281/729 [==========>...................] - ETA: 6s - loss: 0.4449 - mae: 0.3989"
      ]
     },
     {
@@ -10800,7 +8865,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "588/729 [=======================>......] - ETA: 2s - loss: 0.4512 - mae: 0.4001"
+      "285/729 [==========>...................] - ETA: 6s - loss: 0.4449 - mae: 0.3996"
      ]
     },
     {
@@ -10808,7 +8873,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "591/729 [=======================>......] - ETA: 2s - loss: 0.4511 - mae: 0.4001"
+      "289/729 [==========>...................] - ETA: 6s - loss: 0.4464 - mae: 0.4003"
      ]
     },
     {
@@ -10816,7 +8881,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "594/729 [=======================>......] - ETA: 2s - loss: 0.4511 - mae: 0.4001"
+      "293/729 [===========>..................] - ETA: 6s - loss: 0.4458 - mae: 0.3998"
      ]
     },
     {
@@ -10824,7 +8889,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "597/729 [=======================>......] - ETA: 2s - loss: 0.4511 - mae: 0.4001"
+      "297/729 [===========>..................] - ETA: 6s - loss: 0.4458 - mae: 0.4000"
      ]
     },
     {
@@ -10832,7 +8897,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "600/729 [=======================>......] - ETA: 2s - loss: 0.4510 - mae: 0.4001"
+      "301/729 [===========>..................] - ETA: 6s - loss: 0.4456 - mae: 0.4002"
      ]
     },
     {
@@ -10840,7 +8905,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "604/729 [=======================>......] - ETA: 2s - loss: 0.4510 - mae: 0.4001"
+      "305/729 [===========>..................] - ETA: 5s - loss: 0.4495 - mae: 0.4010"
      ]
     },
     {
@@ -10848,7 +8913,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "607/729 [=======================>......] - ETA: 2s - loss: 0.4510 - mae: 0.4001"
+      "309/729 [===========>..................] - ETA: 5s - loss: 0.4516 - mae: 0.4016"
      ]
     },
     {
@@ -10856,7 +8921,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "610/729 [========================>.....] - ETA: 2s - loss: 0.4509 - mae: 0.4001"
+      "313/729 [===========>..................] - ETA: 5s - loss: 0.4506 - mae: 0.4016"
      ]
     },
     {
@@ -10864,7 +8929,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "613/729 [========================>.....] - ETA: 2s - loss: 0.4509 - mae: 0.4001"
+      "317/729 [============>.................] - ETA: 5s - loss: 0.4567 - mae: 0.4024"
      ]
     },
     {
@@ -10872,7 +8937,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "616/729 [========================>.....] - ETA: 1s - loss: 0.4509 - mae: 0.4001"
+      "321/729 [============>.................] - ETA: 5s - loss: 0.4581 - mae: 0.4027"
      ]
     },
     {
@@ -10880,7 +8945,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "619/729 [========================>.....] - ETA: 1s - loss: 0.4509 - mae: 0.4001"
+      "325/729 [============>.................] - ETA: 5s - loss: 0.4557 - mae: 0.4021"
      ]
     },
     {
@@ -10888,7 +8953,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "622/729 [========================>.....] - ETA: 1s - loss: 0.4508 - mae: 0.4001"
+      "329/729 [============>.................] - ETA: 5s - loss: 0.4545 - mae: 0.4021"
      ]
     },
     {
@@ -10896,7 +8961,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "625/729 [========================>.....] - ETA: 1s - loss: 0.4508 - mae: 0.4000"
+      "333/729 [============>.................] - ETA: 5s - loss: 0.4523 - mae: 0.4012"
      ]
     },
     {
@@ -10904,7 +8969,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "628/729 [========================>.....] - ETA: 1s - loss: 0.4508 - mae: 0.4000"
+      "337/729 [============>.................] - ETA: 5s - loss: 0.4512 - mae: 0.4011"
      ]
     },
     {
@@ -10912,7 +8977,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "631/729 [========================>.....] - ETA: 1s - loss: 0.4508 - mae: 0.4000"
+      "341/729 [=============>................] - ETA: 5s - loss: 0.4497 - mae: 0.4007"
      ]
     },
     {
@@ -10920,7 +8985,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "634/729 [=========================>....] - ETA: 1s - loss: 0.4507 - mae: 0.4000"
+      "345/729 [=============>................] - ETA: 5s - loss: 0.4513 - mae: 0.4013"
      ]
     },
     {
@@ -10928,7 +8993,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "637/729 [=========================>....] - ETA: 1s - loss: 0.4507 - mae: 0.4000"
+      "349/729 [=============>................] - ETA: 5s - loss: 0.4515 - mae: 0.4014"
      ]
     },
     {
@@ -10936,7 +9001,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "641/729 [=========================>....] - ETA: 1s - loss: 0.4507 - mae: 0.4000"
+      "353/729 [=============>................] - ETA: 5s - loss: 0.4504 - mae: 0.4012"
      ]
     },
     {
@@ -10944,7 +9009,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "645/729 [=========================>....] - ETA: 1s - loss: 0.4507 - mae: 0.4000"
+      "357/729 [=============>................] - ETA: 5s - loss: 0.4494 - mae: 0.4010"
      ]
     },
     {
@@ -10952,7 +9017,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "648/729 [=========================>....] - ETA: 1s - loss: 0.4507 - mae: 0.4000"
+      "361/729 [=============>................] - ETA: 5s - loss: 0.4489 - mae: 0.4008"
      ]
     },
     {
@@ -10960,7 +9025,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "652/729 [=========================>....] - ETA: 1s - loss: 0.4506 - mae: 0.4000"
+      "365/729 [==============>...............] - ETA: 5s - loss: 0.4504 - mae: 0.4010"
      ]
     },
     {
@@ -10968,7 +9033,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "655/729 [=========================>....] - ETA: 1s - loss: 0.4506 - mae: 0.4000"
+      "369/729 [==============>...............] - ETA: 5s - loss: 0.4503 - mae: 0.4014"
      ]
     },
     {
@@ -10976,7 +9041,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "658/729 [==========================>...] - ETA: 1s - loss: 0.4506 - mae: 0.4000"
+      "373/729 [==============>...............] - ETA: 4s - loss: 0.4490 - mae: 0.4010"
      ]
     },
     {
@@ -10984,7 +9049,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "661/729 [==========================>...] - ETA: 1s - loss: 0.4506 - mae: 0.4000"
+      "377/729 [==============>...............] - ETA: 4s - loss: 0.4483 - mae: 0.4009"
      ]
     },
     {
@@ -10992,7 +9057,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "664/729 [==========================>...] - ETA: 1s - loss: 0.4506 - mae: 0.4000"
+      "381/729 [==============>...............] - ETA: 4s - loss: 0.4517 - mae: 0.4017"
      ]
     },
     {
@@ -11000,7 +9065,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "667/729 [==========================>...] - ETA: 1s - loss: 0.4506 - mae: 0.4000"
+      "385/729 [==============>...............] - ETA: 4s - loss: 0.4511 - mae: 0.4014"
      ]
     },
     {
@@ -11008,7 +9073,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "670/729 [==========================>...] - ETA: 1s - loss: 0.4506 - mae: 0.4000"
+      "389/729 [===============>..............] - ETA: 4s - loss: 0.4495 - mae: 0.4011"
      ]
     },
     {
@@ -11016,7 +9081,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "673/729 [==========================>...] - ETA: 0s - loss: 0.4505 - mae: 0.4000"
+      "393/729 [===============>..............] - ETA: 4s - loss: 0.4482 - mae: 0.4009"
      ]
     },
     {
@@ -11024,7 +9089,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "676/729 [==========================>...] - ETA: 0s - loss: 0.4505 - mae: 0.4000"
+      "397/729 [===============>..............] - ETA: 4s - loss: 0.4491 - mae: 0.4011"
      ]
     },
     {
@@ -11032,7 +9097,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "679/729 [==========================>...] - ETA: 0s - loss: 0.4505 - mae: 0.4000"
+      "401/729 [===============>..............] - ETA: 4s - loss: 0.4498 - mae: 0.4013"
      ]
     },
     {
@@ -11040,7 +9105,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "682/729 [===========================>..] - ETA: 0s - loss: 0.4505 - mae: 0.4000"
+      "405/729 [===============>..............] - ETA: 4s - loss: 0.4499 - mae: 0.4015"
      ]
     },
     {
@@ -11048,7 +9113,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "685/729 [===========================>..] - ETA: 0s - loss: 0.4505 - mae: 0.4000"
+      "409/729 [===============>..............] - ETA: 4s - loss: 0.4497 - mae: 0.4016"
      ]
     },
     {
@@ -11056,7 +9121,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "688/729 [===========================>..] - ETA: 0s - loss: 0.4505 - mae: 0.4000"
+      "413/729 [===============>..............] - ETA: 4s - loss: 0.4483 - mae: 0.4013"
      ]
     },
     {
@@ -11064,7 +9129,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "691/729 [===========================>..] - ETA: 0s - loss: 0.4505 - mae: 0.4000"
+      "417/729 [================>.............] - ETA: 4s - loss: 0.4466 - mae: 0.4008"
      ]
     },
     {
@@ -11072,7 +9137,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "695/729 [===========================>..] - ETA: 0s - loss: 0.4505 - mae: 0.4000"
+      "421/729 [================>.............] - ETA: 4s - loss: 0.4469 - mae: 0.4009"
      ]
     },
     {
@@ -11080,7 +9145,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "698/729 [===========================>..] - ETA: 0s - loss: 0.4505 - mae: 0.4000"
+      "425/729 [================>.............] - ETA: 4s - loss: 0.4483 - mae: 0.4015"
      ]
     },
     {
@@ -11088,7 +9153,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "701/729 [===========================>..] - ETA: 0s - loss: 0.4505 - mae: 0.4000"
+      "429/729 [================>.............] - ETA: 4s - loss: 0.4471 - mae: 0.4012"
      ]
     },
     {
@@ -11096,7 +9161,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "704/729 [===========================>..] - ETA: 0s - loss: 0.4505 - mae: 0.4000"
+      "433/729 [================>.............] - ETA: 4s - loss: 0.4464 - mae: 0.4009"
      ]
     },
     {
@@ -11104,7 +9169,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "707/729 [============================>.] - ETA: 0s - loss: 0.4505 - mae: 0.4000"
+      "437/729 [================>.............] - ETA: 4s - loss: 0.4457 - mae: 0.4005"
      ]
     },
     {
@@ -11112,7 +9177,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "710/729 [============================>.] - ETA: 0s - loss: 0.4505 - mae: 0.4000"
+      "441/729 [=================>............] - ETA: 4s - loss: 0.4438 - mae: 0.3999"
      ]
     },
     {
@@ -11120,7 +9185,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "713/729 [============================>.] - ETA: 0s - loss: 0.4505 - mae: 0.4000"
+      "445/729 [=================>............] - ETA: 3s - loss: 0.4425 - mae: 0.3995"
      ]
     },
     {
@@ -11128,7 +9193,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "716/729 [============================>.] - ETA: 0s - loss: 0.4506 - mae: 0.4000"
+      "449/729 [=================>............] - ETA: 3s - loss: 0.4414 - mae: 0.3993"
      ]
     },
     {
@@ -11136,7 +9201,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "719/729 [============================>.] - ETA: 0s - loss: 0.4506 - mae: 0.4000"
+      "453/729 [=================>............] - ETA: 3s - loss: 0.4402 - mae: 0.3989"
      ]
     },
     {
@@ -11144,7 +9209,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "722/729 [============================>.] - ETA: 0s - loss: 0.4506 - mae: 0.4000"
+      "457/729 [=================>............] - ETA: 3s - loss: 0.4394 - mae: 0.3987"
      ]
     },
     {
@@ -11152,7 +9217,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "725/729 [============================>.] - ETA: 0s - loss: 0.4506 - mae: 0.4000"
+      "461/729 [=================>............] - ETA: 3s - loss: 0.4406 - mae: 0.3992"
      ]
     },
     {
@@ -11160,7 +9225,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "728/729 [============================>.] - ETA: 0s - loss: 0.4506 - mae: 0.4000"
+      "465/729 [==================>...........] - ETA: 3s - loss: 0.4399 - mae: 0.3990"
      ]
     },
     {
@@ -11168,416 +9233,415 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "729/729 [==============================] - 13s 18ms/step - loss: 0.4506 - mae: 0.4000 - val_loss: 0.4299 - val_mae: 0.3820\n"
+      "469/729 [==================>...........] - ETA: 3s - loss: 0.4388 - mae: 0.3986"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 7/10\n",
-      "\r",
-      "  1/729 [..............................] - ETA: 30s - loss: 0.3229 - mae: 0.3640"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "473/729 [==================>...........] - ETA: 3s - loss: 0.4390 - mae: 0.3989"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  4/729 [..............................] - ETA: 12s - loss: 0.2925 - mae: 0.3589"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "477/729 [==================>...........] - ETA: 3s - loss: 0.4382 - mae: 0.3986"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  7/729 [..............................] - ETA: 12s - loss: 0.3046 - mae: 0.3652"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "481/729 [==================>...........] - ETA: 3s - loss: 0.4377 - mae: 0.3984"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 10/729 [..............................] - ETA: 12s - loss: 0.3161 - mae: 0.3714"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "485/729 [==================>...........] - ETA: 3s - loss: 0.4441 - mae: 0.3990"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 13/729 [..............................] - ETA: 12s - loss: 0.3212 - mae: 0.3738"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "489/729 [===================>..........] - ETA: 3s - loss: 0.4426 - mae: 0.3984"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 17/729 [..............................] - ETA: 12s - loss: 0.3290 - mae: 0.3777"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "493/729 [===================>..........] - ETA: 3s - loss: 0.4418 - mae: 0.3982"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 20/729 [..............................] - ETA: 12s - loss: 0.3406 - mae: 0.3809"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "497/729 [===================>..........] - ETA: 3s - loss: 0.4409 - mae: 0.3981"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 23/729 [..............................] - ETA: 12s - loss: 0.3508 - mae: 0.3839"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "501/729 [===================>..........] - ETA: 3s - loss: 0.4423 - mae: 0.3989"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 26/729 [>.............................] - ETA: 12s - loss: 0.3598 - mae: 0.3863"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "505/729 [===================>..........] - ETA: 3s - loss: 0.4416 - mae: 0.3988"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 29/729 [>.............................] - ETA: 12s - loss: 0.3666 - mae: 0.3884"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "509/729 [===================>..........] - ETA: 3s - loss: 0.4409 - mae: 0.3985"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 32/729 [>.............................] - ETA: 12s - loss: 0.3715 - mae: 0.3901"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "513/729 [====================>.........] - ETA: 3s - loss: 0.4405 - mae: 0.3986"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 35/729 [>.............................] - ETA: 11s - loss: 0.3754 - mae: 0.3915"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "517/729 [====================>.........] - ETA: 2s - loss: 0.4435 - mae: 0.3990"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 38/729 [>.............................] - ETA: 11s - loss: 0.3779 - mae: 0.3924"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "521/729 [====================>.........] - ETA: 2s - loss: 0.4428 - mae: 0.3990"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 41/729 [>.............................] - ETA: 11s - loss: 0.3797 - mae: 0.3930"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "525/729 [====================>.........] - ETA: 2s - loss: 0.4422 - mae: 0.3988"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 44/729 [>.............................] - ETA: 11s - loss: 0.3807 - mae: 0.3934"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "529/729 [====================>.........] - ETA: 2s - loss: 0.4420 - mae: 0.3988"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 47/729 [>.............................] - ETA: 11s - loss: 0.3829 - mae: 0.3937"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "533/729 [====================>.........] - ETA: 2s - loss: 0.4407 - mae: 0.3984"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 50/729 [=>............................] - ETA: 11s - loss: 0.3854 - mae: 0.3941"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "537/729 [=====================>........] - ETA: 2s - loss: 0.4391 - mae: 0.3978"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 53/729 [=>............................] - ETA: 11s - loss: 0.3875 - mae: 0.3944"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "541/729 [=====================>........] - ETA: 2s - loss: 0.4419 - mae: 0.3980"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 56/729 [=>............................] - ETA: 11s - loss: 0.3893 - mae: 0.3948"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "545/729 [=====================>........] - ETA: 2s - loss: 0.4433 - mae: 0.3983"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 59/729 [=>............................] - ETA: 11s - loss: 0.3911 - mae: 0.3952"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "549/729 [=====================>........] - ETA: 2s - loss: 0.4421 - mae: 0.3978"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 62/729 [=>............................] - ETA: 11s - loss: 0.3925 - mae: 0.3955"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "553/729 [=====================>........] - ETA: 2s - loss: 0.4424 - mae: 0.3980"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 65/729 [=>............................] - ETA: 11s - loss: 0.3934 - mae: 0.3956"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "557/729 [=====================>........] - ETA: 2s - loss: 0.4427 - mae: 0.3982"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 68/729 [=>............................] - ETA: 11s - loss: 0.3944 - mae: 0.3958"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "561/729 [======================>.......] - ETA: 2s - loss: 0.4424 - mae: 0.3981"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 72/729 [=>............................] - ETA: 11s - loss: 0.3962 - mae: 0.3960"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "565/729 [======================>.......] - ETA: 2s - loss: 0.4436 - mae: 0.3985"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 75/729 [==>...........................] - ETA: 11s - loss: 0.3975 - mae: 0.3962"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "569/729 [======================>.......] - ETA: 2s - loss: 0.4427 - mae: 0.3982"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 78/729 [==>...........................] - ETA: 11s - loss: 0.3986 - mae: 0.3963"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "573/729 [======================>.......] - ETA: 2s - loss: 0.4418 - mae: 0.3981"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 81/729 [==>...........................] - ETA: 11s - loss: 0.3994 - mae: 0.3963"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "577/729 [======================>.......] - ETA: 2s - loss: 0.4410 - mae: 0.3978"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 84/729 [==>...........................] - ETA: 11s - loss: 0.4000 - mae: 0.3963"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "581/729 [======================>.......] - ETA: 2s - loss: 0.4403 - mae: 0.3976"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 87/729 [==>...........................] - ETA: 11s - loss: 0.4006 - mae: 0.3963"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "585/729 [=======================>......] - ETA: 2s - loss: 0.4395 - mae: 0.3975"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 90/729 [==>...........................] - ETA: 11s - loss: 0.4012 - mae: 0.3963"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "589/729 [=======================>......] - ETA: 1s - loss: 0.4401 - mae: 0.3974"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 93/729 [==>...........................] - ETA: 11s - loss: 0.4015 - mae: 0.3963"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "593/729 [=======================>......] - ETA: 1s - loss: 0.4402 - mae: 0.3972"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 96/729 [==>...........................] - ETA: 10s - loss: 0.4017 - mae: 0.3962"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "597/729 [=======================>......] - ETA: 1s - loss: 0.4393 - mae: 0.3969"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 99/729 [===>..........................] - ETA: 10s - loss: 0.4019 - mae: 0.3961"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "601/729 [=======================>......] - ETA: 1s - loss: 0.4385 - mae: 0.3967"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "102/729 [===>..........................] - ETA: 10s - loss: 0.4021 - mae: 0.3961"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "605/729 [=======================>......] - ETA: 1s - loss: 0.4380 - mae: 0.3966"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "105/729 [===>..........................] - ETA: 10s - loss: 0.4023 - mae: 0.3960"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "609/729 [========================>.....] - ETA: 1s - loss: 0.4391 - mae: 0.3969"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "108/729 [===>..........................] - ETA: 10s - loss: 0.4026 - mae: 0.3960"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "613/729 [========================>.....] - ETA: 1s - loss: 0.4390 - mae: 0.3972"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "112/729 [===>..........................] - ETA: 10s - loss: 0.4029 - mae: 0.3961"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "617/729 [========================>.....] - ETA: 1s - loss: 0.4398 - mae: 0.3978"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "115/729 [===>..........................] - ETA: 10s - loss: 0.4031 - mae: 0.3961"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "621/729 [========================>.....] - ETA: 1s - loss: 0.4455 - mae: 0.3982"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "118/729 [===>..........................] - ETA: 10s - loss: 0.4031 - mae: 0.3960"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "625/729 [========================>.....] - ETA: 1s - loss: 0.4445 - mae: 0.3977"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "121/729 [===>..........................] - ETA: 10s - loss: 0.4031 - mae: 0.3960"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "629/729 [========================>.....] - ETA: 1s - loss: 0.4481 - mae: 0.3976"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "124/729 [====>.........................] - ETA: 10s - loss: 0.4031 - mae: 0.3960"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "633/729 [=========================>....] - ETA: 1s - loss: 0.4473 - mae: 0.3975"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "127/729 [====>.........................] - ETA: 10s - loss: 0.4032 - mae: 0.3960"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "637/729 [=========================>....] - ETA: 1s - loss: 0.4470 - mae: 0.3973"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "130/729 [====>.........................] - ETA: 10s - loss: 0.4032 - mae: 0.3960"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "641/729 [=========================>....] - ETA: 1s - loss: 0.4482 - mae: 0.3977"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "133/729 [====>.........................] - ETA: 10s - loss: 0.4033 - mae: 0.3960"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "645/729 [=========================>....] - ETA: 1s - loss: 0.4475 - mae: 0.3976"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "136/729 [====>.........................] - ETA: 10s - loss: 0.4035 - mae: 0.3961"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "649/729 [=========================>....] - ETA: 1s - loss: 0.4481 - mae: 0.3980"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "139/729 [====>.........................] - ETA: 10s - loss: 0.4037 - mae: 0.3961"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "653/729 [=========================>....] - ETA: 1s - loss: 0.4474 - mae: 0.3979"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "142/729 [====>.........................] - ETA: 10s - loss: 0.4040 - mae: 0.3962"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "657/729 [==========================>...] - ETA: 1s - loss: 0.4474 - mae: 0.3982"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "146/729 [=====>........................] - ETA: 10s - loss: 0.4043 - mae: 0.3962"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "661/729 [==========================>...] - ETA: 0s - loss: 0.4466 - mae: 0.3980"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "149/729 [=====>........................] - ETA: 10s - loss: 0.4045 - mae: 0.3963"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "665/729 [==========================>...] - ETA: 0s - loss: 0.4460 - mae: 0.3979"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "152/729 [=====>........................] - ETA: 10s - loss: 0.4047 - mae: 0.3963"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "669/729 [==========================>...] - ETA: 0s - loss: 0.4454 - mae: 0.3979"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "156/729 [=====>........................] - ETA: 9s - loss: 0.4049 - mae: 0.3964 "
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "673/729 [==========================>...] - ETA: 0s - loss: 0.4445 - mae: 0.3976"
      ]
     },
     {
@@ -11585,7 +9649,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "159/729 [=====>........................] - ETA: 9s - loss: 0.4050 - mae: 0.3964"
+      "677/729 [==========================>...] - ETA: 0s - loss: 0.4465 - mae: 0.3981"
      ]
     },
     {
@@ -11593,7 +9657,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "162/729 [=====>........................] - ETA: 9s - loss: 0.4051 - mae: 0.3964"
+      "681/729 [===========================>..] - ETA: 0s - loss: 0.4471 - mae: 0.3981"
      ]
     },
     {
@@ -11601,7 +9665,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "165/729 [=====>........................] - ETA: 9s - loss: 0.4056 - mae: 0.3965"
+      "685/729 [===========================>..] - ETA: 0s - loss: 0.4467 - mae: 0.3980"
      ]
     },
     {
@@ -11609,7 +9673,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "168/729 [=====>........................] - ETA: 9s - loss: 0.4060 - mae: 0.3965"
+      "689/729 [===========================>..] - ETA: 0s - loss: 0.4463 - mae: 0.3979"
      ]
     },
     {
@@ -11617,7 +9681,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "171/729 [======>.......................] - ETA: 9s - loss: 0.4063 - mae: 0.3966"
+      "693/729 [===========================>..] - ETA: 0s - loss: 0.4482 - mae: 0.3983"
      ]
     },
     {
@@ -11625,7 +9689,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "175/729 [======>.......................] - ETA: 9s - loss: 0.4067 - mae: 0.3966"
+      "697/729 [===========================>..] - ETA: 0s - loss: 0.4476 - mae: 0.3982"
      ]
     },
     {
@@ -11633,7 +9697,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "178/729 [======>.......................] - ETA: 9s - loss: 0.4070 - mae: 0.3966"
+      "701/729 [===========================>..] - ETA: 0s - loss: 0.4489 - mae: 0.3986"
      ]
     },
     {
@@ -11641,7 +9705,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "181/729 [======>.......................] - ETA: 9s - loss: 0.4073 - mae: 0.3967"
+      "705/729 [============================>.] - ETA: 0s - loss: 0.4524 - mae: 0.3987"
      ]
     },
     {
@@ -11649,7 +9713,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "184/729 [======>.......................] - ETA: 9s - loss: 0.4076 - mae: 0.3967"
+      "709/729 [============================>.] - ETA: 0s - loss: 0.4531 - mae: 0.3989"
      ]
     },
     {
@@ -11657,7 +9721,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "187/729 [======>.......................] - ETA: 9s - loss: 0.4079 - mae: 0.3967"
+      "713/729 [============================>.] - ETA: 0s - loss: 0.4522 - mae: 0.3987"
      ]
     },
     {
@@ -11665,7 +9729,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "190/729 [======>.......................] - ETA: 9s - loss: 0.4082 - mae: 0.3967"
+      "717/729 [============================>.] - ETA: 0s - loss: 0.4538 - mae: 0.3988"
      ]
     },
     {
@@ -11673,7 +9737,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "193/729 [======>.......................] - ETA: 9s - loss: 0.4085 - mae: 0.3968"
+      "721/729 [============================>.] - ETA: 0s - loss: 0.4535 - mae: 0.3987"
      ]
     },
     {
@@ -11681,7 +9745,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "196/729 [=======>......................] - ETA: 9s - loss: 0.4090 - mae: 0.3968"
+      "725/729 [============================>.] - ETA: 0s - loss: 0.4525 - mae: 0.3985"
      ]
     },
     {
@@ -11689,7 +9753,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "199/729 [=======>......................] - ETA: 9s - loss: 0.4095 - mae: 0.3968"
+      "729/729 [==============================] - ETA: 0s - loss: 0.4520 - mae: 0.3984"
      ]
     },
     {
@@ -11697,15 +9761,16 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "202/729 [=======>......................] - ETA: 9s - loss: 0.4099 - mae: 0.3969"
+      "729/729 [==============================] - 11s 15ms/step - loss: 0.4520 - mae: 0.3984 - val_loss: 0.4339 - val_mae: 0.3768\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "205/729 [=======>......................] - ETA: 9s - loss: 0.4103 - mae: 0.3969"
+      "Epoch 7/10\n",
+      "\r",
+      "  1/729 [..............................] - ETA: 0s - loss: 0.4249 - mae: 0.4114"
      ]
     },
     {
@@ -11713,7 +9778,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "208/729 [=======>......................] - ETA: 9s - loss: 0.4107 - mae: 0.3969"
+      "  5/729 [..............................] - ETA: 8s - loss: 0.6866 - mae: 0.4113"
      ]
     },
     {
@@ -11721,7 +9786,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "211/729 [=======>......................] - ETA: 8s - loss: 0.4111 - mae: 0.3970"
+      "  9/729 [..............................] - ETA: 9s - loss: 0.5295 - mae: 0.3961"
      ]
     },
     {
@@ -11729,7 +9794,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "214/729 [=======>......................] - ETA: 8s - loss: 0.4114 - mae: 0.3970"
+      " 13/729 [..............................] - ETA: 9s - loss: 0.4700 - mae: 0.3872"
      ]
     },
     {
@@ -11737,7 +9802,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "217/729 [=======>......................] - ETA: 8s - loss: 0.4120 - mae: 0.3970"
+      " 17/729 [..............................] - ETA: 9s - loss: 0.4492 - mae: 0.3917"
      ]
     },
     {
@@ -11745,7 +9810,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "220/729 [========>.....................] - ETA: 8s - loss: 0.4125 - mae: 0.3970"
+      " 21/729 [..............................] - ETA: 9s - loss: 0.4334 - mae: 0.3939"
      ]
     },
     {
@@ -11753,7 +9818,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "223/729 [========>.....................] - ETA: 8s - loss: 0.4130 - mae: 0.3971"
+      " 25/729 [>.............................] - ETA: 9s - loss: 0.4069 - mae: 0.3850"
      ]
     },
     {
@@ -11761,7 +9826,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "226/729 [========>.....................] - ETA: 8s - loss: 0.4135 - mae: 0.3971"
+      " 29/729 [>.............................] - ETA: 9s - loss: 0.4000 - mae: 0.3821"
      ]
     },
     {
@@ -11769,7 +9834,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "229/729 [========>.....................] - ETA: 8s - loss: 0.4140 - mae: 0.3972"
+      " 33/729 [>.............................] - ETA: 9s - loss: 0.3992 - mae: 0.3837"
      ]
     },
     {
@@ -11777,7 +9842,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "232/729 [========>.....................] - ETA: 8s - loss: 0.4145 - mae: 0.3972"
+      " 37/729 [>.............................] - ETA: 9s - loss: 0.3992 - mae: 0.3875"
      ]
     },
     {
@@ -11785,7 +9850,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "235/729 [========>.....................] - ETA: 8s - loss: 0.4150 - mae: 0.3973"
+      " 41/729 [>.............................] - ETA: 9s - loss: 0.3899 - mae: 0.3860"
      ]
     },
     {
@@ -11793,7 +9858,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "239/729 [========>.....................] - ETA: 8s - loss: 0.4157 - mae: 0.3974"
+      " 45/729 [>.............................] - ETA: 9s - loss: 0.3937 - mae: 0.3886"
      ]
     },
     {
@@ -11801,7 +9866,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "242/729 [========>.....................] - ETA: 8s - loss: 0.4161 - mae: 0.3974"
+      " 49/729 [=>............................] - ETA: 9s - loss: 0.4016 - mae: 0.3903"
      ]
     },
     {
@@ -11809,7 +9874,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "245/729 [=========>....................] - ETA: 8s - loss: 0.4166 - mae: 0.3975"
+      " 53/729 [=>............................] - ETA: 9s - loss: 0.4024 - mae: 0.3921"
      ]
     },
     {
@@ -11817,7 +9882,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "248/729 [=========>....................] - ETA: 8s - loss: 0.4171 - mae: 0.3975"
+      " 57/729 [=>............................] - ETA: 9s - loss: 0.4003 - mae: 0.3931"
      ]
     },
     {
@@ -11825,7 +9890,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "251/729 [=========>....................] - ETA: 8s - loss: 0.4175 - mae: 0.3975"
+      " 61/729 [=>............................] - ETA: 9s - loss: 0.3972 - mae: 0.3923"
      ]
     },
     {
@@ -11833,7 +9898,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "254/729 [=========>....................] - ETA: 8s - loss: 0.4179 - mae: 0.3976"
+      " 65/729 [=>............................] - ETA: 9s - loss: 0.4222 - mae: 0.3931"
      ]
     },
     {
@@ -11841,7 +9906,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "257/729 [=========>....................] - ETA: 8s - loss: 0.4183 - mae: 0.3976"
+      " 69/729 [=>............................] - ETA: 9s - loss: 0.4181 - mae: 0.3916"
      ]
     },
     {
@@ -11849,7 +9914,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "260/729 [=========>....................] - ETA: 8s - loss: 0.4186 - mae: 0.3976"
+      " 73/729 [==>...........................] - ETA: 9s - loss: 0.4120 - mae: 0.3896"
      ]
     },
     {
@@ -11857,7 +9922,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "263/729 [=========>....................] - ETA: 8s - loss: 0.4190 - mae: 0.3976"
+      " 77/729 [==>...........................] - ETA: 9s - loss: 0.4135 - mae: 0.3909"
      ]
     },
     {
@@ -11865,7 +9930,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "266/729 [=========>....................] - ETA: 8s - loss: 0.4193 - mae: 0.3977"
+      " 81/729 [==>...........................] - ETA: 9s - loss: 0.4183 - mae: 0.3932"
      ]
     },
     {
@@ -11873,7 +9938,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "269/729 [==========>...................] - ETA: 7s - loss: 0.4196 - mae: 0.3977"
+      " 85/729 [==>...........................] - ETA: 8s - loss: 0.4197 - mae: 0.3942"
      ]
     },
     {
@@ -11881,7 +9946,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "272/729 [==========>...................] - ETA: 7s - loss: 0.4199 - mae: 0.3977"
+      " 89/729 [==>...........................] - ETA: 8s - loss: 0.4288 - mae: 0.3933"
      ]
     },
     {
@@ -11889,7 +9954,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "275/729 [==========>...................] - ETA: 7s - loss: 0.4203 - mae: 0.3977"
+      " 93/729 [==>...........................] - ETA: 8s - loss: 0.4288 - mae: 0.3948"
      ]
     },
     {
@@ -11897,7 +9962,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "278/729 [==========>...................] - ETA: 7s - loss: 0.4206 - mae: 0.3978"
+      " 97/729 [==>...........................] - ETA: 8s - loss: 0.4277 - mae: 0.3944"
      ]
     },
     {
@@ -11905,7 +9970,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "281/729 [==========>...................] - ETA: 7s - loss: 0.4209 - mae: 0.3978"
+      "101/729 [===>..........................] - ETA: 8s - loss: 0.4258 - mae: 0.3949"
      ]
     },
     {
@@ -11913,7 +9978,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "284/729 [==========>...................] - ETA: 7s - loss: 0.4212 - mae: 0.3978"
+      "105/729 [===>..........................] - ETA: 8s - loss: 0.4215 - mae: 0.3935"
      ]
     },
     {
@@ -11921,7 +9986,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "287/729 [==========>...................] - ETA: 7s - loss: 0.4214 - mae: 0.3978"
+      "109/729 [===>..........................] - ETA: 8s - loss: 0.4232 - mae: 0.3948"
      ]
     },
     {
@@ -11929,7 +9994,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "290/729 [==========>...................] - ETA: 7s - loss: 0.4216 - mae: 0.3978"
+      "113/729 [===>..........................] - ETA: 8s - loss: 0.4248 - mae: 0.3960"
      ]
     },
     {
@@ -11937,7 +10002,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "293/729 [===========>..................] - ETA: 7s - loss: 0.4219 - mae: 0.3978"
+      "117/729 [===>..........................] - ETA: 8s - loss: 0.4240 - mae: 0.3961"
      ]
     },
     {
@@ -11945,7 +10010,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "296/729 [===========>..................] - ETA: 7s - loss: 0.4221 - mae: 0.3978"
+      "121/729 [===>..........................] - ETA: 8s - loss: 0.4380 - mae: 0.3992"
      ]
     },
     {
@@ -11953,7 +10018,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "299/729 [===========>..................] - ETA: 7s - loss: 0.4223 - mae: 0.3978"
+      "125/729 [====>.........................] - ETA: 8s - loss: 0.4337 - mae: 0.3978"
      ]
     },
     {
@@ -11961,7 +10026,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "302/729 [===========>..................] - ETA: 7s - loss: 0.4224 - mae: 0.3978"
+      "129/729 [====>.........................] - ETA: 8s - loss: 0.4533 - mae: 0.3978"
      ]
     },
     {
@@ -11969,7 +10034,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "305/729 [===========>..................] - ETA: 7s - loss: 0.4227 - mae: 0.3978"
+      "133/729 [====>.........................] - ETA: 8s - loss: 0.4486 - mae: 0.3966"
      ]
     },
     {
@@ -11977,7 +10042,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "308/729 [===========>..................] - ETA: 7s - loss: 0.4229 - mae: 0.3978"
+      "137/729 [====>.........................] - ETA: 8s - loss: 0.4477 - mae: 0.3967"
      ]
     },
     {
@@ -11985,7 +10050,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "312/729 [===========>..................] - ETA: 7s - loss: 0.4231 - mae: 0.3978"
+      "141/729 [====>.........................] - ETA: 8s - loss: 0.4434 - mae: 0.3955"
      ]
     },
     {
@@ -11993,7 +10058,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "315/729 [===========>..................] - ETA: 7s - loss: 0.4233 - mae: 0.3978"
+      "145/729 [====>.........................] - ETA: 8s - loss: 0.4434 - mae: 0.3967"
      ]
     },
     {
@@ -12001,7 +10066,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "318/729 [============>.................] - ETA: 7s - loss: 0.4235 - mae: 0.3978"
+      "149/729 [=====>........................] - ETA: 8s - loss: 0.4390 - mae: 0.3951"
      ]
     },
     {
@@ -12009,7 +10074,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "321/729 [============>.................] - ETA: 7s - loss: 0.4236 - mae: 0.3977"
+      "153/729 [=====>........................] - ETA: 8s - loss: 0.4372 - mae: 0.3943"
      ]
     },
     {
@@ -12017,7 +10082,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "324/729 [============>.................] - ETA: 7s - loss: 0.4238 - mae: 0.3977"
+      "157/729 [=====>........................] - ETA: 8s - loss: 0.4440 - mae: 0.3937"
      ]
     },
     {
@@ -12025,7 +10090,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "327/729 [============>.................] - ETA: 6s - loss: 0.4239 - mae: 0.3977"
+      "161/729 [=====>........................] - ETA: 7s - loss: 0.4532 - mae: 0.3971"
      ]
     },
     {
@@ -12033,7 +10098,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "330/729 [============>.................] - ETA: 6s - loss: 0.4240 - mae: 0.3977"
+      "165/729 [=====>........................] - ETA: 7s - loss: 0.4526 - mae: 0.3975"
      ]
     },
     {
@@ -12041,7 +10106,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "333/729 [============>.................] - ETA: 6s - loss: 0.4242 - mae: 0.3977"
+      "169/729 [=====>........................] - ETA: 7s - loss: 0.4489 - mae: 0.3970"
      ]
     },
     {
@@ -12049,7 +10114,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "337/729 [============>.................] - ETA: 6s - loss: 0.4243 - mae: 0.3976"
+      "173/729 [======>.......................] - ETA: 7s - loss: 0.4473 - mae: 0.3970"
      ]
     },
     {
@@ -12057,7 +10122,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "340/729 [============>.................] - ETA: 6s - loss: 0.4244 - mae: 0.3976"
+      "177/729 [======>.......................] - ETA: 7s - loss: 0.4418 - mae: 0.3951"
      ]
     },
     {
@@ -12065,7 +10130,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "343/729 [=============>................] - ETA: 6s - loss: 0.4244 - mae: 0.3976"
+      "181/729 [======>.......................] - ETA: 7s - loss: 0.4422 - mae: 0.3953"
      ]
     },
     {
@@ -12073,7 +10138,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "346/729 [=============>................] - ETA: 6s - loss: 0.4245 - mae: 0.3975"
+      "185/729 [======>.......................] - ETA: 7s - loss: 0.4390 - mae: 0.3943"
      ]
     },
     {
@@ -12081,7 +10146,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "350/729 [=============>................] - ETA: 6s - loss: 0.4245 - mae: 0.3975"
+      "189/729 [======>.......................] - ETA: 7s - loss: 0.4373 - mae: 0.3943"
      ]
     },
     {
@@ -12089,7 +10154,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "354/729 [=============>................] - ETA: 6s - loss: 0.4246 - mae: 0.3974"
+      "193/729 [======>.......................] - ETA: 7s - loss: 0.4355 - mae: 0.3940"
      ]
     },
     {
@@ -12097,7 +10162,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "357/729 [=============>................] - ETA: 6s - loss: 0.4246 - mae: 0.3974"
+      "197/729 [=======>......................] - ETA: 7s - loss: 0.4422 - mae: 0.3951"
      ]
     },
     {
@@ -12105,7 +10170,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "360/729 [=============>................] - ETA: 6s - loss: 0.4247 - mae: 0.3974"
+      "201/729 [=======>......................] - ETA: 7s - loss: 0.4382 - mae: 0.3937"
      ]
     },
     {
@@ -12113,7 +10178,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "363/729 [=============>................] - ETA: 6s - loss: 0.4247 - mae: 0.3973"
+      "205/729 [=======>......................] - ETA: 7s - loss: 0.4385 - mae: 0.3946"
      ]
     },
     {
@@ -12121,7 +10186,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "366/729 [==============>...............] - ETA: 6s - loss: 0.4248 - mae: 0.3973"
+      "209/729 [=======>......................] - ETA: 7s - loss: 0.4417 - mae: 0.3954"
      ]
     },
     {
@@ -12129,7 +10194,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "370/729 [==============>...............] - ETA: 6s - loss: 0.4249 - mae: 0.3972"
+      "213/729 [=======>......................] - ETA: 7s - loss: 0.4478 - mae: 0.3962"
      ]
     },
     {
@@ -12137,7 +10202,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "373/729 [==============>...............] - ETA: 6s - loss: 0.4249 - mae: 0.3972"
+      "217/729 [=======>......................] - ETA: 7s - loss: 0.4455 - mae: 0.3955"
      ]
     },
     {
@@ -12145,7 +10210,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "376/729 [==============>...............] - ETA: 6s - loss: 0.4249 - mae: 0.3972"
+      "221/729 [========>.....................] - ETA: 7s - loss: 0.4430 - mae: 0.3950"
      ]
     },
     {
@@ -12153,7 +10218,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "380/729 [==============>...............] - ETA: 6s - loss: 0.4250 - mae: 0.3971"
+      "225/729 [========>.....................] - ETA: 7s - loss: 0.4622 - mae: 0.3966"
      ]
     },
     {
@@ -12161,7 +10226,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "383/729 [==============>...............] - ETA: 5s - loss: 0.4251 - mae: 0.3971"
+      "229/729 [========>.....................] - ETA: 7s - loss: 0.4610 - mae: 0.3969"
      ]
     },
     {
@@ -12169,7 +10234,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "386/729 [==============>...............] - ETA: 5s - loss: 0.4251 - mae: 0.3971"
+      "233/729 [========>.....................] - ETA: 6s - loss: 0.4599 - mae: 0.3971"
      ]
     },
     {
@@ -12177,7 +10242,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "389/729 [===============>..............] - ETA: 5s - loss: 0.4252 - mae: 0.3970"
+      "237/729 [========>.....................] - ETA: 6s - loss: 0.4582 - mae: 0.3965"
      ]
     },
     {
@@ -12185,7 +10250,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "392/729 [===============>..............] - ETA: 5s - loss: 0.4252 - mae: 0.3970"
+      "241/729 [========>.....................] - ETA: 6s - loss: 0.4556 - mae: 0.3957"
      ]
     },
     {
@@ -12193,7 +10258,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "396/729 [===============>..............] - ETA: 5s - loss: 0.4253 - mae: 0.3969"
+      "245/729 [=========>....................] - ETA: 6s - loss: 0.4556 - mae: 0.3961"
      ]
     },
     {
@@ -12201,7 +10266,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "399/729 [===============>..............] - ETA: 5s - loss: 0.4253 - mae: 0.3969"
+      "249/729 [=========>....................] - ETA: 6s - loss: 0.4644 - mae: 0.3982"
      ]
     },
     {
@@ -12209,7 +10274,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "402/729 [===============>..............] - ETA: 5s - loss: 0.4254 - mae: 0.3969"
+      "253/729 [=========>....................] - ETA: 6s - loss: 0.4627 - mae: 0.3979"
      ]
     },
     {
@@ -12217,7 +10282,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "405/729 [===============>..............] - ETA: 5s - loss: 0.4255 - mae: 0.3968"
+      "257/729 [=========>....................] - ETA: 6s - loss: 0.4627 - mae: 0.3982"
      ]
     },
     {
@@ -12225,7 +10290,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "408/729 [===============>..............] - ETA: 5s - loss: 0.4255 - mae: 0.3968"
+      "261/729 [=========>....................] - ETA: 6s - loss: 0.4608 - mae: 0.3980"
      ]
     },
     {
@@ -12233,7 +10298,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "411/729 [===============>..............] - ETA: 5s - loss: 0.4256 - mae: 0.3968"
+      "265/729 [=========>....................] - ETA: 6s - loss: 0.4614 - mae: 0.3988"
      ]
     },
     {
@@ -12241,7 +10306,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "414/729 [================>.............] - ETA: 5s - loss: 0.4257 - mae: 0.3968"
+      "269/729 [==========>...................] - ETA: 6s - loss: 0.4584 - mae: 0.3978"
      ]
     },
     {
@@ -12249,7 +10314,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "417/729 [================>.............] - ETA: 5s - loss: 0.4258 - mae: 0.3967"
