01-Embedding-Keras.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
    "\n",
    "# <!-- TITLE --> [IMDB1] - Text embedding with IMDB\n",
    "<!-- DESC --> A very classical example of word embedding for text classification (sentiment analysis)\n",
    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
    "\n",
    "## Objectives :\n",
    " - The objective is to guess whether film reviews are **positive or negative** based on the analysis of the text. \n",
    " - Understand the management of **textual data** and **sentiment analysis**\n",
    "\n",
    "Original dataset can be find **[there](http://ai.stanford.edu/~amaas/data/sentiment/)**  \n",
    "Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/)  \n",
    "For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)\n",
    "\n",
    "## What we're going to do :\n",
    "\n",
    " - Retrieve data\n",
    " - Preparing the data\n",
    " - Build a model\n",
    " - Train the model\n",
    " - Evaluate the result\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1 - Init python stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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      "\n",
      "FIDLE 2020 - Practical Work Module\n",
      "Version              : 0.4.2\n",
      "Run time             : Sunday 23 February 2020, 20:06:09\n",
      "TensorFlow version   : 2.0.0\n",
      "Keras version        : 2.2.4-tf\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "import tensorflow.keras.datasets.imdb as imdb\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "\n",
    "import os,sys,h5py,json\n",
    "from importlib import reload\n",
    "\n",
    "sys.path.append('..')\n",
    "import fidle.pwk as ooo\n",
    "\n",
    "ooo.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Well, we should be at HOME !\n",
      "We are going to use: /home/pjluc/datasets/IMDB\n"
     ]
    }
   ],
   "source": [
    "place, dataset_dir = ooo.good_place( { 'GRICAD' : f'{os.getenv(\"SCRATCH_DIR\",\"\")}/PROJECTS/pr-fidle/datasets/IMDB',\n",
    "                                       'IDRIS'  : f'{os.getenv(\"WORK\",\"\")}/datasets/IMDB',\n",
    "                                       'HOME'   : f'{os.getenv(\"HOME\",\"\")}/datasets/IMDB'} )"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2 - Retrieve data\n",
    "\n",
    "**From Keras :**\n",
    "This IMDb dataset can bet get directly from [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)  \n",
    "\n",
    "Due to their nature, textual data can be somewhat complex.\n",
    "\n",
    "### 2.1 - Data structure :  \n",
    "The dataset is composed of 2 parts: **reviews** and **opinions** (positive/negative),  with a **dictionary**\n",
    "\n",
    "  - dataset = (reviews, opinions)\n",
    "    - reviews = \\[ review_0, review_1, ...\\]\n",
    "      - review_i = [ int1, int2, ...] where int_i is the index of the word in the dictionary.\n",
    "    - opinions = \\[ int0, int1, ...\\] where int_j == 0 if opinion is negative or 1 if opinion is positive.\n",
    "  - dictionary = \\[ mot1:int1, mot2:int2, ... ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 - Get dataset\n",
    "For simplicity, we will use a pre-formatted dataset.  \n",
    "See : https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb/load_data  \n",
    "\n",
    "However, Keras offers some usefull tools for formatting textual data.  \n",
    "See : https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab_size = 10000\n",
    "\n",
    "# ----- Retrieve x,y\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, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Max(x_train,x_test)  :  9999\n",
      "  x_train : (25000,)  y_train : (25000,)\n",
      "  x_test  : (25000,)  y_test  : (25000,)\n",
      "\n",
      "Review example (x_train[12]) :\n",
      "\n",
      " [1, 14, 22, 1367, 53, 206, 159, 4, 636, 898, 74, 26, 11, 436, 363, 108, 7, 14, 432, 14, 22, 9, 1055, 34, 8599, 2, 5, 381, 3705, 4509, 14, 768, 47, 839, 25, 111, 1517, 2579, 1991, 438, 2663, 587, 4, 280, 725, 6, 58, 11, 2714, 201, 4, 206, 16, 702, 5, 5176, 19, 480, 5920, 157, 13, 64, 219, 4, 2, 11, 107, 665, 1212, 39, 4, 206, 4, 65, 410, 16, 565, 5, 24, 43, 343, 17, 5602, 8, 169, 101, 85, 206, 108, 8, 3008, 14, 25, 215, 168, 18, 6, 2579, 1991, 438, 2, 11, 129, 1609, 36, 26, 66, 290, 3303, 46, 5, 633, 115, 4363]\n"