+      "273/729 [==========>...................] - ETA: 6s - loss: 0.4598 - mae: 0.3979"
      ]
     },
     {
@@ -12257,7 +10322,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "420/729 [================>.............] - ETA: 5s - loss: 0.4259 - mae: 0.3967"
+      "277/729 [==========>...................] - ETA: 6s - loss: 0.4570 - mae: 0.3970"
      ]
     },
     {
@@ -12265,7 +10330,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "423/729 [================>.............] - ETA: 5s - loss: 0.4260 - mae: 0.3967"
+      "281/729 [==========>...................] - ETA: 6s - loss: 0.4554 - mae: 0.3967"
      ]
     },
     {
@@ -12273,7 +10338,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "426/729 [================>.............] - ETA: 5s - loss: 0.4261 - mae: 0.3967"
+      "285/729 [==========>...................] - ETA: 6s - loss: 0.4571 - mae: 0.3965"
      ]
     },
     {
@@ -12281,7 +10346,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "429/729 [================>.............] - ETA: 5s - loss: 0.4262 - mae: 0.3967"
+      "289/729 [==========>...................] - ETA: 6s - loss: 0.4556 - mae: 0.3958"
      ]
     },
     {
@@ -12289,7 +10354,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "432/729 [================>.............] - ETA: 5s - loss: 0.4263 - mae: 0.3966"
+      "293/729 [===========>..................] - ETA: 6s - loss: 0.4627 - mae: 0.3955"
      ]
     },
     {
@@ -12297,7 +10362,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "435/729 [================>.............] - ETA: 5s - loss: 0.4264 - mae: 0.3966"
+      "297/729 [===========>..................] - ETA: 6s - loss: 0.4637 - mae: 0.3961"
      ]
     },
     {
@@ -12305,7 +10370,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "438/729 [=================>............] - ETA: 5s - loss: 0.4265 - mae: 0.3966"
+      "301/729 [===========>..................] - ETA: 6s - loss: 0.4632 - mae: 0.3965"
      ]
     },
     {
@@ -12313,7 +10378,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "441/729 [=================>............] - ETA: 4s - loss: 0.4266 - mae: 0.3965"
+      "305/729 [===========>..................] - ETA: 5s - loss: 0.4645 - mae: 0.3975"
      ]
     },
     {
@@ -12321,7 +10386,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "445/729 [=================>............] - ETA: 4s - loss: 0.4267 - mae: 0.3965"
+      "309/729 [===========>..................] - ETA: 5s - loss: 0.4626 - mae: 0.3971"
      ]
     },
     {
@@ -12329,7 +10394,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "448/729 [=================>............] - ETA: 4s - loss: 0.4267 - mae: 0.3965"
+      "313/729 [===========>..................] - ETA: 5s - loss: 0.4613 - mae: 0.3970"
      ]
     },
     {
@@ -12337,7 +10402,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "451/729 [=================>............] - ETA: 4s - loss: 0.4268 - mae: 0.3965"
+      "317/729 [============>.................] - ETA: 5s - loss: 0.4615 - mae: 0.3969"
      ]
     },
     {
@@ -12345,7 +10410,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "454/729 [=================>............] - ETA: 4s - loss: 0.4269 - mae: 0.3964"
+      "321/729 [============>.................] - ETA: 5s - loss: 0.4599 - mae: 0.3965"
      ]
     },
     {
@@ -12353,7 +10418,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "457/729 [=================>............] - ETA: 4s - loss: 0.4270 - mae: 0.3964"
+      "325/729 [============>.................] - ETA: 5s - loss: 0.4589 - mae: 0.3967"
      ]
     },
     {
@@ -12361,7 +10426,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "461/729 [=================>............] - ETA: 4s - loss: 0.4271 - mae: 0.3964"
+      "329/729 [============>.................] - ETA: 5s - loss: 0.4571 - mae: 0.3965"
      ]
     },
     {
@@ -12369,7 +10434,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "464/729 [==================>...........] - ETA: 4s - loss: 0.4272 - mae: 0.3964"
+      "333/729 [============>.................] - ETA: 5s - loss: 0.4561 - mae: 0.3962"
      ]
     },
     {
@@ -12377,7 +10442,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "468/729 [==================>...........] - ETA: 4s - loss: 0.4273 - mae: 0.3964"
+      "337/729 [============>.................] - ETA: 5s - loss: 0.4552 - mae: 0.3962"
      ]
     },
     {
@@ -12385,7 +10450,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "471/729 [==================>...........] - ETA: 4s - loss: 0.4274 - mae: 0.3963"
+      "341/729 [=============>................] - ETA: 5s - loss: 0.4569 - mae: 0.3967"
      ]
     },
     {
@@ -12393,7 +10458,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "474/729 [==================>...........] - ETA: 4s - loss: 0.4275 - mae: 0.3963"
+      "345/729 [=============>................] - ETA: 5s - loss: 0.4555 - mae: 0.3963"
      ]
     },
     {
@@ -12401,7 +10466,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "477/729 [==================>...........] - ETA: 4s - loss: 0.4276 - mae: 0.3963"
+      "349/729 [=============>................] - ETA: 5s - loss: 0.4538 - mae: 0.3959"
      ]
     },
     {
@@ -12409,7 +10474,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "480/729 [==================>...........] - ETA: 4s - loss: 0.4277 - mae: 0.3963"
+      "353/729 [=============>................] - ETA: 5s - loss: 0.4525 - mae: 0.3957"
      ]
     },
     {
@@ -12417,7 +10482,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "483/729 [==================>...........] - ETA: 4s - loss: 0.4278 - mae: 0.3963"
+      "357/729 [=============>................] - ETA: 5s - loss: 0.4520 - mae: 0.3958"
      ]
     },
     {
@@ -12425,7 +10490,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "486/729 [===================>..........] - ETA: 4s - loss: 0.4279 - mae: 0.3963"
+      "361/729 [=============>................] - ETA: 5s - loss: 0.4535 - mae: 0.3964"
      ]
     },
     {
@@ -12433,7 +10498,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "489/729 [===================>..........] - ETA: 4s - loss: 0.4280 - mae: 0.3963"
+      "365/729 [==============>...............] - ETA: 5s - loss: 0.4525 - mae: 0.3961"
      ]
     },
     {
@@ -12441,7 +10506,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "493/729 [===================>..........] - ETA: 4s - loss: 0.4281 - mae: 0.3963"
+      "369/729 [==============>...............] - ETA: 5s - loss: 0.4509 - mae: 0.3957"
      ]
     },
     {
@@ -12449,7 +10514,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "496/729 [===================>..........] - ETA: 4s - loss: 0.4282 - mae: 0.3963"
+      "373/729 [==============>...............] - ETA: 4s - loss: 0.4519 - mae: 0.3959"
      ]
     },
     {
@@ -12457,7 +10522,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "499/729 [===================>..........] - ETA: 3s - loss: 0.4283 - mae: 0.3963"
+      "377/729 [==============>...............] - ETA: 4s - loss: 0.4522 - mae: 0.3962"
      ]
     },
     {
@@ -12465,7 +10530,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "502/729 [===================>..........] - ETA: 3s - loss: 0.4284 - mae: 0.3963"
+      "381/729 [==============>...............] - ETA: 4s - loss: 0.4535 - mae: 0.3968"
      ]
     },
     {
@@ -12473,7 +10538,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "505/729 [===================>..........] - ETA: 3s - loss: 0.4285 - mae: 0.3963"
+      "385/729 [==============>...............] - ETA: 4s - loss: 0.4539 - mae: 0.3971"
      ]
     },
     {
@@ -12481,7 +10546,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "508/729 [===================>..........] - ETA: 3s - loss: 0.4286 - mae: 0.3963"
+      "389/729 [===============>..............] - ETA: 4s - loss: 0.4520 - mae: 0.3966"
      ]
     },
     {
@@ -12489,7 +10554,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "511/729 [====================>.........] - ETA: 3s - loss: 0.4287 - mae: 0.3963"
+      "393/729 [===============>..............] - ETA: 4s - loss: 0.4510 - mae: 0.3965"
      ]
     },
     {
@@ -12497,7 +10562,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "514/729 [====================>.........] - ETA: 3s - loss: 0.4288 - mae: 0.3963"
+      "397/729 [===============>..............] - ETA: 4s - loss: 0.4530 - mae: 0.3970"
      ]
     },
     {
@@ -12505,7 +10570,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "517/729 [====================>.........] - ETA: 3s - loss: 0.4289 - mae: 0.3963"
+      "401/729 [===============>..............] - ETA: 4s - loss: 0.4537 - mae: 0.3972"
      ]
     },
     {
@@ -12513,7 +10578,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "520/729 [====================>.........] - ETA: 3s - loss: 0.4290 - mae: 0.3963"
+      "405/729 [===============>..............] - ETA: 4s - loss: 0.4544 - mae: 0.3974"
      ]
     },
     {
@@ -12521,7 +10586,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "524/729 [====================>.........] - ETA: 3s - loss: 0.4292 - mae: 0.3963"
+      "409/729 [===============>..............] - ETA: 4s - loss: 0.4522 - mae: 0.3966"
      ]
     },
     {
@@ -12529,7 +10594,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "527/729 [====================>.........] - ETA: 3s - loss: 0.4293 - mae: 0.3963"
+      "413/729 [===============>..............] - ETA: 4s - loss: 0.4545 - mae: 0.3971"
      ]
     },
     {
@@ -12537,7 +10602,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "530/729 [====================>.........] - ETA: 3s - loss: 0.4293 - mae: 0.3963"
+      "417/729 [================>.............] - ETA: 4s - loss: 0.4529 - mae: 0.3968"
      ]
     },
     {
@@ -12545,7 +10610,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "533/729 [====================>.........] - ETA: 3s - loss: 0.4294 - mae: 0.3963"
+      "421/729 [================>.............] - ETA: 4s - loss: 0.4512 - mae: 0.3964"
      ]
     },
     {
@@ -12553,7 +10618,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "536/729 [=====================>........] - ETA: 3s - loss: 0.4295 - mae: 0.3963"
+      "425/729 [================>.............] - ETA: 4s - loss: 0.4510 - mae: 0.3966"
      ]
     },
     {
@@ -12561,7 +10626,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "539/729 [=====================>........] - ETA: 3s - loss: 0.4296 - mae: 0.3963"
+      "429/729 [================>.............] - ETA: 4s - loss: 0.4518 - mae: 0.3968"
      ]
     },
     {
@@ -12569,7 +10634,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "542/729 [=====================>........] - ETA: 3s - loss: 0.4297 - mae: 0.3963"
+      "433/729 [================>.............] - ETA: 4s - loss: 0.4509 - mae: 0.3967"
      ]
     },
     {
@@ -12577,7 +10642,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "545/729 [=====================>........] - ETA: 3s - loss: 0.4298 - mae: 0.3963"
+      "437/729 [================>.............] - ETA: 4s - loss: 0.4496 - mae: 0.3964"
      ]
     },
     {
@@ -12585,7 +10650,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "548/729 [=====================>........] - ETA: 3s - loss: 0.4298 - mae: 0.3963"
+      "441/729 [=================>............] - ETA: 4s - loss: 0.4532 - mae: 0.3969"
      ]
     },
     {
@@ -12593,7 +10658,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "551/729 [=====================>........] - ETA: 3s - loss: 0.4299 - mae: 0.3963"
+      "445/729 [=================>............] - ETA: 3s - loss: 0.4521 - mae: 0.3967"
      ]
     },
     {
@@ -12601,7 +10666,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "554/729 [=====================>........] - ETA: 3s - loss: 0.4300 - mae: 0.3963"
+      "449/729 [=================>............] - ETA: 3s - loss: 0.4543 - mae: 0.3973"
      ]
     },
     {
@@ -12609,7 +10674,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "557/729 [=====================>........] - ETA: 2s - loss: 0.4301 - mae: 0.3963"
+      "453/729 [=================>............] - ETA: 3s - loss: 0.4537 - mae: 0.3975"
      ]
     },
     {
@@ -12617,7 +10682,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "560/729 [======================>.......] - ETA: 2s - loss: 0.4301 - mae: 0.3963"
+      "457/729 [=================>............] - ETA: 3s - loss: 0.4523 - mae: 0.3971"
      ]
     },
     {
@@ -12625,7 +10690,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "563/729 [======================>.......] - ETA: 2s - loss: 0.4302 - mae: 0.3963"
+      "461/729 [=================>............] - ETA: 3s - loss: 0.4565 - mae: 0.3972"
      ]
     },
     {
@@ -12633,7 +10698,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "566/729 [======================>.......] - ETA: 2s - loss: 0.4303 - mae: 0.3963"
+      "465/729 [==================>...........] - ETA: 3s - loss: 0.4563 - mae: 0.3973"
      ]
     },
     {
@@ -12641,7 +10706,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "569/729 [======================>.......] - ETA: 2s - loss: 0.4304 - mae: 0.3963"
+      "469/729 [==================>...........] - ETA: 3s - loss: 0.4586 - mae: 0.3971"
      ]
     },
     {
@@ -12649,7 +10714,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "572/729 [======================>.......] - ETA: 2s - loss: 0.4305 - mae: 0.3964"
+      "473/729 [==================>...........] - ETA: 3s - loss: 0.4577 - mae: 0.3972"
      ]
     },
     {
@@ -12657,7 +10722,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "575/729 [======================>.......] - ETA: 2s - loss: 0.4305 - mae: 0.3964"
+      "477/729 [==================>...........] - ETA: 3s - loss: 0.4581 - mae: 0.3974"
      ]
     },
     {
@@ -12665,7 +10730,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "578/729 [======================>.......] - ETA: 2s - loss: 0.4306 - mae: 0.3964"
+      "481/729 [==================>...........] - ETA: 3s - loss: 0.4586 - mae: 0.3977"
      ]
     },
     {
@@ -12673,7 +10738,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "581/729 [======================>.......] - ETA: 2s - loss: 0.4307 - mae: 0.3964"
+      "485/729 [==================>...........] - ETA: 3s - loss: 0.4574 - mae: 0.3974"
      ]
     },
     {
@@ -12681,7 +10746,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "584/729 [=======================>......] - ETA: 2s - loss: 0.4307 - mae: 0.3964"
+      "489/729 [===================>..........] - ETA: 3s - loss: 0.4563 - mae: 0.3971"
      ]
     },
     {
@@ -12689,7 +10754,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "587/729 [=======================>......] - ETA: 2s - loss: 0.4308 - mae: 0.3964"
+      "493/729 [===================>..........] - ETA: 3s - loss: 0.4553 - mae: 0.3968"
      ]
     },
     {
@@ -12697,7 +10762,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "590/729 [=======================>......] - ETA: 2s - loss: 0.4309 - mae: 0.3964"
+      "497/729 [===================>..........] - ETA: 3s - loss: 0.4552 - mae: 0.3968"
      ]
     },
     {
@@ -12705,7 +10770,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "593/729 [=======================>......] - ETA: 2s - loss: 0.4310 - mae: 0.3964"
+      "501/729 [===================>..........] - ETA: 3s - loss: 0.4533 - mae: 0.3961"
      ]
     },
     {
@@ -12713,7 +10778,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "596/729 [=======================>......] - ETA: 2s - loss: 0.4311 - mae: 0.3964"
+      "505/729 [===================>..........] - ETA: 3s - loss: 0.4536 - mae: 0.3964"
      ]
     },
     {
@@ -12721,7 +10786,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "599/729 [=======================>......] - ETA: 2s - loss: 0.4311 - mae: 0.3964"
+      "509/729 [===================>..........] - ETA: 3s - loss: 0.4548 - mae: 0.3969"
      ]
     },
     {
@@ -12729,7 +10794,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "602/729 [=======================>......] - ETA: 2s - loss: 0.4312 - mae: 0.3964"
+      "513/729 [====================>.........] - ETA: 3s - loss: 0.4540 - mae: 0.3967"
      ]
     },
     {
@@ -12737,7 +10802,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "605/729 [=======================>......] - ETA: 2s - loss: 0.4313 - mae: 0.3964"
+      "517/729 [====================>.........] - ETA: 2s - loss: 0.4531 - mae: 0.3966"
      ]
     },
     {
@@ -12745,7 +10810,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "608/729 [========================>.....] - ETA: 2s - loss: 0.4314 - mae: 0.3964"
+      "521/729 [====================>.........] - ETA: 2s - loss: 0.4528 - mae: 0.3965"
      ]
     },
     {
@@ -12753,7 +10818,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "612/729 [========================>.....] - ETA: 2s - loss: 0.4314 - mae: 0.3964"
+      "525/729 [====================>.........] - ETA: 2s - loss: 0.4547 - mae: 0.3966"
      ]
     },
     {
@@ -12761,7 +10826,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "616/729 [========================>.....] - ETA: 1s - loss: 0.4315 - mae: 0.3964"
+      "529/729 [====================>.........] - ETA: 2s - loss: 0.4552 - mae: 0.3968"
      ]
     },
     {
@@ -12769,7 +10834,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "619/729 [========================>.....] - ETA: 1s - loss: 0.4316 - mae: 0.3964"
+      "533/729 [====================>.........] - ETA: 2s - loss: 0.4545 - mae: 0.3968"
      ]
     },
     {
@@ -12777,7 +10842,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "622/729 [========================>.....] - ETA: 1s - loss: 0.4317 - mae: 0.3964"
+      "537/729 [=====================>........] - ETA: 2s - loss: 0.4543 - mae: 0.3970"
      ]
     },
     {
@@ -12785,7 +10850,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "625/729 [========================>.....] - ETA: 1s - loss: 0.4318 - mae: 0.3964"
+      "541/729 [=====================>........] - ETA: 2s - loss: 0.4537 - mae: 0.3970"
      ]
     },
     {
@@ -12793,7 +10858,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "628/729 [========================>.....] - ETA: 1s - loss: 0.4319 - mae: 0.3964"
+      "545/729 [=====================>........] - ETA: 2s - loss: 0.4523 - mae: 0.3965"
      ]
     },
     {
@@ -12801,7 +10866,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "631/729 [========================>.....] - ETA: 1s - loss: 0.4319 - mae: 0.3964"
+      "549/729 [=====================>........] - ETA: 2s - loss: 0.4526 - mae: 0.3968"
      ]
     },
     {
@@ -12809,7 +10874,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "634/729 [=========================>....] - ETA: 1s - loss: 0.4320 - mae: 0.3964"
+      "553/729 [=====================>........] - ETA: 2s - loss: 0.4518 - mae: 0.3966"
      ]
     },
     {
@@ -12817,7 +10882,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "637/729 [=========================>....] - ETA: 1s - loss: 0.4321 - mae: 0.3964"
+      "557/729 [=====================>........] - ETA: 2s - loss: 0.4505 - mae: 0.3962"
      ]
     },
     {
@@ -12825,7 +10890,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "640/729 [=========================>....] - ETA: 1s - loss: 0.4322 - mae: 0.3964"
+      "561/729 [======================>.......] - ETA: 2s - loss: 0.4495 - mae: 0.3959"
      ]
     },
     {
@@ -12833,7 +10898,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "643/729 [=========================>....] - ETA: 1s - loss: 0.4322 - mae: 0.3964"
+      "565/729 [======================>.......] - ETA: 2s - loss: 0.4485 - mae: 0.3957"
      ]
     },
     {
@@ -12841,7 +10906,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "646/729 [=========================>....] - ETA: 1s - loss: 0.4323 - mae: 0.3964"
+      "569/729 [======================>.......] - ETA: 2s - loss: 0.4490 - mae: 0.3957"
      ]
     },
     {
@@ -12849,7 +10914,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "649/729 [=========================>....] - ETA: 1s - loss: 0.4324 - mae: 0.3964"
+      "573/729 [======================>.......] - ETA: 2s - loss: 0.4480 - mae: 0.3953"
      ]
     },
     {
@@ -12857,7 +10922,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "652/729 [=========================>....] - ETA: 1s - loss: 0.4325 - mae: 0.3964"
+      "577/729 [======================>.......] - ETA: 2s - loss: 0.4477 - mae: 0.3954"
      ]
     },
     {
@@ -12865,7 +10930,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "655/729 [=========================>....] - ETA: 1s - loss: 0.4326 - mae: 0.3964"
+      "581/729 [======================>.......] - ETA: 2s - loss: 0.4478 - mae: 0.3958"
      ]
     },
     {
@@ -12873,7 +10938,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "658/729 [==========================>...] - ETA: 1s - loss: 0.4327 - mae: 0.3964"
+      "585/729 [=======================>......] - ETA: 2s - loss: 0.4483 - mae: 0.3960"
      ]
     },
     {
@@ -12881,7 +10946,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "661/729 [==========================>...] - ETA: 1s - loss: 0.4328 - mae: 0.3964"
+      "589/729 [=======================>......] - ETA: 1s - loss: 0.4478 - mae: 0.3960"
      ]
     },
     {
@@ -12889,7 +10954,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "664/729 [==========================>...] - ETA: 1s - loss: 0.4329 - mae: 0.3964"
+      "593/729 [=======================>......] - ETA: 1s - loss: 0.4479 - mae: 0.3963"
      ]
     },
     {
@@ -12897,7 +10962,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "668/729 [==========================>...] - ETA: 1s - loss: 0.4330 - mae: 0.3965"
+      "597/729 [=======================>......] - ETA: 1s - loss: 0.4471 - mae: 0.3961"
      ]
     },
     {
@@ -12905,7 +10970,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "671/729 [==========================>...] - ETA: 1s - loss: 0.4330 - mae: 0.3965"
+      "601/729 [=======================>......] - ETA: 1s - loss: 0.4463 - mae: 0.3960"
      ]
     },
     {
@@ -12913,7 +10978,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "674/729 [==========================>...] - ETA: 0s - loss: 0.4331 - mae: 0.3965"
+      "605/729 [=======================>......] - ETA: 1s - loss: 0.4472 - mae: 0.3964"
      ]
     },
     {
@@ -12921,7 +10986,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "677/729 [==========================>...] - ETA: 0s - loss: 0.4332 - mae: 0.3965"
+      "609/729 [========================>.....] - ETA: 1s - loss: 0.4497 - mae: 0.3969"
      ]
     },
     {
@@ -12929,7 +10994,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "680/729 [==========================>...] - ETA: 0s - loss: 0.4333 - mae: 0.3965"
+      "613/729 [========================>.....] - ETA: 1s - loss: 0.4489 - mae: 0.3967"
      ]
     },
     {
@@ -12937,7 +11002,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "683/729 [===========================>..] - ETA: 0s - loss: 0.4333 - mae: 0.3965"
+      "617/729 [========================>.....] - ETA: 1s - loss: 0.4480 - mae: 0.3965"
      ]
     },
     {
@@ -12945,7 +11010,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "686/729 [===========================>..] - ETA: 0s - loss: 0.4334 - mae: 0.3965"
+      "621/729 [========================>.....] - ETA: 1s - loss: 0.4471 - mae: 0.3963"
      ]
     },
     {
@@ -12953,7 +11018,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "689/729 [===========================>..] - ETA: 0s - loss: 0.4335 - mae: 0.3965"
+      "625/729 [========================>.....] - ETA: 1s - loss: 0.4466 - mae: 0.3962"
      ]
     },
     {
@@ -12961,7 +11026,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "692/729 [===========================>..] - ETA: 0s - loss: 0.4336 - mae: 0.3965"
+      "629/729 [========================>.....] - ETA: 1s - loss: 0.4457 - mae: 0.3959"
      ]
     },
     {
@@ -12969,7 +11034,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "695/729 [===========================>..] - ETA: 0s - loss: 0.4336 - mae: 0.3965"
+      "633/729 [=========================>....] - ETA: 1s - loss: 0.4456 - mae: 0.3961"
      ]
     },
     {
@@ -12977,7 +11042,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "698/729 [===========================>..] - ETA: 0s - loss: 0.4337 - mae: 0.3965"
+      "637/729 [=========================>....] - ETA: 1s - loss: 0.4476 - mae: 0.3964"
      ]
     },
     {
@@ -12985,7 +11050,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "701/729 [===========================>..] - ETA: 0s - loss: 0.4338 - mae: 0.3965"
+      "641/729 [=========================>....] - ETA: 1s - loss: 0.4475 - mae: 0.3964"
      ]
     },
     {
@@ -12993,7 +11058,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "704/729 [===========================>..] - ETA: 0s - loss: 0.4338 - mae: 0.3965"
+      "645/729 [=========================>....] - ETA: 1s - loss: 0.4472 - mae: 0.3963"
      ]
     },
     {
@@ -13001,7 +11066,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "708/729 [============================>.] - ETA: 0s - loss: 0.4339 - mae: 0.3965"
+      "649/729 [=========================>....] - ETA: 1s - loss: 0.4464 - mae: 0.3961"
      ]
     },
     {
@@ -13009,7 +11074,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "711/729 [============================>.] - ETA: 0s - loss: 0.4340 - mae: 0.3965"
+      "653/729 [=========================>....] - ETA: 1s - loss: 0.4461 - mae: 0.3961"
      ]
     },
     {
@@ -13017,7 +11082,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "714/729 [============================>.] - ETA: 0s - loss: 0.4341 - mae: 0.3965"
+      "657/729 [==========================>...] - ETA: 1s - loss: 0.4460 - mae: 0.3963"
      ]
     },
     {
@@ -13025,7 +11090,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "717/729 [============================>.] - ETA: 0s - loss: 0.4342 - mae: 0.3965"
+      "661/729 [==========================>...] - ETA: 0s - loss: 0.4458 - mae: 0.3963"
      ]
     },
     {
@@ -13033,7 +11098,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "720/729 [============================>.] - ETA: 0s - loss: 0.4342 - mae: 0.3965"
+      "665/729 [==========================>...] - ETA: 0s - loss: 0.4456 - mae: 0.3963"
      ]
     },
     {
@@ -13041,7 +11106,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "724/729 [============================>.] - ETA: 0s - loss: 0.4343 - mae: 0.3965"
+      "669/729 [==========================>...] - ETA: 0s - loss: 0.4460 - mae: 0.3966"
      ]
     },
     {
@@ -13049,7 +11114,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "727/729 [============================>.] - ETA: 0s - loss: 0.4344 - mae: 0.3965"
+      "673/729 [==========================>...] - ETA: 0s - loss: 0.4492 - mae: 0.3965"
      ]
     },
     {
@@ -13057,416 +11122,416 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "729/729 [==============================] - 13s 18ms/step - loss: 0.4344 - mae: 0.3965 - val_loss: 0.4385 - val_mae: 0.3805\n"
+      "677/729 [==========================>...] - ETA: 0s - loss: 0.4493 - mae: 0.3964"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 8/10\n",
-      "\r",
-      "  1/729 [..............................] - ETA: 30s - loss: 0.3101 - mae: 0.4105"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "681/729 [===========================>..] - ETA: 0s - loss: 0.4492 - mae: 0.3961"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  4/729 [..............................] - ETA: 13s - loss: 0.3412 - mae: 0.4062"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "685/729 [===========================>..] - ETA: 0s - loss: 0.4492 - mae: 0.3963"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  7/729 [..............................] - ETA: 12s - loss: 0.4129 - mae: 0.4112"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "689/729 [===========================>..] - ETA: 0s - loss: 0.4490 - mae: 0.3962"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 10/729 [..............................] - ETA: 12s - loss: 0.4906 - mae: 0.4122"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "693/729 [===========================>..] - ETA: 0s - loss: 0.4482 - mae: 0.3961"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 13/729 [..............................] - ETA: 12s - loss: 0.5114 - mae: 0.4103"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "697/729 [===========================>..] - ETA: 0s - loss: 0.4476 - mae: 0.3957"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 16/729 [..............................] - ETA: 12s - loss: 0.5129 - mae: 0.4070"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "701/729 [===========================>..] - ETA: 0s - loss: 0.4472 - mae: 0.3957"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 19/729 [..............................] - ETA: 12s - loss: 0.5054 - mae: 0.4021"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "705/729 [============================>.] - ETA: 0s - loss: 0.4466 - mae: 0.3957"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 22/729 [..............................] - ETA: 12s - loss: 0.5019 - mae: 0.3992"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "709/729 [============================>.] - ETA: 0s - loss: 0.4466 - mae: 0.3955"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 25/729 [>.............................] - ETA: 12s - loss: 0.4998 - mae: 0.3974"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "713/729 [============================>.] - ETA: 0s - loss: 0.4479 - mae: 0.3958"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 28/729 [>.............................] - ETA: 12s - loss: 0.4978 - mae: 0.3964"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "717/729 [============================>.] - ETA: 0s - loss: 0.4473 - mae: 0.3957"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 31/729 [>.............................] - ETA: 12s - loss: 0.4994 - mae: 0.3964"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "721/729 [============================>.] - ETA: 0s - loss: 0.4462 - mae: 0.3953"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 34/729 [>.............................] - ETA: 12s - loss: 0.5040 - mae: 0.3967"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "725/729 [============================>.] - ETA: 0s - loss: 0.4454 - mae: 0.3950"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 37/729 [>.............................] - ETA: 12s - loss: 0.5067 - mae: 0.3968"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "729/729 [==============================] - ETA: 0s - loss: 0.4474 - mae: 0.3951"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 40/729 [>.............................] - ETA: 12s - loss: 0.5071 - mae: 0.3965"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "729/729 [==============================] - 11s 15ms/step - loss: 0.4474 - mae: 0.3951 - val_loss: 0.4340 - val_mae: 0.3798\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 43/729 [>.............................] - ETA: 11s - loss: 0.5072 - mae: 0.3963"
+      "Epoch 8/10\n",
+      "\r",
+      "  1/729 [..............................] - ETA: 0s - loss: 0.3090 - mae: 0.3591"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 46/729 [>.............................] - ETA: 11s - loss: 0.5071 - mae: 0.3962"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  5/729 [..............................] - ETA: 8s - loss: 0.2995 - mae: 0.3548"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 49/729 [=>............................] - ETA: 11s - loss: 0.5067 - mae: 0.3962"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  9/729 [..............................] - ETA: 8s - loss: 0.3415 - mae: 0.3780"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 52/729 [=>............................] - ETA: 11s - loss: 0.5058 - mae: 0.3962"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 13/729 [..............................] - ETA: 9s - loss: 0.3115 - mae: 0.3587"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 55/729 [=>............................] - ETA: 11s - loss: 0.5042 - mae: 0.3960"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 17/729 [..............................] - ETA: 9s - loss: 0.3182 - mae: 0.3617"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 58/729 [=>............................] - ETA: 11s - loss: 0.5026 - mae: 0.3958"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 21/729 [..............................] - ETA: 9s - loss: 0.3423 - mae: 0.3763"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 61/729 [=>............................] - ETA: 11s - loss: 0.5006 - mae: 0.3955"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 25/729 [>.............................] - ETA: 9s - loss: 0.3537 - mae: 0.3834"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 64/729 [=>............................] - ETA: 11s - loss: 0.4985 - mae: 0.3951"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 29/729 [>.............................] - ETA: 9s - loss: 0.4088 - mae: 0.3884"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 67/729 [=>............................] - ETA: 11s - loss: 0.4964 - mae: 0.3947"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 33/729 [>.............................] - ETA: 9s - loss: 0.4198 - mae: 0.3910"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 70/729 [=>............................] - ETA: 11s - loss: 0.4944 - mae: 0.3942"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 37/729 [>.............................] - ETA: 9s - loss: 0.4439 - mae: 0.3953"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 73/729 [==>...........................] - ETA: 11s - loss: 0.4923 - mae: 0.3937"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 41/729 [>.............................] - ETA: 9s - loss: 0.4357 - mae: 0.3941"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 76/729 [==>...........................] - ETA: 11s - loss: 0.4902 - mae: 0.3933"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 45/729 [>.............................] - ETA: 9s - loss: 0.4497 - mae: 0.3962"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 79/729 [==>...........................] - ETA: 11s - loss: 0.4883 - mae: 0.3929"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 49/729 [=>............................] - ETA: 9s - loss: 0.4466 - mae: 0.3969"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 82/729 [==>...........................] - ETA: 11s - loss: 0.4866 - mae: 0.3926"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 53/729 [=>............................] - ETA: 9s - loss: 0.4521 - mae: 0.3967"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 85/729 [==>...........................] - ETA: 11s - loss: 0.4851 - mae: 0.3924"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 57/729 [=>............................] - ETA: 9s - loss: 0.4406 - mae: 0.3936"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 88/729 [==>...........................] - ETA: 11s - loss: 0.4837 - mae: 0.3922"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 61/729 [=>............................] - ETA: 9s - loss: 0.4683 - mae: 0.3965"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 91/729 [==>...........................] - ETA: 11s - loss: 0.4823 - mae: 0.3920"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 65/729 [=>............................] - ETA: 9s - loss: 0.4582 - mae: 0.3943"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 94/729 [==>...........................] - ETA: 11s - loss: 0.4809 - mae: 0.3919"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 69/729 [=>............................] - ETA: 9s - loss: 0.4536 - mae: 0.3932"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 97/729 [==>...........................] - ETA: 11s - loss: 0.4796 - mae: 0.3917"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 73/729 [==>...........................] - ETA: 9s - loss: 0.4577 - mae: 0.3976"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "100/729 [===>..........................] - ETA: 10s - loss: 0.4786 - mae: 0.3916"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 77/729 [==>...........................] - ETA: 8s - loss: 0.4556 - mae: 0.3985"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "103/729 [===>..........................] - ETA: 10s - loss: 0.4783 - mae: 0.3915"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 81/729 [==>...........................] - ETA: 8s - loss: 0.4469 - mae: 0.3955"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "106/729 [===>..........................] - ETA: 10s - loss: 0.4779 - mae: 0.3914"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 85/729 [==>...........................] - ETA: 8s - loss: 0.4543 - mae: 0.3983"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "109/729 [===>..........................] - ETA: 10s - loss: 0.4774 - mae: 0.3912"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 89/729 [==>...........................] - ETA: 8s - loss: 0.4507 - mae: 0.3975"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "112/729 [===>..........................] - ETA: 10s - loss: 0.4770 - mae: 0.3911"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 93/729 [==>...........................] - ETA: 8s - loss: 0.4735 - mae: 0.3969"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "115/729 [===>..........................] - ETA: 10s - loss: 0.4766 - mae: 0.3909"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 97/729 [==>...........................] - ETA: 8s - loss: 0.4709 - mae: 0.3972"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "118/729 [===>..........................] - ETA: 10s - loss: 0.4763 - mae: 0.3907"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "101/729 [===>..........................] - ETA: 8s - loss: 0.4646 - mae: 0.3957"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "121/729 [===>..........................] - ETA: 10s - loss: 0.4760 - mae: 0.3906"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "105/729 [===>..........................] - ETA: 8s - loss: 0.4562 - mae: 0.3931"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "124/729 [====>.........................] - ETA: 10s - loss: 0.4755 - mae: 0.3905"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "109/729 [===>..........................] - ETA: 8s - loss: 0.4566 - mae: 0.3924"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "127/729 [====>.........................] - ETA: 10s - loss: 0.4750 - mae: 0.3903"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "113/729 [===>..........................] - ETA: 8s - loss: 0.4647 - mae: 0.3953"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "131/729 [====>.........................] - ETA: 10s - loss: 0.4744 - mae: 0.3901"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "117/729 [===>..........................] - ETA: 8s - loss: 0.4664 - mae: 0.3967"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "135/729 [====>.........................] - ETA: 10s - loss: 0.4742 - mae: 0.3899"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "121/729 [===>..........................] - ETA: 8s - loss: 0.4593 - mae: 0.3949"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "138/729 [====>.........................] - ETA: 10s - loss: 0.4741 - mae: 0.3899"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "125/729 [====>.........................] - ETA: 8s - loss: 0.4654 - mae: 0.3963"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "141/729 [====>.........................] - ETA: 10s - loss: 0.4741 - mae: 0.3899"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "129/729 [====>.........................] - ETA: 8s - loss: 0.4639 - mae: 0.3967"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "144/729 [====>.........................] - ETA: 10s - loss: 0.4740 - mae: 0.3899"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "133/729 [====>.........................] - ETA: 8s - loss: 0.4593 - mae: 0.3957"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "147/729 [=====>........................] - ETA: 10s - loss: 0.4741 - mae: 0.3899"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "137/729 [====>.........................] - ETA: 8s - loss: 0.4550 - mae: 0.3946"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "150/729 [=====>........................] - ETA: 10s - loss: 0.4741 - mae: 0.3899"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "141/729 [====>.........................] - ETA: 8s - loss: 0.4880 - mae: 0.3956"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "154/729 [=====>........................] - ETA: 9s - loss: 0.4742 - mae: 0.3900 "
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "145/729 [====>.........................] - ETA: 8s - loss: 0.5066 - mae: 0.3974"
      ]
     },
     {
@@ -13474,7 +11539,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "157/729 [=====>........................] - ETA: 9s - loss: 0.4742 - mae: 0.3900"
+      "149/729 [=====>........................] - ETA: 8s - loss: 0.4987 - mae: 0.3949"
      ]
     },
     {
@@ -13482,7 +11547,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "160/729 [=====>........................] - ETA: 9s - loss: 0.4742 - mae: 0.3900"
+      "153/729 [=====>........................] - ETA: 8s - loss: 0.4949 - mae: 0.3941"
      ]
     },
     {
@@ -13490,7 +11555,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "163/729 [=====>........................] - ETA: 9s - loss: 0.4741 - mae: 0.3900"
+      "157/729 [=====>........................] - ETA: 7s - loss: 0.5043 - mae: 0.3979"
      ]
     },
     {
@@ -13498,7 +11563,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "166/729 [=====>........................] - ETA: 9s - loss: 0.4740 - mae: 0.3901"
+      "161/729 [=====>........................] - ETA: 7s - loss: 0.5188 - mae: 0.3991"
      ]
     },
     {
@@ -13506,7 +11571,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "169/729 [=====>........................] - ETA: 9s - loss: 0.4738 - mae: 0.3901"
+      "165/729 [=====>........................] - ETA: 7s - loss: 0.5130 - mae: 0.3980"
      ]
     },
     {
@@ -13514,7 +11579,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "172/729 [======>.......................] - ETA: 9s - loss: 0.4736 - mae: 0.3900"
+      "169/729 [=====>........................] - ETA: 7s - loss: 0.5248 - mae: 0.4016"
      ]
     },
     {
@@ -13522,7 +11587,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "175/729 [======>.......................] - ETA: 9s - loss: 0.4733 - mae: 0.3900"
+      "173/729 [======>.......................] - ETA: 7s - loss: 0.5228 - mae: 0.4021"
      ]
     },
     {
@@ -13530,7 +11595,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "178/729 [======>.......................] - ETA: 9s - loss: 0.4730 - mae: 0.3900"
+      "177/729 [======>.......................] - ETA: 7s - loss: 0.5190 - mae: 0.4020"
      ]
     },
     {
@@ -13538,7 +11603,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "181/729 [======>.......................] - ETA: 9s - loss: 0.4728 - mae: 0.3900"
+      "181/729 [======>.......................] - ETA: 7s - loss: 0.5167 - mae: 0.4021"
      ]
     },
     {
@@ -13546,7 +11611,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "184/729 [======>.......................] - ETA: 9s - loss: 0.4727 - mae: 0.3900"
+      "185/729 [======>.......................] - ETA: 7s - loss: 0.5161 - mae: 0.4023"
      ]
     },
     {
@@ -13554,7 +11619,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "187/729 [======>.......................] - ETA: 9s - loss: 0.4726 - mae: 0.3901"
+      "189/729 [======>.......................] - ETA: 7s - loss: 0.5143 - mae: 0.4022"
      ]
     },
     {
@@ -13562,7 +11627,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "190/729 [======>.......................] - ETA: 9s - loss: 0.4725 - mae: 0.3901"
+      "193/729 [======>.......................] - ETA: 7s - loss: 0.5116 - mae: 0.4018"
      ]
     },
     {
@@ -13570,7 +11635,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "193/729 [======>.......................] - ETA: 9s - loss: 0.4725 - mae: 0.3901"
+      "197/729 [=======>......................] - ETA: 7s - loss: 0.5075 - mae: 0.4008"
      ]
     },
     {
@@ -13578,7 +11643,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "197/729 [=======>......................] - ETA: 9s - loss: 0.4724 - mae: 0.3902"
+      "201/729 [=======>......................] - ETA: 7s - loss: 0.5054 - mae: 0.4010"
      ]
     },
     {
@@ -13586,7 +11651,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "200/729 [=======>......................] - ETA: 9s - loss: 0.4722 - mae: 0.3902"
+      "205/729 [=======>......................] - ETA: 7s - loss: 0.5051 - mae: 0.4005"
      ]
     },
     {
@@ -13594,7 +11659,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "203/729 [=======>......................] - ETA: 9s - loss: 0.4721 - mae: 0.3902"
+      "209/729 [=======>......................] - ETA: 7s - loss: 0.5017 - mae: 0.4000"
      ]
     },
     {
@@ -13602,7 +11667,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "207/729 [=======>......................] - ETA: 9s - loss: 0.4720 - mae: 0.3902"
+      "213/729 [=======>......................] - ETA: 7s - loss: 0.4999 - mae: 0.3998"
      ]
     },
     {
@@ -13610,7 +11675,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "210/729 [=======>......................] - ETA: 8s - loss: 0.4718 - mae: 0.3902"
+      "217/729 [=======>......................] - ETA: 7s - loss: 0.4999 - mae: 0.4000"
      ]
     },
     {
@@ -13618,7 +11683,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "213/729 [=======>......................] - ETA: 8s - loss: 0.4716 - mae: 0.3903"
+      "221/729 [========>.....................] - ETA: 7s - loss: 0.5005 - mae: 0.4010"
      ]
     },
     {
@@ -13626,7 +11691,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "216/729 [=======>......................] - ETA: 8s - loss: 0.4714 - mae: 0.3903"
+      "225/729 [========>.....................] - ETA: 7s - loss: 0.4962 - mae: 0.3998"
      ]
     },
     {
@@ -13634,7 +11699,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "220/729 [========>.....................] - ETA: 8s - loss: 0.4712 - mae: 0.3903"
+      "229/729 [========>.....................] - ETA: 6s - loss: 0.4955 - mae: 0.3999"
      ]
     },
     {
@@ -13642,7 +11707,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "224/729 [========>.....................] - ETA: 8s - loss: 0.4709 - mae: 0.3902"
+      "233/729 [========>.....................] - ETA: 6s - loss: 0.4933 - mae: 0.3996"
      ]
     },
     {
@@ -13650,7 +11715,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "228/729 [========>.....................] - ETA: 8s - loss: 0.4706 - mae: 0.3902"
+      "237/729 [========>.....................] - ETA: 6s - loss: 0.4931 - mae: 0.3996"
      ]
     },
     {
@@ -13658,7 +11723,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "232/729 [========>.....................] - ETA: 8s - loss: 0.4703 - mae: 0.3902"
+      "241/729 [========>.....................] - ETA: 6s - loss: 0.4944 - mae: 0.4008"
      ]
     },
     {
@@ -13666,7 +11731,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "236/729 [========>.....................] - ETA: 8s - loss: 0.4699 - mae: 0.3902"
+      "245/729 [=========>....................] - ETA: 6s - loss: 0.4912 - mae: 0.4002"
      ]
     },
     {
@@ -13674,7 +11739,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "240/729 [========>.....................] - ETA: 8s - loss: 0.4695 - mae: 0.3902"
+      "249/729 [=========>....................] - ETA: 6s - loss: 0.4879 - mae: 0.3995"
      ]
     },
     {
@@ -13682,7 +11747,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "244/729 [=========>....................] - ETA: 8s - loss: 0.4691 - mae: 0.3901"
+      "253/729 [=========>....................] - ETA: 6s - loss: 0.4873 - mae: 0.3995"
      ]
     },
     {
@@ -13690,7 +11755,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "248/729 [=========>....................] - ETA: 8s - loss: 0.4687 - mae: 0.3901"
+      "257/729 [=========>....................] - ETA: 6s - loss: 0.4862 - mae: 0.3999"
      ]
     },
     {
@@ -13698,7 +11763,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "252/729 [=========>....................] - ETA: 8s - loss: 0.4682 - mae: 0.3900"
+      "261/729 [=========>....................] - ETA: 6s - loss: 0.4844 - mae: 0.3998"
      ]
     },
     {
@@ -13706,7 +11771,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "256/729 [=========>....................] - ETA: 8s - loss: 0.4678 - mae: 0.3900"