     ]
    }
   ],
   "source": [
    "print(\"  Max(x_train,x_test)  : \", ooo.rmax([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))\n",
    "\n",
    "print('\\nReview example (x_train[12]) :\\n\\n',x_train[12])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 - 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---- Retrieve dictionary {word:index}, and encode it in ascii\n",
    "#\n",
    "word_index = imdb.get_word_index()\n",
    "\n",
    "# ---- Shift the dictionary from +3\n",
    "#\n",
    "word_index = {w:(i+3) for w,i in word_index.items()}\n",
    "\n",
    "# ---- Add <pad>, <start> and unknown tags\n",
    "#\n",
    "word_index.update( {'<pad>':0, '<start>':1, '<unknown>':2} )\n",
    "\n",
    "# ---- Create a reverse dictionary : {index:word}\n",
    "#\n",
    "index_word = {index:word for word,index in word_index.items()} \n",
    "\n",
    "# ---- Add a nice function to transpose :\n",
    "#\n",
    "def dataset2text(review):\n",
    "    return ' '.join([index_word.get(i, '?') for i in review])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dictionary size     :  88587\n",
      "440 : hope\n",
      "441 : entertaining\n",
      "442 : she's\n",
      "443 : mr\n",
      "444 : overall\n",
      "445 : evil\n",
      "446 : called\n",
      "447 : loved\n",
      "448 : based\n",
      "449 : oh\n",
      "450 : several\n",
      "451 : fans\n",
      "452 : mother\n",
      "453 : drama\n",
      "454 : beginning\n",
      "\n",
      "Review example (x_train[12]) :\n",
      "\n",
      " [1, 14, 22, 1367, 53, 206, 159, 4, 636, 898, 74, 26, 11, 436, 363, 108, 7, 14, 432, 14, 22, 9, 1055, 34, 8599, 2, 5, 381, 3705, 4509, 14, 768, 47, 839, 25, 111, 1517, 2579, 1991, 438, 2663, 587, 4, 280, 725, 6, 58, 11, 2714, 201, 4, 206, 16, 702, 5, 5176, 19, 480, 5920, 157, 13, 64, 219, 4, 2, 11, 107, 665, 1212, 39, 4, 206, 4, 65, 410, 16, 565, 5, 24, 43, 343, 17, 5602, 8, 169, 101, 85, 206, 108, 8, 3008, 14, 25, 215, 168, 18, 6, 2579, 1991, 438, 2, 11, 129, 1609, 36, 26, 66, 290, 3303, 46, 5, 633, 115, 4363]\n",
      "\n",
      "In real words :\n",
      "\n",
      " <start> this film contains more action before the opening credits than are in entire hollywood films of this sort this film is produced by tsui <unknown> and stars jet li this team has brought you many worthy hong kong cinema productions including the once upon a time in china series the action was fast and furious with amazing wire work i only saw the <unknown> in two shots aside from the action the story itself was strong and not just used as filler to find any other action films to rival this you must look for a hong kong cinema <unknown> in your area they are really worth checking out and usually never disappoint\n"
     ]
    }
   ],
   "source": [
    "print('\\nDictionary size     : ', len(word_index))\n",
    "for k in range(440,455):print(f'{k:2d} : {index_word[k]}' )\n",
    "print('\\nReview example (x_train[12]) :\\n\\n',x_train[12])\n",
    "print('\\nIn real words :\\n\\n', dataset2text(x_train[12]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 - Have a look for neurons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "ax=sns.distplot([len(i) for i in x_train],bins=60)\n",
    "ax.set_title('Distribution of reviews by size')\n",
    "plt.xlabel(\"Review's sizes\")\n",
    "plt.ylabel('Density')\n",
    "ax.set_xlim(0, 1500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3 - Preprocess the data\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\\>\\)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Review example (x_train[12]) :\n",
      "\n",
      " [   1   14   22 1367   53  206  159    4  636  898   74   26   11  436\n",
      "  363  108    7   14  432   14   22    9 1055   34 8599    2    5  381\n",
      " 3705 4509   14  768   47  839   25  111 1517 2579 1991  438 2663  587\n",
      "    4  280  725    6   58   11 2714  201    4  206   16  702    5 5176\n",
      "   19  480 5920  157   13   64  219    4    2   11  107  665 1212   39\n",
      "    4  206    4   65  410   16  565    5   24   43  343   17 5602    8\n",
      "  169  101   85  206  108    8 3008   14   25  215  168   18    6 2579\n",