+      "265/729 [=========>....................] - ETA: 6s - loss: 0.4887 - mae: 0.4000"
      ]
     },
     {
@@ -13714,7 +11779,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "260/729 [=========>....................] - ETA: 7s - loss: 0.4674 - mae: 0.3899"
+      "269/729 [==========>...................] - ETA: 6s - loss: 0.4850 - mae: 0.3989"
      ]
     },
     {
@@ -13722,7 +11787,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "264/729 [=========>....................] - ETA: 7s - loss: 0.4671 - mae: 0.3899"
+      "273/729 [==========>...................] - ETA: 6s - loss: 0.4860 - mae: 0.3997"
      ]
     },
     {
@@ -13730,7 +11795,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "268/729 [==========>...................] - ETA: 7s - loss: 0.4669 - mae: 0.3899"
+      "277/729 [==========>...................] - ETA: 6s - loss: 0.4851 - mae: 0.3999"
      ]
     },
     {
@@ -13738,7 +11803,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "272/729 [==========>...................] - ETA: 7s - loss: 0.4666 - mae: 0.3899"
+      "281/729 [==========>...................] - ETA: 6s - loss: 0.4823 - mae: 0.3992"
      ]
     },
     {
@@ -13746,7 +11811,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "276/729 [==========>...................] - ETA: 7s - loss: 0.4663 - mae: 0.3899"
+      "285/729 [==========>...................] - ETA: 6s - loss: 0.4812 - mae: 0.3995"
      ]
     },
     {
@@ -13754,7 +11819,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "280/729 [==========>...................] - ETA: 7s - loss: 0.4660 - mae: 0.3898"
+      "289/729 [==========>...................] - ETA: 6s - loss: 0.4782 - mae: 0.3984"
      ]
     },
     {
@@ -13762,7 +11827,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "284/729 [==========>...................] - ETA: 7s - loss: 0.4657 - mae: 0.3898"
+      "293/729 [===========>..................] - ETA: 6s - loss: 0.4780 - mae: 0.3987"
      ]
     },
     {
@@ -13770,7 +11835,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "288/729 [==========>...................] - ETA: 7s - loss: 0.4655 - mae: 0.3898"
+      "297/729 [===========>..................] - ETA: 6s - loss: 0.4779 - mae: 0.3991"
      ]
     },
     {
@@ -13778,7 +11843,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "292/729 [===========>..................] - ETA: 7s - loss: 0.4652 - mae: 0.3898"
+      "301/729 [===========>..................] - ETA: 5s - loss: 0.4827 - mae: 0.4002"
      ]
     },
     {
@@ -13786,7 +11851,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "296/729 [===========>..................] - ETA: 7s - loss: 0.4649 - mae: 0.3898"
+      "305/729 [===========>..................] - ETA: 5s - loss: 0.4835 - mae: 0.4008"
      ]
     },
     {
@@ -13794,7 +11859,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "300/729 [===========>..................] - ETA: 7s - loss: 0.4646 - mae: 0.3897"
+      "309/729 [===========>..................] - ETA: 5s - loss: 0.4830 - mae: 0.4013"
      ]
     },
     {
@@ -13802,7 +11867,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "304/729 [===========>..................] - ETA: 7s - loss: 0.4643 - mae: 0.3897"
+      "313/729 [===========>..................] - ETA: 5s - loss: 0.4819 - mae: 0.4012"
      ]
     },
     {
@@ -13810,7 +11875,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "308/729 [===========>..................] - ETA: 6s - loss: 0.4640 - mae: 0.3897"
+      "317/729 [============>.................] - ETA: 5s - loss: 0.4826 - mae: 0.4013"
      ]
     },
     {
@@ -13818,7 +11883,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "312/729 [===========>..................] - ETA: 6s - loss: 0.4637 - mae: 0.3896"
+      "321/729 [============>.................] - ETA: 5s - loss: 0.4801 - mae: 0.4005"
      ]
     },
     {
@@ -13826,7 +11891,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "316/729 [============>.................] - ETA: 6s - loss: 0.4633 - mae: 0.3896"
+      "325/729 [============>.................] - ETA: 5s - loss: 0.4788 - mae: 0.3999"
      ]
     },
     {
@@ -13834,7 +11899,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "320/729 [============>.................] - ETA: 6s - loss: 0.4630 - mae: 0.3896"
+      "329/729 [============>.................] - ETA: 5s - loss: 0.4775 - mae: 0.3999"
      ]
     },
     {
@@ -13842,7 +11907,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "324/729 [============>.................] - ETA: 6s - loss: 0.4627 - mae: 0.3896"
+      "333/729 [============>.................] - ETA: 5s - loss: 0.4776 - mae: 0.4000"
      ]
     },
     {
@@ -13850,7 +11915,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "328/729 [============>.................] - ETA: 6s - loss: 0.4625 - mae: 0.3896"
+      "337/729 [============>.................] - ETA: 5s - loss: 0.4771 - mae: 0.4001"
      ]
     },
     {
@@ -13858,7 +11923,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "332/729 [============>.................] - ETA: 6s - loss: 0.4622 - mae: 0.3895"
+      "341/729 [=============>................] - ETA: 5s - loss: 0.4781 - mae: 0.4005"
      ]
     },
     {
@@ -13866,7 +11931,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "336/729 [============>.................] - ETA: 6s - loss: 0.4620 - mae: 0.3895"
+      "345/729 [=============>................] - ETA: 5s - loss: 0.4770 - mae: 0.4002"
      ]
     },
     {
@@ -13874,7 +11939,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "340/729 [============>.................] - ETA: 6s - loss: 0.4618 - mae: 0.3895"
+      "349/729 [=============>................] - ETA: 5s - loss: 0.4755 - mae: 0.4000"
      ]
     },
     {
@@ -13882,7 +11947,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "344/729 [=============>................] - ETA: 6s - loss: 0.4616 - mae: 0.3894"
+      "353/729 [=============>................] - ETA: 5s - loss: 0.4746 - mae: 0.3999"
      ]
     },
     {
@@ -13890,7 +11955,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "348/729 [=============>................] - ETA: 6s - loss: 0.4614 - mae: 0.3894"
+      "357/729 [=============>................] - ETA: 5s - loss: 0.4765 - mae: 0.4005"
      ]
     },
     {
@@ -13898,7 +11963,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "352/729 [=============>................] - ETA: 6s - loss: 0.4612 - mae: 0.3894"
+      "361/729 [=============>................] - ETA: 5s - loss: 0.4793 - mae: 0.4007"
      ]
     },
     {
@@ -13906,7 +11971,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "356/729 [=============>................] - ETA: 6s - loss: 0.4610 - mae: 0.3894"
+      "365/729 [==============>...............] - ETA: 5s - loss: 0.4775 - mae: 0.4001"
      ]
     },
     {
@@ -13914,7 +11979,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "360/729 [=============>................] - ETA: 6s - loss: 0.4608 - mae: 0.3894"
+      "369/729 [==============>...............] - ETA: 5s - loss: 0.4760 - mae: 0.3999"
      ]
     },
     {
@@ -13922,7 +11987,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "364/729 [=============>................] - ETA: 5s - loss: 0.4606 - mae: 0.3893"
+      "373/729 [==============>...............] - ETA: 4s - loss: 0.4749 - mae: 0.3997"
      ]
     },
     {
@@ -13930,7 +11995,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "368/729 [==============>...............] - ETA: 5s - loss: 0.4604 - mae: 0.3893"
+      "377/729 [==============>...............] - ETA: 4s - loss: 0.4757 - mae: 0.4000"
      ]
     },
     {
@@ -13938,7 +12003,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "372/729 [==============>...............] - ETA: 5s - loss: 0.4602 - mae: 0.3893"
+      "381/729 [==============>...............] - ETA: 4s - loss: 0.4758 - mae: 0.4002"
      ]
     },
     {
@@ -13946,7 +12011,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "376/729 [==============>...............] - ETA: 5s - loss: 0.4600 - mae: 0.3893"
+      "385/729 [==============>...............] - ETA: 4s - loss: 0.4751 - mae: 0.4002"
      ]
     },
     {
@@ -13954,7 +12019,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "380/729 [==============>...............] - ETA: 5s - loss: 0.4598 - mae: 0.3893"
+      "389/729 [===============>..............] - ETA: 4s - loss: 0.4750 - mae: 0.4001"
      ]
     },
     {
@@ -13962,7 +12027,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "384/729 [==============>...............] - ETA: 5s - loss: 0.4596 - mae: 0.3892"
+      "393/729 [===============>..............] - ETA: 4s - loss: 0.4737 - mae: 0.3996"
      ]
     },
     {
@@ -13970,7 +12035,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "388/729 [==============>...............] - ETA: 5s - loss: 0.4594 - mae: 0.3892"
+      "397/729 [===============>..............] - ETA: 4s - loss: 0.4710 - mae: 0.3985"
      ]
     },
     {
@@ -13978,7 +12043,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "392/729 [===============>..............] - ETA: 5s - loss: 0.4592 - mae: 0.3892"
+      "401/729 [===============>..............] - ETA: 4s - loss: 0.4728 - mae: 0.3984"
      ]
     },
     {
@@ -13986,7 +12051,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "396/729 [===============>..............] - ETA: 5s - loss: 0.4590 - mae: 0.3892"
+      "405/729 [===============>..............] - ETA: 4s - loss: 0.4735 - mae: 0.3987"
      ]
     },
     {
@@ -13994,7 +12059,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "400/729 [===============>..............] - ETA: 5s - loss: 0.4588 - mae: 0.3892"
+      "409/729 [===============>..............] - ETA: 4s - loss: 0.4731 - mae: 0.3989"
      ]
     },
     {
@@ -14002,7 +12067,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "404/729 [===============>..............] - ETA: 5s - loss: 0.4586 - mae: 0.3892"
+      "413/729 [===============>..............] - ETA: 4s - loss: 0.4723 - mae: 0.3989"
      ]
     },
     {
@@ -14010,7 +12075,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "408/729 [===============>..............] - ETA: 5s - loss: 0.4584 - mae: 0.3892"
+      "417/729 [================>.............] - ETA: 4s - loss: 0.4710 - mae: 0.3987"
      ]
     },
     {
@@ -14018,7 +12083,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "412/729 [===============>..............] - ETA: 5s - loss: 0.4583 - mae: 0.3892"
+      "421/729 [================>.............] - ETA: 4s - loss: 0.4710 - mae: 0.3985"
      ]
     },
     {
@@ -14026,7 +12091,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "416/729 [================>.............] - ETA: 5s - loss: 0.4582 - mae: 0.3892"
+      "425/729 [================>.............] - ETA: 4s - loss: 0.4696 - mae: 0.3984"
      ]
     },
     {
@@ -14034,7 +12099,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "420/729 [================>.............] - ETA: 4s - loss: 0.4580 - mae: 0.3892"
+      "429/729 [================>.............] - ETA: 4s - loss: 0.4676 - mae: 0.3977"
      ]
     },
     {
@@ -14042,7 +12107,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "424/729 [================>.............] - ETA: 4s - loss: 0.4579 - mae: 0.3892"
+      "433/729 [================>.............] - ETA: 4s - loss: 0.4658 - mae: 0.3972"
      ]
     },
     {
@@ -14050,7 +12115,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "428/729 [================>.............] - ETA: 4s - loss: 0.4577 - mae: 0.3892"
+      "437/729 [================>.............] - ETA: 4s - loss: 0.4678 - mae: 0.3975"
      ]
     },
     {
@@ -14058,7 +12123,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "432/729 [================>.............] - ETA: 4s - loss: 0.4575 - mae: 0.3892"
+      "441/729 [=================>............] - ETA: 4s - loss: 0.4673 - mae: 0.3976"
      ]
     },
     {
@@ -14066,7 +12131,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "436/729 [================>.............] - ETA: 4s - loss: 0.4574 - mae: 0.3892"
+      "445/729 [=================>............] - ETA: 3s - loss: 0.4671 - mae: 0.3978"
      ]
     },
     {
@@ -14074,7 +12139,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "440/729 [=================>............] - ETA: 4s - loss: 0.4572 - mae: 0.3892"
+      "449/729 [=================>............] - ETA: 3s - loss: 0.4659 - mae: 0.3974"
      ]
     },
     {
@@ -14082,7 +12147,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "444/729 [=================>............] - ETA: 4s - loss: 0.4571 - mae: 0.3893"
+      "453/729 [=================>............] - ETA: 3s - loss: 0.4648 - mae: 0.3971"
      ]
     },
     {
@@ -14090,7 +12155,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "448/729 [=================>............] - ETA: 4s - loss: 0.4570 - mae: 0.3893"
+      "457/729 [=================>............] - ETA: 3s - loss: 0.4665 - mae: 0.3976"
      ]
     },
     {
@@ -14098,7 +12163,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "452/729 [=================>............] - ETA: 4s - loss: 0.4569 - mae: 0.3893"
+      "461/729 [=================>............] - ETA: 3s - loss: 0.4652 - mae: 0.3972"
      ]
     },
     {
@@ -14106,7 +12171,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "456/729 [=================>............] - ETA: 4s - loss: 0.4567 - mae: 0.3893"
+      "465/729 [==================>...........] - ETA: 3s - loss: 0.4653 - mae: 0.3972"
      ]
     },
     {
@@ -14114,7 +12179,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "460/729 [=================>............] - ETA: 4s - loss: 0.4566 - mae: 0.3893"
+      "469/729 [==================>...........] - ETA: 3s - loss: 0.4637 - mae: 0.3967"
      ]
     },
     {
@@ -14122,7 +12187,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "464/729 [==================>...........] - ETA: 4s - loss: 0.4565 - mae: 0.3893"
+      "473/729 [==================>...........] - ETA: 3s - loss: 0.4626 - mae: 0.3965"
      ]
     },
     {
@@ -14130,7 +12195,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "468/729 [==================>...........] - ETA: 4s - loss: 0.4564 - mae: 0.3893"
+      "477/729 [==================>...........] - ETA: 3s - loss: 0.4611 - mae: 0.3960"
      ]
     },
     {
@@ -14138,7 +12203,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "472/729 [==================>...........] - ETA: 4s - loss: 0.4563 - mae: 0.3893"
+      "481/729 [==================>...........] - ETA: 3s - loss: 0.4608 - mae: 0.3961"
      ]
     },
     {
@@ -14146,7 +12211,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "476/729 [==================>...........] - ETA: 4s - loss: 0.4562 - mae: 0.3893"
+      "485/729 [==================>...........] - ETA: 3s - loss: 0.4599 - mae: 0.3960"
      ]
     },
     {
@@ -14154,7 +12219,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "480/729 [==================>...........] - ETA: 3s - loss: 0.4561 - mae: 0.3894"
+      "489/729 [===================>..........] - ETA: 3s - loss: 0.4594 - mae: 0.3961"
      ]
     },
     {
@@ -14162,7 +12227,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "484/729 [==================>...........] - ETA: 3s - loss: 0.4561 - mae: 0.3894"
+      "493/729 [===================>..........] - ETA: 3s - loss: 0.4577 - mae: 0.3954"
      ]
     },
     {
@@ -14170,7 +12235,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "488/729 [===================>..........] - ETA: 3s - loss: 0.4560 - mae: 0.3894"
+      "497/729 [===================>..........] - ETA: 3s - loss: 0.4566 - mae: 0.3951"
      ]
     },
     {
@@ -14178,7 +12243,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "492/729 [===================>..........] - ETA: 3s - loss: 0.4560 - mae: 0.3894"
+      "501/729 [===================>..........] - ETA: 3s - loss: 0.4550 - mae: 0.3946"
      ]
     },
     {
@@ -14186,7 +12251,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "496/729 [===================>..........] - ETA: 3s - loss: 0.4560 - mae: 0.3895"
+      "505/729 [===================>..........] - ETA: 3s - loss: 0.4535 - mae: 0.3942"
      ]
     },
     {
@@ -14194,7 +12259,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "500/729 [===================>..........] - ETA: 3s - loss: 0.4559 - mae: 0.3895"
+      "509/729 [===================>..........] - ETA: 3s - loss: 0.4521 - mae: 0.3938"
      ]
     },
     {
@@ -14202,7 +12267,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "504/729 [===================>..........] - ETA: 3s - loss: 0.4559 - mae: 0.3895"
+      "513/729 [====================>.........] - ETA: 3s - loss: 0.4516 - mae: 0.3939"
      ]
     },
     {
@@ -14210,7 +12275,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "508/729 [===================>..........] - ETA: 3s - loss: 0.4559 - mae: 0.3895"
+      "517/729 [====================>.........] - ETA: 2s - loss: 0.4501 - mae: 0.3934"
      ]
     },
     {
@@ -14218,7 +12283,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "512/729 [====================>.........] - ETA: 3s - loss: 0.4559 - mae: 0.3896"
+      "521/729 [====================>.........] - ETA: 2s - loss: 0.4489 - mae: 0.3930"
      ]
     },
     {
@@ -14226,7 +12291,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "516/729 [====================>.........] - ETA: 3s - loss: 0.4558 - mae: 0.3896"
+      "525/729 [====================>.........] - ETA: 2s - loss: 0.4495 - mae: 0.3930"
      ]
     },
     {
@@ -14234,7 +12299,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "520/729 [====================>.........] - ETA: 3s - loss: 0.4558 - mae: 0.3897"
+      "529/729 [====================>.........] - ETA: 2s - loss: 0.4491 - mae: 0.3929"
      ]
     },
     {
@@ -14242,7 +12307,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "524/729 [====================>.........] - ETA: 3s - loss: 0.4558 - mae: 0.3897"
+      "533/729 [====================>.........] - ETA: 2s - loss: 0.4483 - mae: 0.3928"
      ]
     },
     {
@@ -14250,7 +12315,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "528/729 [====================>.........] - ETA: 3s - loss: 0.4557 - mae: 0.3897"
+      "537/729 [=====================>........] - ETA: 2s - loss: 0.4478 - mae: 0.3928"
      ]
     },
     {
@@ -14258,7 +12323,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "532/729 [====================>.........] - ETA: 3s - loss: 0.4557 - mae: 0.3897"
+      "541/729 [=====================>........] - ETA: 2s - loss: 0.4470 - mae: 0.3927"
      ]
     },
     {
@@ -14266,7 +12331,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "536/729 [=====================>........] - ETA: 3s - loss: 0.4556 - mae: 0.3897"
+      "545/729 [=====================>........] - ETA: 2s - loss: 0.4470 - mae: 0.3927"
      ]
     },
     {
@@ -14274,7 +12339,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "540/729 [=====================>........] - ETA: 3s - loss: 0.4556 - mae: 0.3898"
+      "549/729 [=====================>........] - ETA: 2s - loss: 0.4509 - mae: 0.3928"
      ]
     },
     {
@@ -14282,7 +12347,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "544/729 [=====================>........] - ETA: 2s - loss: 0.4555 - mae: 0.3898"
+      "553/729 [=====================>........] - ETA: 2s - loss: 0.4504 - mae: 0.3927"
      ]
     },
     {
@@ -14290,7 +12355,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "548/729 [=====================>........] - ETA: 2s - loss: 0.4554 - mae: 0.3898"
+      "557/729 [=====================>........] - ETA: 2s - loss: 0.4514 - mae: 0.3934"
      ]
     },
     {
@@ -14298,7 +12363,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "552/729 [=====================>........] - ETA: 2s - loss: 0.4554 - mae: 0.3898"
+      "561/729 [======================>.......] - ETA: 2s - loss: 0.4520 - mae: 0.3934"
      ]
     },
     {
@@ -14306,7 +12371,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "556/729 [=====================>........] - ETA: 2s - loss: 0.4553 - mae: 0.3898"
+      "565/729 [======================>.......] - ETA: 2s - loss: 0.4518 - mae: 0.3936"
      ]
     },
     {
@@ -14314,7 +12379,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "560/729 [======================>.......] - ETA: 2s - loss: 0.4553 - mae: 0.3899"
+      "569/729 [======================>.......] - ETA: 2s - loss: 0.4518 - mae: 0.3938"
      ]
     },
     {
@@ -14322,7 +12387,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "564/729 [======================>.......] - ETA: 2s - loss: 0.4552 - mae: 0.3899"
+      "573/729 [======================>.......] - ETA: 2s - loss: 0.4539 - mae: 0.3940"
      ]
     },
     {
@@ -14330,7 +12395,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "568/729 [======================>.......] - ETA: 2s - loss: 0.4551 - mae: 0.3899"
+      "577/729 [======================>.......] - ETA: 2s - loss: 0.4531 - mae: 0.3937"
      ]
     },
     {
@@ -14338,7 +12403,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "572/729 [======================>.......] - ETA: 2s - loss: 0.4551 - mae: 0.3899"
+      "580/729 [======================>.......] - ETA: 2s - loss: 0.4527 - mae: 0.3937"
      ]
     },
     {
@@ -14346,7 +12411,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "576/729 [======================>.......] - ETA: 2s - loss: 0.4550 - mae: 0.3899"
+      "584/729 [=======================>......] - ETA: 2s - loss: 0.4529 - mae: 0.3939"
      ]
     },
     {
@@ -14354,7 +12419,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "580/729 [======================>.......] - ETA: 2s - loss: 0.4550 - mae: 0.3899"
+      "587/729 [=======================>......] - ETA: 2s - loss: 0.4520 - mae: 0.3937"
      ]
     },
     {
@@ -14362,7 +12427,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "584/729 [=======================>......] - ETA: 2s - loss: 0.4549 - mae: 0.3900"
+      "591/729 [=======================>......] - ETA: 1s - loss: 0.4516 - mae: 0.3937"
      ]
     },
     {
@@ -14370,7 +12435,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "588/729 [=======================>......] - ETA: 2s - loss: 0.4549 - mae: 0.3900"
+      "595/729 [=======================>......] - ETA: 1s - loss: 0.4510 - mae: 0.3935"
      ]
     },
     {
@@ -14378,7 +12443,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "592/729 [=======================>......] - ETA: 2s - loss: 0.4548 - mae: 0.3900"
+      "599/729 [=======================>......] - ETA: 1s - loss: 0.4505 - mae: 0.3934"
      ]
     },
     {
@@ -14386,7 +12451,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "596/729 [=======================>......] - ETA: 2s - loss: 0.4548 - mae: 0.3900"
+      "603/729 [=======================>......] - ETA: 1s - loss: 0.4501 - mae: 0.3936"
      ]
     },
     {
@@ -14394,7 +12459,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "600/729 [=======================>......] - ETA: 2s - loss: 0.4547 - mae: 0.3900"
+      "607/729 [=======================>......] - ETA: 1s - loss: 0.4489 - mae: 0.3932"
      ]
     },
     {
@@ -14402,7 +12467,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "604/729 [=======================>......] - ETA: 1s - loss: 0.4546 - mae: 0.3900"
+      "611/729 [========================>.....] - ETA: 1s - loss: 0.4482 - mae: 0.3931"
      ]
     },
     {
@@ -14410,7 +12475,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "608/729 [========================>.....] - ETA: 1s - loss: 0.4545 - mae: 0.3900"
+      "615/729 [========================>.....] - ETA: 1s - loss: 0.4477 - mae: 0.3931"
      ]
     },
     {
@@ -14418,7 +12483,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "612/729 [========================>.....] - ETA: 1s - loss: 0.4545 - mae: 0.3901"
+      "619/729 [========================>.....] - ETA: 1s - loss: 0.4474 - mae: 0.3932"
      ]
     },
     {
@@ -14426,7 +12491,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "616/729 [========================>.....] - ETA: 1s - loss: 0.4544 - mae: 0.3901"
+      "623/729 [========================>.....] - ETA: 1s - loss: 0.4482 - mae: 0.3935"
      ]
     },
     {
@@ -14434,7 +12499,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "620/729 [========================>.....] - ETA: 1s - loss: 0.4543 - mae: 0.3901"
+      "627/729 [========================>.....] - ETA: 1s - loss: 0.4475 - mae: 0.3933"
      ]
     },
     {
@@ -14442,7 +12507,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "624/729 [========================>.....] - ETA: 1s - loss: 0.4543 - mae: 0.3901"
+      "631/729 [========================>.....] - ETA: 1s - loss: 0.4466 - mae: 0.3931"
      ]
     },
     {
@@ -14450,7 +12515,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "628/729 [========================>.....] - ETA: 1s - loss: 0.4542 - mae: 0.3901"
+      "635/729 [=========================>....] - ETA: 1s - loss: 0.4457 - mae: 0.3929"
      ]
     },
     {
@@ -14458,7 +12523,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "632/729 [=========================>....] - ETA: 1s - loss: 0.4541 - mae: 0.3901"
+      "639/729 [=========================>....] - ETA: 1s - loss: 0.4456 - mae: 0.3927"
      ]
     },
     {
@@ -14466,7 +12531,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "636/729 [=========================>....] - ETA: 1s - loss: 0.4540 - mae: 0.3901"
+      "643/729 [=========================>....] - ETA: 1s - loss: 0.4456 - mae: 0.3928"
      ]
     },
     {
@@ -14474,7 +12539,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "640/729 [=========================>....] - ETA: 1s - loss: 0.4540 - mae: 0.3901"
+      "647/729 [=========================>....] - ETA: 1s - loss: 0.4453 - mae: 0.3930"
      ]
     },
     {
@@ -14482,7 +12547,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "644/729 [=========================>....] - ETA: 1s - loss: 0.4539 - mae: 0.3901"
+      "651/729 [=========================>....] - ETA: 1s - loss: 0.4448 - mae: 0.3928"
      ]
     },
     {
@@ -14490,7 +12555,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "648/729 [=========================>....] - ETA: 1s - loss: 0.4538 - mae: 0.3901"
+      "654/729 [=========================>....] - ETA: 1s - loss: 0.4446 - mae: 0.3929"
      ]
     },
     {
@@ -14498,7 +12563,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "652/729 [=========================>....] - ETA: 1s - loss: 0.4538 - mae: 0.3901"
+      "657/729 [==========================>...] - ETA: 1s - loss: 0.4443 - mae: 0.3929"
      ]
     },
     {
@@ -14506,7 +12571,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "656/729 [=========================>....] - ETA: 1s - loss: 0.4537 - mae: 0.3902"
+      "661/729 [==========================>...] - ETA: 0s - loss: 0.4436 - mae: 0.3928"
      ]
     },
     {
@@ -14514,7 +12579,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "660/729 [==========================>...] - ETA: 1s - loss: 0.4536 - mae: 0.3902"
+      "665/729 [==========================>...] - ETA: 0s - loss: 0.4435 - mae: 0.3927"
      ]
     },
     {
@@ -14522,7 +12587,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "664/729 [==========================>...] - ETA: 1s - loss: 0.4536 - mae: 0.3902"
+      "669/729 [==========================>...] - ETA: 0s - loss: 0.4426 - mae: 0.3925"
      ]
     },
     {
@@ -14530,7 +12595,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "668/729 [==========================>...] - ETA: 0s - loss: 0.4535 - mae: 0.3902"
+      "673/729 [==========================>...] - ETA: 0s - loss: 0.4420 - mae: 0.3924"
      ]
     },
     {
@@ -14538,7 +12603,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "672/729 [==========================>...] - ETA: 0s - loss: 0.4534 - mae: 0.3902"
+      "677/729 [==========================>...] - ETA: 0s - loss: 0.4418 - mae: 0.3922"
      ]
     },
     {
@@ -14546,7 +12611,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "676/729 [==========================>...] - ETA: 0s - loss: 0.4534 - mae: 0.3902"
+      "681/729 [===========================>..] - ETA: 0s - loss: 0.4421 - mae: 0.3926"
      ]
     },
     {
@@ -14554,7 +12619,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "680/729 [==========================>...] - ETA: 0s - loss: 0.4533 - mae: 0.3902"
+      "685/729 [===========================>..] - ETA: 0s - loss: 0.4416 - mae: 0.3924"
      ]
     },
     {
@@ -14562,7 +12627,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "684/729 [===========================>..] - ETA: 0s - loss: 0.4532 - mae: 0.3902"
+      "689/729 [===========================>..] - ETA: 0s - loss: 0.4406 - mae: 0.3920"
      ]
     },
     {
@@ -14570,7 +12635,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "688/729 [===========================>..] - ETA: 0s - loss: 0.4532 - mae: 0.3902"
+      "693/729 [===========================>..] - ETA: 0s - loss: 0.4408 - mae: 0.3920"
      ]
     },
     {
@@ -14578,7 +12643,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "692/729 [===========================>..] - ETA: 0s - loss: 0.4531 - mae: 0.3903"
+      "697/729 [===========================>..] - ETA: 0s - loss: 0.4401 - mae: 0.3916"
      ]
     },
     {
@@ -14586,7 +12651,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "696/729 [===========================>..] - ETA: 0s - loss: 0.4531 - mae: 0.3903"
+      "701/729 [===========================>..] - ETA: 0s - loss: 0.4430 - mae: 0.3919"
      ]
     },
     {
@@ -14594,7 +12659,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "700/729 [===========================>..] - ETA: 0s - loss: 0.4530 - mae: 0.3903"
+      "705/729 [============================>.] - ETA: 0s - loss: 0.4419 - mae: 0.3916"
      ]
     },
     {
@@ -14602,7 +12667,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "704/729 [===========================>..] - ETA: 0s - loss: 0.4530 - mae: 0.3903"
+      "709/729 [============================>.] - ETA: 0s - loss: 0.4410 - mae: 0.3913"
      ]
     },
     {
@@ -14610,7 +12675,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "708/729 [============================>.] - ETA: 0s - loss: 0.4530 - mae: 0.3903"
+      "713/729 [============================>.] - ETA: 0s - loss: 0.4407 - mae: 0.3913"
      ]
     },
     {
@@ -14618,7 +12683,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "712/729 [============================>.] - ETA: 0s - loss: 0.4529 - mae: 0.3903"
+      "717/729 [============================>.] - ETA: 0s - loss: 0.4407 - mae: 0.3915"
      ]
     },
     {
@@ -14626,7 +12691,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "716/729 [============================>.] - ETA: 0s - loss: 0.4529 - mae: 0.3903"
+      "721/729 [============================>.] - ETA: 0s - loss: 0.4406 - mae: 0.3914"
      ]
     },
     {
@@ -14634,7 +12699,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "720/729 [============================>.] - ETA: 0s - loss: 0.4528 - mae: 0.3903"
+      "725/729 [============================>.] - ETA: 0s - loss: 0.4401 - mae: 0.3914"
      ]
     },
     {
@@ -14642,7 +12707,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "724/729 [============================>.] - ETA: 0s - loss: 0.4528 - mae: 0.3904"
+      "729/729 [==============================] - ETA: 0s - loss: 0.4397 - mae: 0.3913"
      ]
     },
     {
@@ -14650,32 +12715,32 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "728/729 [============================>.] - ETA: 0s - loss: 0.4527 - mae: 0.3904"
+      "729/729 [==============================] - 12s 16ms/step - loss: 0.4397 - mae: 0.3913 - val_loss: 0.4273 - val_mae: 0.3675\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "729/729 [==============================] - 12s 16ms/step - loss: 0.4527 - mae: 0.3904 - val_loss: 0.4370 - val_mae: 0.3732\n"
+      "Epoch 9/10\n",
+      "\r",
+      "  1/729 [..............................] - ETA: 0s - loss: 0.2486 - mae: 0.3318"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 9/10\n",
-      "\r",
-      "  1/729 [..............................] - ETA: 28s - loss: 1.7737 - mae: 0.5914"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  4/729 [..............................] - ETA: 9s - loss: 0.3027 - mae: 0.3608"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  5/729 [..............................] - ETA: 10s - loss: 1.0861 - mae: 0.5069"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  8/729 [..............................] - ETA: 10s - loss: 0.3422 - mae: 0.3785"
      ]
     },
     {
@@ -14683,7 +12748,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  9/729 [..............................] - ETA: 10s - loss: 0.8747 - mae: 0.4752"
+      " 12/729 [..............................] - ETA: 10s - loss: 0.4235 - mae: 0.3882"
      ]
     },
     {
@@ -14691,7 +12756,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 13/729 [..............................] - ETA: 10s - loss: 0.7610 - mae: 0.4528"
+      " 16/729 [..............................] - ETA: 10s - loss: 0.4200 - mae: 0.3987"
      ]
     },
     {
@@ -14699,7 +12764,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 17/729 [..............................] - ETA: 10s - loss: 0.6942 - mae: 0.4403"
+      " 20/729 [..............................] - ETA: 10s - loss: 0.4811 - mae: 0.4117"
      ]
     },
     {
@@ -14707,7 +12772,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 21/729 [..............................] - ETA: 10s - loss: 0.6468 - mae: 0.4311"
+      " 23/729 [..............................] - ETA: 11s - loss: 0.4923 - mae: 0.4134"
      ]
     },
     {
@@ -14715,7 +12780,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 25/729 [>.............................] - ETA: 10s - loss: 0.6112 - mae: 0.4241"
+      " 27/729 [>.............................] - ETA: 11s - loss: 0.4728 - mae: 0.4084"
      ]
     },
     {
@@ -14723,7 +12788,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 29/729 [>.............................] - ETA: 10s - loss: 0.5825 - mae: 0.4182"
+      " 31/729 [>.............................] - ETA: 11s - loss: 0.4822 - mae: 0.4148"
      ]
     },
     {
@@ -14731,7 +12796,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 33/729 [>.............................] - ETA: 10s - loss: 0.5596 - mae: 0.4134"
+      " 35/729 [>.............................] - ETA: 10s - loss: 0.4658 - mae: 0.4118"
      ]
     },
     {
@@ -14739,7 +12804,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 37/729 [>.............................] - ETA: 10s - loss: 0.5409 - mae: 0.4095"
+      " 39/729 [>.............................] - ETA: 10s - loss: 0.4603 - mae: 0.4067"
      ]
     },
     {
@@ -14747,7 +12812,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 41/729 [>.............................] - ETA: 10s - loss: 0.5255 - mae: 0.4063"
+      " 42/729 [>.............................] - ETA: 10s - loss: 0.4456 - mae: 0.4009"
      ]
     },
     {
@@ -14755,7 +12820,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 45/729 [>.............................] - ETA: 10s - loss: 0.5160 - mae: 0.4042"
+      " 45/729 [>.............................] - ETA: 11s - loss: 0.4401 - mae: 0.4003"
      ]
     },
     {
@@ -14763,7 +12828,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 49/729 [=>............................] - ETA: 10s - loss: 0.5107 - mae: 0.4028"
+      " 48/729 [>.............................] - ETA: 10s - loss: 0.4336 - mae: 0.3986"
      ]
     },
     {
@@ -14771,7 +12836,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 53/729 [=>............................] - ETA: 10s - loss: 0.5053 - mae: 0.4014"
+      " 52/729 [=>............................] - ETA: 10s - loss: 0.4256 - mae: 0.3977"
      ]
     },
     {
@@ -14779,71 +12844,71 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 57/729 [=>............................] - ETA: 9s - loss: 0.5007 - mae: 0.4003 "
+      " 55/729 [=>............................] - ETA: 10s - loss: 0.4150 - mae: 0.3932"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 61/729 [=>............................] - ETA: 9s - loss: 0.4962 - mae: 0.3992"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 59/729 [=>............................] - ETA: 10s - loss: 0.4066 - mae: 0.3902"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 65/729 [=>............................] - ETA: 9s - loss: 0.4920 - mae: 0.3982"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 63/729 [=>............................] - ETA: 10s - loss: 0.4129 - mae: 0.3926"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 69/729 [=>............................] - ETA: 9s - loss: 0.4901 - mae: 0.3974"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 67/729 [=>............................] - ETA: 10s - loss: 0.4078 - mae: 0.3906"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 73/729 [==>...........................] - ETA: 9s - loss: 0.4899 - mae: 0.3969"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 71/729 [=>............................] - ETA: 10s - loss: 0.4051 - mae: 0.3891"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 77/729 [==>...........................] - ETA: 9s - loss: 0.4891 - mae: 0.3964"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 75/729 [==>...........................] - ETA: 10s - loss: 0.4040 - mae: 0.3891"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 81/729 [==>...........................] - ETA: 9s - loss: 0.4880 - mae: 0.3959"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 79/729 [==>...........................] - ETA: 10s - loss: 0.4003 - mae: 0.3877"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 85/729 [==>...........................] - ETA: 9s - loss: 0.4872 - mae: 0.3955"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 83/729 [==>...........................] - ETA: 10s - loss: 0.4023 - mae: 0.3872"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 89/729 [==>...........................] - ETA: 9s - loss: 0.4870 - mae: 0.3954"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 87/729 [==>...........................] - ETA: 9s - loss: 0.4041 - mae: 0.3872 "
      ]
     },
     {
@@ -14851,7 +12916,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 93/729 [==>...........................] - ETA: 9s - loss: 0.4869 - mae: 0.3953"
+      " 91/729 [==>...........................] - ETA: 9s - loss: 0.4193 - mae: 0.3910"
      ]
     },
     {
@@ -14859,7 +12924,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 97/729 [==>...........................] - ETA: 9s - loss: 0.4869 - mae: 0.3954"
+      " 95/729 [==>...........................] - ETA: 9s - loss: 0.4130 - mae: 0.3891"
      ]
     },
     {
@@ -14867,7 +12932,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "101/729 [===>..........................] - ETA: 9s - loss: 0.4868 - mae: 0.3954"
+      " 99/729 [===>..........................] - ETA: 9s - loss: 0.4080 - mae: 0.3872"
      ]
     },
     {
@@ -14875,7 +12940,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "105/729 [===>..........................] - ETA: 9s - loss: 0.4864 - mae: 0.3955"
+      "103/729 [===>..........................] - ETA: 9s - loss: 0.4062 - mae: 0.3872"
      ]
     },
     {
@@ -14883,7 +12948,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "109/729 [===>..........................] - ETA: 9s - loss: 0.4859 - mae: 0.3955"
+      "107/729 [===>..........................] - ETA: 9s - loss: 0.4121 - mae: 0.3882"
      ]
     },
     {
@@ -14891,7 +12956,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "113/729 [===>..........................] - ETA: 9s - loss: 0.4854 - mae: 0.3955"
+      "111/729 [===>..........................] - ETA: 9s - loss: 0.4085 - mae: 0.3871"
      ]
     },
     {
@@ -14899,7 +12964,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "117/729 [===>..........................] - ETA: 9s - loss: 0.4848 - mae: 0.3955"
+      "115/729 [===>..........................] - ETA: 9s - loss: 0.4097 - mae: 0.3865"
      ]
     },
     {
@@ -14907,7 +12972,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "121/729 [===>..........................] - ETA: 9s - loss: 0.4840 - mae: 0.3955"
+      "119/729 [===>..........................] - ETA: 9s - loss: 0.4071 - mae: 0.3850"
      ]
     },
     {
@@ -14915,7 +12980,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "125/729 [====>.........................] - ETA: 8s - loss: 0.4833 - mae: 0.3954"
+      "123/729 [====>.........................] - ETA: 9s - loss: 0.4077 - mae: 0.3858"
      ]
     },
     {
@@ -14923,7 +12988,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "129/729 [====>.........................] - ETA: 8s - loss: 0.4832 - mae: 0.3954"
+      "127/729 [====>.........................] - ETA: 9s - loss: 0.4125 - mae: 0.3882"
      ]
     },
     {
@@ -14931,7 +12996,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "133/729 [====>.........................] - ETA: 8s - loss: 0.4830 - mae: 0.3953"
+      "131/729 [====>.........................] - ETA: 9s - loss: 0.4117 - mae: 0.3880"
      ]
     },
     {
@@ -14939,7 +13004,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "137/729 [====>.........................] - ETA: 8s - loss: 0.4827 - mae: 0.3952"
+      "135/729 [====>.........................] - ETA: 8s - loss: 0.4102 - mae: 0.3877"
      ]
     },
     {
@@ -14947,7 +13012,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "141/729 [====>.........................] - ETA: 8s - loss: 0.4824 - mae: 0.3952"
+      "139/729 [====>.........................] - ETA: 8s - loss: 0.4298 - mae: 0.3907"
      ]
     },
     {
@@ -14955,7 +13020,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "145/729 [====>.........................] - ETA: 8s - loss: 0.4820 - mae: 0.3951"
+      "143/729 [====>.........................] - ETA: 8s - loss: 0.4263 - mae: 0.3897"
      ]
     },
     {
@@ -14963,7 +13028,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "149/729 [=====>........................] - ETA: 8s - loss: 0.4815 - mae: 0.3950"
+      "147/729 [=====>........................] - ETA: 8s - loss: 0.4221 - mae: 0.3883"
      ]
     },
     {
@@ -14971,7 +13036,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "153/729 [=====>........................] - ETA: 8s - loss: 0.4810 - mae: 0.3949"
+      "151/729 [=====>........................] - ETA: 8s - loss: 0.4221 - mae: 0.3874"
      ]
     },
     {
@@ -14979,7 +13044,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "157/729 [=====>........................] - ETA: 8s - loss: 0.4805 - mae: 0.3948"
+      "155/729 [=====>........................] - ETA: 8s - loss: 0.4228 - mae: 0.3881"
      ]
     },
     {
@@ -14987,7 +13052,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "161/729 [=====>........................] - ETA: 8s - loss: 0.4801 - mae: 0.3948"
+      "159/729 [=====>........................] - ETA: 8s - loss: 0.4394 - mae: 0.3894"
      ]
     },
     {
@@ -14995,7 +13060,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "165/729 [=====>........................] - ETA: 8s - loss: 0.4797 - mae: 0.3947"
+      "163/729 [=====>........................] - ETA: 8s - loss: 0.4539 - mae: 0.3899"
      ]
     },
     {
@@ -15003,7 +13068,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "169/729 [=====>........................] - ETA: 8s - loss: 0.4792 - mae: 0.3946"
+      "167/729 [=====>........................] - ETA: 8s - loss: 0.4516 - mae: 0.3902"
      ]
     },
     {
@@ -15011,7 +13076,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "173/729 [======>.......................] - ETA: 8s - loss: 0.4788 - mae: 0.3946"
+      "171/729 [======>.......................] - ETA: 8s - loss: 0.4539 - mae: 0.3910"
      ]
     },
     {
@@ -15019,7 +13084,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "177/729 [======>.......................] - ETA: 8s - loss: 0.4782 - mae: 0.3945"
+      "175/729 [======>.......................] - ETA: 8s - loss: 0.4540 - mae: 0.3916"
      ]
     },
     {
@@ -15027,7 +13092,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "181/729 [======>.......................] - ETA: 8s - loss: 0.4777 - mae: 0.3944"
+      "179/729 [======>.......................] - ETA: 8s - loss: 0.4518 - mae: 0.3916"
      ]
     },
     {
@@ -15035,7 +13100,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "185/729 [======>.......................] - ETA: 8s - loss: 0.4771 - mae: 0.3943"
+      "183/729 [======>.......................] - ETA: 8s - loss: 0.4499 - mae: 0.3915"
      ]
     },
     {
@@ -15043,7 +13108,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "189/729 [======>.......................] - ETA: 8s - loss: 0.4766 - mae: 0.3942"
+      "187/729 [======>.......................] - ETA: 8s - loss: 0.4557 - mae: 0.3933"
      ]
     },
     {
@@ -15051,7 +13116,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "193/729 [======>.......................] - ETA: 7s - loss: 0.4760 - mae: 0.3941"
+      "191/729 [======>.......................] - ETA: 7s - loss: 0.4546 - mae: 0.3935"
      ]
     },
     {
@@ -15059,7 +13124,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "197/729 [=======>......................] - ETA: 7s - loss: 0.4753 - mae: 0.3940"
+      "195/729 [=======>......................] - ETA: 7s - loss: 0.4523 - mae: 0.3932"
      ]
     },
     {
@@ -15067,7 +13132,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "201/729 [=======>......................] - ETA: 7s - loss: 0.4749 - mae: 0.3939"
+      "199/729 [=======>......................] - ETA: 7s - loss: 0.4515 - mae: 0.3936"
      ]
     },
     {
@@ -15075,7 +13140,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "205/729 [=======>......................] - ETA: 7s - loss: 0.4746 - mae: 0.3939"
+      "203/729 [=======>......................] - ETA: 7s - loss: 0.4503 - mae: 0.3936"
      ]
     },
     {
@@ -15083,7 +13148,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "209/729 [=======>......................] - ETA: 7s - loss: 0.4742 - mae: 0.3938"
+      "207/729 [=======>......................] - ETA: 7s - loss: 0.4519 - mae: 0.3939"
      ]
     },
     {
@@ -15091,7 +13156,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "213/729 [=======>......................] - ETA: 7s - loss: 0.4739 - mae: 0.3938"
+      "211/729 [=======>......................] - ETA: 7s - loss: 0.4542 - mae: 0.3943"
      ]
     },
     {
@@ -15099,7 +13164,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "217/729 [=======>......................] - ETA: 7s - loss: 0.4736 - mae: 0.3937"
+      "215/729 [=======>......................] - ETA: 7s - loss: 0.4519 - mae: 0.3941"
      ]
     },
     {
@@ -15107,7 +13172,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "221/729 [========>.....................] - ETA: 7s - loss: 0.4734 - mae: 0.3936"
+      "219/729 [========>.....................] - ETA: 7s - loss: 0.4510 - mae: 0.3942"
      ]
     },
     {
@@ -15115,7 +13180,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "225/729 [========>.....................] - ETA: 7s - loss: 0.4732 - mae: 0.3936"
+      "223/729 [========>.....................] - ETA: 7s - loss: 0.4504 - mae: 0.3940"
      ]
     },
     {
@@ -15123,7 +13188,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "229/729 [========>.....................] - ETA: 7s - loss: 0.4730 - mae: 0.3936"
+      "227/729 [========>.....................] - ETA: 7s - loss: 0.4565 - mae: 0.3942"
      ]
     },
     {
@@ -15131,7 +13196,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "233/729 [========>.....................] - ETA: 7s - loss: 0.4728 - mae: 0.3935"
+      "231/729 [========>.....................] - ETA: 7s - loss: 0.4572 - mae: 0.3950"
      ]
     },
     {
@@ -15139,7 +13204,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "237/729 [========>.....................] - ETA: 7s - loss: 0.4725 - mae: 0.3935"
+      "235/729 [========>.....................] - ETA: 7s - loss: 0.4598 - mae: 0.3955"
      ]
     },
     {
@@ -15147,7 +13212,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "241/729 [========>.....................] - ETA: 7s - loss: 0.4723 - mae: 0.3935"
+      "239/729 [========>.....................] - ETA: 7s - loss: 0.4589 - mae: 0.3958"
      ]
     },
     {
@@ -15155,7 +13220,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "245/729 [=========>....................] - ETA: 7s - loss: 0.4721 - mae: 0.3935"
+      "243/729 [=========>....................] - ETA: 7s - loss: 0.4572 - mae: 0.3955"
      ]
     },
     {
@@ -15163,7 +13228,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "249/729 [=========>....................] - ETA: 7s - loss: 0.4720 - mae: 0.3934"