      " 1991  438    2   11  129 1609   36   26   66  290 3303   46    5  633\n",
      "  115 4363    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0]\n",
      "\n",
      "In real words :\n",
      "\n",
      " <start> this film contains more action before the opening credits than are in entire hollywood films of this sort this film is produced by tsui <unknown> and stars jet li this team has brought you many worthy hong kong cinema productions including the once upon a time in china series the action was fast and furious with amazing wire work i only saw the <unknown> in two shots aside from the action the story itself was strong and not just used as filler to find any other action films to rival this you must look for a hong kong cinema <unknown> in your area they are really worth checking out and usually never disappoint <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>\n"
     ]
    }
   ],
   "source": [
    "review_len = 256\n",
    "\n",
    "x_train = keras.preprocessing.sequence.pad_sequences(x_train,\n",
    "                                                     value   = 0,\n",
    "                                                     padding = 'post',\n",
    "                                                     maxlen  = review_len)\n",
    "\n",
    "x_test  = keras.preprocessing.sequence.pad_sequences(x_test,\n",
    "                                                     value   = 0 ,\n",
    "                                                     padding = 'post',\n",
    "                                                     maxlen  = review_len)\n",
    "\n",
    "print('\\nReview example (x_train[12]) :\\n\\n',x_train[12])\n",
    "print('\\nIn real words :\\n\\n', dataset2text(x_train[12]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save dataset and dictionary (For future use)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved.\n"
     ]
    }
   ],
   "source": [
    "# ---- To write dataset in the project place\n",
    "#\n",
    "output_dir = dataset_dir\n",
    "\n",
    "# ---- To write h5 dataset in a test place\n",
    "#      For small tests only !\n",
    "#\n",
    "# output_dir = './data'\n",
    "# ooo.mkdir(output_dir)\n",
    "\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",
    "1. We'll choose a dense vector size for the embedding output with **dense_vector_size**\n",
    "2. **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",
    "3. 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 : https://stats.stackexchange.com/questions/324992/how-the-embedding-layer-is-trained-in-keras-embedding-layer\n",
    "\n",
    "A SUIVRE : https://www.liip.ch/en/blog/sentiment-detection-with-keras-word-embeddings-and-lstm-deep-learning-networks\n",
    "### 4.1 - Build\n",
    "More documentation about :\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model(dense_vector_size=32):\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.GlobalAveragePooling1D())\n",
    "    model.add(keras.layers.Dense(dense_vector_size, activation='relu'))\n",
    "    model.add(keras.layers.Dense(1,                 activation='sigmoid'))\n",
    "\n",
    "    model.compile(optimizer = 'adam',\n",
    "                  loss      = 'binary_crossentropy',\n",
    "                  metrics   = ['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5 - Train the model\n",
    "### 5.1 - Get it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, 256, 32)           320000    \n",
      "_________________________________________________________________\n",
      "global_average_pooling1d (Gl (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 321,089\n",
      "Trainable params: 321,089\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = get_model(32)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 - Add callback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs('./run/models',   mode=0o750, exist_ok=True)\n",
    "save_dir = \"./run/models/best_model.h5\"\n",
    "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 - Train it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/30\n",
      "25000/25000 [==============================] - 2s 63us/sample - loss: 0.6872 - accuracy: 0.6386 - val_loss: 0.6756 - val_accuracy: 0.7404\n",
      "Epoch 2/30\n",