+      "247/729 [=========>....................] - ETA: 7s - loss: 0.4569 - mae: 0.3957"
      ]
     },
     {
@@ -15171,7 +13236,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "253/729 [=========>....................] - ETA: 7s - loss: 0.4718 - mae: 0.3934"
+      "251/729 [=========>....................] - ETA: 6s - loss: 0.4550 - mae: 0.3952"
      ]
     },
     {
@@ -15179,7 +13244,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "257/729 [=========>....................] - ETA: 7s - loss: 0.4716 - mae: 0.3934"
+      "255/729 [=========>....................] - ETA: 6s - loss: 0.4578 - mae: 0.3953"
      ]
     },
     {
@@ -15187,7 +13252,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "261/729 [=========>....................] - ETA: 6s - loss: 0.4714 - mae: 0.3934"
+      "259/729 [=========>....................] - ETA: 6s - loss: 0.4554 - mae: 0.3947"
      ]
     },
     {
@@ -15195,7 +13260,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "265/729 [=========>....................] - ETA: 6s - loss: 0.4711 - mae: 0.3934"
+      "263/729 [=========>....................] - ETA: 6s - loss: 0.4549 - mae: 0.3950"
      ]
     },
     {
@@ -15203,7 +13268,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "269/729 [==========>...................] - ETA: 6s - loss: 0.4709 - mae: 0.3933"
+      "267/729 [=========>....................] - ETA: 6s - loss: 0.4577 - mae: 0.3945"
      ]
     },
     {
@@ -15211,7 +13276,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "273/729 [==========>...................] - ETA: 6s - loss: 0.4706 - mae: 0.3933"
+      "271/729 [==========>...................] - ETA: 6s - loss: 0.4552 - mae: 0.3937"
      ]
     },
     {
@@ -15219,7 +13284,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "277/729 [==========>...................] - ETA: 6s - loss: 0.4703 - mae: 0.3932"
+      "275/729 [==========>...................] - ETA: 6s - loss: 0.4547 - mae: 0.3934"
      ]
     },
     {
@@ -15227,7 +13292,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "281/729 [==========>...................] - ETA: 6s - loss: 0.4701 - mae: 0.3932"
+      "279/729 [==========>...................] - ETA: 6s - loss: 0.4526 - mae: 0.3927"
      ]
     },
     {
@@ -15235,7 +13300,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "285/729 [==========>...................] - ETA: 6s - loss: 0.4699 - mae: 0.3932"
+      "283/729 [==========>...................] - ETA: 6s - loss: 0.4515 - mae: 0.3926"
      ]
     },
     {
@@ -15243,7 +13308,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "289/729 [==========>...................] - ETA: 6s - loss: 0.4697 - mae: 0.3932"
+      "287/729 [==========>...................] - ETA: 6s - loss: 0.4516 - mae: 0.3927"
      ]
     },
     {
@@ -15251,7 +13316,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "293/729 [===========>..................] - ETA: 6s - loss: 0.4696 - mae: 0.3932"
+      "291/729 [==========>...................] - ETA: 6s - loss: 0.4663 - mae: 0.3938"
      ]
     },
     {
@@ -15259,7 +13324,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "297/729 [===========>..................] - ETA: 6s - loss: 0.4694 - mae: 0.3932"
+      "295/729 [===========>..................] - ETA: 6s - loss: 0.4637 - mae: 0.3931"
      ]
     },
     {
@@ -15267,7 +13332,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "301/729 [===========>..................] - ETA: 6s - loss: 0.4693 - mae: 0.3932"
+      "299/729 [===========>..................] - ETA: 6s - loss: 0.4611 - mae: 0.3923"
      ]
     },
     {
@@ -15275,7 +13340,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "305/729 [===========>..................] - ETA: 6s - loss: 0.4691 - mae: 0.3932"
+      "303/729 [===========>..................] - ETA: 6s - loss: 0.4600 - mae: 0.3920"
      ]
     },
     {
@@ -15283,7 +13348,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "309/729 [===========>..................] - ETA: 6s - loss: 0.4690 - mae: 0.3932"
+      "307/729 [===========>..................] - ETA: 6s - loss: 0.4579 - mae: 0.3917"
      ]
     },
     {
@@ -15291,7 +13356,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "313/729 [===========>..................] - ETA: 6s - loss: 0.4688 - mae: 0.3932"
+      "311/729 [===========>..................] - ETA: 6s - loss: 0.4590 - mae: 0.3922"
      ]
     },
     {
@@ -15299,7 +13364,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "317/729 [============>.................] - ETA: 6s - loss: 0.4686 - mae: 0.3932"
+      "315/729 [===========>..................] - ETA: 6s - loss: 0.4619 - mae: 0.3934"
      ]
     },
     {
@@ -15307,7 +13372,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "321/729 [============>.................] - ETA: 6s - loss: 0.4685 - mae: 0.3932"
+      "319/729 [============>.................] - ETA: 5s - loss: 0.4650 - mae: 0.3936"
      ]
     },
     {
@@ -15315,7 +13380,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "325/729 [============>.................] - ETA: 6s - loss: 0.4683 - mae: 0.3932"
+      "323/729 [============>.................] - ETA: 5s - loss: 0.4640 - mae: 0.3935"
      ]
     },
     {
@@ -15323,7 +13388,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "329/729 [============>.................] - ETA: 5s - loss: 0.4682 - mae: 0.3932"
+      "327/729 [============>.................] - ETA: 5s - loss: 0.4625 - mae: 0.3932"
      ]
     },
     {
@@ -15331,7 +13396,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "333/729 [============>.................] - ETA: 5s - loss: 0.4680 - mae: 0.3932"
+      "331/729 [============>.................] - ETA: 5s - loss: 0.4603 - mae: 0.3926"
      ]
     },
     {
@@ -15339,7 +13404,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "337/729 [============>.................] - ETA: 5s - loss: 0.4678 - mae: 0.3932"
+      "335/729 [============>.................] - ETA: 5s - loss: 0.4589 - mae: 0.3926"
      ]
     },
     {
@@ -15347,7 +13412,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "341/729 [=============>................] - ETA: 5s - loss: 0.4676 - mae: 0.3932"
+      "339/729 [============>.................] - ETA: 5s - loss: 0.4580 - mae: 0.3927"
      ]
     },
     {
@@ -15355,7 +13420,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "345/729 [=============>................] - ETA: 5s - loss: 0.4674 - mae: 0.3932"
+      "343/729 [=============>................] - ETA: 5s - loss: 0.4576 - mae: 0.3929"
      ]
     },
     {
@@ -15363,7 +13428,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "349/729 [=============>................] - ETA: 5s - loss: 0.4672 - mae: 0.3932"
+      "347/729 [=============>................] - ETA: 5s - loss: 0.4567 - mae: 0.3928"
      ]
     },
     {
@@ -15371,7 +13436,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "353/729 [=============>................] - ETA: 5s - loss: 0.4670 - mae: 0.3932"
+      "351/729 [=============>................] - ETA: 5s - loss: 0.4555 - mae: 0.3926"
      ]
     },
     {
@@ -15379,7 +13444,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "357/729 [=============>................] - ETA: 5s - loss: 0.4667 - mae: 0.3932"
+      "355/729 [=============>................] - ETA: 5s - loss: 0.4546 - mae: 0.3926"
      ]
     },
     {
@@ -15387,7 +13452,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "361/729 [=============>................] - ETA: 5s - loss: 0.4665 - mae: 0.3932"
+      "359/729 [=============>................] - ETA: 5s - loss: 0.4537 - mae: 0.3926"
      ]
     },
     {
@@ -15395,7 +13460,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "365/729 [==============>...............] - ETA: 5s - loss: 0.4662 - mae: 0.3932"
+      "363/729 [=============>................] - ETA: 5s - loss: 0.4578 - mae: 0.3931"
      ]
     },
     {
@@ -15403,7 +13468,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "369/729 [==============>...............] - ETA: 5s - loss: 0.4659 - mae: 0.3931"
+      "367/729 [==============>...............] - ETA: 5s - loss: 0.4566 - mae: 0.3931"
      ]
     },
     {
@@ -15411,7 +13476,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "373/729 [==============>...............] - ETA: 5s - loss: 0.4657 - mae: 0.3931"
+      "371/729 [==============>...............] - ETA: 5s - loss: 0.4548 - mae: 0.3926"
      ]
     },
     {
@@ -15419,7 +13484,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "377/729 [==============>...............] - ETA: 5s - loss: 0.4654 - mae: 0.3931"
+      "375/729 [==============>...............] - ETA: 5s - loss: 0.4531 - mae: 0.3922"
      ]
     },
     {
@@ -15427,7 +13492,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "381/729 [==============>...............] - ETA: 5s - loss: 0.4652 - mae: 0.3931"
+      "379/729 [==============>...............] - ETA: 5s - loss: 0.4518 - mae: 0.3919"
      ]
     },
     {
@@ -15435,7 +13500,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "385/729 [==============>...............] - ETA: 5s - loss: 0.4650 - mae: 0.3931"
+      "383/729 [==============>...............] - ETA: 5s - loss: 0.4525 - mae: 0.3923"
      ]
     },
     {
@@ -15443,7 +13508,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "389/729 [===============>..............] - ETA: 5s - loss: 0.4649 - mae: 0.3931"
+      "387/729 [==============>...............] - ETA: 4s - loss: 0.4515 - mae: 0.3922"
      ]
     },
     {
@@ -15451,7 +13516,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "393/729 [===============>..............] - ETA: 5s - loss: 0.4648 - mae: 0.3931"
+      "391/729 [===============>..............] - ETA: 4s - loss: 0.4496 - mae: 0.3917"
      ]
     },
     {
@@ -15459,7 +13524,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "397/729 [===============>..............] - ETA: 4s - loss: 0.4647 - mae: 0.3931"
+      "395/729 [===============>..............] - ETA: 4s - loss: 0.4483 - mae: 0.3915"
      ]
     },
     {
@@ -15467,7 +13532,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "401/729 [===============>..............] - ETA: 4s - loss: 0.4646 - mae: 0.3931"
+      "399/729 [===============>..............] - ETA: 4s - loss: 0.4475 - mae: 0.3911"
      ]
     },
     {
@@ -15475,7 +13540,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "405/729 [===============>..............] - ETA: 4s - loss: 0.4644 - mae: 0.3931"
+      "403/729 [===============>..............] - ETA: 4s - loss: 0.4473 - mae: 0.3915"
      ]
     },
     {
@@ -15483,7 +13548,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "409/729 [===============>..............] - ETA: 4s - loss: 0.4643 - mae: 0.3932"
+      "407/729 [===============>..............] - ETA: 4s - loss: 0.4522 - mae: 0.3917"
      ]
     },
     {
@@ -15491,7 +13556,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "413/729 [===============>..............] - ETA: 4s - loss: 0.4641 - mae: 0.3932"
+      "411/729 [===============>..............] - ETA: 4s - loss: 0.4504 - mae: 0.3911"
      ]
     },
     {
@@ -15499,7 +13564,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "417/729 [================>.............] - ETA: 4s - loss: 0.4640 - mae: 0.3932"
+      "415/729 [================>.............] - ETA: 4s - loss: 0.4512 - mae: 0.3916"
      ]
     },
     {
@@ -15507,7 +13572,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "421/729 [================>.............] - ETA: 4s - loss: 0.4639 - mae: 0.3932"
+      "419/729 [================>.............] - ETA: 4s - loss: 0.4511 - mae: 0.3918"
      ]
     },
     {
@@ -15515,7 +13580,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "425/729 [================>.............] - ETA: 4s - loss: 0.4637 - mae: 0.3932"
+      "423/729 [================>.............] - ETA: 4s - loss: 0.4507 - mae: 0.3920"
      ]
     },
     {
@@ -15523,7 +13588,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "429/729 [================>.............] - ETA: 4s - loss: 0.4636 - mae: 0.3932"
+      "427/729 [================>.............] - ETA: 4s - loss: 0.4512 - mae: 0.3923"
      ]
     },
     {
@@ -15531,7 +13596,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "433/729 [================>.............] - ETA: 4s - loss: 0.4635 - mae: 0.3932"
+      "431/729 [================>.............] - ETA: 4s - loss: 0.4515 - mae: 0.3927"
      ]
     },
     {
@@ -15539,7 +13604,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "437/729 [================>.............] - ETA: 4s - loss: 0.4634 - mae: 0.3932"
+      "435/729 [================>.............] - ETA: 4s - loss: 0.4550 - mae: 0.3941"
      ]
     },
     {
@@ -15547,7 +13612,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "441/729 [=================>............] - ETA: 4s - loss: 0.4633 - mae: 0.3932"
+      "439/729 [=================>............] - ETA: 4s - loss: 0.4536 - mae: 0.3938"
      ]
     },
     {
@@ -15555,7 +13620,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "445/729 [=================>............] - ETA: 4s - loss: 0.4632 - mae: 0.3932"
+      "443/729 [=================>............] - ETA: 4s - loss: 0.4520 - mae: 0.3934"
      ]
     },
     {
@@ -15563,7 +13628,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "449/729 [=================>............] - ETA: 4s - loss: 0.4630 - mae: 0.3933"
+      "447/729 [=================>............] - ETA: 4s - loss: 0.4554 - mae: 0.3937"
      ]
     },
     {
@@ -15571,7 +13636,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "453/729 [=================>............] - ETA: 4s - loss: 0.4629 - mae: 0.3933"
+      "451/729 [=================>............] - ETA: 4s - loss: 0.4556 - mae: 0.3937"
      ]
     },
     {
@@ -15579,7 +13644,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "457/729 [=================>............] - ETA: 4s - loss: 0.4628 - mae: 0.3933"
+      "455/729 [=================>............] - ETA: 3s - loss: 0.4587 - mae: 0.3945"
      ]
     },
     {
@@ -15587,7 +13652,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "461/729 [=================>............] - ETA: 3s - loss: 0.4626 - mae: 0.3933"
+      "459/729 [=================>............] - ETA: 3s - loss: 0.4570 - mae: 0.3939"
      ]
     },
     {
@@ -15595,7 +13660,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "465/729 [==================>...........] - ETA: 3s - loss: 0.4625 - mae: 0.3933"
+      "463/729 [==================>...........] - ETA: 3s - loss: 0.4596 - mae: 0.3939"
      ]
     },
     {
@@ -15603,7 +13668,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "469/729 [==================>...........] - ETA: 3s - loss: 0.4624 - mae: 0.3933"
+      "467/729 [==================>...........] - ETA: 3s - loss: 0.4587 - mae: 0.3939"
      ]
     },
     {
@@ -15611,7 +13676,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "473/729 [==================>...........] - ETA: 3s - loss: 0.4622 - mae: 0.3933"
+      "471/729 [==================>...........] - ETA: 3s - loss: 0.4573 - mae: 0.3934"
      ]
     },
     {
@@ -15619,7 +13684,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "477/729 [==================>...........] - ETA: 3s - loss: 0.4621 - mae: 0.3933"
+      "475/729 [==================>...........] - ETA: 3s - loss: 0.4562 - mae: 0.3932"
      ]
     },
     {
@@ -15627,7 +13692,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "481/729 [==================>...........] - ETA: 3s - loss: 0.4619 - mae: 0.3933"
+      "479/729 [==================>...........] - ETA: 3s - loss: 0.4548 - mae: 0.3928"
      ]
     },
     {
@@ -15635,7 +13700,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "485/729 [==================>...........] - ETA: 3s - loss: 0.4618 - mae: 0.3933"
+      "483/729 [==================>...........] - ETA: 3s - loss: 0.4550 - mae: 0.3929"
      ]
     },
     {
@@ -15643,7 +13708,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "489/729 [===================>..........] - ETA: 3s - loss: 0.4616 - mae: 0.3933"
+      "487/729 [===================>..........] - ETA: 3s - loss: 0.4560 - mae: 0.3929"
      ]
     },
     {
@@ -15651,7 +13716,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "493/729 [===================>..........] - ETA: 3s - loss: 0.4614 - mae: 0.3933"
+      "491/729 [===================>..........] - ETA: 3s - loss: 0.4564 - mae: 0.3931"
      ]
     },
     {
@@ -15659,7 +13724,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "497/729 [===================>..........] - ETA: 3s - loss: 0.4613 - mae: 0.3933"
+      "495/729 [===================>..........] - ETA: 3s - loss: 0.4553 - mae: 0.3929"
      ]
     },
     {
@@ -15667,7 +13732,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "501/729 [===================>..........] - ETA: 3s - loss: 0.4611 - mae: 0.3932"
+      "499/729 [===================>..........] - ETA: 3s - loss: 0.4537 - mae: 0.3924"
      ]
     },
     {
@@ -15675,7 +13740,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "505/729 [===================>..........] - ETA: 3s - loss: 0.4610 - mae: 0.3932"
+      "503/729 [===================>..........] - ETA: 3s - loss: 0.4528 - mae: 0.3921"
      ]
     },
     {
@@ -15683,7 +13748,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "509/729 [===================>..........] - ETA: 3s - loss: 0.4608 - mae: 0.3932"
+      "507/729 [===================>..........] - ETA: 3s - loss: 0.4532 - mae: 0.3924"
      ]
     },
     {
@@ -15691,7 +13756,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "513/729 [====================>.........] - ETA: 3s - loss: 0.4607 - mae: 0.3932"
+      "511/729 [====================>.........] - ETA: 3s - loss: 0.4518 - mae: 0.3920"
      ]
     },
     {
@@ -15699,7 +13764,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "517/729 [====================>.........] - ETA: 3s - loss: 0.4605 - mae: 0.3932"
+      "515/729 [====================>.........] - ETA: 3s - loss: 0.4518 - mae: 0.3920"
      ]
     },
     {
@@ -15707,7 +13772,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "521/729 [====================>.........] - ETA: 3s - loss: 0.4604 - mae: 0.3932"
+      "519/729 [====================>.........] - ETA: 3s - loss: 0.4502 - mae: 0.3914"
      ]
     },
     {
@@ -15715,7 +13780,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "525/729 [====================>.........] - ETA: 3s - loss: 0.4602 - mae: 0.3932"
+      "523/729 [====================>.........] - ETA: 2s - loss: 0.4500 - mae: 0.3916"
      ]
     },
     {
@@ -15723,7 +13788,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "529/729 [====================>.........] - ETA: 2s - loss: 0.4601 - mae: 0.3932"
+      "527/729 [====================>.........] - ETA: 2s - loss: 0.4508 - mae: 0.3919"
      ]
     },
     {
@@ -15731,7 +13796,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "533/729 [====================>.........] - ETA: 2s - loss: 0.4600 - mae: 0.3931"
+      "531/729 [====================>.........] - ETA: 2s - loss: 0.4496 - mae: 0.3915"
      ]
     },
     {
@@ -15739,7 +13804,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "537/729 [=====================>........] - ETA: 2s - loss: 0.4599 - mae: 0.3931"
+      "535/729 [=====================>........] - ETA: 2s - loss: 0.4485 - mae: 0.3912"
      ]
     },
     {
@@ -15747,7 +13812,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "541/729 [=====================>........] - ETA: 2s - loss: 0.4598 - mae: 0.3931"
+      "539/729 [=====================>........] - ETA: 2s - loss: 0.4476 - mae: 0.3910"
      ]
     },
     {
@@ -15755,7 +13820,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "545/729 [=====================>........] - ETA: 2s - loss: 0.4596 - mae: 0.3931"
+      "543/729 [=====================>........] - ETA: 2s - loss: 0.4479 - mae: 0.3913"
      ]
     },
     {
@@ -15763,7 +13828,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "549/729 [=====================>........] - ETA: 2s - loss: 0.4595 - mae: 0.3931"
+      "547/729 [=====================>........] - ETA: 2s - loss: 0.4474 - mae: 0.3912"
      ]
     },
     {
@@ -15771,7 +13836,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "553/729 [=====================>........] - ETA: 2s - loss: 0.4594 - mae: 0.3931"
+      "551/729 [=====================>........] - ETA: 2s - loss: 0.4462 - mae: 0.3909"
      ]
     },
     {
@@ -15779,7 +13844,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "557/729 [=====================>........] - ETA: 2s - loss: 0.4593 - mae: 0.3931"
+      "555/729 [=====================>........] - ETA: 2s - loss: 0.4447 - mae: 0.3904"
      ]
     },
     {
@@ -15787,7 +13852,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "561/729 [======================>.......] - ETA: 2s - loss: 0.4591 - mae: 0.3931"
+      "559/729 [======================>.......] - ETA: 2s - loss: 0.4439 - mae: 0.3901"
      ]
     },
     {
@@ -15795,7 +13860,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "565/729 [======================>.......] - ETA: 2s - loss: 0.4590 - mae: 0.3931"
+      "563/729 [======================>.......] - ETA: 2s - loss: 0.4443 - mae: 0.3904"
      ]
     },
     {
@@ -15803,7 +13868,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "569/729 [======================>.......] - ETA: 2s - loss: 0.4589 - mae: 0.3931"
+      "567/729 [======================>.......] - ETA: 2s - loss: 0.4438 - mae: 0.3904"
      ]
     },
     {
@@ -15811,7 +13876,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "573/729 [======================>.......] - ETA: 2s - loss: 0.4587 - mae: 0.3930"
+      "571/729 [======================>.......] - ETA: 2s - loss: 0.4432 - mae: 0.3903"
      ]
     },
     {
@@ -15819,7 +13884,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "577/729 [======================>.......] - ETA: 2s - loss: 0.4586 - mae: 0.3930"
+      "575/729 [======================>.......] - ETA: 2s - loss: 0.4490 - mae: 0.3906"
      ]
     },
     {
@@ -15827,7 +13892,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "581/729 [======================>.......] - ETA: 2s - loss: 0.4585 - mae: 0.3930"
+      "579/729 [======================>.......] - ETA: 2s - loss: 0.4479 - mae: 0.3902"
      ]
     },
     {
@@ -15835,7 +13900,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "585/729 [=======================>......] - ETA: 2s - loss: 0.4584 - mae: 0.3930"
+      "583/729 [======================>.......] - ETA: 2s - loss: 0.4475 - mae: 0.3903"
      ]
     },
     {
@@ -15843,7 +13908,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "589/729 [=======================>......] - ETA: 2s - loss: 0.4582 - mae: 0.3930"
+      "587/729 [=======================>......] - ETA: 2s - loss: 0.4462 - mae: 0.3898"
      ]
     },
     {
@@ -15851,7 +13916,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "593/729 [=======================>......] - ETA: 2s - loss: 0.4581 - mae: 0.3930"
+      "591/729 [=======================>......] - ETA: 1s - loss: 0.4451 - mae: 0.3894"
      ]
     },
     {
@@ -15859,7 +13924,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "597/729 [=======================>......] - ETA: 1s - loss: 0.4579 - mae: 0.3930"
+      "595/729 [=======================>......] - ETA: 1s - loss: 0.4440 - mae: 0.3891"
      ]
     },
     {
@@ -15867,7 +13932,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "601/729 [=======================>......] - ETA: 1s - loss: 0.4578 - mae: 0.3929"
+      "599/729 [=======================>......] - ETA: 1s - loss: 0.4436 - mae: 0.3890"
      ]
     },
     {
@@ -15875,7 +13940,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "605/729 [=======================>......] - ETA: 1s - loss: 0.4577 - mae: 0.3929"
+      "603/729 [=======================>......] - ETA: 1s - loss: 0.4431 - mae: 0.3890"
      ]
     },
     {
@@ -15883,7 +13948,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "609/729 [========================>.....] - ETA: 1s - loss: 0.4576 - mae: 0.3929"
+      "607/729 [=======================>......] - ETA: 1s - loss: 0.4418 - mae: 0.3886"
      ]
     },
     {
@@ -15891,7 +13956,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "613/729 [========================>.....] - ETA: 1s - loss: 0.4574 - mae: 0.3929"
+      "611/729 [========================>.....] - ETA: 1s - loss: 0.4407 - mae: 0.3882"
      ]
     },
     {
@@ -15899,7 +13964,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "617/729 [========================>.....] - ETA: 1s - loss: 0.4573 - mae: 0.3929"
+      "615/729 [========================>.....] - ETA: 1s - loss: 0.4404 - mae: 0.3882"
      ]
     },
     {
@@ -15907,7 +13972,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "621/729 [========================>.....] - ETA: 1s - loss: 0.4572 - mae: 0.3929"
+      "619/729 [========================>.....] - ETA: 1s - loss: 0.4398 - mae: 0.3881"
      ]
     },
     {
@@ -15915,7 +13980,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "625/729 [========================>.....] - ETA: 1s - loss: 0.4571 - mae: 0.3929"
+      "623/729 [========================>.....] - ETA: 1s - loss: 0.4387 - mae: 0.3878"
      ]
     },
     {
@@ -15923,7 +13988,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "629/729 [========================>.....] - ETA: 1s - loss: 0.4570 - mae: 0.3928"
+      "627/729 [========================>.....] - ETA: 1s - loss: 0.4381 - mae: 0.3877"
      ]
     },
     {
@@ -15931,7 +13996,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "633/729 [=========================>....] - ETA: 1s - loss: 0.4569 - mae: 0.3928"
+      "631/729 [========================>.....] - ETA: 1s - loss: 0.4380 - mae: 0.3879"
      ]
     },
     {
@@ -15939,7 +14004,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "637/729 [=========================>....] - ETA: 1s - loss: 0.4568 - mae: 0.3928"
+      "635/729 [=========================>....] - ETA: 1s - loss: 0.4379 - mae: 0.3882"
      ]
     },
     {
@@ -15947,7 +14012,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "641/729 [=========================>....] - ETA: 1s - loss: 0.4567 - mae: 0.3928"
+      "639/729 [=========================>....] - ETA: 1s - loss: 0.4370 - mae: 0.3879"
      ]
     },
     {
@@ -15955,7 +14020,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "645/729 [=========================>....] - ETA: 1s - loss: 0.4566 - mae: 0.3928"
+      "643/729 [=========================>....] - ETA: 1s - loss: 0.4365 - mae: 0.3876"
      ]
     },
     {
@@ -15963,7 +14028,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "649/729 [=========================>....] - ETA: 1s - loss: 0.4564 - mae: 0.3928"
+      "647/729 [=========================>....] - ETA: 1s - loss: 0.4359 - mae: 0.3875"
      ]
     },
     {
@@ -15971,7 +14036,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "653/729 [=========================>....] - ETA: 1s - loss: 0.4563 - mae: 0.3928"
+      "651/729 [=========================>....] - ETA: 1s - loss: 0.4355 - mae: 0.3875"
      ]
     },
     {
@@ -15979,7 +14044,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "657/729 [==========================>...] - ETA: 1s - loss: 0.4562 - mae: 0.3928"
+      "655/729 [=========================>....] - ETA: 1s - loss: 0.4352 - mae: 0.3874"
      ]
     },
     {
@@ -15987,7 +14052,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "661/729 [==========================>...] - ETA: 1s - loss: 0.4561 - mae: 0.3928"
+      "659/729 [==========================>...] - ETA: 1s - loss: 0.4345 - mae: 0.3871"
      ]
     },
     {
@@ -15995,7 +14060,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "665/729 [==========================>...] - ETA: 0s - loss: 0.4561 - mae: 0.3927"
+      "663/729 [==========================>...] - ETA: 0s - loss: 0.4342 - mae: 0.3870"
      ]
     },
     {
@@ -16003,7 +14068,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "669/729 [==========================>...] - ETA: 0s - loss: 0.4560 - mae: 0.3927"
+      "667/729 [==========================>...] - ETA: 0s - loss: 0.4340 - mae: 0.3870"
      ]
     },
     {
@@ -16011,7 +14076,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "673/729 [==========================>...] - ETA: 0s - loss: 0.4559 - mae: 0.3927"
+      "671/729 [==========================>...] - ETA: 0s - loss: 0.4356 - mae: 0.3872"
      ]
     },
     {
@@ -16019,7 +14084,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "677/729 [==========================>...] - ETA: 0s - loss: 0.4558 - mae: 0.3927"
+      "675/729 [==========================>...] - ETA: 0s - loss: 0.4348 - mae: 0.3871"
      ]
     },
     {
@@ -16027,7 +14092,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "681/729 [===========================>..] - ETA: 0s - loss: 0.4557 - mae: 0.3927"
+      "679/729 [==========================>...] - ETA: 0s - loss: 0.4359 - mae: 0.3874"
      ]
     },
     {
@@ -16035,7 +14100,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "685/729 [===========================>..] - ETA: 0s - loss: 0.4557 - mae: 0.3927"
+      "683/729 [===========================>..] - ETA: 0s - loss: 0.4359 - mae: 0.3877"
      ]
     },
     {
@@ -16043,7 +14108,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "689/729 [===========================>..] - ETA: 0s - loss: 0.4556 - mae: 0.3927"
+      "687/729 [===========================>..] - ETA: 0s - loss: 0.4376 - mae: 0.3879"
      ]
     },
     {
@@ -16051,7 +14116,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "693/729 [===========================>..] - ETA: 0s - loss: 0.4555 - mae: 0.3927"
+      "691/729 [===========================>..] - ETA: 0s - loss: 0.4385 - mae: 0.3884"
      ]
     },
     {
@@ -16059,7 +14124,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "697/729 [===========================>..] - ETA: 0s - loss: 0.4554 - mae: 0.3927"
+      "695/729 [===========================>..] - ETA: 0s - loss: 0.4383 - mae: 0.3883"
      ]
     },
     {
@@ -16067,7 +14132,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "701/729 [===========================>..] - ETA: 0s - loss: 0.4553 - mae: 0.3927"
+      "699/729 [===========================>..] - ETA: 0s - loss: 0.4373 - mae: 0.3880"
      ]
     },
     {
@@ -16075,7 +14140,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "705/729 [============================>.] - ETA: 0s - loss: 0.4552 - mae: 0.3926"
+      "703/729 [===========================>..] - ETA: 0s - loss: 0.4364 - mae: 0.3878"
      ]
     },
     {
@@ -16083,7 +14148,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "709/729 [============================>.] - ETA: 0s - loss: 0.4551 - mae: 0.3926"
+      "707/729 [============================>.] - ETA: 0s - loss: 0.4360 - mae: 0.3878"
      ]
     },
     {
@@ -16091,7 +14156,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "713/729 [============================>.] - ETA: 0s - loss: 0.4551 - mae: 0.3926"
+      "711/729 [============================>.] - ETA: 0s - loss: 0.4353 - mae: 0.3877"
      ]
     },
     {
@@ -16099,7 +14164,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "717/729 [============================>.] - ETA: 0s - loss: 0.4550 - mae: 0.3926"
+      "715/729 [============================>.] - ETA: 0s - loss: 0.4350 - mae: 0.3877"
      ]
     },
     {
@@ -16107,7 +14172,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "721/729 [============================>.] - ETA: 0s - loss: 0.4549 - mae: 0.3926"
+      "719/729 [============================>.] - ETA: 0s - loss: 0.4354 - mae: 0.3880"
      ]
     },
     {
@@ -16115,7 +14180,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "725/729 [============================>.] - ETA: 0s - loss: 0.4548 - mae: 0.3926"
+      "723/729 [============================>.] - ETA: 0s - loss: 0.4356 - mae: 0.3882"
      ]
     },
     {
@@ -16123,7 +14188,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "729/729 [==============================] - ETA: 0s - loss: 0.4547 - mae: 0.3926"
+      "727/729 [============================>.] - ETA: 0s - loss: 0.4361 - mae: 0.3885"
      ]
     },
     {
@@ -16131,7 +14196,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "729/729 [==============================] - 11s 16ms/step - loss: 0.4547 - mae: 0.3926 - val_loss: 0.4251 - val_mae: 0.3694\n"
+      "729/729 [==============================] - 11s 16ms/step - loss: 0.4358 - mae: 0.3884 - val_loss: 0.4268 - val_mae: 0.3690\n"
      ]
     },
     {
@@ -16140,127 +14205,127 @@
      "text": [
       "Epoch 10/10\n",
       "\r",
-      "  1/729 [..............................] - ETA: 27s - loss: 0.2605 - mae: 0.3381"
+      "  1/729 [..............................] - ETA: 0s - loss: 0.2139 - mae: 0.3216"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  5/729 [..............................] - ETA: 10s - loss: 0.3488 - mae: 0.3756"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  5/729 [..............................] - ETA: 7s - loss: 0.5691 - mae: 0.4183"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "  9/729 [..............................] - ETA: 10s - loss: 0.3828 - mae: 0.3871"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      "  9/729 [..............................] - ETA: 8s - loss: 0.4839 - mae: 0.4022"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 13/729 [..............................] - ETA: 10s - loss: 0.4315 - mae: 0.3971"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 13/729 [..............................] - ETA: 9s - loss: 0.4521 - mae: 0.4000"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 17/729 [..............................] - ETA: 10s - loss: 0.4564 - mae: 0.4036"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 17/729 [..............................] - ETA: 9s - loss: 0.4866 - mae: 0.4137"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 21/729 [..............................] - ETA: 10s - loss: 0.4736 - mae: 0.4086"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 21/729 [..............................] - ETA: 9s - loss: 0.4945 - mae: 0.4076"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 25/729 [>.............................] - ETA: 10s - loss: 0.4779 - mae: 0.4099"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 25/729 [>.............................] - ETA: 9s - loss: 0.4725 - mae: 0.4063"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 29/729 [>.............................] - ETA: 10s - loss: 0.4825 - mae: 0.4100"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 29/729 [>.............................] - ETA: 9s - loss: 0.4505 - mae: 0.4029"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 33/729 [>.............................] - ETA: 10s - loss: 0.4857 - mae: 0.4100"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 33/729 [>.............................] - ETA: 9s - loss: 0.4483 - mae: 0.4059"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 37/729 [>.............................] - ETA: 10s - loss: 0.4854 - mae: 0.4093"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 37/729 [>.............................] - ETA: 9s - loss: 0.4529 - mae: 0.4036"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 41/729 [>.............................] - ETA: 10s - loss: 0.4844 - mae: 0.4088"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 41/729 [>.............................] - ETA: 9s - loss: 0.4561 - mae: 0.4079"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 45/729 [>.............................] - ETA: 10s - loss: 0.4838 - mae: 0.4089"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 45/729 [>.............................] - ETA: 9s - loss: 0.4628 - mae: 0.4077"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 49/729 [=>............................] - ETA: 10s - loss: 0.4837 - mae: 0.4092"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 49/729 [=>............................] - ETA: 9s - loss: 0.4631 - mae: 0.4106"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 53/729 [=>............................] - ETA: 10s - loss: 0.4832 - mae: 0.4093"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 53/729 [=>............................] - ETA: 9s - loss: 0.4511 - mae: 0.4071"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 57/729 [=>............................] - ETA: 10s - loss: 0.4824 - mae: 0.4093"
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 57/729 [=>............................] - ETA: 9s - loss: 0.4398 - mae: 0.4039"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 61/729 [=>............................] - ETA: 9s - loss: 0.4810 - mae: 0.4091 "
+      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+      " 61/729 [=>............................] - ETA: 9s - loss: 0.4297 - mae: 0.3993"
      ]
     },
     {
@@ -16268,7 +14333,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 65/729 [=>............................] - ETA: 9s - loss: 0.4801 - mae: 0.4089"
+      " 65/729 [=>............................] - ETA: 9s - loss: 0.4299 - mae: 0.3997"
      ]
     },
     {
@@ -16276,7 +14341,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 69/729 [=>............................] - ETA: 9s - loss: 0.4788 - mae: 0.4086"
+      " 69/729 [=>............................] - ETA: 9s - loss: 0.4260 - mae: 0.3975"
      ]
     },
     {
@@ -16284,7 +14349,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 73/729 [==>...........................] - ETA: 9s - loss: 0.4773 - mae: 0.4082"
+      " 73/729 [==>...........................] - ETA: 9s - loss: 0.4188 - mae: 0.3940"
      ]
     },
     {
@@ -16292,7 +14357,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 77/729 [==>...........................] - ETA: 9s - loss: 0.4758 - mae: 0.4078"
+      " 77/729 [==>...........................] - ETA: 9s - loss: 0.4269 - mae: 0.3965"
      ]
     },
     {
@@ -16300,7 +14365,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 81/729 [==>...........................] - ETA: 9s - loss: 0.4747 - mae: 0.4076"
+      " 81/729 [==>...........................] - ETA: 8s - loss: 0.4231 - mae: 0.3959"
      ]
     },
     {
@@ -16308,7 +14373,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 85/729 [==>...........................] - ETA: 9s - loss: 0.4734 - mae: 0.4073"
+      " 85/729 [==>...........................] - ETA: 8s - loss: 0.4155 - mae: 0.3927"
      ]
     },
     {
@@ -16316,7 +14381,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 89/729 [==>...........................] - ETA: 9s - loss: 0.4719 - mae: 0.4069"
+      " 89/729 [==>...........................] - ETA: 8s - loss: 0.4075 - mae: 0.3894"
      ]
     },
     {
@@ -16324,7 +14389,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 93/729 [==>...........................] - ETA: 9s - loss: 0.4701 - mae: 0.4064"
+      " 93/729 [==>...........................] - ETA: 8s - loss: 0.4040 - mae: 0.3889"
      ]
     },
     {
@@ -16332,7 +14397,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      " 97/729 [==>...........................] - ETA: 9s - loss: 0.4684 - mae: 0.4059"
+      " 97/729 [==>...........................] - ETA: 8s - loss: 0.3998 - mae: 0.3871"
      ]
     },
     {
@@ -16340,7 +14405,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "101/729 [===>..........................] - ETA: 9s - loss: 0.4667 - mae: 0.4054"
+      "101/729 [===>..........................] - ETA: 8s - loss: 0.3989 - mae: 0.3866"
      ]
     },
     {
@@ -16348,7 +14413,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "105/729 [===>..........................] - ETA: 9s - loss: 0.4650 - mae: 0.4048"
+      "105/729 [===>..........................] - ETA: 8s - loss: 0.4121 - mae: 0.3888"
      ]
     },
     {
@@ -16356,7 +14421,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "109/729 [===>..........................] - ETA: 9s - loss: 0.4634 - mae: 0.4043"
+      "109/729 [===>..........................] - ETA: 8s - loss: 0.4199 - mae: 0.3899"
      ]
     },
     {
@@ -16364,7 +14429,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "113/729 [===>..........................] - ETA: 9s - loss: 0.4619 - mae: 0.4037"
+      "113/729 [===>..........................] - ETA: 8s - loss: 0.4697 - mae: 0.3945"
      ]
     },
     {
@@ -16372,7 +14437,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "117/729 [===>..........................] - ETA: 9s - loss: 0.4612 - mae: 0.4032"
+      "117/729 [===>..........................] - ETA: 8s - loss: 0.4657 - mae: 0.3945"
      ]
     },
     {
@@ -16380,7 +14445,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "121/729 [===>..........................] - ETA: 9s - loss: 0.4609 - mae: 0.4029"
+      "121/729 [===>..........................] - ETA: 8s - loss: 0.4631 - mae: 0.3937"
      ]
     },
     {
@@ -16388,7 +14453,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "125/729 [====>.........................] - ETA: 9s - loss: 0.4607 - mae: 0.4027"
+      "125/729 [====>.........................] - ETA: 8s - loss: 0.4628 - mae: 0.3930"
      ]
     },
     {
@@ -16396,7 +14461,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "129/729 [====>.........................] - ETA: 8s - loss: 0.4607 - mae: 0.4025"
+      "129/729 [====>.........................] - ETA: 8s - loss: 0.4755 - mae: 0.3950"
      ]
     },
     {
@@ -16404,7 +14469,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "133/729 [====>.........................] - ETA: 8s - loss: 0.4608 - mae: 0.4023"
+      "133/729 [====>.........................] - ETA: 8s - loss: 0.4727 - mae: 0.3942"
      ]
     },
     {
@@ -16412,7 +14477,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "137/729 [====>.........................] - ETA: 8s - loss: 0.4610 - mae: 0.4021"
+      "137/729 [====>.........................] - ETA: 8s - loss: 0.4845 - mae: 0.3975"
      ]
     },
     {
@@ -16420,7 +14485,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "141/729 [====>.........................] - ETA: 8s - loss: 0.4610 - mae: 0.4019"
+      "141/729 [====>.........................] - ETA: 8s - loss: 0.4811 - mae: 0.3972"
      ]
     },
     {
@@ -16428,7 +14493,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "145/729 [====>.........................] - ETA: 8s - loss: 0.4608 - mae: 0.4017"
+      "145/729 [====>.........................] - ETA: 8s - loss: 0.4775 - mae: 0.3973"
      ]
     },
     {
@@ -16436,7 +14501,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "149/729 [=====>........................] - ETA: 8s - loss: 0.4606 - mae: 0.4015"
+      "149/729 [=====>........................] - ETA: 8s - loss: 0.4750 - mae: 0.3971"
      ]
     },
     {
@@ -16444,7 +14509,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "153/729 [=====>........................] - ETA: 8s - loss: 0.4603 - mae: 0.4012"
+      "153/729 [=====>........................] - ETA: 8s - loss: 0.4898 - mae: 0.3979"
      ]
     },
     {
@@ -16452,7 +14517,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "157/729 [=====>........................] - ETA: 8s - loss: 0.4599 - mae: 0.4010"
+      "157/729 [=====>........................] - ETA: 8s - loss: 0.4875 - mae: 0.3973"
      ]
     },
     {
@@ -16460,7 +14525,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "161/729 [=====>........................] - ETA: 8s - loss: 0.4595 - mae: 0.4007"
+      "161/729 [=====>........................] - ETA: 7s - loss: 0.4818 - mae: 0.3955"
      ]
     },
     {
@@ -16468,7 +14533,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "165/729 [=====>........................] - ETA: 8s - loss: 0.4591 - mae: 0.4005"
+      "165/729 [=====>........................] - ETA: 7s - loss: 0.4831 - mae: 0.3957"
      ]
     },
     {
@@ -16476,7 +14541,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "169/729 [=====>........................] - ETA: 8s - loss: 0.4587 - mae: 0.4003"
+      "169/729 [=====>........................] - ETA: 7s - loss: 0.5048 - mae: 0.3969"
      ]
     },
     {
@@ -16484,7 +14549,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "173/729 [======>.......................] - ETA: 8s - loss: 0.4583 - mae: 0.4001"
+      "173/729 [======>.......................] - ETA: 7s - loss: 0.5042 - mae: 0.3972"
      ]
     },
     {
@@ -16492,7 +14557,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "177/729 [======>.......................] - ETA: 8s - loss: 0.4580 - mae: 0.3999"
+      "177/729 [======>.......................] - ETA: 7s - loss: 0.4994 - mae: 0.3968"
      ]
     },
     {
@@ -16500,7 +14565,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "181/729 [======>.......................] - ETA: 8s - loss: 0.4575 - mae: 0.3997"
+      "181/729 [======>.......................] - ETA: 7s - loss: 0.4950 - mae: 0.3963"
      ]
     },
     {
@@ -16508,7 +14573,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "185/729 [======>.......................] - ETA: 8s - loss: 0.4571 - mae: 0.3995"
+      "185/729 [======>.......................] - ETA: 7s - loss: 0.4909 - mae: 0.3955"
      ]
     },
     {
@@ -16516,7 +14581,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "189/729 [======>.......................] - ETA: 8s - loss: 0.4567 - mae: 0.3994"
+      "189/729 [======>.......................] - ETA: 7s - loss: 0.4880 - mae: 0.3955"
      ]
     },
     {
@@ -16524,7 +14589,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "193/729 [======>.......................] - ETA: 7s - loss: 0.4563 - mae: 0.3992"
+      "193/729 [======>.......................] - ETA: 7s - loss: 0.4874 - mae: 0.3955"
      ]
     },
     {
@@ -16532,7 +14597,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "197/729 [=======>......................] - ETA: 7s - loss: 0.4560 - mae: 0.3990"
+      "197/729 [=======>......................] - ETA: 7s - loss: 0.4847 - mae: 0.3954"
      ]
     },
     {
@@ -16540,7 +14605,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "201/729 [=======>......................] - ETA: 7s - loss: 0.4556 - mae: 0.3989"
+      "201/729 [=======>......................] - ETA: 7s - loss: 0.4912 - mae: 0.3959"
      ]
     },
     {
@@ -16548,7 +14613,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "205/729 [=======>......................] - ETA: 7s - loss: 0.4554 - mae: 0.3987"
+      "205/729 [=======>......................] - ETA: 7s - loss: 0.4883 - mae: 0.3953"
      ]
     },
     {
@@ -16556,7 +14621,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "209/729 [=======>......................] - ETA: 7s - loss: 0.4555 - mae: 0.3986"
+      "209/729 [=======>......................] - ETA: 7s - loss: 0.4850 - mae: 0.3951"
      ]
     },
     {
@@ -16564,7 +14629,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "213/729 [=======>......................] - ETA: 7s - loss: 0.4557 - mae: 0.3985"
+      "213/729 [=======>......................] - ETA: 7s - loss: 0.4835 - mae: 0.3950"
      ]
     },
     {
@@ -16572,7 +14637,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "217/729 [=======>......................] - ETA: 7s - loss: 0.4559 - mae: 0.3983"
+      "217/729 [=======>......................] - ETA: 7s - loss: 0.4817 - mae: 0.3951"
      ]
     },
     {
@@ -16580,7 +14645,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "221/729 [========>.....................] - ETA: 7s - loss: 0.4560 - mae: 0.3982"
+      "221/729 [========>.....................] - ETA: 7s - loss: 0.4785 - mae: 0.3945"
      ]
     },
     {
@@ -16588,7 +14653,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "225/729 [========>.....................] - ETA: 7s - loss: 0.4562 - mae: 0.3981"
+      "225/729 [========>.....................] - ETA: 7s - loss: 0.4745 - mae: 0.3934"
      ]
     },
     {
@@ -16596,7 +14661,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "229/729 [========>.....................] - ETA: 7s - loss: 0.4563 - mae: 0.3980"
+      "229/729 [========>.....................] - ETA: 7s - loss: 0.4732 - mae: 0.3937"
      ]
     },
     {
@@ -16604,7 +14669,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "233/729 [========>.....................] - ETA: 7s - loss: 0.4564 - mae: 0.3979"
+      "233/729 [========>.....................] - ETA: 6s - loss: 0.4717 - mae: 0.3935"
      ]
     },
     {
@@ -16612,7 +14677,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "237/729 [========>.....................] - ETA: 7s - loss: 0.4565 - mae: 0.3978"
+      "237/729 [========>.....................] - ETA: 6s - loss: 0.4706 - mae: 0.3936"
      ]
     },
     {