      "25000/25000 [==============================] - 1s 32us/sample - loss: 0.6445 - accuracy: 0.7591 - val_loss: 0.6068 - val_accuracy: 0.7795\n",
      "Epoch 3/30\n",
      "25000/25000 [==============================] - 1s 32us/sample - loss: 0.5446 - accuracy: 0.8122 - val_loss: 0.5002 - val_accuracy: 0.8214\n",
      "Epoch 4/30\n",
      "25000/25000 [==============================] - 1s 33us/sample - loss: 0.4322 - accuracy: 0.8539 - val_loss: 0.4097 - val_accuracy: 0.8488\n",
      "Epoch 5/30\n",
      "25000/25000 [==============================] - 1s 32us/sample - loss: 0.3517 - accuracy: 0.8778 - val_loss: 0.3574 - val_accuracy: 0.8615\n",
      "Epoch 6/30\n",
      "25000/25000 [==============================] - 1s 32us/sample - loss: 0.3012 - accuracy: 0.8926 - val_loss: 0.3281 - val_accuracy: 0.8691\n",
      "Epoch 7/30\n",
      "25000/25000 [==============================] - 1s 31us/sample - loss: 0.2668 - accuracy: 0.9040 - val_loss: 0.3108 - val_accuracy: 0.8737\n",
      "Epoch 8/30\n",
      "25000/25000 [==============================] - 1s 31us/sample - loss: 0.2420 - accuracy: 0.9116 - val_loss: 0.2996 - val_accuracy: 0.8772\n",
      "Epoch 9/30\n",
      "25000/25000 [==============================] - 1s 32us/sample - loss: 0.2218 - accuracy: 0.9199 - val_loss: 0.2921 - val_accuracy: 0.8796\n",
      "Epoch 10/30\n",
      "25000/25000 [==============================] - 1s 33us/sample - loss: 0.2048 - accuracy: 0.9266 - val_loss: 0.2890 - val_accuracy: 0.8813\n",
      "Epoch 11/30\n",
      "25000/25000 [==============================] - 1s 32us/sample - loss: 0.1908 - accuracy: 0.9331 - val_loss: 0.2873 - val_accuracy: 0.8828\n",
      "Epoch 12/30\n",
      "25000/25000 [==============================] - 1s 32us/sample - loss: 0.1790 - accuracy: 0.9368 - val_loss: 0.2868 - val_accuracy: 0.8825\n",
      "Epoch 13/30\n",
      "25000/25000 [==============================] - 1s 31us/sample - loss: 0.1672 - accuracy: 0.9419 - val_loss: 0.2887 - val_accuracy: 0.8831\n",
      "Epoch 14/30\n",
      "25000/25000 [==============================] - 1s 32us/sample - loss: 0.1582 - accuracy: 0.9466 - val_loss: 0.2973 - val_accuracy: 0.8790\n",
      "Epoch 15/30\n",
      "25000/25000 [==============================] - 1s 30us/sample - loss: 0.1494 - accuracy: 0.9498 - val_loss: 0.2953 - val_accuracy: 0.8822\n",
      "Epoch 16/30\n",
      "25000/25000 [==============================] - 1s 33us/sample - loss: 0.1408 - accuracy: 0.9532 - val_loss: 0.3004 - val_accuracy: 0.8817\n",
      "Epoch 17/30\n",
      "25000/25000 [==============================] - 1s 32us/sample - loss: 0.1335 - accuracy: 0.9554 - val_loss: 0.3063 - val_accuracy: 0.8803\n",
      "Epoch 18/30\n",
      "25000/25000 [==============================] - 1s 32us/sample - loss: 0.1267 - accuracy: 0.9589 - val_loss: 0.3131 - val_accuracy: 0.8778\n",
      "Epoch 19/30\n",
      "25000/25000 [==============================] - 1s 32us/sample - loss: 0.1210 - accuracy: 0.9609 - val_loss: 0.3205 - val_accuracy: 0.8760\n",
      "Epoch 20/30\n",
      "25000/25000 [==============================] - 1s 33us/sample - loss: 0.1145 - accuracy: 0.9638 - val_loss: 0.3283 - val_accuracy: 0.8743\n",
      "Epoch 21/30\n",
      "25000/25000 [==============================] - 1s 32us/sample - loss: 0.1085 - accuracy: 0.9670 - val_loss: 0.3345 - val_accuracy: 0.8751\n",
      "Epoch 22/30\n",
      "25000/25000 [==============================] - 1s 32us/sample - loss: 0.1033 - accuracy: 0.9691 - val_loss: 0.3436 - val_accuracy: 0.8738\n",
      "Epoch 23/30\n",
      "25000/25000 [==============================] - 1s 31us/sample - loss: 0.0992 - accuracy: 0.9704 - val_loss: 0.3523 - val_accuracy: 0.8721\n",
      "Epoch 24/30\n",
      "25000/25000 [==============================] - 1s 32us/sample - loss: 0.0940 - accuracy: 0.9733 - val_loss: 0.3613 - val_accuracy: 0.8707\n",
      "Epoch 25/30\n",
      "25000/25000 [==============================] - 1s 31us/sample - loss: 0.0892 - accuracy: 0.9746 - val_loss: 0.3775 - val_accuracy: 0.8659\n",
      "Epoch 26/30\n",
      "25000/25000 [==============================] - 1s 31us/sample - loss: 0.0852 - accuracy: 0.9766 - val_loss: 0.3809 - val_accuracy: 0.8687\n",
      "Epoch 27/30\n",