@@ -16620,7 +14685,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "241/729 [========>.....................] - ETA: 7s - loss: 0.4566 - mae: 0.3977"
+      "241/729 [========>.....................] - ETA: 6s - loss: 0.4690 - mae: 0.3937"
      ]
     },
     {
@@ -16628,7 +14693,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "245/729 [=========>....................] - ETA: 7s - loss: 0.4567 - mae: 0.3977"
+      "245/729 [=========>....................] - ETA: 6s - loss: 0.4749 - mae: 0.3949"
      ]
     },
     {
@@ -16636,7 +14701,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "249/729 [=========>....................] - ETA: 7s - loss: 0.4568 - mae: 0.3976"
+      "249/729 [=========>....................] - ETA: 6s - loss: 0.4720 - mae: 0.3944"
      ]
     },
     {
@@ -16644,7 +14709,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "253/729 [=========>....................] - ETA: 7s - loss: 0.4568 - mae: 0.3975"
+      "253/729 [=========>....................] - ETA: 6s - loss: 0.4687 - mae: 0.3935"
      ]
     },
     {
@@ -16652,7 +14717,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "257/729 [=========>....................] - ETA: 7s - loss: 0.4569 - mae: 0.3975"
+      "257/729 [=========>....................] - ETA: 6s - loss: 0.4672 - mae: 0.3936"
      ]
     },
     {
@@ -16660,7 +14725,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "261/729 [=========>....................] - ETA: 6s - loss: 0.4569 - mae: 0.3974"
+      "261/729 [=========>....................] - ETA: 6s - loss: 0.4660 - mae: 0.3935"
      ]
     },
     {
@@ -16668,7 +14733,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "265/729 [=========>....................] - ETA: 6s - loss: 0.4569 - mae: 0.3973"
+      "265/729 [=========>....................] - ETA: 6s - loss: 0.4638 - mae: 0.3936"
      ]
     },
     {
@@ -16676,7 +14741,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "269/729 [==========>...................] - ETA: 6s - loss: 0.4570 - mae: 0.3973"
+      "269/729 [==========>...................] - ETA: 6s - loss: 0.4628 - mae: 0.3935"
      ]
     },
     {
@@ -16684,7 +14749,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "273/729 [==========>...................] - ETA: 6s - loss: 0.4569 - mae: 0.3972"
+      "273/729 [==========>...................] - ETA: 6s - loss: 0.4631 - mae: 0.3933"
      ]
     },
     {
@@ -16692,7 +14757,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "277/729 [==========>...................] - ETA: 6s - loss: 0.4569 - mae: 0.3971"
+      "277/729 [==========>...................] - ETA: 6s - loss: 0.4613 - mae: 0.3931"
      ]
     },
     {
@@ -16700,7 +14765,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "281/729 [==========>...................] - ETA: 6s - loss: 0.4568 - mae: 0.3971"
+      "281/729 [==========>...................] - ETA: 6s - loss: 0.4588 - mae: 0.3925"
      ]
     },
     {
@@ -16708,7 +14773,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "285/729 [==========>...................] - ETA: 6s - loss: 0.4567 - mae: 0.3970"
+      "285/729 [==========>...................] - ETA: 6s - loss: 0.4601 - mae: 0.3925"
      ]
     },
     {
@@ -16716,7 +14781,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "289/729 [==========>...................] - ETA: 6s - loss: 0.4566 - mae: 0.3969"
+      "289/729 [==========>...................] - ETA: 6s - loss: 0.4617 - mae: 0.3926"
      ]
     },
     {
@@ -16724,7 +14789,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "293/729 [===========>..................] - ETA: 6s - loss: 0.4565 - mae: 0.3968"
+      "293/729 [===========>..................] - ETA: 6s - loss: 0.4618 - mae: 0.3933"
      ]
     },
     {
@@ -16732,7 +14797,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "297/729 [===========>..................] - ETA: 6s - loss: 0.4564 - mae: 0.3968"
+      "297/729 [===========>..................] - ETA: 6s - loss: 0.4606 - mae: 0.3929"
      ]
     },
     {
@@ -16740,7 +14805,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "301/729 [===========>..................] - ETA: 6s - loss: 0.4562 - mae: 0.3967"
+      "301/729 [===========>..................] - ETA: 6s - loss: 0.4579 - mae: 0.3920"
      ]
     },
     {
@@ -16748,7 +14813,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "305/729 [===========>..................] - ETA: 6s - loss: 0.4561 - mae: 0.3966"
+      "305/729 [===========>..................] - ETA: 5s - loss: 0.4579 - mae: 0.3927"
      ]
     },
     {
@@ -16756,7 +14821,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "309/729 [===========>..................] - ETA: 6s - loss: 0.4560 - mae: 0.3965"
+      "309/729 [===========>..................] - ETA: 5s - loss: 0.4571 - mae: 0.3927"
      ]
     },
     {
@@ -16764,7 +14829,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "313/729 [===========>..................] - ETA: 6s - loss: 0.4558 - mae: 0.3964"
+      "313/729 [===========>..................] - ETA: 5s - loss: 0.4582 - mae: 0.3932"
      ]
     },
     {
@@ -16772,7 +14837,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "317/729 [============>.................] - ETA: 6s - loss: 0.4556 - mae: 0.3964"
+      "317/729 [============>.................] - ETA: 5s - loss: 0.4620 - mae: 0.3943"
      ]
     },
     {
@@ -16780,7 +14845,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "321/729 [============>.................] - ETA: 6s - loss: 0.4554 - mae: 0.3963"
+      "321/729 [============>.................] - ETA: 5s - loss: 0.4601 - mae: 0.3938"
      ]
     },
     {
@@ -16788,7 +14853,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "325/729 [============>.................] - ETA: 6s - loss: 0.4553 - mae: 0.3962"
+      "325/729 [============>.................] - ETA: 5s - loss: 0.4578 - mae: 0.3931"
      ]
     },
     {
@@ -16796,7 +14861,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "329/729 [============>.................] - ETA: 5s - loss: 0.4551 - mae: 0.3961"
+      "329/729 [============>.................] - ETA: 5s - loss: 0.4589 - mae: 0.3937"
      ]
     },
     {
@@ -16804,7 +14869,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "333/729 [============>.................] - ETA: 5s - loss: 0.4550 - mae: 0.3961"
+      "333/729 [============>.................] - ETA: 5s - loss: 0.4571 - mae: 0.3932"
      ]
     },
     {
@@ -16812,7 +14877,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "337/729 [============>.................] - ETA: 5s - loss: 0.4548 - mae: 0.3960"
+      "337/729 [============>.................] - ETA: 5s - loss: 0.4561 - mae: 0.3931"
      ]
     },
     {
@@ -16820,7 +14885,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "341/729 [=============>................] - ETA: 5s - loss: 0.4547 - mae: 0.3959"
+      "341/729 [=============>................] - ETA: 5s - loss: 0.4549 - mae: 0.3931"
      ]
     },
     {
@@ -16828,7 +14893,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "345/729 [=============>................] - ETA: 5s - loss: 0.4547 - mae: 0.3959"
+      "345/729 [=============>................] - ETA: 5s - loss: 0.4551 - mae: 0.3938"
      ]
     },
     {
@@ -16836,7 +14901,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "349/729 [=============>................] - ETA: 5s - loss: 0.4546 - mae: 0.3958"
+      "349/729 [=============>................] - ETA: 5s - loss: 0.4543 - mae: 0.3941"
      ]
     },
     {
@@ -16844,7 +14909,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "353/729 [=============>................] - ETA: 5s - loss: 0.4546 - mae: 0.3958"
+      "353/729 [=============>................] - ETA: 5s - loss: 0.4530 - mae: 0.3939"
      ]
     },
     {
@@ -16852,7 +14917,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "357/729 [=============>................] - ETA: 5s - loss: 0.4545 - mae: 0.3957"
+      "357/729 [=============>................] - ETA: 5s - loss: 0.4517 - mae: 0.3935"
      ]
     },
     {
@@ -16860,7 +14925,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "361/729 [=============>................] - ETA: 5s - loss: 0.4545 - mae: 0.3956"
+      "361/729 [=============>................] - ETA: 5s - loss: 0.4510 - mae: 0.3936"
      ]
     },
     {
@@ -16868,7 +14933,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "365/729 [==============>...............] - ETA: 5s - loss: 0.4544 - mae: 0.3956"
+      "365/729 [==============>...............] - ETA: 5s - loss: 0.4523 - mae: 0.3937"
      ]
     },
     {
@@ -16876,7 +14941,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "369/729 [==============>...............] - ETA: 5s - loss: 0.4543 - mae: 0.3955"
+      "369/729 [==============>...............] - ETA: 5s - loss: 0.4506 - mae: 0.3931"
      ]
     },
     {
@@ -16884,7 +14949,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "373/729 [==============>...............] - ETA: 5s - loss: 0.4542 - mae: 0.3955"
+      "373/729 [==============>...............] - ETA: 5s - loss: 0.4498 - mae: 0.3932"
      ]
     },
     {
@@ -16892,7 +14957,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "377/729 [==============>...............] - ETA: 5s - loss: 0.4541 - mae: 0.3954"
+      "377/729 [==============>...............] - ETA: 4s - loss: 0.4537 - mae: 0.3936"
      ]
     },
     {
@@ -16900,7 +14965,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "381/729 [==============>...............] - ETA: 5s - loss: 0.4540 - mae: 0.3954"
+      "381/729 [==============>...............] - ETA: 4s - loss: 0.4529 - mae: 0.3935"
      ]
     },
     {
@@ -16908,7 +14973,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "385/729 [==============>...............] - ETA: 5s - loss: 0.4539 - mae: 0.3953"
+      "385/729 [==============>...............] - ETA: 4s - loss: 0.4552 - mae: 0.3940"
      ]
     },
     {
@@ -16916,7 +14981,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "389/729 [===============>..............] - ETA: 5s - loss: 0.4538 - mae: 0.3953"
+      "389/729 [===============>..............] - ETA: 4s - loss: 0.4535 - mae: 0.3934"
      ]
     },
     {
@@ -16924,7 +14989,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "393/729 [===============>..............] - ETA: 5s - loss: 0.4537 - mae: 0.3952"
+      "393/729 [===============>..............] - ETA: 4s - loss: 0.4545 - mae: 0.3943"
      ]
     },
     {
@@ -16932,7 +14997,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "397/729 [===============>..............] - ETA: 4s - loss: 0.4536 - mae: 0.3952"
+      "397/729 [===============>..............] - ETA: 4s - loss: 0.4539 - mae: 0.3943"
      ]
     },
     {
@@ -16940,7 +15005,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "401/729 [===============>..............] - ETA: 4s - loss: 0.4535 - mae: 0.3951"
+      "401/729 [===============>..............] - ETA: 4s - loss: 0.4594 - mae: 0.3945"
      ]
     },
     {
@@ -16948,7 +15013,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "405/729 [===============>..............] - ETA: 4s - loss: 0.4533 - mae: 0.3951"
+      "405/729 [===============>..............] - ETA: 4s - loss: 0.4593 - mae: 0.3944"
      ]
     },
     {
@@ -16956,7 +15021,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "409/729 [===============>..............] - ETA: 4s - loss: 0.4532 - mae: 0.3950"
+      "409/729 [===============>..............] - ETA: 4s - loss: 0.4571 - mae: 0.3937"
      ]
     },
     {
@@ -16964,7 +15029,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "413/729 [===============>..............] - ETA: 4s - loss: 0.4530 - mae: 0.3950"
+      "413/729 [===============>..............] - ETA: 4s - loss: 0.4556 - mae: 0.3932"
      ]
     },
     {
@@ -16972,7 +15037,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "417/729 [================>.............] - ETA: 4s - loss: 0.4529 - mae: 0.3949"
+      "417/729 [================>.............] - ETA: 4s - loss: 0.4542 - mae: 0.3930"
      ]
     },
     {
@@ -16980,7 +15045,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "421/729 [================>.............] - ETA: 4s - loss: 0.4528 - mae: 0.3949"
+      "421/729 [================>.............] - ETA: 4s - loss: 0.4534 - mae: 0.3930"
      ]
     },
     {
@@ -16988,7 +15053,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "425/729 [================>.............] - ETA: 4s - loss: 0.4527 - mae: 0.3948"
+      "425/729 [================>.............] - ETA: 4s - loss: 0.4519 - mae: 0.3926"
      ]
     },
     {
@@ -16996,7 +15061,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "429/729 [================>.............] - ETA: 4s - loss: 0.4526 - mae: 0.3948"
+      "429/729 [================>.............] - ETA: 4s - loss: 0.4503 - mae: 0.3922"
      ]
     },
     {
@@ -17004,7 +15069,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "433/729 [================>.............] - ETA: 4s - loss: 0.4524 - mae: 0.3947"
+      "433/729 [================>.............] - ETA: 4s - loss: 0.4496 - mae: 0.3922"
      ]
     },
     {
@@ -17012,7 +15077,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "437/729 [================>.............] - ETA: 4s - loss: 0.4523 - mae: 0.3947"
+      "437/729 [================>.............] - ETA: 4s - loss: 0.4489 - mae: 0.3919"
      ]
     },
     {
@@ -17020,7 +15085,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "441/729 [=================>............] - ETA: 4s - loss: 0.4521 - mae: 0.3946"
+      "441/729 [=================>............] - ETA: 4s - loss: 0.4484 - mae: 0.3921"
      ]
     },
     {
@@ -17028,7 +15093,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "445/729 [=================>............] - ETA: 4s - loss: 0.4520 - mae: 0.3946"
+      "445/729 [=================>............] - ETA: 3s - loss: 0.4481 - mae: 0.3922"
      ]
     },
     {
@@ -17036,7 +15101,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "449/729 [=================>............] - ETA: 4s - loss: 0.4518 - mae: 0.3945"
+      "449/729 [=================>............] - ETA: 3s - loss: 0.4468 - mae: 0.3918"
      ]
     },
     {
@@ -17044,7 +15109,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "453/729 [=================>............] - ETA: 4s - loss: 0.4517 - mae: 0.3945"
+      "453/729 [=================>............] - ETA: 3s - loss: 0.4449 - mae: 0.3911"
      ]
     },
     {
@@ -17052,7 +15117,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "457/729 [=================>............] - ETA: 4s - loss: 0.4515 - mae: 0.3944"
+      "457/729 [=================>............] - ETA: 3s - loss: 0.4435 - mae: 0.3907"
      ]
     },
     {
@@ -17060,7 +15125,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "461/729 [=================>............] - ETA: 3s - loss: 0.4514 - mae: 0.3944"
+      "461/729 [=================>............] - ETA: 3s - loss: 0.4433 - mae: 0.3909"
      ]
     },
     {
@@ -17068,7 +15133,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "465/729 [==================>...........] - ETA: 3s - loss: 0.4512 - mae: 0.3944"
+      "465/729 [==================>...........] - ETA: 3s - loss: 0.4445 - mae: 0.3916"
      ]
     },
     {
@@ -17076,7 +15141,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "469/729 [==================>...........] - ETA: 3s - loss: 0.4511 - mae: 0.3943"
+      "469/729 [==================>...........] - ETA: 3s - loss: 0.4481 - mae: 0.3921"
      ]
     },
     {
@@ -17084,7 +15149,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "473/729 [==================>...........] - ETA: 3s - loss: 0.4509 - mae: 0.3943"
+      "473/729 [==================>...........] - ETA: 3s - loss: 0.4474 - mae: 0.3922"
      ]
     },
     {
@@ -17092,7 +15157,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "477/729 [==================>...........] - ETA: 3s - loss: 0.4507 - mae: 0.3942"
+      "477/729 [==================>...........] - ETA: 3s - loss: 0.4464 - mae: 0.3921"
      ]
     },
     {
@@ -17100,7 +15165,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "481/729 [==================>...........] - ETA: 3s - loss: 0.4506 - mae: 0.3942"
+      "481/729 [==================>...........] - ETA: 3s - loss: 0.4471 - mae: 0.3924"
      ]
     },
     {
@@ -17108,7 +15173,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "485/729 [==================>...........] - ETA: 3s - loss: 0.4504 - mae: 0.3941"
+      "485/729 [==================>...........] - ETA: 3s - loss: 0.4455 - mae: 0.3918"
      ]
     },
     {
@@ -17116,7 +15181,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "489/729 [===================>..........] - ETA: 3s - loss: 0.4502 - mae: 0.3941"
+      "489/729 [===================>..........] - ETA: 3s - loss: 0.4451 - mae: 0.3918"
      ]
     },
     {
@@ -17124,7 +15189,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "493/729 [===================>..........] - ETA: 3s - loss: 0.4500 - mae: 0.3941"
+      "493/729 [===================>..........] - ETA: 3s - loss: 0.4440 - mae: 0.3914"
      ]
     },
     {
@@ -17132,7 +15197,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "497/729 [===================>..........] - ETA: 3s - loss: 0.4498 - mae: 0.3940"
+      "497/729 [===================>..........] - ETA: 3s - loss: 0.4435 - mae: 0.3914"
      ]
     },
     {
@@ -17140,7 +15205,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "501/729 [===================>..........] - ETA: 3s - loss: 0.4497 - mae: 0.3940"
+      "501/729 [===================>..........] - ETA: 3s - loss: 0.4421 - mae: 0.3909"
      ]
     },
     {
@@ -17148,7 +15213,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "505/729 [===================>..........] - ETA: 3s - loss: 0.4495 - mae: 0.3939"
+      "505/729 [===================>..........] - ETA: 3s - loss: 0.4441 - mae: 0.3910"
      ]
     },
     {
@@ -17156,7 +15221,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "509/729 [===================>..........] - ETA: 3s - loss: 0.4493 - mae: 0.3939"
+      "509/729 [===================>..........] - ETA: 3s - loss: 0.4443 - mae: 0.3913"
      ]
     },
     {
@@ -17164,7 +15229,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "513/729 [====================>.........] - ETA: 3s - loss: 0.4492 - mae: 0.3938"
+      "513/729 [====================>.........] - ETA: 3s - loss: 0.4441 - mae: 0.3914"
      ]
     },
     {
@@ -17172,7 +15237,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "517/729 [====================>.........] - ETA: 3s - loss: 0.4490 - mae: 0.3938"
+      "517/729 [====================>.........] - ETA: 2s - loss: 0.4430 - mae: 0.3909"
      ]
     },
     {
@@ -17180,7 +15245,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "521/729 [====================>.........] - ETA: 3s - loss: 0.4488 - mae: 0.3938"
+      "521/729 [====================>.........] - ETA: 2s - loss: 0.4452 - mae: 0.3911"
      ]
     },
     {
@@ -17188,7 +15253,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "525/729 [====================>.........] - ETA: 3s - loss: 0.4486 - mae: 0.3937"
+      "525/729 [====================>.........] - ETA: 2s - loss: 0.4453 - mae: 0.3912"
      ]
     },
     {
@@ -17196,7 +15261,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "529/729 [====================>.........] - ETA: 2s - loss: 0.4485 - mae: 0.3937"
+      "529/729 [====================>.........] - ETA: 2s - loss: 0.4445 - mae: 0.3911"
      ]
     },
     {
@@ -17204,7 +15269,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "533/729 [====================>.........] - ETA: 2s - loss: 0.4483 - mae: 0.3936"
+      "533/729 [====================>.........] - ETA: 2s - loss: 0.4445 - mae: 0.3915"
      ]
     },
     {
@@ -17212,7 +15277,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "537/729 [=====================>........] - ETA: 2s - loss: 0.4481 - mae: 0.3936"
+      "537/729 [=====================>........] - ETA: 2s - loss: 0.4479 - mae: 0.3919"
      ]
     },
     {
@@ -17220,7 +15285,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "541/729 [=====================>........] - ETA: 2s - loss: 0.4479 - mae: 0.3935"
+      "541/729 [=====================>........] - ETA: 2s - loss: 0.4468 - mae: 0.3916"
      ]
     },
     {
@@ -17228,7 +15293,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "545/729 [=====================>........] - ETA: 2s - loss: 0.4478 - mae: 0.3935"
+      "545/729 [=====================>........] - ETA: 2s - loss: 0.4462 - mae: 0.3916"
      ]
     },
     {
@@ -17236,7 +15301,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "549/729 [=====================>........] - ETA: 2s - loss: 0.4476 - mae: 0.3935"
+      "549/729 [=====================>........] - ETA: 2s - loss: 0.4453 - mae: 0.3913"
      ]
     },
     {
@@ -17244,7 +15309,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "553/729 [=====================>........] - ETA: 2s - loss: 0.4475 - mae: 0.3934"
+      "553/729 [=====================>........] - ETA: 2s - loss: 0.4446 - mae: 0.3910"
      ]
     },
     {
@@ -17252,7 +15317,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "557/729 [=====================>........] - ETA: 2s - loss: 0.4473 - mae: 0.3934"
+      "557/729 [=====================>........] - ETA: 2s - loss: 0.4450 - mae: 0.3912"
      ]
     },
     {
@@ -17260,7 +15325,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "561/729 [======================>.......] - ETA: 2s - loss: 0.4471 - mae: 0.3933"
+      "561/729 [======================>.......] - ETA: 2s - loss: 0.4471 - mae: 0.3917"
      ]
     },
     {
@@ -17268,7 +15333,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "565/729 [======================>.......] - ETA: 2s - loss: 0.4470 - mae: 0.3933"
+      "565/729 [======================>.......] - ETA: 2s - loss: 0.4467 - mae: 0.3916"
      ]
     },
     {
@@ -17276,7 +15341,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "569/729 [======================>.......] - ETA: 2s - loss: 0.4468 - mae: 0.3933"
+      "569/729 [======================>.......] - ETA: 2s - loss: 0.4458 - mae: 0.3913"
      ]
     },
     {
@@ -17284,7 +15349,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "573/729 [======================>.......] - ETA: 2s - loss: 0.4467 - mae: 0.3932"
+      "573/729 [======================>.......] - ETA: 2s - loss: 0.4453 - mae: 0.3912"
      ]
     },
     {
@@ -17292,7 +15357,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "577/729 [======================>.......] - ETA: 2s - loss: 0.4465 - mae: 0.3932"
+      "577/729 [======================>.......] - ETA: 2s - loss: 0.4464 - mae: 0.3915"
      ]
     },
     {
@@ -17300,7 +15365,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "581/729 [======================>.......] - ETA: 2s - loss: 0.4464 - mae: 0.3931"
+      "581/729 [======================>.......] - ETA: 2s - loss: 0.4470 - mae: 0.3917"
      ]
     },
     {
@@ -17308,7 +15373,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "585/729 [=======================>......] - ETA: 2s - loss: 0.4462 - mae: 0.3931"
+      "585/729 [=======================>......] - ETA: 2s - loss: 0.4466 - mae: 0.3917"
      ]
     },
     {
@@ -17316,7 +15381,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "589/729 [=======================>......] - ETA: 2s - loss: 0.4461 - mae: 0.3931"
+      "589/729 [=======================>......] - ETA: 1s - loss: 0.4458 - mae: 0.3915"
      ]
     },
     {
@@ -17324,7 +15389,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "593/729 [=======================>......] - ETA: 2s - loss: 0.4460 - mae: 0.3930"
+      "593/729 [=======================>......] - ETA: 1s - loss: 0.4461 - mae: 0.3918"
      ]
     },
     {
@@ -17332,7 +15397,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "597/729 [=======================>......] - ETA: 1s - loss: 0.4459 - mae: 0.3930"
+      "597/729 [=======================>......] - ETA: 1s - loss: 0.4461 - mae: 0.3918"
      ]
     },
     {
@@ -17340,7 +15405,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "601/729 [=======================>......] - ETA: 1s - loss: 0.4458 - mae: 0.3930"
+      "601/729 [=======================>......] - ETA: 1s - loss: 0.4452 - mae: 0.3916"
      ]
     },
     {
@@ -17348,7 +15413,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "605/729 [=======================>......] - ETA: 1s - loss: 0.4457 - mae: 0.3930"
+      "605/729 [=======================>......] - ETA: 1s - loss: 0.4447 - mae: 0.3916"
      ]
     },
     {
@@ -17356,7 +15421,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "609/729 [========================>.....] - ETA: 1s - loss: 0.4456 - mae: 0.3929"
+      "609/729 [========================>.....] - ETA: 1s - loss: 0.4438 - mae: 0.3913"
      ]
     },
     {
@@ -17364,7 +15429,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "613/729 [========================>.....] - ETA: 1s - loss: 0.4455 - mae: 0.3929"
+      "613/729 [========================>.....] - ETA: 1s - loss: 0.4437 - mae: 0.3914"
      ]
     },
     {
@@ -17372,7 +15437,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "617/729 [========================>.....] - ETA: 1s - loss: 0.4454 - mae: 0.3929"
+      "617/729 [========================>.....] - ETA: 1s - loss: 0.4425 - mae: 0.3909"
      ]
     },
     {
@@ -17380,7 +15445,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "621/729 [========================>.....] - ETA: 1s - loss: 0.4453 - mae: 0.3928"
+      "621/729 [========================>.....] - ETA: 1s - loss: 0.4418 - mae: 0.3908"
      ]
     },
     {
@@ -17388,7 +15453,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "625/729 [========================>.....] - ETA: 1s - loss: 0.4452 - mae: 0.3928"
+      "625/729 [========================>.....] - ETA: 1s - loss: 0.4415 - mae: 0.3907"
      ]
     },
     {
@@ -17396,7 +15461,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "629/729 [========================>.....] - ETA: 1s - loss: 0.4451 - mae: 0.3928"
+      "629/729 [========================>.....] - ETA: 1s - loss: 0.4419 - mae: 0.3911"
      ]
     },
     {
@@ -17404,7 +15469,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "633/729 [=========================>....] - ETA: 1s - loss: 0.4450 - mae: 0.3927"
+      "633/729 [=========================>....] - ETA: 1s - loss: 0.4413 - mae: 0.3911"
      ]
     },
     {
@@ -17412,7 +15477,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "637/729 [=========================>....] - ETA: 1s - loss: 0.4449 - mae: 0.3927"
+      "637/729 [=========================>....] - ETA: 1s - loss: 0.4410 - mae: 0.3908"
      ]
     },
     {
@@ -17420,7 +15485,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "641/729 [=========================>....] - ETA: 1s - loss: 0.4448 - mae: 0.3927"
+      "641/729 [=========================>....] - ETA: 1s - loss: 0.4405 - mae: 0.3908"
      ]
     },
     {
@@ -17428,7 +15493,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "645/729 [=========================>....] - ETA: 1s - loss: 0.4447 - mae: 0.3927"
+      "645/729 [=========================>....] - ETA: 1s - loss: 0.4405 - mae: 0.3909"
      ]
     },
     {
@@ -17436,7 +15501,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "649/729 [=========================>....] - ETA: 1s - loss: 0.4446 - mae: 0.3926"
+      "649/729 [=========================>....] - ETA: 1s - loss: 0.4413 - mae: 0.3909"
      ]
     },
     {
@@ -17444,7 +15509,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "653/729 [=========================>....] - ETA: 1s - loss: 0.4445 - mae: 0.3926"
+      "653/729 [=========================>....] - ETA: 1s - loss: 0.4402 - mae: 0.3906"
      ]
     },
     {
@@ -17452,7 +15517,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "657/729 [==========================>...] - ETA: 1s - loss: 0.4444 - mae: 0.3926"
+      "657/729 [==========================>...] - ETA: 1s - loss: 0.4395 - mae: 0.3905"
      ]
     },
     {
@@ -17460,7 +15525,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "661/729 [==========================>...] - ETA: 1s - loss: 0.4443 - mae: 0.3925"
+      "661/729 [==========================>...] - ETA: 0s - loss: 0.4389 - mae: 0.3903"
      ]
     },
     {
@@ -17468,7 +15533,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "665/729 [==========================>...] - ETA: 0s - loss: 0.4442 - mae: 0.3925"
+      "665/729 [==========================>...] - ETA: 0s - loss: 0.4382 - mae: 0.3902"
      ]
     },
     {
@@ -17476,7 +15541,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "669/729 [==========================>...] - ETA: 0s - loss: 0.4442 - mae: 0.3925"
+      "669/729 [==========================>...] - ETA: 0s - loss: 0.4378 - mae: 0.3902"
      ]
     },
     {
@@ -17484,7 +15549,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "673/729 [==========================>...] - ETA: 0s - loss: 0.4441 - mae: 0.3925"
+      "673/729 [==========================>...] - ETA: 0s - loss: 0.4373 - mae: 0.3900"
      ]
     },
     {
@@ -17492,7 +15557,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "677/729 [==========================>...] - ETA: 0s - loss: 0.4440 - mae: 0.3924"
+      "677/729 [==========================>...] - ETA: 0s - loss: 0.4378 - mae: 0.3902"
      ]
     },
     {
@@ -17500,7 +15565,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "681/729 [===========================>..] - ETA: 0s - loss: 0.4439 - mae: 0.3924"
+      "681/729 [===========================>..] - ETA: 0s - loss: 0.4372 - mae: 0.3900"
      ]
     },
     {
@@ -17508,7 +15573,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "685/729 [===========================>..] - ETA: 0s - loss: 0.4438 - mae: 0.3924"
+      "685/729 [===========================>..] - ETA: 0s - loss: 0.4384 - mae: 0.3901"
      ]
     },
     {
@@ -17516,7 +15581,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "689/729 [===========================>..] - ETA: 0s - loss: 0.4437 - mae: 0.3923"
+      "689/729 [===========================>..] - ETA: 0s - loss: 0.4390 - mae: 0.3904"
      ]
     },
     {
@@ -17524,7 +15589,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "693/729 [===========================>..] - ETA: 0s - loss: 0.4436 - mae: 0.3923"
+      "693/729 [===========================>..] - ETA: 0s - loss: 0.4379 - mae: 0.3900"
      ]
     },
     {
@@ -17532,7 +15597,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "697/729 [===========================>..] - ETA: 0s - loss: 0.4435 - mae: 0.3923"
+      "697/729 [===========================>..] - ETA: 0s - loss: 0.4375 - mae: 0.3898"
      ]
     },
     {
@@ -17540,7 +15605,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "701/729 [===========================>..] - ETA: 0s - loss: 0.4434 - mae: 0.3923"
+      "701/729 [===========================>..] - ETA: 0s - loss: 0.4372 - mae: 0.3897"
      ]
     },
     {
@@ -17548,7 +15613,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "705/729 [============================>.] - ETA: 0s - loss: 0.4433 - mae: 0.3922"
+      "705/729 [============================>.] - ETA: 0s - loss: 0.4359 - mae: 0.3893"
      ]
     },
     {
@@ -17556,7 +15621,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "709/729 [============================>.] - ETA: 0s - loss: 0.4433 - mae: 0.3922"
+      "709/729 [============================>.] - ETA: 0s - loss: 0.4359 - mae: 0.3894"
      ]
     },
     {
@@ -17564,7 +15629,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "713/729 [============================>.] - ETA: 0s - loss: 0.4432 - mae: 0.3922"
+      "713/729 [============================>.] - ETA: 0s - loss: 0.4348 - mae: 0.3890"
      ]
     },
     {
@@ -17572,7 +15637,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "717/729 [============================>.] - ETA: 0s - loss: 0.4431 - mae: 0.3922"
+      "717/729 [============================>.] - ETA: 0s - loss: 0.4340 - mae: 0.3887"
      ]
     },
     {
@@ -17580,7 +15645,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "721/729 [============================>.] - ETA: 0s - loss: 0.4431 - mae: 0.3921"
+      "721/729 [============================>.] - ETA: 0s - loss: 0.4337 - mae: 0.3888"
      ]
     },
     {
@@ -17588,7 +15653,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "725/729 [============================>.] - ETA: 0s - loss: 0.4430 - mae: 0.3921"
+      "725/729 [============================>.] - ETA: 0s - loss: 0.4334 - mae: 0.3886"
      ]
     },
     {
@@ -17596,7 +15661,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "729/729 [==============================] - ETA: 0s - loss: 0.4429 - mae: 0.3921"
+      "729/729 [==============================] - ETA: 0s - loss: 0.4339 - mae: 0.3888"
      ]
     },
     {
@@ -17604,7 +15669,7 @@
      "output_type": "stream",
      "text": [
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "729/729 [==============================] - 11s 16ms/step - loss: 0.4429 - mae: 0.3921 - val_loss: 0.4237 - val_mae: 0.3686\n"
+      "729/729 [==============================] - 11s 15ms/step - loss: 0.4339 - mae: 0.3888 - val_loss: 0.4251 - val_mae: 0.3618\n"
      ]
     },
     {
@@ -17612,7 +15677,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Duration :  00:02:08 509ms\n"
+      "Duration :  00:01:53 852ms\n"
      ]
     }
    ],
@@ -17633,10 +15698,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2021-03-01T19:32:17.835082Z",
-     "iopub.status.busy": "2021-03-01T19:32:17.834604Z",
-     "iopub.status.idle": "2021-03-01T19:32:19.148070Z",
-     "shell.execute_reply": "2021-03-01T19:32:19.148560Z"
+     "iopub.execute_input": "2021-03-07T20:17:57.012692Z",
+     "iopub.status.busy": "2021-03-07T20:17:57.012250Z",
+     "iopub.status.idle": "2021-03-07T20:17:57.742521Z",
+     "shell.execute_reply": "2021-03-07T20:17:57.742190Z"
     }
    },
    "outputs": [
@@ -17654,7 +15719,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x432 with 1 Axes>"
       ]
@@ -17678,7 +15743,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x432 with 1 Axes>"
       ]
@@ -17712,21 +15777,13 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2021-03-01T19:32:19.151920Z",
-     "iopub.status.busy": "2021-03-01T19:32:19.151440Z",
-     "iopub.status.idle": "2021-03-01T19:32:19.270741Z",
-     "shell.execute_reply": "2021-03-01T19:32:19.271238Z"
+     "iopub.execute_input": "2021-03-07T20:17:57.752837Z",
+     "iopub.status.busy": "2021-03-07T20:17:57.750313Z",
+     "iopub.status.idle": "2021-03-07T20:17:57.828283Z",
+     "shell.execute_reply": "2021-03-07T20:17:57.828675Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')"
    ]
@@ -17744,10 +15801,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2021-03-01T19:32:19.275849Z",
-     "iopub.status.busy": "2021-03-01T19:32:19.275375Z",
-     "iopub.status.idle": "2021-03-01T19:32:23.022804Z",
-     "shell.execute_reply": "2021-03-01T19:32:23.023320Z"
+     "iopub.execute_input": "2021-03-07T20:17:57.835232Z",
+     "iopub.status.busy": "2021-03-07T20:17:57.833460Z",
+     "iopub.status.idle": "2021-03-07T20:18:00.088335Z",
+     "shell.execute_reply": "2021-03-07T20:18:00.087948Z"
     }
    },
    "outputs": [
@@ -17765,7 +15822,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x1152 with 12 Axes>"
       ]
@@ -17801,10 +15858,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2021-03-01T19:32:23.030478Z",
-     "iopub.status.busy": "2021-03-01T19:32:23.029991Z",
-     "iopub.status.idle": "2021-03-01T19:32:23.921604Z",
-     "shell.execute_reply": "2021-03-01T19:32:23.922102Z"
+     "iopub.execute_input": "2021-03-07T20:18:00.094136Z",
+     "iopub.status.busy": "2021-03-07T20:18:00.093074Z",
+     "iopub.status.idle": "2021-03-07T20:18:01.465109Z",
+     "shell.execute_reply": "2021-03-07T20:18:01.464820Z"
     }
    },
    "outputs": [
@@ -17822,7 +15879,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 3024x2304 with 1 Axes>"
       ]
@@ -17836,7 +15893,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Gap between prediction and reality : 0.93 °C\n"
+      "Gap between prediction and reality : 2.08 °C\n"
      ]
     }
    ],
@@ -17878,10 +15935,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2021-03-01T19:32:23.925437Z",
-     "iopub.status.busy": "2021-03-01T19:32:23.924974Z",
-     "iopub.status.idle": "2021-03-01T19:32:23.927309Z",
-     "shell.execute_reply": "2021-03-01T19:32:23.927784Z"
+     "iopub.execute_input": "2021-03-07T20:18:01.468085Z",
+     "iopub.status.busy": "2021-03-07T20:18:01.467636Z",
+     "iopub.status.idle": "2021-03-07T20:18:01.471238Z",
+     "shell.execute_reply": "2021-03-07T20:18:01.470922Z"
     }
    },
    "outputs": [
@@ -17889,8 +15946,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "End time is : Monday 01 March 2021, 20:32:23\n",
-      "Duration is : 00:02:15 133ms\n",
+      "End time is : Sunday 07 March 2021, 21:18:01\n",
+      "Duration is : 00:01:58 640ms\n",
       "This notebook ends here\n"
      ]
     }
@@ -17924,7 +15981,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/SYNOP/03-12h-predictions.ipynb b/SYNOP/SYNOP3-12h-predictions.ipynb
similarity index 98%
rename from SYNOP/03-12h-predictions.ipynb
rename to SYNOP/SYNOP3-12h-predictions.ipynb
index 7b5094c6fb84488618afa4731738de96c4b191c4..8884d41ac33e399dae1578b7b5018510a580915b 100644
--- a/SYNOP/03-12h-predictions.ipynb
+++ b/SYNOP/SYNOP3-12h-predictions.ipynb
@@ -272,7 +272,8 @@
     "\n",
     "feat=11\n",
     "\n",
-    "pwk.plot_multivariate_serie(sequence_true, predictions=pred, labels=features, only_features=[feat],width=14, height=8, save_as='02-prediction')"
+    "pwk.plot_multivariate_serie(sequence_true, predictions=pred, labels=features,\n",
+    "                            only_features=[feat],width=14, height=8, save_as='02-prediction')"
    ]
   },
   {
@@ -322,7 +323,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/SYNOP/SYNOP3-12h-predictions==done==.ipynb b/SYNOP/SYNOP3-12h-predictions==done==.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..42aef1eb71045175954a93985b3617ba8f5a1a2f
--- /dev/null
+++ b/SYNOP/SYNOP3-12h-predictions==done==.ipynb
@@ -0,0 +1,552 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+    "\n",
+    "# <!-- TITLE --> [SYNOP3] - 12h predictions\n",
+    "<!-- DESC --> Episode 3: Attempt to predict in a more longer term \n",
+    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+    "\n",
+    "## Objectives :\n",
+    " - Prediction at 12:00\n",
+    " - Understand the principle of using recurrent neurons... and the limitations of our example !\n",
+    "\n",
+    "\n",
+    "SYNOP meteorological data, available at: https://public.opendatasoft.com\n",
+    "\n",
+    "## What we're going to do :\n",
+    "\n",
+    " - Read the data\n",
+    " - Make a reccurent prediction\n",
+    "\n",
+    "## Step 1 - Import and init\n",
+    "### 1.1 - Python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:18:04.444230Z",
+     "iopub.status.busy": "2021-03-07T20:18:04.443901Z",
+     "iopub.status.idle": "2021-03-07T20:18:05.777775Z",
+     "shell.execute_reply": "2021-03-07T20:18:05.777395Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "\n",
+       "div.warn {    \n",
+       "    background-color: #fcf2f2;\n",
+       "    border-color: #dFb5b4;\n",
+       "    border-left: 5px solid #dfb5b4;\n",
+       "    padding: 0.5em;\n",
+       "    font-weight: bold;\n",
+       "    font-size: 1.1em;;\n",
+       "    }\n",
+       "\n",
+       "\n",
+       "\n",
+       "div.nota {    \n",
+       "    background-color: #DAFFDE;\n",
+       "    border-left: 5px solid #92CC99;\n",
+       "    padding: 0.5em;\n",
+       "    }\n",
+       "\n",
+       "div.todo:before { content:url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSI1My44OTEyIiBoZWlnaHQ9IjE0My4zOTAyIiB2aWV3Qm94PSIwIDAgNTMuODkxMiAxNDMuMzkwMiI+PHRpdGxlPjAwLUJvYi10b2RvPC90aXRsZT48cGF0aCBkPSJNMjMuNDU2OCwxMTQuMzAxNmExLjgwNjMsMS44MDYzLDAsMSwxLDEuODE1NywxLjgyNEExLjgyMDksMS44MjA5LDAsMCwxLDIzLjQ1NjgsMTE0LjMwMTZabS0xMC42NjEyLDEuODIyQTEuODI3MiwxLjgyNzIsMCwxLDAsMTAuOTgsMTE0LjMsMS44MiwxLjgyLDAsMCwwLDEyLjc5NTYsMTE2LjEyMzZabS03LjcwNyw0LjU4NzR2LTVzLjQ4NjMtOS4xMjIzLDguMDIxNS0xMS45Njc1YTE5LjIwODIsMTkuMjA4MiwwLDAsMSw2LjA0ODYtMS4yNDU0LDE5LjE3NzgsMTkuMTc3OCwwLDAsMSw2LjA0ODcsMS4yNDc1YzcuNTM1MSwyLjgzNDcsOC4wMTc0LDExLjk2NzQsOC4wMTc0LDExLjk2NzR2NS4wMjM0bC4wMDQyLDcuNjgydjIuNGMuMDE2Ny4xOTkyLjAzMzYuMzkyMS4wMzM2LjU4NzEsMCwuMjEzOC0uMDE2OC40MTA5LS4wMzM2LjYzMzJ2LjA1ODdoLS4wMDg0YTguMzcxOSw4LjM3MTksMCwwLDEtNy4zNzM4LDcuNjU0N3MtLjk5NTMsMy42MzgtNi42OTMzLDMuNjM4LTYuNjkzNC0zLjYzOC02LjY5MzQtMy42MzhhOC4zNyw4LjM3LDAsMCwxLTcuMzcxNi03LjY1NDdINS4wODQzdi0uMDU4N2MtLjAxODktLjIyLS4wMjk0LS40MTk0LS4wMjk0LS42MzMyLDAtLjE5MjkuMDE2Ny0uMzgzNy4wMjk0LS41ODcxdi0yLjRtMTguMDkzNy00LjA0YTEuMTU2NSwxLjE1NjUsMCwxLDAtMi4zMTI2LDAsMS4xNTY0LDEuMTU2NCwwLDEsMCwyLjMxMjYsMFptNC4wODM0LDBhMS4xNTk1LDEuMTU5NSwwLDEsMC0xLjE2MzYsMS4xN0ExLjE3NSwxLjE3NSwwLDAsMCwyNy4yNjE0LDEyNC4zNzc5Wk05LjM3MzksMTE0LjYzNWMwLDMuMTA5MywyLjQxMzIsMy4zMSwyLjQxMzIsMy4zMWExMzMuOTI0MywxMzMuOTI0MywwLDAsMCwxNC43MzQ4LDBzMi40MTExLS4xOTI5LDIuNDExMS0zLjMxYTguMDc3Myw4LjA3NzMsMCwwLDAtMi40MTExLTUuNTUxOWMtNC41LTMuNTAzMy05LjkxMjYtMy41MDMzLTE0Ljc0MTEsMEE4LjA4NTEsOC4wODUxLDAsMCwwLDkuMzczOSwxMTQuNjM1WiIgc3R5bGU9ImZpbGw6IzAxMDEwMSIvPjxjaXJjbGUgY3g9IjMzLjE0MzYiIGN5PSIxMjQuNTM0IiByPSIzLjgzNjMiIHN0eWxlPSJmaWxsOiMwMTAxMDEiLz48cmVjdCB4PSIzNS42NjU5IiB5PSIxMTIuOTYyNSIgd2lkdGg9IjIuMDc3IiBoZWlnaHQ9IjEwLjU0NTgiIHRyYW5zZm9ybT0idHJhbnNsYXRlKDIxLjYgMjQxLjExMjEpIHJvdGF0ZSgtMTU1Ljc0NikiIHN0eWxlPSJmaWxsOiMwMTAxMDEiLz48Y2lyY2xlIGN4PSIzOC44NzA0IiBjeT0iMTEzLjQyNzkiIHI9IjIuNDA4NSIgc3R5bGU9ImZpbGw6IzAxMDEwMSIvPjxjaXJjbGUgY3g9IjUuMjI0OCIgY3k9IjEyNC41MzQiIHI9IjMuODM2MyIgc3R5bGU9ImZpbGw6IzAxMDEwMSIvPjxyZWN0IHg9IjEuNDE2NCIgeT0iMTI0LjYzMDEiIHdpZHRoPSIyLjA3NyIgaGVpZ2h0PSIxMC41NDU4IiB0cmFuc2Zvcm09InRyYW5zbGF0ZSg0LjkwOTcgMjU5LjgwNikgcm90YXRlKC0xODApIiBzdHlsZT0iZmlsbDojMDEwMTAxIi8+PGNpcmNsZSBjeD0iMi40MDkxIiBjeT0iMTM3LjA5OTYiIHI9IjIuNDA4NSIgc3R5bGU9ImZpbGw6IzAxMDEwMSIvPjxwYXRoIGQ9Ik0xOC4wNTExLDEwMC4xMDY2aC0uMDE0NlYxMDIuNjFoMi4zdi0yLjQyNzlhMi40MjI5LDIuNDIyOSwwLDEsMC0yLjI4NTQtLjA3NTVaIiBzdHlsZT0iZmlsbDojMDEwMTAxIi8+PHBhdGggZD0iTTM5LjQyMTQsMjcuMjU4djEuMDVBMTEuOTQ1MiwxMS45NDUyLDAsMCwwLDQ0LjU5NTQsNS43OWEuMjQ0OS4yNDQ5LDAsMCwxLS4wMjM1LS40MjI3TDQ2Ljc1LDMuOTUxNWEuMzg5Mi4zODkyLDAsMCwxLC40MjYyLDAsMTQuODQ0MiwxNC44NDQyLDAsMCwxLTcuNzU0MywyNy4yNTkxdjEuMDY3YS40NS40NSwwLDAsMS0uNzA0Ny4zNzU4bC0zLjg0MTktMi41MWEuNDUuNDUsMCwwLDEsMC0uNzUxNmwzLjg0MTktMi41MWEuNDUuNDUsMCwwLDEsLjY5NDYuMzc1OFpNNDMuMjMsMi41ODkyLDM5LjM4NzguMDc5NGEuNDUuNDUsMCwwLDAtLjcwNDYuMzc1OHYxLjA2N2ExNC44NDQyLDE0Ljg0NDIsMCwwLDAtNy43NTQzLDI3LjI1OTEuMzg5LjM4OSwwLDAsMCwuNDI2MSwwbDIuMTc3Ny0xLjQxOTNhLjI0NS4yNDUsMCwwLDAtLjAyMzUtLjQyMjgsMTEuOTQ1MSwxMS45NDUxLDAsMCwxLDUuMTc0LTIyLjUxNDZ2MS4wNWEuNDUuNDUsMCwwLDAsLjcwNDYuMzc1OGwzLjg1NTMtMi41MWEuNDUuNDUsMCwwLDAsMC0uNzUxNlpNMzkuMDUyMywxNC4yNDU4YTIuMTIwNiwyLjEyMDYsMCwxLDAsMi4xMjA2LDIuMTIwNmgwQTIuMTI0LDIuMTI0LDAsMCwwLDM5LjA1MjMsMTQuMjQ1OFptNi4wNzMyLTQuNzc4MS44MjU0LjgyNTVhMS4wNTY4LDEuMDU2OCwwLDAsMSwuMTE3NSwxLjM0MjFsLS44MDIsMS4xNDQyYTcuMTAxOCw3LjEwMTgsMCwwLDEsLjcxMTQsMS43MTEybDEuMzc1Ny4yNDE2YTEuMDU2OSwxLjA1NjksMCwwLDEsLjg3NTcsMS4wNHYxLjE2NDNhMS4wNTY5LDEuMDU2OSwwLDAsMS0uODc1NywxLjA0bC0xLjM3MjQuMjQxNkE3LjExLDcuMTEsMCwwLDEsNDUuMjcsMTkuOTNsLjgwMTksMS4xNDQyYTEuMDU3LDEuMDU3LDAsMCwxLS4xMTc0LDEuMzQyMmwtLjgyODguODQ4OWExLjA1NywxLjA1NywwLDAsMS0xLjM0MjEuMTE3NGwtMS4xNDQyLS44MDE5YTcuMTMzOCw3LjEzMzgsMCwwLDEtMS43MTEzLjcxMTNsLS4yNDE2LDEuMzcyNGExLjA1NjgsMS4wNTY4LDAsMCwxLTEuMDQuODc1N0gzOC40Njg0YTEuMDU2OCwxLjA1NjgsMCwwLDEtMS4wNC0uODc1N2wtLjI0MTYtMS4zNzI0YTcuMTM1NSw3LjEzNTUsMCwwLDEtMS43MTEzLS43MTEzbC0xLjE0NDEuODAxOWExLjA1NzEsMS4wNTcxLDAsMCwxLTEuMzQyMi0uMTE3NGwtLjgzNTUtLjgyNTVhMS4wNTcsMS4wNTcsMCwwLDEtLjExNzQtMS4zNDIxbC44MDE5LTEuMTQ0MmE3LjEyMSw3LjEyMSwwLDAsMS0uNzExMy0xLjcxMTJsLTEuMzcyNC0uMjQxNmExLjA1NjksMS4wNTY5LDAsMCwxLS44NzU3LTEuMDRWMTUuNzgyNmExLjA1NjksMS4wNTY5LDAsMCwxLC44NzU3LTEuMDRsMS4zNzU3LS4yNDE2YTcuMTEsNy4xMSwwLDAsMSwuNzExNC0xLjcxMTJsLS44MDItMS4xNDQyYTEuMDU3LDEuMDU3LDAsMCwxLC4xMTc1LTEuMzQyMmwuODI1NC0uODI1NEExLjA1NjgsMS4wNTY4LDAsMCwxLDM0LjMyNDUsOS4zNmwxLjE0NDIuODAxOUE3LjEzNTUsNy4xMzU1LDAsMCwxLDM3LjE4LDkuNDUxbC4yNDE2LTEuMzcyNGExLjA1NjgsMS4wNTY4LDAsMCwxLDEuMDQtLjg3NTdoMS4xNjc3YTEuMDU2OSwxLjA1NjksMCwwLDEsMS4wNC44NzU3bC4yNDE2LDEuMzcyNGE3LjEyNSw3LjEyNSwwLDAsMSwxLjcxMTIuNzExM0w0My43NjY2LDkuMzZBMS4wNTY5LDEuMDU2OSwwLDAsMSw0NS4xMjU1LDkuNDY3N1ptLTIuMDMsNi44OTg3QTQuMDQzMyw0LjA0MzMsMCwxLDAsMzkuMDUyMywyMC40MWgwQTQuMDQ2NSw0LjA0NjUsMCwwLDAsNDMuMDk1NSwxNi4zNjY0WiIgc3R5bGU9ImZpbGw6I2UxMjIyOSIvPjxwb2x5Z29uIHBvaW50cz0iMzkuNDEzIDM0Ljc1NyAzOS41MzcgMzQuNzU3IDM5LjY3NSAzNC43NTcgMzkuNjc1IDEwOS41MSAzOS41MzcgMTA5LjUxIDM5LjQxMyAxMDkuNTEgMzkuNDEzIDM0Ljc1NyAzOS40MTMgMzQuNzU3IiBzdHlsZT0iZmlsbDpub25lO3N0cm9rZTojOTk5O3N0cm9rZS1saW5lY2FwOnJvdW5kO3N0cm9rZS1taXRlcmxpbWl0OjEwO3N0cm9rZS13aWR0aDowLjMwODg1NDQ1MDU2MDE2MThweDtmaWxsLXJ1bGU6ZXZlbm9kZCIvPjwvc3ZnPg==);\n",
+       "    float:left;\n",
+       "    margin-right:20px;\n",
+       "    margin-top:-20px;\n",
+       "    margin-bottom:20px;\n",
+       "}\n",
+       "div.todo{\n",
+       "    font-weight: bold;\n",
+       "    font-size: 1.1em;\n",
+       "    margin-top:40px;\n",
+       "}\n",
+       "div.todo ul{\n",
+       "    margin: 0.2em;\n",
+       "}\n",
+       "div.todo li{\n",
+       "    margin-left:60px;\n",
+       "    margin-top:0;\n",
+       "    margin-bottom:0;\n",
+       "}\n",
+       "\n",
+       "div .comment{\n",
+       "    font-size:0.8em;\n",
+       "    color:#696969;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "</style>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "<br>**FIDLE 2020 - Practical Work Module**"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Version              : 2.0.18\n",
+      "Notebook id          : SYNOP3\n",
+      "Run time             : Sunday 07 March 2021, 21:18:05\n",
+      "TensorFlow version   : 2.2.0\n",
+      "Keras version        : 2.3.0-tf\n",
+      "Datasets dir         : /home/pjluc/datasets/fidle\n",
+      "Run dir              : ./run\n",
+      "Update keras cache   : False\n",
+      "Save figs            : True\n",
+      "Path figs            : ./run/figs\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras.callbacks import TensorBoard\n",
+    "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
+    "\n",
+    "import numpy as np\n",
+    "import math, random\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import pandas as pd\n",
+    "import h5py, json\n",
+    "import os,time,sys\n",
+    "\n",
+    "from importlib import reload\n",
+    "\n",
+    "sys.path.append('..')\n",
+    "import fidle.pwk as pwk\n",
+    "\n",
+    "datasets_dir = pwk.init('SYNOP3')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2 - Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:18:05.781284Z",
+     "iopub.status.busy": "2021-03-07T20:18:05.780977Z",
+     "iopub.status.idle": "2021-03-07T20:18:05.783322Z",
+     "shell.execute_reply": "2021-03-07T20:18:05.782997Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# ---- About dataset\n",
+    "#\n",
+    "dataset_dir      = './data'\n",
+    "dataset_filename = 'synop-LYS.csv'\n",
+    "schema_filename  = 'synop.json'\n",
+    "features         = ['tend', 'cod_tend', 'dd', 'ff', 'td', 'u', 'ww', 'pres', 'rafper', 'rr1', 'rr3', 'tc']\n",
+    "features_len     = len(features)\n",
+    "\n",
+    "# ---- About training\n",
+    "#\n",
+    "iterations       = 4        # number of iterations for prediction (1 iteration = 3h)\n",
+    "\n",
+    "scale            = 1        # Percentage of dataset to be used (1=all)\n",
+    "train_prop       = .8       # Percentage for train (the rest being for the test)\n",
+    "sequence_len     = 16\n",
+    "batch_size       = 32\n",
+    "epochs           = 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Override parameters (batch mode) - Just forget this cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:18:05.786165Z",
+     "iopub.status.busy": "2021-03-07T20:18:05.785860Z",
+     "iopub.status.idle": "2021-03-07T20:18:05.788358Z",
+     "shell.execute_reply": "2021-03-07T20:18:05.788041Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pwk.override('iterations', 'scale', 'train_prop', 'sequence_len', 'batch_size', 'epochs')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Read and prepare dataset\n",
+    "As before, in episode 2... ;-)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:18:05.793226Z",
+     "iopub.status.busy": "2021-03-07T20:18:05.792892Z",
+     "iopub.status.idle": "2021-03-07T20:18:05.889093Z",
+     "shell.execute_reply": "2021-03-07T20:18:05.888818Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset       :  (29165, 14)\n",