      "25000/25000 [==============================] - 1s 32us/sample - loss: 0.0814 - accuracy: 0.9776 - val_loss: 0.3921 - val_accuracy: 0.8666\n",
      "Epoch 28/30\n",
      "25000/25000 [==============================] - 1s 31us/sample - loss: 0.0776 - accuracy: 0.9794 - val_loss: 0.4015 - val_accuracy: 0.8657\n",
      "Epoch 29/30\n",
      "25000/25000 [==============================] - 1s 32us/sample - loss: 0.0733 - accuracy: 0.9810 - val_loss: 0.4121 - val_accuracy: 0.8642\n",
      "Epoch 30/30\n",
      "25000/25000 [==============================] - 1s 31us/sample - loss: 0.0704 - accuracy: 0.9823 - val_loss: 0.4246 - val_accuracy: 0.8627\n",
      "CPU times: user 1min 30s, sys: 4.75 s, total: 1min 35s\n",
      "Wall time: 24.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "n_epochs   = 30\n",
    "batch_size = 512\n",
    "\n",
    "history = model.fit(x_train,\n",
    "                    y_train,\n",
    "                    epochs          = n_epochs,\n",
    "                    batch_size      = batch_size,\n",
    "                    validation_data = (x_test, y_test),\n",
    "                    verbose         = 1,\n",
    "                    callbacks       = [savemodel_callback])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6 - Evaluate\n",
    "### 6.1 - Training history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": [
    "ooo.plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 - Reload and evaluate best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_test / loss      : 0.2868\n",
      "x_test / accuracy  : 0.8825\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "#### Accuracy donut is :"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Confusion matrix is :"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_c9924bca_5673_11ea_b7f2_ddc51b15627crow0_col0 {\n",
       "            background-color:  #008000;\n",
       "            color:  #f1f1f1;\n",
       "            font-size:  12pt;\n",
       "        }    #T_c9924bca_5673_11ea_b7f2_ddc51b15627crow0_col1 {\n",
       "            background-color:  #e5ffe5;\n",
       "            color:  #000000;\n",
       "            font-size:  12pt;\n",
       "        }    #T_c9924bca_5673_11ea_b7f2_ddc51b15627crow1_col0 {\n",
       "            background-color:  #e5ffe5;\n",
       "            color:  #000000;\n",
       "            font-size:  12pt;\n",
       "        }    #T_c9924bca_5673_11ea_b7f2_ddc51b15627crow1_col1 {\n",
       "            background-color:  #008000;\n",
       "            color:  #f1f1f1;\n",
       "            font-size:  12pt;\n",
       "        }</style><table id=\"T_c9924bca_5673_11ea_b7f2_ddc51b15627c\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_c9924bca_5673_11ea_b7f2_ddc51b15627clevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_c9924bca_5673_11ea_b7f2_ddc51b15627crow0_col0\" class=\"data row0 col0\" >0.87</td>\n",
       "                        <td id=\"T_c9924bca_5673_11ea_b7f2_ddc51b15627crow0_col1\" class=\"data row0 col1\" >0.13</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c9924bca_5673_11ea_b7f2_ddc51b15627clevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_c9924bca_5673_11ea_b7f2_ddc51b15627crow1_col0\" class=\"data row1 col0\" >0.11</td>\n",
       "                        <td id=\"T_c9924bca_5673_11ea_b7f2_ddc51b15627crow1_col1\" class=\"data row1 col1\" >0.89</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f2bff5efd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = keras.models.load_model('./run/models/best_model.h5')\n",
    "\n",
    "# ---- Evaluate\n",
    "reload(ooo)\n",
    "score  = model.evaluate(x_test, y_test, verbose=0)\n",
    "\n",
    "print('x_test / loss      : {:5.4f}'.format(score[0]))\n",
    "print('x_test / accuracy  : {:5.4f}'.format(score[1]))\n",
    "\n",
    "values=[score[1], 1-score[1]]\n",
    "ooo.plot_donut(values,[\"Accuracy\",\"Errors\"], title=\"#### Accuracy donut is :\")\n",
    "\n",
    "# ---- Confusion matrix\n",
    "\n",
    "y_pred   = model.predict_classes(x_test)\n",
    "\n",
    "# ooo.display_confusion_matrix(y_test,y_pred,labels=range(2),color='orange',font_size='20pt')\n",
    "ooo.display_confusion_matrix(y_test,y_pred,labels=range(2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
   ]
  }
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