+      "Train dataset :  (23332, 12)\n",
+      "Test  dataset :  (5833, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ---- Read dataset\n",
+    "\n",
+    "df = pd.read_csv(f'{dataset_dir}/{dataset_filename}', header=0, sep=';')\n",
+    "\n",
+    "# ---- Scaling\n",
+    "\n",
+    "df = df[:int(scale*len(df))]\n",
+    "train_len=int(train_prop*len(df))\n",
+    "\n",
+    "# ---- Train / Test\n",
+    "dataset_train = df.loc[ :train_len-1, features ]\n",
+    "dataset_test  = df.loc[train_len:,    features ]\n",
+    "\n",
+    "# ---- Normalize, and convert to numpy array\n",
+    "mean = dataset_train.mean()\n",
+    "std  = dataset_train.std()\n",
+    "dataset_train = np.array( (dataset_train - mean) / std )\n",
+    "dataset_test  = np.array( (dataset_test  - mean) / std )\n",
+    "\n",
+    "print('Dataset       : ',df.shape)\n",
+    "print('Train dataset : ',dataset_train.shape)\n",
+    "print('Test  dataset : ',dataset_test.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Predict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1 - Load model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:18:05.892800Z",
+     "iopub.status.busy": "2021-03-07T20:18:05.892494Z",
+     "iopub.status.idle": "2021-03-07T20:18:05.996565Z",
+     "shell.execute_reply": "2021-03-07T20:18:05.996888Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2 Make a 12h prediction\n",
+    "Note : Our predictions are normalized"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:18:06.002134Z",
+     "iopub.status.busy": "2021-03-07T20:18:06.001363Z",
+     "iopub.status.idle": "2021-03-07T20:18:08.080168Z",
+     "shell.execute_reply": "2021-03-07T20:18:08.079833Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"comment\">Saved: ./run/figs/SYNOP3-01-prediction-norm</div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x1152 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# ---- Initial sequence\n",
+    "\n",
+    "s=random.randint(0,len(dataset_test)-sequence_len-iterations)\n",
+    "\n",
+    "sequence_pred = dataset_test[s:s+sequence_len].copy()\n",
+    "sequence_true = dataset_test[s:s+sequence_len+iterations].copy()\n",
+    "\n",
+    "# ---- Iterate on 4 predictions\n",
+    "\n",
+    "sequence_pred=list(sequence_pred)\n",
+    "\n",
+    "for i in range(iterations):\n",
+    "    sequence=sequence_pred[-sequence_len:]\n",
+    "    pred = loaded_model.predict( np.array([sequence]) )\n",
+    "    sequence_pred.append(pred[0])\n",
+    "\n",
+    "# ---- Extract the predictions    \n",
+    "\n",
+    "pred=np.array(sequence_pred[-iterations:])\n",
+    "       \n",
+    "# ---- Show result\n",
+    "\n",
+    "pwk.plot_multivariate_serie(sequence_true, predictions=pred, labels=features, save_as='01-prediction-norm')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.3 Full prediction\n",
+    "#### Some cool functions that do the job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:18:08.084892Z",
+     "iopub.status.busy": "2021-03-07T20:18:08.084581Z",
+     "iopub.status.idle": "2021-03-07T20:18:08.086665Z",
+     "shell.execute_reply": "2021-03-07T20:18:08.086973Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def denormalize(mean,std,seq):\n",
+    "    nseq = seq.copy()\n",
+    "    for i,s in enumerate(nseq):\n",
+    "        s = s*std + mean\n",
+    "        nseq[i]=s\n",
+    "    return nseq\n",
+    "\n",
+    "\n",
+    "def get_prediction(dataset, model, iterations=4,sequence_len=16):\n",
+    "\n",
+    "    # ---- Initial sequence\n",
+    "\n",
+    "    s=random.randint(0,len(dataset)-sequence_len-iterations)\n",
+    "\n",
+    "    sequence_pred = dataset[s:s+sequence_len].copy()\n",
+    "    sequence_true = dataset[s:s+sequence_len+iterations].copy()\n",
+    "\n",
+    "    # ---- Iterate\n",
+    "\n",
+    "    sequence_pred=list(sequence_pred)\n",
+    "\n",
+    "    for i in range(iterations):\n",
+    "        sequence=sequence_pred[-sequence_len:]\n",
+    "        pred = model.predict( np.array([sequence]) )\n",
+    "        sequence_pred.append(pred[0])\n",
+    "\n",
+    "    # ---- Extract the predictions    \n",
+    "\n",
+    "    pred=np.array(sequence_pred[-iterations:])\n",
+    "\n",
+    "    # ---- De-normalization\n",
+    "\n",
+    "    sequence_true = denormalize(mean,std, sequence_true)\n",
+    "    pred          = denormalize(mean,std, pred)\n",
+    "\n",
+    "    return sequence_true,pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### And the result is..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:18:08.090404Z",
+     "iopub.status.busy": "2021-03-07T20:18:08.090040Z",
+     "iopub.status.idle": "2021-03-07T20:18:08.590268Z",
+     "shell.execute_reply": "2021-03-07T20:18:08.590522Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"comment\">Saved: ./run/figs/SYNOP3-02-prediction</div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 3024x2304 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "  \n",
+    "sequence_true, pred = get_prediction(dataset_test, loaded_model,iterations=4)\n",
+    "\n",
+    "feat=11\n",
+    "\n",
+    "pwk.plot_multivariate_serie(sequence_true, predictions=pred, labels=features,\n",
+    "                            only_features=[feat],width=14, height=8, save_as='02-prediction')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-07T20:18:08.593576Z",
+     "iopub.status.busy": "2021-03-07T20:18:08.593185Z",
+     "iopub.status.idle": "2021-03-07T20:18:08.597507Z",
+     "shell.execute_reply": "2021-03-07T20:18:08.597143Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "End time is : Sunday 07 March 2021, 21:18:08\n",
+      "Duration is : 00:00:03 822ms\n",
+      "This notebook ends here\n"
+     ]
+    }
+   ],
+   "source": [
+    "pwk.end()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"todo\">\n",
+    "    What you can do:\n",
+    "    <ul>\n",
+    "        <li>Trying to increase the forecasting time</li>\n",
+    "        <li>What could we do to try to improve our forecasts?</li>\n",
+    "    </ul>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/fidle/01-update-index.ipynb b/fidle/01-update-index.ipynb
index e92ea29b9191578b59d5000ec1e94543416d248b..34e3954fa790f2fc9d7f91376214cca7dc5706c1 100644
--- a/fidle/01-update-index.ipynb
+++ b/fidle/01-update-index.ipynb
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -113,9 +113,10 @@
       "Read :  IMDB/03-Prediction.ipynb\n",
       "Read :  IMDB/04-Show-vectors.ipynb\n",
       "Read :  IMDB/05-LSTM-Keras.ipynb\n",
-      "Read :  SYNOP/01-Preparation-of-data.ipynb\n",
-      "Read :  SYNOP/02-First-predictions.ipynb\n",
-      "Read :  SYNOP/03-12h-predictions.ipynb\n",
+      "Read :  SYNOP/LADYB1-Ladybug.ipynb\n",
+      "Read :  SYNOP/SYNOP1-Preparation-of-data.ipynb\n",
+      "Read :  SYNOP/SYNOP2-First-predictions.ipynb\n",
+      "Read :  SYNOP/SYNOP3-12h-predictions.ipynb\n",
       "Read :  AE/01-AE-with-MNIST.ipynb\n",
       "Read :  AE/02-AE-with-MNIST-post.ipynb\n",
       "Read :  VAE/01-VAE-with-MNIST.ipynb\n",
@@ -129,7 +130,7 @@
       "Read :  Misc/Numpy.ipynb\n",
       "Read :  Misc/Using-Tensorboard.ipynb\n",
       "Catalog saved as ../fidle/logs/catalog.json\n",
-      "Entries :  39\n"
+      "Entries :  40\n"
      ]
     }
    ],
@@ -157,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -231,15 +232,17 @@
        "Retrieving a saved model to perform a sentiment analysis (movie review)\n",
        "- **[IMDB4](IMDB/04-Show-vectors.ipynb)** - [Reload embedded vectors](IMDB/04-Show-vectors.ipynb)  \n",
        "Retrieving embedded vectors from our trained model\n",
-       "- **[IMDB5](IMDB/05-LSTM-Keras.ipynb)** - [Sentiment analysis with a LSTM network](IMDB/05-LSTM-Keras.ipynb)  \n",
-       "Still the same problem, but with a network combining embedding and LSTM\n",
+       "- **[IMDB5](IMDB/05-LSTM-Keras.ipynb)** - [Sentiment analysis with a RNN network](IMDB/05-LSTM-Keras.ipynb)  \n",
+       "Still the same problem, but with a network combining embedding and RNN\n",
        "\n",
        "### Time series with Recurrent Neural Network (RNN)\n",
-       "- **[SYNOP1](SYNOP/01-Preparation-of-data.ipynb)** - [Preparation of data](SYNOP/01-Preparation-of-data.ipynb)  \n",
+       "- **[LADYB1](SYNOP/LADYB1-Ladybug.ipynb)** - [Prediction of a 2D trajectory via RNN](SYNOP/LADYB1-Ladybug.ipynb)  \n",
+       "Artificial dataset generation and prediction attempt via a recurrent network\n",
+       "- **[SYNOP1](SYNOP/SYNOP1-Preparation-of-data.ipynb)** - [Preparation of data](SYNOP/SYNOP1-Preparation-of-data.ipynb)  \n",
        "Episode 1 : Data analysis and preparation of a meteorological dataset (SYNOP)\n",
-       "- **[SYNOP2](SYNOP/02-First-predictions.ipynb)** - [First predictions at 3h](SYNOP/02-First-predictions.ipynb)  \n",
+       "- **[SYNOP2](SYNOP/SYNOP2-First-predictions.ipynb)** - [First predictions at 3h](SYNOP/SYNOP2-First-predictions.ipynb)  \n",
        "Episode 2 : Learning session and weather prediction attempt at 3h\n",
-       "- **[SYNOP3](SYNOP/03-12h-predictions.ipynb)** - [12h predictions](SYNOP/03-12h-predictions.ipynb)  \n",
+       "- **[SYNOP3](SYNOP/SYNOP3-12h-predictions.ipynb)** - [12h predictions](SYNOP/SYNOP3-12h-predictions.ipynb)  \n",
        "Episode 3: Attempt to predict in a more longer term \n",
        "\n",
        "### Unsupervised learning with an autoencoder neural network (AE)\n",
@@ -333,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -408,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -453,14 +456,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Completed on :  Monday 01 March 2021, 15:12:43\n"
+      "Completed on :  Sunday 07 March 2021, 21:23:29\n"
      ]
     }
    ],
diff --git a/fidle/02-running-ci-tests.ipynb b/fidle/02-running-ci-tests.ipynb
index e2b96bfba5b4dc8d30cc561fdbeee2c212d15eff..04673656980807f7607ac52fd35115e7dc979338 100644
--- a/fidle/02-running-ci-tests.ipynb
+++ b/fidle/02-running-ci-tests.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,9 +43,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Profile saved as ./ci/default.yml\n",
+      "Entries :  40\n"
+     ]
+    }
+   ],
    "source": [
     "profile = cookci.get_default_profile()\n",
     "cookci.save_profile(profile, './ci/default.yml')"
@@ -60,13 +69,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "# ---- Profile of tests\n",
     "#\n",
-    "profile_name = './ci/smart_cpu.yml'"
+    "# profile_name = './ci/smart_cpu.yml'\n",
+    "profile_name = './ci/fidle-ad_s04.yml'"
    ]
   },
   {
@@ -78,9 +88,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Run profile session - FIDLE 2021\n",
+      "Version : 1.0\n",
+      "\n",
+      "Load profile :./ci/fidle-ad_s04.yml\n",
+      "    Entries :  4\n",
+      "\n",
+      "Create new ci report : /home/pjluc/dev/fidle/fidle/logs/ci_report.json\n",
+      "Remove error file    : /home/pjluc/dev/fidle/fidle/logs/ci_ERROR.txt\n",
+      "\n",
+      "Run : Nb_LADYB1\n",
+      "    set overrides :\n",
+      "    Run notebook.....done.\n",
+      "    Duration :  0:02:32\n",
+      "    Saved as :  LADYB1-Ladybug==done==.ipynb\n",
+      "\n",
+      "Run : Nb_SYNOP1\n",
+      "    set overrides :\n",
+      "    Run notebook.....done.\n",
+      "    Duration :  0:00:05\n",
+      "    Saved as :  SYNOP1-Preparation-of-data==done==.ipynb\n",
+      "\n",
+      "Run : Nb_SYNOP2\n",
+      "    set overrides :\n",
+      "    Run notebook.....done.\n",
+      "    Duration :  0:02:01\n",
+      "    Saved as :  SYNOP2-First-predictions==done==.ipynb\n",
+      "\n",
+      "Run : Nb_SYNOP3\n",
+      "    set overrides :\n",
+      "    Run notebook.....done.\n",
+      "    Duration :  0:00:07\n",
+      "    Saved as :  SYNOP3-12h-predictions==done==.ipynb\n",
+      "\n",
+      "End of running process\n",
+      "    Duration : 0:04:47\n",
+      "\n",
+      "Complete ci report : /home/pjluc/dev/fidle/fidle/logs/ci_report.json\n"
+     ]
+    }
+   ],
    "source": [
     "# ---- Override for batch mode\n",
     "#\n",
@@ -91,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/fidle/03-ci-report.ipynb b/fidle/03-ci-report.ipynb
index 3c2f4894f2f6992b11d0b7d4ba094737582c0fe5..8fe55eba7adcf73198a6f87bd002a3057b0b0937 100644
--- a/fidle/03-ci-report.ipynb
+++ b/fidle/03-ci-report.ipynb
@@ -34,14 +34,14 @@
      "data": {
       "text/markdown": [
        "**Version** : 1.0  \n",
-       "**Output_Tag** : ==ci==  \n",
+       "**Output_Tag** : ==done==  \n",
        "**Save_Figs** : True  \n",
-       "**Description** : Smart profile, for cpu  \n",
+       "**Description** : Light profile for S04 with CPU  \n",
        "**Host** : Oban  \n",
-       "**Profile** : ./ci/smart_cpu.yml  \n",
-       "**Start** : 09/02/21 22:22:08  \n",
-       "**End** : 09/02/21 22:31:28  \n",
-       "**Duration** : 0:09:20  \n"
+       "**Profile** : ./ci/fidle-ad_s04.yml  \n",
+       "**Start** : 07/03/21 21:13:23  \n",
+       "**End** : 07/03/21 21:18:10  \n",
+       "**Duration** : 0:04:47  \n"
       ],
       "text/plain": [
        "<IPython.core.display.Markdown object>"
@@ -66,193 +66,61 @@
      "data": {
       "text/html": [
        "<style  type=\"text/css\" >\n",
-       "    #T_ed857_ td {\n",
+       "    #T_82c8a_ td {\n",
        "          font-size: 110%;\n",
        "          text-align: left;\n",
-       "    }    #T_ed857_ th {\n",
+       "    }    #T_82c8a_ th {\n",
        "          font-size: 110%;\n",
        "          text-align: left;\n",
-       "    }</style><table id=\"T_ed857_\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >Run</th>        <th class=\"col_heading level0 col1\" >Id</th>        <th class=\"col_heading level0 col2\" >Dir</th>        <th class=\"col_heading level0 col3\" >Src</th>        <th class=\"col_heading level0 col4\" >Out</th>        <th class=\"col_heading level0 col5\" >Start</th>        <th class=\"col_heading level0 col6\" >End</th>        <th class=\"col_heading level0 col7\" >Duration</th>        <th class=\"col_heading level0 col8\" >State</th>    </tr></thead><tbody>\n",
+       "    }</style><table id=\"T_82c8a_\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >Run</th>        <th class=\"col_heading level0 col1\" >Id</th>        <th class=\"col_heading level0 col2\" >Dir</th>        <th class=\"col_heading level0 col3\" >Src</th>        <th class=\"col_heading level0 col4\" >Out</th>        <th class=\"col_heading level0 col5\" >Start</th>        <th class=\"col_heading level0 col6\" >End</th>        <th class=\"col_heading level0 col7\" >Duration</th>        <th class=\"col_heading level0 col8\" >State</th>    </tr></thead><tbody>\n",
        "                <tr>\n",
-       "                                <td id=\"T_ed857_row0_col0\" class=\"data row0 col0\" >LINR1</td>\n",
-       "                        <td id=\"T_ed857_row0_col1\" class=\"data row0 col1\" ><a href='../LinearReg/01-Linear-Regression.ipynb'>LINR1</a></td>\n",
-       "                        <td id=\"T_ed857_row0_col2\" class=\"data row0 col2\" >LinearReg</td>\n",
-       "                        <td id=\"T_ed857_row0_col3\" class=\"data row0 col3\" ><a href='../LinearReg/01-Linear-Regression.ipynb'>01-Linear-Regression.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row0_col4\" class=\"data row0 col4\" ><a href='../LinearReg/01-Linear-Regression==ci==.ipynb'>01-Linear-Regression==ci==.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row0_col5\" class=\"data row0 col5\" >09/02/21 22:22:08</td>\n",
-       "                        <td id=\"T_ed857_row0_col6\" class=\"data row0 col6\" >09/02/21 22:22:12</td>\n",
-       "                        <td id=\"T_ed857_row0_col7\" class=\"data row0 col7\" >0:00:04</td>\n",
-       "                        <td id=\"T_ed857_row0_col8\" class=\"data row0 col8\" >ok</td>\n",
+       "                                <td id=\"T_82c8a_row0_col0\" class=\"data row0 col0\" >Nb_LADYB1</td>\n",
+       "                        <td id=\"T_82c8a_row0_col1\" class=\"data row0 col1\" ><a href='../SYNOP/LADYB1-Ladybug.ipynb'>LADYB1</a></td>\n",
+       "                        <td id=\"T_82c8a_row0_col2\" class=\"data row0 col2\" >SYNOP</td>\n",
+       "                        <td id=\"T_82c8a_row0_col3\" class=\"data row0 col3\" ><a href='../SYNOP/LADYB1-Ladybug.ipynb'>LADYB1-Ladybug.ipynb</a></td>\n",
+       "                        <td id=\"T_82c8a_row0_col4\" class=\"data row0 col4\" ><a href='../SYNOP/LADYB1-Ladybug==done==.ipynb'>LADYB1-Ladybug==done==.ipynb</a></td>\n",
+       "                        <td id=\"T_82c8a_row0_col5\" class=\"data row0 col5\" >07/03/21 21:13:23</td>\n",
+       "                        <td id=\"T_82c8a_row0_col6\" class=\"data row0 col6\" >07/03/21 21:15:55</td>\n",
+       "                        <td id=\"T_82c8a_row0_col7\" class=\"data row0 col7\" >0:02:32</td>\n",
+       "                        <td id=\"T_82c8a_row0_col8\" class=\"data row0 col8\" >ok</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                                <td id=\"T_ed857_row1_col0\" class=\"data row1 col0\" >GRAD1</td>\n",
-       "                        <td id=\"T_ed857_row1_col1\" class=\"data row1 col1\" ><a href='../LinearReg/02-Gradient-descent.ipynb'>GRAD1</a></td>\n",
-       "                        <td id=\"T_ed857_row1_col2\" class=\"data row1 col2\" >LinearReg</td>\n",
-       "                        <td id=\"T_ed857_row1_col3\" class=\"data row1 col3\" ><a href='../LinearReg/02-Gradient-descent.ipynb'>02-Gradient-descent.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row1_col4\" class=\"data row1 col4\" ><a href='../LinearReg/02-Gradient-descent==ci==.ipynb'>02-Gradient-descent==ci==.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row1_col5\" class=\"data row1 col5\" >09/02/21 22:22:12</td>\n",
-       "                        <td id=\"T_ed857_row1_col6\" class=\"data row1 col6\" >09/02/21 22:22:20</td>\n",
-       "                        <td id=\"T_ed857_row1_col7\" class=\"data row1 col7\" >0:00:07</td>\n",
-       "                        <td id=\"T_ed857_row1_col8\" class=\"data row1 col8\" >ok</td>\n",
+       "                                <td id=\"T_82c8a_row1_col0\" class=\"data row1 col0\" >Nb_SYNOP1</td>\n",
+       "                        <td id=\"T_82c8a_row1_col1\" class=\"data row1 col1\" ><a href='../SYNOP/SYNOP1-Preparation-of-data.ipynb'>SYNOP1</a></td>\n",
+       "                        <td id=\"T_82c8a_row1_col2\" class=\"data row1 col2\" >SYNOP</td>\n",
+       "                        <td id=\"T_82c8a_row1_col3\" class=\"data row1 col3\" ><a href='../SYNOP/SYNOP1-Preparation-of-data.ipynb'>SYNOP1-Preparation-of-data.ipynb</a></td>\n",
+       "                        <td id=\"T_82c8a_row1_col4\" class=\"data row1 col4\" ><a href='../SYNOP/SYNOP1-Preparation-of-data==done==.ipynb'>SYNOP1-Preparation-of-data==done==.ipynb</a></td>\n",
+       "                        <td id=\"T_82c8a_row1_col5\" class=\"data row1 col5\" >07/03/21 21:15:56</td>\n",
+       "                        <td id=\"T_82c8a_row1_col6\" class=\"data row1 col6\" >07/03/21 21:16:01</td>\n",
+       "                        <td id=\"T_82c8a_row1_col7\" class=\"data row1 col7\" >0:00:05</td>\n",
+       "                        <td id=\"T_82c8a_row1_col8\" class=\"data row1 col8\" >ok</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                                <td id=\"T_ed857_row2_col0\" class=\"data row2 col0\" >POLR1</td>\n",
-       "                        <td id=\"T_ed857_row2_col1\" class=\"data row2 col1\" ><a href='../LinearReg/03-Polynomial-Regression.ipynb'>POLR1</a></td>\n",
-       "                        <td id=\"T_ed857_row2_col2\" class=\"data row2 col2\" >LinearReg</td>\n",
-       "                        <td id=\"T_ed857_row2_col3\" class=\"data row2 col3\" ><a href='../LinearReg/03-Polynomial-Regression.ipynb'>03-Polynomial-Regression.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row2_col4\" class=\"data row2 col4\" ><a href='../LinearReg/03-Polynomial-Regression==ci==.ipynb'>03-Polynomial-Regression==ci==.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row2_col5\" class=\"data row2 col5\" >09/02/21 22:22:20</td>\n",
-       "                        <td id=\"T_ed857_row2_col6\" class=\"data row2 col6\" >09/02/21 22:22:24</td>\n",
-       "                        <td id=\"T_ed857_row2_col7\" class=\"data row2 col7\" >0:00:03</td>\n",
-       "                        <td id=\"T_ed857_row2_col8\" class=\"data row2 col8\" >ok</td>\n",
+       "                                <td id=\"T_82c8a_row2_col0\" class=\"data row2 col0\" >Nb_SYNOP2</td>\n",
+       "                        <td id=\"T_82c8a_row2_col1\" class=\"data row2 col1\" ><a href='../SYNOP/SYNOP2-First-predictions.ipynb'>SYNOP2</a></td>\n",
+       "                        <td id=\"T_82c8a_row2_col2\" class=\"data row2 col2\" >SYNOP</td>\n",
+       "                        <td id=\"T_82c8a_row2_col3\" class=\"data row2 col3\" ><a href='../SYNOP/SYNOP2-First-predictions.ipynb'>SYNOP2-First-predictions.ipynb</a></td>\n",
+       "                        <td id=\"T_82c8a_row2_col4\" class=\"data row2 col4\" ><a href='../SYNOP/SYNOP2-First-predictions==done==.ipynb'>SYNOP2-First-predictions==done==.ipynb</a></td>\n",
+       "                        <td id=\"T_82c8a_row2_col5\" class=\"data row2 col5\" >07/03/21 21:16:01</td>\n",
+       "                        <td id=\"T_82c8a_row2_col6\" class=\"data row2 col6\" >07/03/21 21:18:02</td>\n",
+       "                        <td id=\"T_82c8a_row2_col7\" class=\"data row2 col7\" >0:02:01</td>\n",
+       "                        <td id=\"T_82c8a_row2_col8\" class=\"data row2 col8\" >ok</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                                <td id=\"T_ed857_row3_col0\" class=\"data row3 col0\" >LOGR1</td>\n",
-       "                        <td id=\"T_ed857_row3_col1\" class=\"data row3 col1\" ><a href='../LinearReg/04-Logistic-Regression.ipynb'>LOGR1</a></td>\n",
-       "                        <td id=\"T_ed857_row3_col2\" class=\"data row3 col2\" >LinearReg</td>\n",
-       "                        <td id=\"T_ed857_row3_col3\" class=\"data row3 col3\" ><a href='../LinearReg/04-Logistic-Regression.ipynb'>04-Logistic-Regression.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row3_col4\" class=\"data row3 col4\" ><a href='../LinearReg/04-Logistic-Regression==ci==.ipynb'>04-Logistic-Regression==ci==.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row3_col5\" class=\"data row3 col5\" >09/02/21 22:22:24</td>\n",
-       "                        <td id=\"T_ed857_row3_col6\" class=\"data row3 col6\" >09/02/21 22:22:28</td>\n",
-       "                        <td id=\"T_ed857_row3_col7\" class=\"data row3 col7\" >0:00:03</td>\n",
-       "                        <td id=\"T_ed857_row3_col8\" class=\"data row3 col8\" >ok</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                                <td id=\"T_ed857_row4_col0\" class=\"data row4 col0\" >PER57</td>\n",
-       "                        <td id=\"T_ed857_row4_col1\" class=\"data row4 col1\" ><a href='../IRIS/01-Simple-Perceptron.ipynb'>PER57</a></td>\n",
-       "                        <td id=\"T_ed857_row4_col2\" class=\"data row4 col2\" >IRIS</td>\n",
-       "                        <td id=\"T_ed857_row4_col3\" class=\"data row4 col3\" ><a href='../IRIS/01-Simple-Perceptron.ipynb'>01-Simple-Perceptron.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row4_col4\" class=\"data row4 col4\" ><a href='../IRIS/01-Simple-Perceptron==ci==.ipynb'>01-Simple-Perceptron==ci==.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row4_col5\" class=\"data row4 col5\" >09/02/21 22:22:28</td>\n",
-       "                        <td id=\"T_ed857_row4_col6\" class=\"data row4 col6\" >09/02/21 22:22:32</td>\n",
-       "                        <td id=\"T_ed857_row4_col7\" class=\"data row4 col7\" >0:00:04</td>\n",
-       "                        <td id=\"T_ed857_row4_col8\" class=\"data row4 col8\" >ok</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                                <td id=\"T_ed857_row5_col0\" class=\"data row5 col0\" >BHPD1</td>\n",
-       "                        <td id=\"T_ed857_row5_col1\" class=\"data row5 col1\" ><a href='../BHPD/01-DNN-Regression.ipynb'>BHPD1</a></td>\n",
-       "                        <td id=\"T_ed857_row5_col2\" class=\"data row5 col2\" >BHPD</td>\n",
-       "                        <td id=\"T_ed857_row5_col3\" class=\"data row5 col3\" ><a href='../BHPD/01-DNN-Regression.ipynb'>01-DNN-Regression.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row5_col4\" class=\"data row5 col4\" ><a href='../BHPD/01-DNN-Regression==ci==.ipynb'>01-DNN-Regression==ci==.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row5_col5\" class=\"data row5 col5\" >09/02/21 22:22:32</td>\n",
-       "                        <td id=\"T_ed857_row5_col6\" class=\"data row5 col6\" >09/02/21 22:22:41</td>\n",
-       "                        <td id=\"T_ed857_row5_col7\" class=\"data row5 col7\" >0:00:09</td>\n",
-       "                        <td id=\"T_ed857_row5_col8\" class=\"data row5 col8\" >ok</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                                <td id=\"T_ed857_row6_col0\" class=\"data row6 col0\" >BHPD2</td>\n",
-       "                        <td id=\"T_ed857_row6_col1\" class=\"data row6 col1\" ><a href='../BHPD/02-DNN-Regression-Premium.ipynb'>BHPH2</a></td>\n",
-       "                        <td id=\"T_ed857_row6_col2\" class=\"data row6 col2\" >BHPD</td>\n",
-       "                        <td id=\"T_ed857_row6_col3\" class=\"data row6 col3\" ><a href='../BHPD/02-DNN-Regression-Premium.ipynb'>02-DNN-Regression-Premium.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row6_col4\" class=\"data row6 col4\" ><a href='../BHPD/02-DNN-Regression-Premium==ci==.ipynb'>02-DNN-Regression-Premium==ci==.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row6_col5\" class=\"data row6 col5\" >09/02/21 22:22:41</td>\n",
-       "                        <td id=\"T_ed857_row6_col6\" class=\"data row6 col6\" >09/02/21 22:22:55</td>\n",
-       "                        <td id=\"T_ed857_row6_col7\" class=\"data row6 col7\" >0:00:13</td>\n",
-       "                        <td id=\"T_ed857_row6_col8\" class=\"data row6 col8\" >ok</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                                <td id=\"T_ed857_row7_col0\" class=\"data row7 col0\" >MNIST1</td>\n",
-       "                        <td id=\"T_ed857_row7_col1\" class=\"data row7 col1\" ><a href='../MNIST/01-DNN-MNIST.ipynb'>MNIST1</a></td>\n",
-       "                        <td id=\"T_ed857_row7_col2\" class=\"data row7 col2\" >MNIST</td>\n",
-       "                        <td id=\"T_ed857_row7_col3\" class=\"data row7 col3\" ><a href='../MNIST/01-DNN-MNIST.ipynb'>01-DNN-MNIST.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row7_col4\" class=\"data row7 col4\" ><a href='../MNIST/01-DNN-MNIST==ci==.ipynb'>01-DNN-MNIST==ci==.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row7_col5\" class=\"data row7 col5\" >09/02/21 22:22:55</td>\n",
-       "                        <td id=\"T_ed857_row7_col6\" class=\"data row7 col6\" >09/02/21 22:23:32</td>\n",
-       "                        <td id=\"T_ed857_row7_col7\" class=\"data row7 col7\" >0:00:36</td>\n",
-       "                        <td id=\"T_ed857_row7_col8\" class=\"data row7 col8\" >ok</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                                <td id=\"T_ed857_row8_col0\" class=\"data row8 col0\" >MNIST2</td>\n",
-       "                        <td id=\"T_ed857_row8_col1\" class=\"data row8 col1\" ><a href='../MNIST/02-CNN-MNIST.ipynb'>MNIST2</a></td>\n",
-       "                        <td id=\"T_ed857_row8_col2\" class=\"data row8 col2\" >MNIST</td>\n",
-       "                        <td id=\"T_ed857_row8_col3\" class=\"data row8 col3\" ><a href='../MNIST/02-CNN-MNIST.ipynb'>02-CNN-MNIST.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row8_col4\" class=\"data row8 col4\" ><a href='../MNIST/02-CNN-MNIST==ci==.ipynb'>02-CNN-MNIST==ci==.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row8_col5\" class=\"data row8 col5\" >09/02/21 22:23:32</td>\n",
-       "                        <td id=\"T_ed857_row8_col6\" class=\"data row8 col6\" >09/02/21 22:25:27</td>\n",
-       "                        <td id=\"T_ed857_row8_col7\" class=\"data row8 col7\" >0:01:55</td>\n",
-       "                        <td id=\"T_ed857_row8_col8\" class=\"data row8 col8\" >ok</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                                <td id=\"T_ed857_row9_col0\" class=\"data row9 col0\" >GTSRB1</td>\n",
-       "                        <td id=\"T_ed857_row9_col1\" class=\"data row9 col1\" ><a href='../GTSRB/01-Preparation-of-data.ipynb'>GTSRG1</a></td>\n",
-       "                        <td id=\"T_ed857_row9_col2\" class=\"data row9 col2\" >GTSRB</td>\n",
-       "                        <td id=\"T_ed857_row9_col3\" class=\"data row9 col3\" ><a href='../GTSRB/01-Preparation-of-data.ipynb'>01-Preparation-of-data.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row9_col4\" class=\"data row9 col4\" ><a href='../GTSRB/01-Preparation-of-data==ci==.ipynb'>01-Preparation-of-data==ci==.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row9_col5\" class=\"data row9 col5\" >09/02/21 22:25:28</td>\n",
-       "                        <td id=\"T_ed857_row9_col6\" class=\"data row9 col6\" >09/02/21 22:27:27</td>\n",
-       "                        <td id=\"T_ed857_row9_col7\" class=\"data row9 col7\" >0:01:59</td>\n",
-       "                        <td id=\"T_ed857_row9_col8\" class=\"data row9 col8\" >ok</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                                <td id=\"T_ed857_row10_col0\" class=\"data row10 col0\" >GTSRB2</td>\n",
-       "                        <td id=\"T_ed857_row10_col1\" class=\"data row10 col1\" ><a href='../GTSRB/02-First-convolutions.ipynb'>GTSRB2</a></td>\n",
-       "                        <td id=\"T_ed857_row10_col2\" class=\"data row10 col2\" >GTSRB</td>\n",
-       "                        <td id=\"T_ed857_row10_col3\" class=\"data row10 col3\" ><a href='../GTSRB/02-First-convolutions.ipynb'>02-First-convolutions.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row10_col4\" class=\"data row10 col4\" ><a href='../GTSRB/02-First-convolutions==ci==.ipynb'>02-First-convolutions==ci==.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row10_col5\" class=\"data row10 col5\" >09/02/21 22:27:27</td>\n",
-       "                        <td id=\"T_ed857_row10_col6\" class=\"data row10 col6\" >09/02/21 22:27:57</td>\n",
-       "                        <td id=\"T_ed857_row10_col7\" class=\"data row10 col7\" >0:00:29</td>\n",
-       "                        <td id=\"T_ed857_row10_col8\" class=\"data row10 col8\" >ok</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                                <td id=\"T_ed857_row11_col0\" class=\"data row11 col0\" >GTSRB3</td>\n",
-       "                        <td id=\"T_ed857_row11_col1\" class=\"data row11 col1\" ><a href='../GTSRB/03-Tracking-and-visualizing.ipynb'>GTSRB3</a></td>\n",
-       "                        <td id=\"T_ed857_row11_col2\" class=\"data row11 col2\" >GTSRB</td>\n",
-       "                        <td id=\"T_ed857_row11_col3\" class=\"data row11 col3\" ><a href='../GTSRB/03-Tracking-and-visualizing.ipynb'>03-Tracking-and-visualizing.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row11_col4\" class=\"data row11 col4\" ><a href='../GTSRB/03-Tracking-and-visualizing==ci==.ipynb'>03-Tracking-and-visualizing==ci==.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row11_col5\" class=\"data row11 col5\" >09/02/21 22:27:57</td>\n",
-       "                        <td id=\"T_ed857_row11_col6\" class=\"data row11 col6\" >09/02/21 22:28:52</td>\n",
-       "                        <td id=\"T_ed857_row11_col7\" class=\"data row11 col7\" >0:00:54</td>\n",
-       "                        <td id=\"T_ed857_row11_col8\" class=\"data row11 col8\" >ok</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                                <td id=\"T_ed857_row12_col0\" class=\"data row12 col0\" >GTSRB4</td>\n",
-       "                        <td id=\"T_ed857_row12_col1\" class=\"data row12 col1\" ><a href='../GTSRB/04-Data-augmentation.ipynb'>GTSRB4</a></td>\n",
-       "                        <td id=\"T_ed857_row12_col2\" class=\"data row12 col2\" >GTSRB</td>\n",
-       "                        <td id=\"T_ed857_row12_col3\" class=\"data row12 col3\" ><a href='../GTSRB/04-Data-augmentation.ipynb'>04-Data-augmentation.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row12_col4\" class=\"data row12 col4\" ><a href='../GTSRB/04-Data-augmentation==ci==.ipynb'>04-Data-augmentation==ci==.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row12_col5\" class=\"data row12 col5\" >09/02/21 22:28:52</td>\n",
-       "                        <td id=\"T_ed857_row12_col6\" class=\"data row12 col6\" >09/02/21 22:29:41</td>\n",
-       "                        <td id=\"T_ed857_row12_col7\" class=\"data row12 col7\" >0:00:49</td>\n",
-       "                        <td id=\"T_ed857_row12_col8\" class=\"data row12 col8\" >ok</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                                <td id=\"T_ed857_row13_col0\" class=\"data row13 col0\" >GTSRB5</td>\n",
-       "                        <td id=\"T_ed857_row13_col1\" class=\"data row13 col1\" ><a href='../GTSRB/05-Full-convolutions.ipynb'>GTSRB5</a></td>\n",
-       "                        <td id=\"T_ed857_row13_col2\" class=\"data row13 col2\" >GTSRB</td>\n",
-       "                        <td id=\"T_ed857_row13_col3\" class=\"data row13 col3\" ><a href='../GTSRB/05-Full-convolutions.ipynb'>05-Full-convolutions.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row13_col4\" class=\"data row13 col4\" ><a href='../GTSRB/05-Full-convolutions==ci==.ipynb'>05-Full-convolutions==ci==.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row13_col5\" class=\"data row13 col5\" >09/02/21 22:29:41</td>\n",
-       "                        <td id=\"T_ed857_row13_col6\" class=\"data row13 col6\" >09/02/21 22:31:24</td>\n",
-       "                        <td id=\"T_ed857_row13_col7\" class=\"data row13 col7\" >0:01:43</td>\n",
-       "                        <td id=\"T_ed857_row13_col8\" class=\"data row13 col8\" >ok</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                                <td id=\"T_ed857_row14_col0\" class=\"data row14 col0\" >GTSRB6</td>\n",
-       "                        <td id=\"T_ed857_row14_col1\" class=\"data row14 col1\" ><a href='../GTSRB/06-Notebook-as-a-batch.ipynb'>GTSRB6</a></td>\n",
-       "                        <td id=\"T_ed857_row14_col2\" class=\"data row14 col2\" >GTSRB</td>\n",
-       "                        <td id=\"T_ed857_row14_col3\" class=\"data row14 col3\" ><a href='../GTSRB/06-Notebook-as-a-batch.ipynb'>06-Notebook-as-a-batch.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row14_col4\" class=\"data row14 col4\" ><a href='../GTSRB/06-Notebook-as-a-batch==ci==.ipynb'>06-Notebook-as-a-batch==ci==.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row14_col5\" class=\"data row14 col5\" >09/02/21 22:31:24</td>\n",
-       "                        <td id=\"T_ed857_row14_col6\" class=\"data row14 col6\" >09/02/21 22:31:26</td>\n",
-       "                        <td id=\"T_ed857_row14_col7\" class=\"data row14 col7\" >0:00:01</td>\n",
-       "                        <td id=\"T_ed857_row14_col8\" class=\"data row14 col8\" >ok</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                                <td id=\"T_ed857_row15_col0\" class=\"data row15 col0\" >GTSRB7</td>\n",
-       "                        <td id=\"T_ed857_row15_col1\" class=\"data row15 col1\" ><a href='../GTSRB/07-Show-report.ipynb'>GTSRB7</a></td>\n",
-       "                        <td id=\"T_ed857_row15_col2\" class=\"data row15 col2\" >GTSRB</td>\n",
-       "                        <td id=\"T_ed857_row15_col3\" class=\"data row15 col3\" ><a href='../GTSRB/07-Show-report.ipynb'>07-Show-report.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row15_col4\" class=\"data row15 col4\" ><a href='../GTSRB/07-Show-report==ci==.ipynb'>07-Show-report==ci==.ipynb</a></td>\n",
-       "                        <td id=\"T_ed857_row15_col5\" class=\"data row15 col5\" >09/02/21 22:31:26</td>\n",
-       "                        <td id=\"T_ed857_row15_col6\" class=\"data row15 col6\" >09/02/21 22:31:28</td>\n",
-       "                        <td id=\"T_ed857_row15_col7\" class=\"data row15 col7\" >0:00:02</td>\n",
-       "                        <td id=\"T_ed857_row15_col8\" class=\"data row15 col8\" >ok</td>\n",
+       "                                <td id=\"T_82c8a_row3_col0\" class=\"data row3 col0\" >Nb_SYNOP3</td>\n",
+       "                        <td id=\"T_82c8a_row3_col1\" class=\"data row3 col1\" ><a href='../SYNOP/SYNOP3-12h-predictions.ipynb'>SYNOP3</a></td>\n",
+       "                        <td id=\"T_82c8a_row3_col2\" class=\"data row3 col2\" >SYNOP</td>\n",
+       "                        <td id=\"T_82c8a_row3_col3\" class=\"data row3 col3\" ><a href='../SYNOP/SYNOP3-12h-predictions.ipynb'>SYNOP3-12h-predictions.ipynb</a></td>\n",
+       "                        <td id=\"T_82c8a_row3_col4\" class=\"data row3 col4\" ><a href='../SYNOP/SYNOP3-12h-predictions==done==.ipynb'>SYNOP3-12h-predictions==done==.ipynb</a></td>\n",
+       "                        <td id=\"T_82c8a_row3_col5\" class=\"data row3 col5\" >07/03/21 21:18:03</td>\n",
+       "                        <td id=\"T_82c8a_row3_col6\" class=\"data row3 col6\" >07/03/21 21:18:10</td>\n",
+       "                        <td id=\"T_82c8a_row3_col7\" class=\"data row3 col7\" >0:00:07</td>\n",
+       "                        <td id=\"T_82c8a_row3_col8\" class=\"data row3 col8\" >ok</td>\n",
        "            </tr>\n",
        "    </tbody></table>"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f71d6ce2dc0>"
+       "<pandas.io.formats.style.Styler at 0x7f7dde7f9040>"
       ]
      },
      "metadata": {},
diff --git a/fidle/ci/default.yml b/fidle/ci/default.yml
index 0e9ed689fd1beeafd3c78714f8c0a13c0dca724c..d647c20555b265efa44b019d8d4ec5b3eb2bc843 100644
--- a/fidle/ci/default.yml
+++ b/fidle/ci/default.yml
@@ -180,15 +180,38 @@ Nb_IMDB5:
   notebook_dir: IMDB
   notebook_src: 05-LSTM-Keras.ipynb
   notebook_tag: default
+  overrides:
+    vocab_size: default
+    hide_most_frequently: default
+    review_len: default
+    dense_vector_size: default
+    batch_size: default
+    epochs: default
+    output_dir: default
+Nb_LADYB1:
+  notebook_id: LADYB1
+  notebook_dir: SYNOP
+  notebook_src: LADYB1-Ladybug.ipynb
+  notebook_tag: default
+  overrides:
+    run_dir: default
+    scale: default
+    train_prop: default
+    sequence_len: default
+    predict_len: default
+    batch_size: default
+    epochs: default
 Nb_SYNOP1:
   notebook_id: SYNOP1
   notebook_dir: SYNOP
-  notebook_src: 01-Preparation-of-data.ipynb
+  notebook_src: SYNOP1-Preparation-of-data.ipynb
   notebook_tag: default
+  overrides:
+    output_dir: default
 Nb_SYNOP2:
   notebook_id: SYNOP2
   notebook_dir: SYNOP
-  notebook_src: 02-First-predictions.ipynb
+  notebook_src: SYNOP2-First-predictions.ipynb
   notebook_tag: default
   overrides:
     scale: default
@@ -199,7 +222,7 @@ Nb_SYNOP2:
 Nb_SYNOP3:
   notebook_id: SYNOP3
   notebook_dir: SYNOP
-  notebook_src: 03-12h-predictions.ipynb
+  notebook_src: SYNOP3-12h-predictions.ipynb
   notebook_tag: default
   overrides:
     iterations: default
diff --git a/fidle/ci/fidle-ad_s04.yml b/fidle/ci/fidle-ad_s04.yml
new file mode 100644
index 0000000000000000000000000000000000000000..d3deb8c4138eef67768b17f8f8670f9318320f3a
--- /dev/null
+++ b/fidle/ci/fidle-ad_s04.yml
@@ -0,0 +1,51 @@
+_metadata_:
+  version: '1.0'
+  output_tag: ==done==
+  save_figs: true
+  description: Light profile for S04 with CPU
+#
+# ------ SYNOP -----------------------------------------------------
+#
+Nb_LADYB1:
+  notebook_id: LADYB1
+  notebook_dir: SYNOP
+  notebook_src: LADYB1-Ladybug.ipynb
+  notebook_tag: default
+  overrides:
+    run_dir: default
+    scale: default
+    train_prop: default
+    sequence_len: default
+    predict_len: default
+    batch_size: default
+    epochs: default
+Nb_SYNOP1:
+  notebook_id: SYNOP1
+  notebook_dir: SYNOP
+  notebook_src: SYNOP1-Preparation-of-data.ipynb
+  notebook_tag: default
+  overrides:
+    output_dir: default
+Nb_SYNOP2:
+  notebook_id: SYNOP2
+  notebook_dir: SYNOP
+  notebook_src: SYNOP2-First-predictions.ipynb
+  notebook_tag: default
+  overrides:
+    scale: default
+    train_prop: default
+    sequence_len: default
+    batch_size: default
+    epochs: default
+Nb_SYNOP3:
+  notebook_id: SYNOP3
+  notebook_dir: SYNOP
+  notebook_src: SYNOP3-12h-predictions.ipynb
+  notebook_tag: default
+  overrides:
+    iterations: default
+    scale: default
+    train_prop: default
+    sequence_len: default
+    batch_size: default
+    epochs: default
\ No newline at end of file
diff --git a/fidle/ci/full_fad_s01.yml b/fidle/ci/full_fad_s01.yml
deleted file mode 100644
index 528c7693e9dc3175f6f205731cbb745683e1364b..0000000000000000000000000000000000000000
--- a/fidle/ci/full_fad_s01.yml
+++ /dev/null
@@ -1,45 +0,0 @@
-_metadata_:
-  version: '1.0'
-  output_tag: ==ci==
-  save_figs: true
-  description: Runned notebooks (done)
-LINR1:
-  notebook_id: LINR1
-  notebook_dir: LinearReg
-  notebook_src: 01-Linear-Regression.ipynb
-  notebook_tag: default
-GRAD1:
-  notebook_id: GRAD1
-  notebook_dir: LinearReg
-  notebook_src: 02-Gradient-descent.ipynb
-  notebook_tag: default
-POLR1:
-  notebook_id: POLR1
-  notebook_dir: LinearReg
-  notebook_src: 03-Polynomial-Regression.ipynb
-  notebook_tag: default
-LOGR1:
-  notebook_id: LOGR1
-  notebook_dir: LinearReg
-  notebook_src: 04-Logistic-Regression.ipynb
-  notebook_tag: default
-PER57:
-  notebook_id: PER57
-  notebook_dir: IRIS
-  notebook_src: 01-Simple-Perceptron.ipynb
-  notebook_tag: default
-BHPD1:
-  notebook_id: BHPD1
-  notebook_dir: BHPD
-  notebook_src: 01-DNN-Regression.ipynb
-  notebook_tag: default
-BHPD2:
-  notebook_id: BHPH2
-  notebook_dir: BHPD
-  notebook_src: 02-DNN-Regression-Premium.ipynb
-  notebook_tag: default
-MNIST1:
-  notebook_id: MNIST1
-  notebook_dir: MNIST
-  notebook_src: 01-DNN-MNIST.ipynb
-  notebook_tag: default
diff --git a/fidle/ci/full_gpu.yml b/fidle/ci/full_gpu.yml
index 1e15ca9c0062e20d8219de72ae0ffff82676a8c7..511dba89d0c288894c29682d8f22dac1f47d912c 100644
--- a/fidle/ci/full_gpu.yml
+++ b/fidle/ci/full_gpu.yml
@@ -218,34 +218,49 @@ Nb_IMDB5:
 #
 # ------ SYNOP -----------------------------------------------------
 #
+Nb_LADYB1:
+  notebook_id: LADYB1
+  notebook_dir: SYNOP
+  notebook_src: LADYB1-Ladybug.ipynb
+  notebook_tag: default
+  overrides:
+    run_dir: default
+    scale: default
+    train_prop: default
+    sequence_len: default
+    predict_len: default
+    batch_size: default
+    epochs: default
 Nb_SYNOP1:
   notebook_id: SYNOP1
   notebook_dir: SYNOP
-  notebook_src: 01-Preparation-of-data.ipynb
+  notebook_src: SYNOP1-Preparation-of-data.ipynb
   notebook_tag: default
+  overrides:
+    output_dir: default
 Nb_SYNOP2:
   notebook_id: SYNOP2
   notebook_dir: SYNOP
-  notebook_src: 02-First-predictions.ipynb
+  notebook_src: SYNOP2-First-predictions.ipynb
   notebook_tag: default
   overrides:
-    scale: 1
-    train_prop: 0.8
-    sequence_len: 16
-    batch_size: 32
-    epochs: 10
+    scale: default
+    train_prop: default
+    sequence_len: default
+    batch_size: default
+    epochs: default
 Nb_SYNOP3:
   notebook_id: SYNOP3
   notebook_dir: SYNOP
-  notebook_src: 03-12h-predictions.ipynb
+  notebook_src: SYNOP3-12h-predictions.ipynb
   notebook_tag: default
   overrides:
-    iterations: 4
-    scale: 1
-    train_prop: 0.8
-    sequence_len: 16
-    batch_size: 32
-    epochs: 10
+    iterations: default
+    scale: default
+    train_prop: default
+    sequence_len: default
+    batch_size: default
+    epochs: default
 #
 # ------ AE --------------------------------------------------------
 #
diff --git a/fidle/logs/catalog.json b/fidle/logs/catalog.json
index 4f6419042469c703cc54d7790cce24ee730e1eb4..4b9640e3745bde10cc3f81a7e142625bfa0e05d4 100644
--- a/fidle/logs/catalog.json
+++ b/fidle/logs/catalog.json
@@ -241,22 +241,48 @@
         "id": "IMDB5",
         "dirname": "IMDB",
         "basename": "05-LSTM-Keras.ipynb",
-        "title": "Sentiment analysis with a LSTM network",
-        "description": "Still the same problem, but with a network combining embedding and LSTM",
-        "overrides": []
+        "title": "Sentiment analysis with a RNN network",
+        "description": "Still the same problem, but with a network combining embedding and RNN",
+        "overrides": [
+            "vocab_size",
+            "hide_most_frequently",
+            "review_len",
+            "dense_vector_size",
+            "batch_size",
+            "epochs",
+            "output_dir"
+        ]
+    },
+    "LADYB1": {
+        "id": "LADYB1",
+        "dirname": "SYNOP",
+        "basename": "LADYB1-Ladybug.ipynb",
+        "title": "Prediction of a 2D trajectory via RNN",
+        "description": "Artificial dataset generation and prediction attempt via a recurrent network",
+        "overrides": [
+            "run_dir",
+            "scale",
+            "train_prop",
+            "sequence_len",
+            "predict_len",
+            "batch_size",
+            "epochs"
+        ]
     },
     "SYNOP1": {
         "id": "SYNOP1",
         "dirname": "SYNOP",
-        "basename": "01-Preparation-of-data.ipynb",
+        "basename": "SYNOP1-Preparation-of-data.ipynb",
         "title": "Preparation of data",
         "description": "Episode 1 : Data analysis and preparation of a meteorological dataset (SYNOP)",
-        "overrides": []
+        "overrides": [
+            "output_dir"
+        ]
     },
     "SYNOP2": {
         "id": "SYNOP2",
         "dirname": "SYNOP",
-        "basename": "02-First-predictions.ipynb",
+        "basename": "SYNOP2-First-predictions.ipynb",
         "title": "First predictions at 3h",
         "description": "Episode 2 : Learning session and weather prediction attempt at 3h",
         "overrides": [
@@ -270,7 +296,7 @@
     "SYNOP3": {
         "id": "SYNOP3",
         "dirname": "SYNOP",
-        "basename": "03-12h-predictions.ipynb",
+        "basename": "SYNOP3-12h-predictions.ipynb",
         "title": "12h predictions",
         "description": "Episode 3: Attempt to predict in a more longer term ",
         "overrides": [
diff --git a/fidle/logs/ci_report-2021-03-01.html b/fidle/logs/ci_report-2021-03-01.html
new file mode 100644
index 0000000000000000000000000000000000000000..dd1a64e2df55dec892f4bf6f2e49dd55c3902e11
--- /dev/null
+++ b/fidle/logs/ci_report-2021-03-01.html
@@ -0,0 +1,475 @@
+    <html>
+        <head><title>FIDLE - CI Report</title></head>
+        <body>
+        <style>
+            body{
+                  font-family: sans-serif;
+            }
+            div.title{ 
+                font-size: 1.2em;
+                font-weight: bold;
+                padding: 15px 0px 10px 0px; }
+            a{
+                color: SteelBlue;
+                text-decoration:none;
+            }
+            table{      
+                  border-collapse : collapse;
+                  font-size : 0.9em;
+            }
+            td{
+                  border-style: solid;
+                  border-width:  thin;
+                  border-color:  lightgrey;
+                  padding: 5px;
+            }
+            .metadata{ padding: 10px 0px 10px 30px; font-size: 0.9em; }
+            .result{ padding: 10px 0px 10px 30px; }
+        </style>
+            <br>Hi,
+            <p>Below is the result of the continuous integration tests of the Fidle project:</p>
+            <div class='title'>About :</div>
+            <div class="metadata"><b>Version</b> : 1.0  <br>
+<b>Output_Tag</b> : ==done==  <br>
+<b>Save_Figs</b> : True  <br>
+<b>Description</b> : Full profile for GPU  <br>
+<b>Host</b> : r7i0n6  <br>
+<b>Profile</b> : ./ci/full_gpu.yml  <br>
+<b>Start</b> : 01/03/21 18:40:12  <br>
+<b>End</b> : 01/03/21 21:45:30  <br>
+<b>Duration</b> : 3:05:17  <br>
+</div>
+            <div class='title'>Details :</div>
+            <div class="result"><style  type="text/css" >
+    #T_159526f0_7acf_11eb_b7a6_0cc47af5a44f td {
+          font-size: 110%;
+          text-align: left;
+    }    #T_159526f0_7acf_11eb_b7a6_0cc47af5a44f th {
+          font-size: 110%;
+          text-align: left;
+    }</style><table id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44f" ><thead>    <tr>        <th class="col_heading level0 col0" >Run</th>        <th class="col_heading level0 col1" >Id</th>        <th class="col_heading level0 col2" >Dir</th>        <th class="col_heading level0 col3" >Src</th>        <th class="col_heading level0 col4" >Out</th>        <th class="col_heading level0 col5" >Start</th>        <th class="col_heading level0 col6" >End</th>        <th class="col_heading level0 col7" >Duration</th>        <th class="col_heading level0 col8" >State</th>    </tr></thead><tbody>
+                <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow0_col0" class="data row0 col0" >Nb_LINR1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow0_col1" class="data row0 col1" >LINR1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow0_col2" class="data row0 col2" >LinearReg</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow0_col3" class="data row0 col3" >01-Linear-Regression.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow0_col4" class="data row0 col4" >01-Linear-Regression==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow0_col5" class="data row0 col5" >01/03/21 18:40:13</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow0_col6" class="data row0 col6" >01/03/21 18:40:42</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow0_col7" class="data row0 col7" >0:00:29</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow0_col8" class="data row0 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow1_col0" class="data row1 col0" >Nb_GRAD1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow1_col1" class="data row1 col1" >GRAD1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow1_col2" class="data row1 col2" >LinearReg</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow1_col3" class="data row1 col3" >02-Gradient-descent.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow1_col4" class="data row1 col4" >02-Gradient-descent==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow1_col5" class="data row1 col5" >01/03/21 18:40:42</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow1_col6" class="data row1 col6" >01/03/21 18:40:52</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow1_col7" class="data row1 col7" >0:00:09</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow1_col8" class="data row1 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow2_col0" class="data row2 col0" >Nb_POLR1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow2_col1" class="data row2 col1" >POLR1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow2_col2" class="data row2 col2" >LinearReg</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow2_col3" class="data row2 col3" >03-Polynomial-Regression.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow2_col4" class="data row2 col4" >03-Polynomial-Regression==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow2_col5" class="data row2 col5" >01/03/21 18:40:52</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow2_col6" class="data row2 col6" >01/03/21 18:40:59</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow2_col7" class="data row2 col7" >0:00:07</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow2_col8" class="data row2 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow3_col0" class="data row3 col0" >Nb_LOGR1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow3_col1" class="data row3 col1" >LOGR1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow3_col2" class="data row3 col2" >LinearReg</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow3_col3" class="data row3 col3" >04-Logistic-Regression.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow3_col4" class="data row3 col4" >04-Logistic-Regression==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow3_col5" class="data row3 col5" >01/03/21 18:40:59</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow3_col6" class="data row3 col6" >01/03/21 18:41:06</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow3_col7" class="data row3 col7" >0:00:06</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow3_col8" class="data row3 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow4_col0" class="data row4 col0" >Nb_PER57</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow4_col1" class="data row4 col1" >PER57</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow4_col2" class="data row4 col2" >IRIS</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow4_col3" class="data row4 col3" >01-Simple-Perceptron.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow4_col4" class="data row4 col4" >01-Simple-Perceptron==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow4_col5" class="data row4 col5" >01/03/21 18:41:06</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow4_col6" class="data row4 col6" >01/03/21 18:41:12</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow4_col7" class="data row4 col7" >0:00:06</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow4_col8" class="data row4 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow5_col0" class="data row5 col0" >Nb_BHPD1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow5_col1" class="data row5 col1" >BHPD1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow5_col2" class="data row5 col2" >BHPD</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow5_col3" class="data row5 col3" >01-DNN-Regression.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow5_col4" class="data row5 col4" >01-DNN-Regression==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow5_col5" class="data row5 col5" >01/03/21 18:41:12</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow5_col6" class="data row5 col6" >01/03/21 18:41:29</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow5_col7" class="data row5 col7" >0:00:16</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow5_col8" class="data row5 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow6_col0" class="data row6 col0" >Nb_BHPD2</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow6_col1" class="data row6 col1" >BHPD2</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow6_col2" class="data row6 col2" >BHPD</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow6_col3" class="data row6 col3" >02-DNN-Regression-Premium.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow6_col4" class="data row6 col4" >02-DNN-Regression-Premium==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow6_col5" class="data row6 col5" >01/03/21 18:41:29</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow6_col6" class="data row6 col6" >01/03/21 18:41:54</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow6_col7" class="data row6 col7" >0:00:24</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow6_col8" class="data row6 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow7_col0" class="data row7 col0" >Nb_MNIST1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow7_col1" class="data row7 col1" >MNIST1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow7_col2" class="data row7 col2" >MNIST</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow7_col3" class="data row7 col3" >01-DNN-MNIST.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow7_col4" class="data row7 col4" >01-DNN-MNIST==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow7_col5" class="data row7 col5" >01/03/21 18:41:54</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow7_col6" class="data row7 col6" >01/03/21 18:42:42</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow7_col7" class="data row7 col7" >0:00:48</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow7_col8" class="data row7 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow8_col0" class="data row8 col0" >Nb_MNIST2</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow8_col1" class="data row8 col1" >MNIST2</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow8_col2" class="data row8 col2" >MNIST</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow8_col3" class="data row8 col3" >02-CNN-MNIST.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow8_col4" class="data row8 col4" >02-CNN-MNIST==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow8_col5" class="data row8 col5" >01/03/21 18:42:42</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow8_col6" class="data row8 col6" >01/03/21 18:43:34</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow8_col7" class="data row8 col7" >0:00:52</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow8_col8" class="data row8 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow9_col0" class="data row9 col0" >Nb_GTSRB1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow9_col1" class="data row9 col1" >GTSRB1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow9_col2" class="data row9 col2" >GTSRB</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow9_col3" class="data row9 col3" >01-Preparation-of-data.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow9_col4" class="data row9 col4" >01-Preparation-of-data==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow9_col5" class="data row9 col5" >01/03/21 18:43:34</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow9_col6" class="data row9 col6" >01/03/21 18:48:18</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow9_col7" class="data row9 col7" >0:04:43</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow9_col8" class="data row9 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow10_col0" class="data row10 col0" >Nb_GTSRB2</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow10_col1" class="data row10 col1" >GTSRB2</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow10_col2" class="data row10 col2" >GTSRB</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow10_col3" class="data row10 col3" >02-First-convolutions.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow10_col4" class="data row10 col4" >02-First-convolutions==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow10_col5" class="data row10 col5" >01/03/21 18:48:18</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow10_col6" class="data row10 col6" >01/03/21 18:48:49</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow10_col7" class="data row10 col7" >0:00:31</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow10_col8" class="data row10 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow11_col0" class="data row11 col0" >Nb_GTSRB3</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow11_col1" class="data row11 col1" >GTSRB3</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow11_col2" class="data row11 col2" >GTSRB</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow11_col3" class="data row11 col3" >03-Tracking-and-visualizing.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow11_col4" class="data row11 col4" >03-Tracking-and-visualizing==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow11_col5" class="data row11 col5" >01/03/21 18:48:49</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow11_col6" class="data row11 col6" >01/03/21 18:49:55</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow11_col7" class="data row11 col7" >0:01:06</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow11_col8" class="data row11 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow12_col0" class="data row12 col0" >Nb_GTSRB4</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow12_col1" class="data row12 col1" >GTSRB4</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow12_col2" class="data row12 col2" >GTSRB</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow12_col3" class="data row12 col3" >04-Data-augmentation.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow12_col4" class="data row12 col4" >04-Data-augmentation==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow12_col5" class="data row12 col5" >01/03/21 18:49:55</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow12_col6" class="data row12 col6" >01/03/21 18:51:31</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow12_col7" class="data row12 col7" >0:01:36</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow12_col8" class="data row12 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow13_col0" class="data row13 col0" >Nb_GTSRB5_r1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow13_col1" class="data row13 col1" >GTSRB5</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow13_col2" class="data row13 col2" >GTSRB</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow13_col3" class="data row13 col3" >05-Full-convolutions.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow13_col4" class="data row13 col4" >05-Full-convolutions=1==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow13_col5" class="data row13 col5" >01/03/21 18:51:32</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow13_col6" class="data row13 col6" >01/03/21 19:23:13</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow13_col7" class="data row13 col7" >0:31:41</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow13_col8" class="data row13 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow14_col0" class="data row14 col0" >Nb_GTSRB5_r2</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow14_col1" class="data row14 col1" >GTSRB5</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow14_col2" class="data row14 col2" >GTSRB</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow14_col3" class="data row14 col3" >05-Full-convolutions.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow14_col4" class="data row14 col4" >05-Full-convolutions=2==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow14_col5" class="data row14 col5" >01/03/21 19:23:13</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow14_col6" class="data row14 col6" >01/03/21 19:54:55</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow14_col7" class="data row14 col7" >0:31:42</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow14_col8" class="data row14 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow15_col0" class="data row15 col0" >Nb_GTSRB5_r3</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow15_col1" class="data row15 col1" >GTSRB5</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow15_col2" class="data row15 col2" >GTSRB</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow15_col3" class="data row15 col3" >05-Full-convolutions.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow15_col4" class="data row15 col4" >05-Full-convolutions=3==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow15_col5" class="data row15 col5" >01/03/21 19:54:55</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow15_col6" class="data row15 col6" >01/03/21 20:19:35</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow15_col7" class="data row15 col7" >0:24:39</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow15_col8" class="data row15 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow16_col0" class="data row16 col0" >Nb_GTSRB6</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow16_col1" class="data row16 col1" >GTSRB6</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow16_col2" class="data row16 col2" >GTSRB</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow16_col3" class="data row16 col3" >06-Notebook-as-a-batch.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow16_col4" class="data row16 col4" >06-Notebook-as-a-batch==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow16_col5" class="data row16 col5" >01/03/21 20:19:35</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow16_col6" class="data row16 col6" >01/03/21 20:19:39</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow16_col7" class="data row16 col7" >0:00:04</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow16_col8" class="data row16 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow17_col0" class="data row17 col0" >Nb_GTSRB7</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow17_col1" class="data row17 col1" >GTSRB7</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow17_col2" class="data row17 col2" >GTSRB</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow17_col3" class="data row17 col3" >07-Show-report.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow17_col4" class="data row17 col4" >07-Show-report==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow17_col5" class="data row17 col5" >01/03/21 20:19:39</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow17_col6" class="data row17 col6" >01/03/21 20:19:45</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow17_col7" class="data row17 col7" >0:00:05</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow17_col8" class="data row17 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow18_col0" class="data row18 col0" >Nb_IMDB1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow18_col1" class="data row18 col1" >IMDB1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow18_col2" class="data row18 col2" >IMDB</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow18_col3" class="data row18 col3" >01-One-hot-encoding.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow18_col4" class="data row18 col4" >01-One-hot-encoding==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow18_col5" class="data row18 col5" >01/03/21 20:19:45</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow18_col6" class="data row18 col6" >01/03/21 20:20:31</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow18_col7" class="data row18 col7" >0:00:46</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow18_col8" class="data row18 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow19_col0" class="data row19 col0" >Nb_IMDB2</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow19_col1" class="data row19 col1" >IMDB2</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow19_col2" class="data row19 col2" >IMDB</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow19_col3" class="data row19 col3" >02-Keras-embedding.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow19_col4" class="data row19 col4" >02-Keras-embedding==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow19_col5" class="data row19 col5" >01/03/21 20:20:31</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow19_col6" class="data row19 col6" >01/03/21 20:21:19</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow19_col7" class="data row19 col7" >0:00:48</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow19_col8" class="data row19 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow20_col0" class="data row20 col0" >Nb_IMDB3</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow20_col1" class="data row20 col1" >IMDB3</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow20_col2" class="data row20 col2" >IMDB</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow20_col3" class="data row20 col3" >03-Prediction.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow20_col4" class="data row20 col4" >03-Prediction==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow20_col5" class="data row20 col5" >01/03/21 20:21:19</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow20_col6" class="data row20 col6" >01/03/21 20:21:25</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow20_col7" class="data row20 col7" >0:00:05</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow20_col8" class="data row20 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow21_col0" class="data row21 col0" >Nb_IMDB4</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow21_col1" class="data row21 col1" >IMDB4</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow21_col2" class="data row21 col2" >IMDB</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow21_col3" class="data row21 col3" >04-Show-vectors.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow21_col4" class="data row21 col4" >04-Show-vectors==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow21_col5" class="data row21 col5" >01/03/21 20:21:25</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow21_col6" class="data row21 col6" >01/03/21 20:21:31</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow21_col7" class="data row21 col7" >0:00:06</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow21_col8" class="data row21 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow22_col0" class="data row22 col0" >Nb_IMDB5</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow22_col1" class="data row22 col1" >IMDB5</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow22_col2" class="data row22 col2" >IMDB</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow22_col3" class="data row22 col3" >05-LSTM-Keras.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow22_col4" class="data row22 col4" >05-LSTM-Keras==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow22_col5" class="data row22 col5" >01/03/21 20:21:31</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow22_col6" class="data row22 col6" >01/03/21 20:29:54</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow22_col7" class="data row22 col7" >0:08:23</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow22_col8" class="data row22 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow23_col0" class="data row23 col0" >Nb_SYNOP1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow23_col1" class="data row23 col1" >SYNOP1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow23_col2" class="data row23 col2" >SYNOP</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow23_col3" class="data row23 col3" >01-Preparation-of-data.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow23_col4" class="data row23 col4" >01-Preparation-of-data==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow23_col5" class="data row23 col5" >01/03/21 20:29:55</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow23_col6" class="data row23 col6" >01/03/21 20:30:05</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow23_col7" class="data row23 col7" >0:00:09</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow23_col8" class="data row23 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow24_col0" class="data row24 col0" >Nb_SYNOP2</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow24_col1" class="data row24 col1" >SYNOP2</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow24_col2" class="data row24 col2" >SYNOP</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow24_col3" class="data row24 col3" >02-First-predictions.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow24_col4" class="data row24 col4" >02-First-predictions==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow24_col5" class="data row24 col5" >01/03/21 20:30:05</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow24_col6" class="data row24 col6" >01/03/21 20:32:25</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow24_col7" class="data row24 col7" >0:02:20</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow24_col8" class="data row24 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow25_col0" class="data row25 col0" >Nb_SYNOP3</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow25_col1" class="data row25 col1" >SYNOP3</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow25_col2" class="data row25 col2" >SYNOP</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow25_col3" class="data row25 col3" >03-12h-predictions.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow25_col4" class="data row25 col4" >03-12h-predictions==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow25_col5" class="data row25 col5" >01/03/21 20:32:26</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow25_col6" class="data row25 col6" >01/03/21 20:32:37</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow25_col7" class="data row25 col7" >0:00:11</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow25_col8" class="data row25 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow26_col0" class="data row26 col0" >Nb_AE1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow26_col1" class="data row26 col1" >AE1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow26_col2" class="data row26 col2" >AE</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow26_col3" class="data row26 col3" >01-AE-with-MNIST.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow26_col4" class="data row26 col4" >01-AE-with-MNIST==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow26_col5" class="data row26 col5" >01/03/21 20:32:37</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow26_col6" class="data row26 col6" >01/03/21 20:34:55</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow26_col7" class="data row26 col7" >0:02:18</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow26_col8" class="data row26 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow27_col0" class="data row27 col0" >Nb_AE2</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow27_col1" class="data row27 col1" >AE2</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow27_col2" class="data row27 col2" >AE</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow27_col3" class="data row27 col3" >02-AE-with-MNIST-post.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow27_col4" class="data row27 col4" >02-AE-with-MNIST-post==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow27_col5" class="data row27 col5" >01/03/21 20:34:55</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow27_col6" class="data row27 col6" >01/03/21 20:35:06</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow27_col7" class="data row27 col7" >0:00:11</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow27_col8" class="data row27 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow28_col0" class="data row28 col0" >Nb_VAE1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow28_col1" class="data row28 col1" >VAE1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow28_col2" class="data row28 col2" >VAE</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow28_col3" class="data row28 col3" >01-VAE-with-MNIST.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow28_col4" class="data row28 col4" >01-VAE-with-MNIST==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow28_col5" class="data row28 col5" >01/03/21 20:35:07</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow28_col6" class="data row28 col6" >01/03/21 20:36:27</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow28_col7" class="data row28 col7" >0:01:20</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow28_col8" class="data row28 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow29_col0" class="data row29 col0" >Nb_VAE2</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow29_col1" class="data row29 col1" >VAE2</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow29_col2" class="data row29 col2" >VAE</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow29_col3" class="data row29 col3" >02-VAE-with-MNIST-post.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow29_col4" class="data row29 col4" >02-VAE-with-MNIST-post==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow29_col5" class="data row29 col5" >01/03/21 20:36:27</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow29_col6" class="data row29 col6" >01/03/21 20:37:25</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow29_col7" class="data row29 col7" >0:00:58</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow29_col8" class="data row29 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow30_col0" class="data row30 col0" >Nb_VAE5</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow30_col1" class="data row30 col1" >VAE5</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow30_col2" class="data row30 col2" >VAE</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow30_col3" class="data row30 col3" >05-About-CelebA.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow30_col4" class="data row30 col4" >05-About-CelebA==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow30_col5" class="data row30 col5" >01/03/21 20:37:25</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow30_col6" class="data row30 col6" >01/03/21 20:37:50</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow30_col7" class="data row30 col7" >0:00:25</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow30_col8" class="data row30 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow31_col0" class="data row31 col0" >Nb_VAE6</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow31_col1" class="data row31 col1" >VAE6</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow31_col2" class="data row31 col2" >VAE</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow31_col3" class="data row31 col3" >06-Prepare-CelebA-datasets.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow31_col4" class="data row31 col4" >06-Prepare-CelebA-datasets==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow31_col5" class="data row31 col5" >01/03/21 20:37:50</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow31_col6" class="data row31 col6" >01/03/21 20:39:29</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow31_col7" class="data row31 col7" >0:01:38</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow31_col8" class="data row31 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow32_col0" class="data row32 col0" >Nb_VAE7</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow32_col1" class="data row32 col1" >VAE7</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow32_col2" class="data row32 col2" >VAE</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow32_col3" class="data row32 col3" >07-Check-CelebA.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow32_col4" class="data row32 col4" >07-Check-CelebA==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow32_col5" class="data row32 col5" >01/03/21 20:39:29</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow32_col6" class="data row32 col6" >01/03/21 20:40:59</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow32_col7" class="data row32 col7" >0:01:29</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow32_col8" class="data row32 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow33_col0" class="data row33 col0" >Nb_VAE8</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow33_col1" class="data row33 col1" >VAE8</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow33_col2" class="data row33 col2" >VAE</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow33_col3" class="data row33 col3" >08-VAE-with-CelebA.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow33_col4" class="data row33 col4" >08-VAE-with-CelebA==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow33_col5" class="data row33 col5" >01/03/21 20:40:59</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow33_col6" class="data row33 col6" >01/03/21 21:42:45</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow33_col7" class="data row33 col7" >1:01:46</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow33_col8" class="data row33 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow34_col0" class="data row34 col0" >Nb_VAE9</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow34_col1" class="data row34 col1" >VAE9</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow34_col2" class="data row34 col2" >VAE</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow34_col3" class="data row34 col3" >09-VAE-with-CelebA-post.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow34_col4" class="data row34 col4" >09-VAE-with-CelebA-post==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow34_col5" class="data row34 col5" >01/03/21 21:42:58</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow34_col6" class="data row34 col6" >01/03/21 21:45:06</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow34_col7" class="data row34 col7" >0:02:08</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow34_col8" class="data row34 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow35_col0" class="data row35 col0" >Nb_ACTF1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow35_col1" class="data row35 col1" >ACTF1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow35_col2" class="data row35 col2" >Misc</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow35_col3" class="data row35 col3" >Activation-Functions.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow35_col4" class="data row35 col4" >Activation-Functions==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow35_col5" class="data row35 col5" >01/03/21 21:45:07</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow35_col6" class="data row35 col6" >01/03/21 21:45:27</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow35_col7" class="data row35 col7" >0:00:20</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow35_col8" class="data row35 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow36_col0" class="data row36 col0" >Nb_NP1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow36_col1" class="data row36 col1" >NP1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow36_col2" class="data row36 col2" >Misc</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow36_col3" class="data row36 col3" >Numpy.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow36_col4" class="data row36 col4" >Numpy==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow36_col5" class="data row36 col5" >01/03/21 21:45:28</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow36_col6" class="data row36 col6" >01/03/21 21:45:29</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow36_col7" class="data row36 col7" >0:00:01</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow36_col8" class="data row36 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow37_col0" class="data row37 col0" >Nb_TSB1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow37_col1" class="data row37 col1" >TSB1</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow37_col2" class="data row37 col2" >Misc</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow37_col3" class="data row37 col3" >Using-Tensorboard.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow37_col4" class="data row37 col4" >Using-Tensorboard==done==.ipynb</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow37_col5" class="data row37 col5" >01/03/21 21:45:29</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow37_col6" class="data row37 col6" >01/03/21 21:45:30</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow37_col7" class="data row37 col7" >0:00:01</td>
+                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow37_col8" class="data row37 col8" >ok</td>
+            </tr>
+    </tbody></table></div>
+
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 140.2164 40.848" width="80px"><title>00-Fidle-logo-01</title><g id="Calque_2" data-name="Calque 2"><g id="Calque_4" data-name="Calque 4"><path d="M46.1913,31.74a12.9222,12.9222,0,0,0,5.2755-1.77,6.4763,6.4763,0,0,1,2.3629-.9239,14.6364,14.6364,0,0,0-5.7616-16.4446,17.0565,17.0565,0,0,0-11.8732-2.0051c-4.1719.69-8.4957,3.8461-10.189,5.2622-1.0189.8536-13.1385,12.3424-18.1936,10.0527-3.42-1.5492,2.6862-7.1873-.1144-12.3393a.2236.2236,0,0,0-.373-.0248c-1.4257,1.9233-2.8193,4.2317-4.7179,3.1953-.8482-.4632-1.6116-1.9422-2.2-2.8775A.2216.2216,0,0,0,0,13.9917,23.35,23.35,0,0,0,5.87,28.2417a35.3776,35.3776,0,0,0,24.34,12.518c5.3439.5321,18.0193-1.1527,23.0835-10.2646a12.7681,12.7681,0,0,0-1.2217.6066,14.2177,14.2177,0,0,1-5.7629,1.9167c-.1761.0163-.3511.0236-.5261.0236a10.1733,10.1733,0,0,1-5.7446-2.303,1.0764,1.0764,0,1,1,.8227-1.0443c0,.0176-.0042.0339-.0054.0515C41.8966,30.5423,44.0669,31.9474,46.1913,31.74ZM30.0385,36.5091a19.6093,19.6093,0,0,1-4.6162.8385c-1.0425.0006-1.476-.2954-1.6824-.7392-.5431-1.1678,1.4136-2.8563,3.1493-4.0677a.6418.6418,0,1,1,.7343,1.0528,10.5781,10.5781,0,0,0-2.651,2.4368c.339.0732,1.44.12,4.733-.7616a.6422.6422,0,0,1,.333,1.24Zm14.87-15.6442a2.4512,2.4512,0,0,1,2.38,2.3617,1.6015,1.6015,0,1,0-1.4179,2.34,1.6573,1.6573,0,0,0,.2973-.03,2.28,2.28,0,0,1-1.2593.3875,2.5337,2.5337,0,0,1,0-5.06ZM36.6423,4.436A1.2835,1.2835,0,0,0,37.1466,6.18c.6211.342,1.9294-.402,2.7231.7071.4122.5763-.8627-2.6129-1.4839-2.9556A1.2827,1.2827,0,0,0,36.6423,4.436Zm6.5389.1374c-1.5995.9378-1.8961,4.8154-1.4838,4.2391a7.2989,7.2989,0,0,1,2.7231-1.9906,1.2837,1.2837,0,0,0-1.2393-2.2485ZM41.5587.2981c-.8179.9462-.2579,3.4-.1114,2.95a5.2169,5.2169,0,0,1,1.3174-1.8537A.8415.8415,0,0,0,42.7441.2054.8332.8332,0,0,0,41.5587.2981Z" style="fill:#e12229"/><path d="M65.6671,13.7493H77.3946V15.158H67.3223v9.4379h9.2271v1.4087H67.3223v11.481H65.6671Z" style="fill:#808285"/><path d="M83.5909,13.7493V37.4856H81.9356V13.7493Z" style="fill:#808285"/><path d="M89.3658,14.0662a39.0353,39.0353,0,0,1,6.0576-.4932c4.3316,0,7.607,1.1621,9.5791,3.24a11.2256,11.2256,0,0,1,2.958,8.2056,13.0738,13.0738,0,0,1-3.0991,9.0156c-2.1128,2.2891-5.67,3.6275-10.248,3.6275a50.7148,50.7148,0,0,1-5.2476-.2115Zm1.6553,22.0107a29.8576,29.8576,0,0,0,3.8388.1763c7.607,0,11.375-4.2617,11.375-11.1289.0352-6.022-3.31-10.1426-10.9174-10.1426a25.2377,25.2377,0,0,0-4.2964.352Z" style="fill:#808285"/><path d="M112.15,13.7493h1.6553V36.0769h10.6006v1.4087H112.15Z" style="fill:#808285"/><path d="M139.0894,25.6877h-9.5088V36.0769h10.6358v1.4087h-12.291V13.7493h11.7275V15.158H129.5806v9.1211h9.5088Z" style="fill:#808285"/></g></g></svg>
+
+            </body>
+    </html>
+    
\ No newline at end of file
diff --git a/fidle/logs/ci_report.html b/fidle/logs/ci_report.html
index dd1a64e2df55dec892f4bf6f2e49dd55c3902e11..210a50bc9be99224e177e061f81cbe1a9147ff5a 100644
--- a/fidle/logs/ci_report.html
+++ b/fidle/logs/ci_report.html
@@ -32,439 +32,65 @@
             <div class="metadata"><b>Version</b> : 1.0  <br>
 <b>Output_Tag</b> : ==done==  <br>
 <b>Save_Figs</b> : True  <br>
-<b>Description</b> : Full profile for GPU  <br>
-<b>Host</b> : r7i0n6  <br>
-<b>Profile</b> : ./ci/full_gpu.yml  <br>
-<b>Start</b> : 01/03/21 18:40:12  <br>
-<b>End</b> : 01/03/21 21:45:30  <br>
-<b>Duration</b> : 3:05:17  <br>
+<b>Description</b> : Light profile for S04 with CPU  <br>
+<b>Host</b> : Oban  <br>
+<b>Profile</b> : ./ci/fidle-ad_s04.yml  <br>
+<b>Start</b> : 07/03/21 21:13:23  <br>
+<b>End</b> : 07/03/21 21:18:10  <br>
+<b>Duration</b> : 0:04:47  <br>
 </div>
             <div class='title'>Details :</div>
             <div class="result"><style  type="text/css" >
-    #T_159526f0_7acf_11eb_b7a6_0cc47af5a44f td {
+    #T_22cc1_ td {
           font-size: 110%;
           text-align: left;
-    }    #T_159526f0_7acf_11eb_b7a6_0cc47af5a44f th {
+    }    #T_22cc1_ th {
           font-size: 110%;
           text-align: left;
-    }</style><table id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44f" ><thead>    <tr>        <th class="col_heading level0 col0" >Run</th>        <th class="col_heading level0 col1" >Id</th>        <th class="col_heading level0 col2" >Dir</th>        <th class="col_heading level0 col3" >Src</th>        <th class="col_heading level0 col4" >Out</th>        <th class="col_heading level0 col5" >Start</th>        <th class="col_heading level0 col6" >End</th>        <th class="col_heading level0 col7" >Duration</th>        <th class="col_heading level0 col8" >State</th>    </tr></thead><tbody>
+    }</style><table id="T_22cc1_" ><thead>    <tr>        <th class="col_heading level0 col0" >Run</th>        <th class="col_heading level0 col1" >Id</th>        <th class="col_heading level0 col2" >Dir</th>        <th class="col_heading level0 col3" >Src</th>        <th class="col_heading level0 col4" >Out</th>        <th class="col_heading level0 col5" >Start</th>        <th class="col_heading level0 col6" >End</th>        <th class="col_heading level0 col7" >Duration</th>        <th class="col_heading level0 col8" >State</th>    </tr></thead><tbody>
                 <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow0_col0" class="data row0 col0" >Nb_LINR1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow0_col1" class="data row0 col1" >LINR1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow0_col2" class="data row0 col2" >LinearReg</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow0_col3" class="data row0 col3" >01-Linear-Regression.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow0_col4" class="data row0 col4" >01-Linear-Regression==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow0_col5" class="data row0 col5" >01/03/21 18:40:13</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow0_col6" class="data row0 col6" >01/03/21 18:40:42</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow0_col7" class="data row0 col7" >0:00:29</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow0_col8" class="data row0 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow1_col0" class="data row1 col0" >Nb_GRAD1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow1_col1" class="data row1 col1" >GRAD1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow1_col2" class="data row1 col2" >LinearReg</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow1_col3" class="data row1 col3" >02-Gradient-descent.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow1_col4" class="data row1 col4" >02-Gradient-descent==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow1_col5" class="data row1 col5" >01/03/21 18:40:42</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow1_col6" class="data row1 col6" >01/03/21 18:40:52</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow1_col7" class="data row1 col7" >0:00:09</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow1_col8" class="data row1 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow2_col0" class="data row2 col0" >Nb_POLR1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow2_col1" class="data row2 col1" >POLR1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow2_col2" class="data row2 col2" >LinearReg</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow2_col3" class="data row2 col3" >03-Polynomial-Regression.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow2_col4" class="data row2 col4" >03-Polynomial-Regression==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow2_col5" class="data row2 col5" >01/03/21 18:40:52</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow2_col6" class="data row2 col6" >01/03/21 18:40:59</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow2_col7" class="data row2 col7" >0:00:07</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow2_col8" class="data row2 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow3_col0" class="data row3 col0" >Nb_LOGR1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow3_col1" class="data row3 col1" >LOGR1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow3_col2" class="data row3 col2" >LinearReg</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow3_col3" class="data row3 col3" >04-Logistic-Regression.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow3_col4" class="data row3 col4" >04-Logistic-Regression==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow3_col5" class="data row3 col5" >01/03/21 18:40:59</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow3_col6" class="data row3 col6" >01/03/21 18:41:06</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow3_col7" class="data row3 col7" >0:00:06</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow3_col8" class="data row3 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow4_col0" class="data row4 col0" >Nb_PER57</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow4_col1" class="data row4 col1" >PER57</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow4_col2" class="data row4 col2" >IRIS</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow4_col3" class="data row4 col3" >01-Simple-Perceptron.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow4_col4" class="data row4 col4" >01-Simple-Perceptron==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow4_col5" class="data row4 col5" >01/03/21 18:41:06</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow4_col6" class="data row4 col6" >01/03/21 18:41:12</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow4_col7" class="data row4 col7" >0:00:06</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow4_col8" class="data row4 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow5_col0" class="data row5 col0" >Nb_BHPD1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow5_col1" class="data row5 col1" >BHPD1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow5_col2" class="data row5 col2" >BHPD</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow5_col3" class="data row5 col3" >01-DNN-Regression.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow5_col4" class="data row5 col4" >01-DNN-Regression==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow5_col5" class="data row5 col5" >01/03/21 18:41:12</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow5_col6" class="data row5 col6" >01/03/21 18:41:29</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow5_col7" class="data row5 col7" >0:00:16</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow5_col8" class="data row5 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow6_col0" class="data row6 col0" >Nb_BHPD2</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow6_col1" class="data row6 col1" >BHPD2</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow6_col2" class="data row6 col2" >BHPD</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow6_col3" class="data row6 col3" >02-DNN-Regression-Premium.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow6_col4" class="data row6 col4" >02-DNN-Regression-Premium==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow6_col5" class="data row6 col5" >01/03/21 18:41:29</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow6_col6" class="data row6 col6" >01/03/21 18:41:54</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow6_col7" class="data row6 col7" >0:00:24</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow6_col8" class="data row6 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow7_col0" class="data row7 col0" >Nb_MNIST1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow7_col1" class="data row7 col1" >MNIST1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow7_col2" class="data row7 col2" >MNIST</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow7_col3" class="data row7 col3" >01-DNN-MNIST.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow7_col4" class="data row7 col4" >01-DNN-MNIST==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow7_col5" class="data row7 col5" >01/03/21 18:41:54</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow7_col6" class="data row7 col6" >01/03/21 18:42:42</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow7_col7" class="data row7 col7" >0:00:48</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow7_col8" class="data row7 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow8_col0" class="data row8 col0" >Nb_MNIST2</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow8_col1" class="data row8 col1" >MNIST2</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow8_col2" class="data row8 col2" >MNIST</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow8_col3" class="data row8 col3" >02-CNN-MNIST.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow8_col4" class="data row8 col4" >02-CNN-MNIST==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow8_col5" class="data row8 col5" >01/03/21 18:42:42</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow8_col6" class="data row8 col6" >01/03/21 18:43:34</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow8_col7" class="data row8 col7" >0:00:52</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow8_col8" class="data row8 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow9_col0" class="data row9 col0" >Nb_GTSRB1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow9_col1" class="data row9 col1" >GTSRB1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow9_col2" class="data row9 col2" >GTSRB</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow9_col3" class="data row9 col3" >01-Preparation-of-data.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow9_col4" class="data row9 col4" >01-Preparation-of-data==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow9_col5" class="data row9 col5" >01/03/21 18:43:34</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow9_col6" class="data row9 col6" >01/03/21 18:48:18</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow9_col7" class="data row9 col7" >0:04:43</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow9_col8" class="data row9 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow10_col0" class="data row10 col0" >Nb_GTSRB2</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow10_col1" class="data row10 col1" >GTSRB2</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow10_col2" class="data row10 col2" >GTSRB</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow10_col3" class="data row10 col3" >02-First-convolutions.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow10_col4" class="data row10 col4" >02-First-convolutions==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow10_col5" class="data row10 col5" >01/03/21 18:48:18</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow10_col6" class="data row10 col6" >01/03/21 18:48:49</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow10_col7" class="data row10 col7" >0:00:31</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow10_col8" class="data row10 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow11_col0" class="data row11 col0" >Nb_GTSRB3</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow11_col1" class="data row11 col1" >GTSRB3</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow11_col2" class="data row11 col2" >GTSRB</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow11_col3" class="data row11 col3" >03-Tracking-and-visualizing.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow11_col4" class="data row11 col4" >03-Tracking-and-visualizing==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow11_col5" class="data row11 col5" >01/03/21 18:48:49</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow11_col6" class="data row11 col6" >01/03/21 18:49:55</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow11_col7" class="data row11 col7" >0:01:06</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow11_col8" class="data row11 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow12_col0" class="data row12 col0" >Nb_GTSRB4</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow12_col1" class="data row12 col1" >GTSRB4</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow12_col2" class="data row12 col2" >GTSRB</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow12_col3" class="data row12 col3" >04-Data-augmentation.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow12_col4" class="data row12 col4" >04-Data-augmentation==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow12_col5" class="data row12 col5" >01/03/21 18:49:55</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow12_col6" class="data row12 col6" >01/03/21 18:51:31</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow12_col7" class="data row12 col7" >0:01:36</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow12_col8" class="data row12 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow13_col0" class="data row13 col0" >Nb_GTSRB5_r1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow13_col1" class="data row13 col1" >GTSRB5</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow13_col2" class="data row13 col2" >GTSRB</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow13_col3" class="data row13 col3" >05-Full-convolutions.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow13_col4" class="data row13 col4" >05-Full-convolutions=1==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow13_col5" class="data row13 col5" >01/03/21 18:51:32</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow13_col6" class="data row13 col6" >01/03/21 19:23:13</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow13_col7" class="data row13 col7" >0:31:41</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow13_col8" class="data row13 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow14_col0" class="data row14 col0" >Nb_GTSRB5_r2</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow14_col1" class="data row14 col1" >GTSRB5</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow14_col2" class="data row14 col2" >GTSRB</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow14_col3" class="data row14 col3" >05-Full-convolutions.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow14_col4" class="data row14 col4" >05-Full-convolutions=2==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow14_col5" class="data row14 col5" >01/03/21 19:23:13</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow14_col6" class="data row14 col6" >01/03/21 19:54:55</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow14_col7" class="data row14 col7" >0:31:42</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow14_col8" class="data row14 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow15_col0" class="data row15 col0" >Nb_GTSRB5_r3</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow15_col1" class="data row15 col1" >GTSRB5</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow15_col2" class="data row15 col2" >GTSRB</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow15_col3" class="data row15 col3" >05-Full-convolutions.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow15_col4" class="data row15 col4" >05-Full-convolutions=3==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow15_col5" class="data row15 col5" >01/03/21 19:54:55</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow15_col6" class="data row15 col6" >01/03/21 20:19:35</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow15_col7" class="data row15 col7" >0:24:39</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow15_col8" class="data row15 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow16_col0" class="data row16 col0" >Nb_GTSRB6</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow16_col1" class="data row16 col1" >GTSRB6</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow16_col2" class="data row16 col2" >GTSRB</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow16_col3" class="data row16 col3" >06-Notebook-as-a-batch.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow16_col4" class="data row16 col4" >06-Notebook-as-a-batch==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow16_col5" class="data row16 col5" >01/03/21 20:19:35</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow16_col6" class="data row16 col6" >01/03/21 20:19:39</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow16_col7" class="data row16 col7" >0:00:04</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow16_col8" class="data row16 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow17_col0" class="data row17 col0" >Nb_GTSRB7</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow17_col1" class="data row17 col1" >GTSRB7</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow17_col2" class="data row17 col2" >GTSRB</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow17_col3" class="data row17 col3" >07-Show-report.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow17_col4" class="data row17 col4" >07-Show-report==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow17_col5" class="data row17 col5" >01/03/21 20:19:39</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow17_col6" class="data row17 col6" >01/03/21 20:19:45</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow17_col7" class="data row17 col7" >0:00:05</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow17_col8" class="data row17 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow18_col0" class="data row18 col0" >Nb_IMDB1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow18_col1" class="data row18 col1" >IMDB1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow18_col2" class="data row18 col2" >IMDB</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow18_col3" class="data row18 col3" >01-One-hot-encoding.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow18_col4" class="data row18 col4" >01-One-hot-encoding==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow18_col5" class="data row18 col5" >01/03/21 20:19:45</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow18_col6" class="data row18 col6" >01/03/21 20:20:31</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow18_col7" class="data row18 col7" >0:00:46</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow18_col8" class="data row18 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow19_col0" class="data row19 col0" >Nb_IMDB2</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow19_col1" class="data row19 col1" >IMDB2</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow19_col2" class="data row19 col2" >IMDB</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow19_col3" class="data row19 col3" >02-Keras-embedding.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow19_col4" class="data row19 col4" >02-Keras-embedding==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow19_col5" class="data row19 col5" >01/03/21 20:20:31</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow19_col6" class="data row19 col6" >01/03/21 20:21:19</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow19_col7" class="data row19 col7" >0:00:48</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow19_col8" class="data row19 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow20_col0" class="data row20 col0" >Nb_IMDB3</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow20_col1" class="data row20 col1" >IMDB3</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow20_col2" class="data row20 col2" >IMDB</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow20_col3" class="data row20 col3" >03-Prediction.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow20_col4" class="data row20 col4" >03-Prediction==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow20_col5" class="data row20 col5" >01/03/21 20:21:19</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow20_col6" class="data row20 col6" >01/03/21 20:21:25</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow20_col7" class="data row20 col7" >0:00:05</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow20_col8" class="data row20 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow21_col0" class="data row21 col0" >Nb_IMDB4</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow21_col1" class="data row21 col1" >IMDB4</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow21_col2" class="data row21 col2" >IMDB</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow21_col3" class="data row21 col3" >04-Show-vectors.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow21_col4" class="data row21 col4" >04-Show-vectors==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow21_col5" class="data row21 col5" >01/03/21 20:21:25</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow21_col6" class="data row21 col6" >01/03/21 20:21:31</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow21_col7" class="data row21 col7" >0:00:06</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow21_col8" class="data row21 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow22_col0" class="data row22 col0" >Nb_IMDB5</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow22_col1" class="data row22 col1" >IMDB5</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow22_col2" class="data row22 col2" >IMDB</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow22_col3" class="data row22 col3" >05-LSTM-Keras.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow22_col4" class="data row22 col4" >05-LSTM-Keras==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow22_col5" class="data row22 col5" >01/03/21 20:21:31</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow22_col6" class="data row22 col6" >01/03/21 20:29:54</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow22_col7" class="data row22 col7" >0:08:23</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow22_col8" class="data row22 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow23_col0" class="data row23 col0" >Nb_SYNOP1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow23_col1" class="data row23 col1" >SYNOP1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow23_col2" class="data row23 col2" >SYNOP</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow23_col3" class="data row23 col3" >01-Preparation-of-data.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow23_col4" class="data row23 col4" >01-Preparation-of-data==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow23_col5" class="data row23 col5" >01/03/21 20:29:55</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow23_col6" class="data row23 col6" >01/03/21 20:30:05</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow23_col7" class="data row23 col7" >0:00:09</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow23_col8" class="data row23 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow24_col0" class="data row24 col0" >Nb_SYNOP2</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow24_col1" class="data row24 col1" >SYNOP2</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow24_col2" class="data row24 col2" >SYNOP</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow24_col3" class="data row24 col3" >02-First-predictions.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow24_col4" class="data row24 col4" >02-First-predictions==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow24_col5" class="data row24 col5" >01/03/21 20:30:05</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow24_col6" class="data row24 col6" >01/03/21 20:32:25</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow24_col7" class="data row24 col7" >0:02:20</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow24_col8" class="data row24 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow25_col0" class="data row25 col0" >Nb_SYNOP3</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow25_col1" class="data row25 col1" >SYNOP3</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow25_col2" class="data row25 col2" >SYNOP</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow25_col3" class="data row25 col3" >03-12h-predictions.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow25_col4" class="data row25 col4" >03-12h-predictions==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow25_col5" class="data row25 col5" >01/03/21 20:32:26</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow25_col6" class="data row25 col6" >01/03/21 20:32:37</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow25_col7" class="data row25 col7" >0:00:11</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow25_col8" class="data row25 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow26_col0" class="data row26 col0" >Nb_AE1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow26_col1" class="data row26 col1" >AE1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow26_col2" class="data row26 col2" >AE</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow26_col3" class="data row26 col3" >01-AE-with-MNIST.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow26_col4" class="data row26 col4" >01-AE-with-MNIST==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow26_col5" class="data row26 col5" >01/03/21 20:32:37</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow26_col6" class="data row26 col6" >01/03/21 20:34:55</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow26_col7" class="data row26 col7" >0:02:18</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow26_col8" class="data row26 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow27_col0" class="data row27 col0" >Nb_AE2</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow27_col1" class="data row27 col1" >AE2</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow27_col2" class="data row27 col2" >AE</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow27_col3" class="data row27 col3" >02-AE-with-MNIST-post.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow27_col4" class="data row27 col4" >02-AE-with-MNIST-post==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow27_col5" class="data row27 col5" >01/03/21 20:34:55</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow27_col6" class="data row27 col6" >01/03/21 20:35:06</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow27_col7" class="data row27 col7" >0:00:11</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow27_col8" class="data row27 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow28_col0" class="data row28 col0" >Nb_VAE1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow28_col1" class="data row28 col1" >VAE1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow28_col2" class="data row28 col2" >VAE</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow28_col3" class="data row28 col3" >01-VAE-with-MNIST.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow28_col4" class="data row28 col4" >01-VAE-with-MNIST==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow28_col5" class="data row28 col5" >01/03/21 20:35:07</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow28_col6" class="data row28 col6" >01/03/21 20:36:27</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow28_col7" class="data row28 col7" >0:01:20</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow28_col8" class="data row28 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow29_col0" class="data row29 col0" >Nb_VAE2</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow29_col1" class="data row29 col1" >VAE2</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow29_col2" class="data row29 col2" >VAE</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow29_col3" class="data row29 col3" >02-VAE-with-MNIST-post.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow29_col4" class="data row29 col4" >02-VAE-with-MNIST-post==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow29_col5" class="data row29 col5" >01/03/21 20:36:27</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow29_col6" class="data row29 col6" >01/03/21 20:37:25</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow29_col7" class="data row29 col7" >0:00:58</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow29_col8" class="data row29 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow30_col0" class="data row30 col0" >Nb_VAE5</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow30_col1" class="data row30 col1" >VAE5</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow30_col2" class="data row30 col2" >VAE</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow30_col3" class="data row30 col3" >05-About-CelebA.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow30_col4" class="data row30 col4" >05-About-CelebA==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow30_col5" class="data row30 col5" >01/03/21 20:37:25</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow30_col6" class="data row30 col6" >01/03/21 20:37:50</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow30_col7" class="data row30 col7" >0:00:25</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow30_col8" class="data row30 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow31_col0" class="data row31 col0" >Nb_VAE6</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow31_col1" class="data row31 col1" >VAE6</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow31_col2" class="data row31 col2" >VAE</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow31_col3" class="data row31 col3" >06-Prepare-CelebA-datasets.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow31_col4" class="data row31 col4" >06-Prepare-CelebA-datasets==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow31_col5" class="data row31 col5" >01/03/21 20:37:50</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow31_col6" class="data row31 col6" >01/03/21 20:39:29</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow31_col7" class="data row31 col7" >0:01:38</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow31_col8" class="data row31 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow32_col0" class="data row32 col0" >Nb_VAE7</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow32_col1" class="data row32 col1" >VAE7</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow32_col2" class="data row32 col2" >VAE</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow32_col3" class="data row32 col3" >07-Check-CelebA.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow32_col4" class="data row32 col4" >07-Check-CelebA==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow32_col5" class="data row32 col5" >01/03/21 20:39:29</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow32_col6" class="data row32 col6" >01/03/21 20:40:59</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow32_col7" class="data row32 col7" >0:01:29</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow32_col8" class="data row32 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow33_col0" class="data row33 col0" >Nb_VAE8</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow33_col1" class="data row33 col1" >VAE8</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow33_col2" class="data row33 col2" >VAE</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow33_col3" class="data row33 col3" >08-VAE-with-CelebA.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow33_col4" class="data row33 col4" >08-VAE-with-CelebA==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow33_col5" class="data row33 col5" >01/03/21 20:40:59</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow33_col6" class="data row33 col6" >01/03/21 21:42:45</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow33_col7" class="data row33 col7" >1:01:46</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow33_col8" class="data row33 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow34_col0" class="data row34 col0" >Nb_VAE9</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow34_col1" class="data row34 col1" >VAE9</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow34_col2" class="data row34 col2" >VAE</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow34_col3" class="data row34 col3" >09-VAE-with-CelebA-post.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow34_col4" class="data row34 col4" >09-VAE-with-CelebA-post==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow34_col5" class="data row34 col5" >01/03/21 21:42:58</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow34_col6" class="data row34 col6" >01/03/21 21:45:06</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow34_col7" class="data row34 col7" >0:02:08</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow34_col8" class="data row34 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow35_col0" class="data row35 col0" >Nb_ACTF1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow35_col1" class="data row35 col1" >ACTF1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow35_col2" class="data row35 col2" >Misc</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow35_col3" class="data row35 col3" >Activation-Functions.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow35_col4" class="data row35 col4" >Activation-Functions==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow35_col5" class="data row35 col5" >01/03/21 21:45:07</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow35_col6" class="data row35 col6" >01/03/21 21:45:27</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow35_col7" class="data row35 col7" >0:00:20</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow35_col8" class="data row35 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow36_col0" class="data row36 col0" >Nb_NP1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow36_col1" class="data row36 col1" >NP1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow36_col2" class="data row36 col2" >Misc</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow36_col3" class="data row36 col3" >Numpy.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow36_col4" class="data row36 col4" >Numpy==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow36_col5" class="data row36 col5" >01/03/21 21:45:28</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow36_col6" class="data row36 col6" >01/03/21 21:45:29</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow36_col7" class="data row36 col7" >0:00:01</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow36_col8" class="data row36 col8" >ok</td>
-            </tr>
-            <tr>
-                                <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow37_col0" class="data row37 col0" >Nb_TSB1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow37_col1" class="data row37 col1" >TSB1</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow37_col2" class="data row37 col2" >Misc</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow37_col3" class="data row37 col3" >Using-Tensorboard.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow37_col4" class="data row37 col4" >Using-Tensorboard==done==.ipynb</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow37_col5" class="data row37 col5" >01/03/21 21:45:29</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow37_col6" class="data row37 col6" >01/03/21 21:45:30</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow37_col7" class="data row37 col7" >0:00:01</td>
-                        <td id="T_159526f0_7acf_11eb_b7a6_0cc47af5a44frow37_col8" class="data row37 col8" >ok</td>
+                                <td id="T_22cc1_row0_col0" class="data row0 col0" >Nb_LADYB1</td>
+                        <td id="T_22cc1_row0_col1" class="data row0 col1" >LADYB1</td>
+                        <td id="T_22cc1_row0_col2" class="data row0 col2" >SYNOP</td>
+                        <td id="T_22cc1_row0_col3" class="data row0 col3" >LADYB1-Ladybug.ipynb</td>
+                        <td id="T_22cc1_row0_col4" class="data row0 col4" >LADYB1-Ladybug==done==.ipynb</td>
+                        <td id="T_22cc1_row0_col5" class="data row0 col5" >07/03/21 21:13:23</td>
+                        <td id="T_22cc1_row0_col6" class="data row0 col6" >07/03/21 21:15:55</td>
+                        <td id="T_22cc1_row0_col7" class="data row0 col7" >0:02:32</td>
+                        <td id="T_22cc1_row0_col8" class="data row0 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_22cc1_row1_col0" class="data row1 col0" >Nb_SYNOP1</td>
+                        <td id="T_22cc1_row1_col1" class="data row1 col1" >SYNOP1</td>
+                        <td id="T_22cc1_row1_col2" class="data row1 col2" >SYNOP</td>
+                        <td id="T_22cc1_row1_col3" class="data row1 col3" >SYNOP1-Preparation-of-data.ipynb</td>
+                        <td id="T_22cc1_row1_col4" class="data row1 col4" >SYNOP1-Preparation-of-data==done==.ipynb</td>
+                        <td id="T_22cc1_row1_col5" class="data row1 col5" >07/03/21 21:15:56</td>
+                        <td id="T_22cc1_row1_col6" class="data row1 col6" >07/03/21 21:16:01</td>
+                        <td id="T_22cc1_row1_col7" class="data row1 col7" >0:00:05</td>
+                        <td id="T_22cc1_row1_col8" class="data row1 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_22cc1_row2_col0" class="data row2 col0" >Nb_SYNOP2</td>
+                        <td id="T_22cc1_row2_col1" class="data row2 col1" >SYNOP2</td>
+                        <td id="T_22cc1_row2_col2" class="data row2 col2" >SYNOP</td>
+                        <td id="T_22cc1_row2_col3" class="data row2 col3" >SYNOP2-First-predictions.ipynb</td>
+                        <td id="T_22cc1_row2_col4" class="data row2 col4" >SYNOP2-First-predictions==done==.ipynb</td>
+                        <td id="T_22cc1_row2_col5" class="data row2 col5" >07/03/21 21:16:01</td>
+                        <td id="T_22cc1_row2_col6" class="data row2 col6" >07/03/21 21:18:02</td>
+                        <td id="T_22cc1_row2_col7" class="data row2 col7" >0:02:01</td>
+                        <td id="T_22cc1_row2_col8" class="data row2 col8" >ok</td>
+            </tr>
+            <tr>
+                                <td id="T_22cc1_row3_col0" class="data row3 col0" >Nb_SYNOP3</td>
+                        <td id="T_22cc1_row3_col1" class="data row3 col1" >SYNOP3</td>
+                        <td id="T_22cc1_row3_col2" class="data row3 col2" >SYNOP</td>
+                        <td id="T_22cc1_row3_col3" class="data row3 col3" >SYNOP3-12h-predictions.ipynb</td>
+                        <td id="T_22cc1_row3_col4" class="data row3 col4" >SYNOP3-12h-predictions==done==.ipynb</td>
+                        <td id="T_22cc1_row3_col5" class="data row3 col5" >07/03/21 21:18:03</td>
+                        <td id="T_22cc1_row3_col6" class="data row3 col6" >07/03/21 21:18:10</td>
+                        <td id="T_22cc1_row3_col7" class="data row3 col7" >0:00:07</td>
+                        <td id="T_22cc1_row3_col8" class="data row3 col8" >ok</td>
             </tr>
     </tbody></table></div>
 
diff --git a/fidle/logs/ci_report.json b/fidle/logs/ci_report.json
index 8ee5a9cce60f13ad75a0b74bbb6f4a95120084c2..af58344ce3f2584e163b0d529844f28445cf73ca 100644
--- a/fidle/logs/ci_report.json
+++ b/fidle/logs/ci_report.json
@@ -3,391 +3,51 @@
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         "save_figs": true,
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-        "host": "r7i0n6",
-        "profile": "./ci/full_gpu.yml",
-        "start": "01/03/21 18:40:12",
-        "end": "01/03/21 21:45:30",
-        "duration": "3:05:17"
-    },
-    "Nb_LINR1": {
-        "id": "LINR1",
-        "dir": "LinearReg",
-        "src": "01-Linear-Regression.ipynb",
-        "out": "01-Linear-Regression==done==.ipynb",
-        "start": "01/03/21 18:40:13",
-        "end": "01/03/21 18:40:42",
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-        "state": "ok"
-    },
-    "Nb_GRAD1": {
-        "id": "GRAD1",
-        "dir": "LinearReg",
-        "src": "02-Gradient-descent.ipynb",
-        "out": "02-Gradient-descent==done==.ipynb",
-        "start": "01/03/21 18:40:42",
-        "end": "01/03/21 18:40:52",
-        "duration": "0:00:09",
-        "state": "ok"
-    },
-    "Nb_POLR1": {
-        "id": "POLR1",
-        "dir": "LinearReg",
-        "src": "03-Polynomial-Regression.ipynb",
-        "out": "03-Polynomial-Regression==done==.ipynb",
-        "start": "01/03/21 18:40:52",
-        "end": "01/03/21 18:40:59",
-        "duration": "0:00:07",
-        "state": "ok"
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-    "Nb_LOGR1": {
-        "id": "LOGR1",
-        "dir": "LinearReg",
-        "src": "04-Logistic-Regression.ipynb",
-        "out": "04-Logistic-Regression==done==.ipynb",
-        "start": "01/03/21 18:40:59",
-        "end": "01/03/21 18:41:06",
-        "duration": "0:00:06",
-        "state": "ok"
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-    "Nb_PER57": {
-        "id": "PER57",
-        "dir": "IRIS",
-        "src": "01-Simple-Perceptron.ipynb",
-        "out": "01-Simple-Perceptron==done==.ipynb",
-        "start": "01/03/21 18:41:06",
-        "end": "01/03/21 18:41:12",
-        "duration": "0:00:06",
-        "state": "ok"
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-    "Nb_BHPD1": {
-        "id": "BHPD1",
-        "dir": "BHPD",
-        "src": "01-DNN-Regression.ipynb",
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-        "start": "01/03/21 18:41:12",
-        "end": "01/03/21 18:41:29",
-        "duration": "0:00:16",
-        "state": "ok"
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-    "Nb_BHPD2": {
-        "id": "BHPD2",
-        "dir": "BHPD",
-        "src": "02-DNN-Regression-Premium.ipynb",
-        "out": "02-DNN-Regression-Premium==done==.ipynb",
-        "start": "01/03/21 18:41:29",
-        "end": "01/03/21 18:41:54",
-        "duration": "0:00:24",
-        "state": "ok"
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-    "Nb_MNIST1": {
-        "id": "MNIST1",
-        "dir": "MNIST",
-        "src": "01-DNN-MNIST.ipynb",
-        "out": "01-DNN-MNIST==done==.ipynb",
-        "start": "01/03/21 18:41:54",
-        "end": "01/03/21 18:42:42",
-        "duration": "0:00:48",
-        "state": "ok"
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-    "Nb_MNIST2": {
-        "id": "MNIST2",
-        "dir": "MNIST",
-        "src": "02-CNN-MNIST.ipynb",
-        "out": "02-CNN-MNIST==done==.ipynb",
-        "start": "01/03/21 18:42:42",
-        "end": "01/03/21 18:43:34",
-        "duration": "0:00:52",
-        "state": "ok"
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-    "Nb_GTSRB1": {
-        "id": "GTSRB1",
-        "dir": "GTSRB",
-        "src": "01-Preparation-of-data.ipynb",
-        "out": "01-Preparation-of-data==done==.ipynb",
-        "start": "01/03/21 18:43:34",
-        "end": "01/03/21 18:48:18",
-        "duration": "0:04:43",
-        "state": "ok"
-    },
-    "Nb_GTSRB2": {
-        "id": "GTSRB2",
-        "dir": "GTSRB",
-        "src": "02-First-convolutions.ipynb",
-        "out": "02-First-convolutions==done==.ipynb",
-        "start": "01/03/21 18:48:18",
-        "end": "01/03/21 18:48:49",
-        "duration": "0:00:31",
-        "state": "ok"
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-    "Nb_GTSRB3": {
-        "id": "GTSRB3",
-        "dir": "GTSRB",
-        "src": "03-Tracking-and-visualizing.ipynb",
-        "out": "03-Tracking-and-visualizing==done==.ipynb",
-        "start": "01/03/21 18:48:49",
-        "end": "01/03/21 18:49:55",
-        "duration": "0:01:06",
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-    "Nb_GTSRB4": {
-        "id": "GTSRB4",
-        "dir": "GTSRB",
-        "src": "04-Data-augmentation.ipynb",
-        "out": "04-Data-augmentation==done==.ipynb",
-        "start": "01/03/21 18:49:55",
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-        "state": "ok"
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-    "Nb_GTSRB5_r1": {
-        "id": "GTSRB5",
-        "dir": "GTSRB",
-        "src": "05-Full-convolutions.ipynb",
-        "out": "05-Full-convolutions=1==done==.ipynb",
-        "start": "01/03/21 18:51:32",
-        "end": "01/03/21 19:23:13",
-        "duration": "0:31:41",
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-    "Nb_GTSRB5_r2": {
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-        "out": "05-Full-convolutions=2==done==.ipynb",
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-        "end": "01/03/21 19:54:55",
-        "duration": "0:31:42",
-        "state": "ok"
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-    "Nb_GTSRB5_r3": {
-        "id": "GTSRB5",
-        "dir": "GTSRB",
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-        "out": "05-Full-convolutions=3==done==.ipynb",
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-        "state": "ok"
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-    "Nb_GTSRB6": {
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-        "dir": "GTSRB",
-        "src": "06-Notebook-as-a-batch.ipynb",
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-        "duration": "0:00:04",
-        "state": "ok"
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-    "Nb_GTSRB7": {
-        "id": "GTSRB7",
-        "dir": "GTSRB",
-        "src": "07-Show-report.ipynb",
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-        "duration": "0:00:05",
-        "state": "ok"
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-        "id": "IMDB1",
-        "dir": "IMDB",
-        "src": "01-One-hot-encoding.ipynb",
-        "out": "01-One-hot-encoding==done==.ipynb",
-        "start": "01/03/21 20:19:45",
-        "end": "01/03/21 20:20:31",
-        "duration": "0:00:46",
-        "state": "ok"
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-    "Nb_IMDB2": {
-        "id": "IMDB2",
-        "dir": "IMDB",
-        "src": "02-Keras-embedding.ipynb",
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-        "duration": "0:00:48",
-        "state": "ok"
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-    "Nb_IMDB3": {
-        "id": "IMDB3",
-        "dir": "IMDB",
-        "src": "03-Prediction.ipynb",
-        "out": "03-Prediction==done==.ipynb",
-        "start": "01/03/21 20:21:19",
-        "end": "01/03/21 20:21:25",
-        "duration": "0:00:05",
-        "state": "ok"
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-    "Nb_IMDB4": {
-        "id": "IMDB4",
-        "dir": "IMDB",
-        "src": "04-Show-vectors.ipynb",
-        "out": "04-Show-vectors==done==.ipynb",
-        "start": "01/03/21 20:21:25",
-        "end": "01/03/21 20:21:31",
-        "duration": "0:00:06",
-        "state": "ok"
-    },
-    "Nb_IMDB5": {
-        "id": "IMDB5",
-        "dir": "IMDB",
-        "src": "05-LSTM-Keras.ipynb",
-        "out": "05-LSTM-Keras==done==.ipynb",
-        "start": "01/03/21 20:21:31",
-        "end": "01/03/21 20:29:54",
-        "duration": "0:08:23",
+        "description": "Light profile for S04 with CPU",
+        "host": "Oban",
+        "profile": "./ci/fidle-ad_s04.yml",
+        "start": "07/03/21 21:13:23",
+        "end": "07/03/21 21:18:10",
+        "duration": "0:04:47"
+    },
+    "Nb_LADYB1": {
+        "id": "LADYB1",
+        "dir": "SYNOP",
+        "src": "LADYB1-Ladybug.ipynb",
+        "out": "LADYB1-Ladybug==done==.ipynb",
+        "start": "07/03/21 21:13:23",
+        "end": "07/03/21 21:15:55",
+        "duration": "0:02:32",
         "state": "ok"
     },
     "Nb_SYNOP1": {
         "id": "SYNOP1",
         "dir": "SYNOP",
-        "src": "01-Preparation-of-data.ipynb",
-        "out": "01-Preparation-of-data==done==.ipynb",
-        "start": "01/03/21 20:29:55",
-        "end": "01/03/21 20:30:05",
-        "duration": "0:00:09",
+        "src": "SYNOP1-Preparation-of-data.ipynb",
+        "out": "SYNOP1-Preparation-of-data==done==.ipynb",
+        "start": "07/03/21 21:15:56",
+        "end": "07/03/21 21:16:01",
+        "duration": "0:00:05",
         "state": "ok"
     },
     "Nb_SYNOP2": {
         "id": "SYNOP2",
         "dir": "SYNOP",
-        "src": "02-First-predictions.ipynb",
-        "out": "02-First-predictions==done==.ipynb",
-        "start": "01/03/21 20:30:05",
-        "end": "01/03/21 20:32:25",
-        "duration": "0:02:20",
+        "src": "SYNOP2-First-predictions.ipynb",
+        "out": "SYNOP2-First-predictions==done==.ipynb",
+        "start": "07/03/21 21:16:01",
+        "end": "07/03/21 21:18:02",
+        "duration": "0:02:01",
         "state": "ok"
     },
     "Nb_SYNOP3": {
         "id": "SYNOP3",
         "dir": "SYNOP",
-        "src": "03-12h-predictions.ipynb",
-        "out": "03-12h-predictions==done==.ipynb",
-        "start": "01/03/21 20:32:26",
-        "end": "01/03/21 20:32:37",
-        "duration": "0:00:11",
-        "state": "ok"
-    },
-    "Nb_AE1": {
-        "id": "AE1",
-        "dir": "AE",
-        "src": "01-AE-with-MNIST.ipynb",
-        "out": "01-AE-with-MNIST==done==.ipynb",
-        "start": "01/03/21 20:32:37",
-        "end": "01/03/21 20:34:55",
-        "duration": "0:02:18",
-        "state": "ok"
-    },
-    "Nb_AE2": {
-        "id": "AE2",
-        "dir": "AE",
-        "src": "02-AE-with-MNIST-post.ipynb",
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-        "start": "01/03/21 20:34:55",
-        "end": "01/03/21 20:35:06",
-        "duration": "0:00:11",
-        "state": "ok"
-    },
-    "Nb_VAE1": {
-        "id": "VAE1",
-        "dir": "VAE",
-        "src": "01-VAE-with-MNIST.ipynb",
-        "out": "01-VAE-with-MNIST==done==.ipynb",
-        "start": "01/03/21 20:35:07",
-        "end": "01/03/21 20:36:27",
-        "duration": "0:01:20",
-        "state": "ok"
-    },
-    "Nb_VAE2": {
-        "id": "VAE2",
-        "dir": "VAE",
-        "src": "02-VAE-with-MNIST-post.ipynb",
-        "out": "02-VAE-with-MNIST-post==done==.ipynb",
-        "start": "01/03/21 20:36:27",
-        "end": "01/03/21 20:37:25",
-        "duration": "0:00:58",
-        "state": "ok"
-    },
-    "Nb_VAE5": {
-        "id": "VAE5",
-        "dir": "VAE",
-        "src": "05-About-CelebA.ipynb",
-        "out": "05-About-CelebA==done==.ipynb",
-        "start": "01/03/21 20:37:25",
-        "end": "01/03/21 20:37:50",
-        "duration": "0:00:25",
-        "state": "ok"
-    },
-    "Nb_VAE6": {
-        "id": "VAE6",
-        "dir": "VAE",
-        "src": "06-Prepare-CelebA-datasets.ipynb",
-        "out": "06-Prepare-CelebA-datasets==done==.ipynb",
-        "start": "01/03/21 20:37:50",
-        "end": "01/03/21 20:39:29",
-        "duration": "0:01:38",
-        "state": "ok"
-    },
-    "Nb_VAE7": {
-        "id": "VAE7",
-        "dir": "VAE",
-        "src": "07-Check-CelebA.ipynb",
-        "out": "07-Check-CelebA==done==.ipynb",
-        "start": "01/03/21 20:39:29",
-        "end": "01/03/21 20:40:59",
-        "duration": "0:01:29",
-        "state": "ok"
-    },
-    "Nb_VAE8": {
-        "id": "VAE8",
-        "dir": "VAE",
-        "src": "08-VAE-with-CelebA.ipynb",
-        "out": "08-VAE-with-CelebA==done==.ipynb",
-        "start": "01/03/21 20:40:59",
-        "end": "01/03/21 21:42:45",
-        "duration": "1:01:46",
-        "state": "ok"
-    },
-    "Nb_VAE9": {
-        "id": "VAE9",
-        "dir": "VAE",
-        "src": "09-VAE-with-CelebA-post.ipynb",
-        "out": "09-VAE-with-CelebA-post==done==.ipynb",
-        "start": "01/03/21 21:42:58",
-        "end": "01/03/21 21:45:06",
-        "duration": "0:02:08",
-        "state": "ok"
-    },
-    "Nb_ACTF1": {
-        "id": "ACTF1",
-        "dir": "Misc",
-        "src": "Activation-Functions.ipynb",
-        "out": "Activation-Functions==done==.ipynb",
-        "start": "01/03/21 21:45:07",
-        "end": "01/03/21 21:45:27",
-        "duration": "0:00:20",
-        "state": "ok"
-    },
-    "Nb_NP1": {
-        "id": "NP1",
-        "dir": "Misc",
-        "src": "Numpy.ipynb",
-        "out": "Numpy==done==.ipynb",
-        "start": "01/03/21 21:45:28",
-        "end": "01/03/21 21:45:29",
-        "duration": "0:00:01",
-        "state": "ok"
-    },
-    "Nb_TSB1": {
-        "id": "TSB1",
-        "dir": "Misc",
-        "src": "Using-Tensorboard.ipynb",
-        "out": "Using-Tensorboard==done==.ipynb",
-        "start": "01/03/21 21:45:29",
-        "end": "01/03/21 21:45:30",
-        "duration": "0:00:01",
+        "src": "SYNOP3-12h-predictions.ipynb",
+        "out": "SYNOP3-12h-predictions==done==.ipynb",
+        "start": "07/03/21 21:18:03",
+        "end": "07/03/21 21:18:10",
+        "duration": "0:00:07",
         "state": "ok"
     }
 }
\ No newline at end of file
diff --git a/fidle/pwk.py b/fidle/pwk.py
index f714b3c36abf2d06d9718c7f8499e2b48770095a..c210294e21957ba356e431de61f1d222e7be7f53 100644
--- a/fidle/pwk.py
+++ b/fidle/pwk.py
@@ -605,7 +605,7 @@ def plot_donut(values, labels, colors=["lightsteelblue","coral"], figsize=(6,6),
 
     
 def plot_multivariate_serie(sequence, labels=None, predictions=None, only_features=None,
-                            columns=3, width=5,height=4,wspace=0.3,hspace=0.2,
+                            columns=3, width=5,height=4,wspace=0.3,hspace=0.2,ms=6,lw=1,
                             save_as='auto', time_dt=1, hide_ticks=False):
     
     sequence_len = len(sequence)
@@ -627,12 +627,19 @@ def plot_multivariate_serie(sequence, labels=None, predictions=None, only_featur
     n=1
     for i in only_features:
         ax=fig.add_subplot(rows, columns, n)
-        ax.plot(t[:-dt],       sequence[:-dt,i],    '-',  linewidth=1,  color='steelblue', label=labels[i])
-        ax.plot(t[:-dt],       sequence[:-dt,i],    'o',  markersize=4, color='steelblue')
-        ax.plot(t[-dt-1:], sequence[-dt-1:,i],'--o', linewidth=1, fillstyle='none',  markersize=6, color='steelblue')
+        
+        # ---- Real sequence without prediction
+        #
+        ax.plot( t[:-dt],sequence[:-dt,i], 'o',  markersize=ms, color='C0', zorder=2)
+        ax.plot( t,sequence[:,i],          '-',  linewidth=lw,  color='C0', label=labels[i],zorder=1)
+
+        # ---- What we expect
+        #
+        ax.plot(t[-dt:], sequence[-dt:,i], 'o', markeredgecolor='C0',markerfacecolor='white',ms=6)
+        
         if predictions is not None:
-            ax.plot(t[-dt-1:],     sequence_with_pred[-dt-1:,i],     '--',  linewidth=1, fillstyle='full',  markersize=6, color='red')
-            ax.plot(t[-dt:],       predictions[:,i],     'o',  linewidth=1, fillstyle='full',  markersize=6, color='red')
+            ax.plot(t[-dt-1:], sequence_with_pred[-dt-1:,i], '--',  lw=lw, fillstyle='full',  ms=ms, color='C1',zorder=1)
+            ax.plot(t[-dt:],   predictions[:,i],             'o',   lw=lw, fillstyle='full',  ms=ms, color='C1',zorder=2)
 
         if hide_ticks:
             ax.set_yticks([])
@@ -649,7 +656,7 @@ def plot_multivariate_serie(sequence, labels=None, predictions=None, only_featur
 # -------------------------------------------------------------
 #
 
-def plot_2d_serie(data, figsize=(10,8), monocolor=False, hide_ticks=True, save_as='auto'):
+def plot_2d_serie(data, figsize=(10,8), monocolor=False, hide_ticks=True, lw=2, ms=4, save_as='auto'):
     """
     Plot a 2d dataset as a trajectory
     args:
@@ -677,11 +684,11 @@ def plot_2d_serie(data, figsize=(10,8), monocolor=False, hide_ticks=True, save_a
     else:
         for i in range(0,100):
             a= (200-i)/200
-            ax.plot(x[i*k:(i+1)*k+1], y[i*k:(i+1)*k+1], '-', color=(a,a,a),lw='2')
+            ax.plot(x[i*k:(i+1)*k+1], y[i*k:(i+1)*k+1], '-', color=(a,a,a),lw=lw,zorder=1)
 
     # ---- Last point
     #
-    ax.plot(x[n-1], y[n-1], 'o', color='#e12229',ms='4')
+    ax.plot(x[n-1], y[n-1], 'o', color='C1',ms=ms,zorder=2)
     
     ax.set_aspect('equal', 'box')
     ax.set_xlabel('axis=0')
@@ -697,7 +704,7 @@ def plot_2d_serie(data, figsize=(10,8), monocolor=False, hide_ticks=True, save_a
     
 
     
-def plot_2d_segment(sequence_real, sequence_pred, figsize=(10,8), ms=6, hide_ticks=True, save_as='auto'):
+def plot_2d_segment(sequence_real, sequence_pred, figsize=(10,8), ms=6, lw=1, hide_ticks=True, save_as='auto'):
     """
     Plot a 2d segment real and predicted
     args:
@@ -718,8 +725,8 @@ def plot_2d_segment(sequence_real, sequence_pred, figsize=(10,8), ms=6, hide_tic
     
     # ---- Draw real sequence without prediction
     #
-    ax.plot(x[:-k], y[:-k], 'o', ms=ms, markeredgecolor='C0', markerfacecolor='white', zorder=2)
-    ax.plot(x, y,'-', alpha=0.5, color='C0', lw=1, zorder=1)
+    ax.plot(x[:-k], y[:-k],   'o', color='C0', fillstyle='full', zorder=2, ms=ms)
+    ax.plot(x, y,             '-', color='C0', lw=lw, zorder=1)
     
     # ---- What we expect
     #
@@ -727,9 +734,9 @@ def plot_2d_segment(sequence_real, sequence_pred, figsize=(10,8), ms=6, hide_tic
 
     # ---- What we have
     #
-    ax.plot(u, v,'o', ms=ms, markeredgecolor='C1', markerfacecolor='white', zorder=2)
-    ax.plot( [x[-1-k],u[0]], [y[-1-k],v[0]], '--',alpha=1., color='C1',lw=1, zorder=1)
-    ax.plot(u, v,'--', alpha=0.5, color='C1',lw=1, zorder=1)
+    ax.plot(u, v,                            'o',  color='C1',fillstyle='full',zorder=2, ms=ms)
+    ax.plot( [x[-1-k],u[0]], [y[-1-k],v[0]], '--', color='C1',lw=lw, zorder=1)
+    ax.plot(u, v,                            '--', color='C1',lw=lw, zorder=1)
 
     ax.set_aspect('equal', 'box')
     ax.set_xlabel('axis=0')