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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Text Embedding - IMDB dataset\n",
    "=============================\n",
    "---\n",
    "Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020  \n",
    "\n",
    "## Text classification using **Text embedding** :\n",
    "\n",
    "The objective is to guess whether film reviews are **positive or negative** based on the analysis of the text. \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": [
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    "## Step 1 - Init python stuff"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDLE 2020 - Practical Work Module\n",
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      "  Version            : 0.2.4\n",
      "  Run time           : Monday 27 January 2020, 23:33:47\n",
      "  Matplotlib style   : fidle/talk.mplstyle\n",
      "  TensorFlow version : 2.0.0\n",
      "  Keras version      : 2.2.4-tf\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
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    "\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",
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    "import seaborn as sns\n",
    "\n",
    "import os,h5py,json\n",
    "\n",
    "import fidle.pwk as ooo\n",
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    "from importlib import reload\n",
    "\n",
    "ooo.init()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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    "## 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",
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    "### 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": [
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    "### 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",
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   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
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    "vocab_size = 10000\n",
    "\n",
    "# ----- Retrieve x,y\n",
    "#\n",
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    "(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",
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   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  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": [
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    "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": [
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    "### 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",
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   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
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    "# ---- 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",
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    "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",
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   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dictionary size     :  88587\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",
    "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": [
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    "### 2.4 - Have a look for neurons"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
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    "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",
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    "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",
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    "We chose a review length of **review_len**  \n",
    "We will therefore complete them with a padding (of \\<pad\\>\\)  "
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 7,
   "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": [
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    "review_len = 256\n",
    "\n",
    "x_train = keras.preprocessing.sequence.pad_sequences(x_train,\n",
    "                                                     value   = 0,\n",
    "                                                     padding = 'post',\n",
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    "                                                     maxlen  = review_len)\n",
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    "x_test  = keras.preprocessing.sequence.pad_sequences(x_test,\n",
    "                                                     value   = 0 ,\n",
    "                                                     padding = 'post',\n",
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    "                                                     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": [
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    "### Save dataset and dictionary (can be usefull)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved.\n"
     ]
    }
   ],
   "source": [
    "os.makedirs('./data',   mode=0o750, exist_ok=True)\n",
    "\n",
    "with h5py.File('./data/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('./data/word_index.json', 'w') as fp:\n",
    "    json.dump(word_index, fp)\n",
    "\n",
    "with open('./data/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",
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    "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": 9,
   "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": null,
   "metadata": {},
   "source": [
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    "model = get_model(32)\n",
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    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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    "### 5.2 - Add callback"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 11,
   "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",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "n_epochs   = 30\n",
    "batch_size = 512\n",
    "\n",
    "history = model.fit(x_train,\n",
    "                    y_train,\n",
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    "                    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": [
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    "## Step 6 - Evaluate\n",
    "### 6.1 - Training history"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 13,
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "x_test / loss      : 0.2869\n",
      "x_test / accuracy  : 0.8834\n"
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      "text/markdown": [
       "#### Accuracy donut is :"
      ],
      "text/plain": [
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       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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Jean-Luc Parouty committed
      "image/png": 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JQVARd6bldHYnYTmdWNJ1eOGv8PhdEDTdqpAPPAV80tqOqUuCT3K4mBESC7Ye6GTd7nZCsr4j4szlsJFtVk5HxSk3Mxtegv/7XrRAawfuxTiOXjLhJkmCT+JdCzyBcTjUMMFgiHd3tLCnoSfyu4SIg+jldNYkpkOJULMV7rkZ2uujPeI2jDOzTEtbibGR4JNYNwD3YXIX5fEFeH1LE00dIxa6FSKmzMvpvJEa5XRiqbMZ7vk67NsY7RGfxjijJ+puVTEyCT6J878Yh0xF6B3wsWpTE939Pou7JNJZ9HI6r1jel6TgGYAHfzDS3qYPYpwiGvOim+lAgk9ifBbj/PQILV2DvLa5Ebd3suWehBifBdOLIqfcupqhbkdiOpQMggF47FfwZtRqO6dirNfKXqBxkuBjvc9jVKWO0Ng+wDvbWwgEJbFAWM+8nM6q1C6nEwu6Ds/9BZ69L9ojFiMjoHGT4GOtL2FUpo7Q0N7Pml2taf8+F4lRVZxtXk4nXafczLz9JDx6Z7RU7BXAMxgH1YkxkOBjna8SpTR5fVs/a3e2SeARCTNvakFkY/1O6GyyvjPJbMvr8MBt4DVNBDoBeBLItrZTqUmCjzW+AfzE7EJdWz/rdrUhcUckUqFZOZ1kPio7kfZvhge+Dz632dUzMKpiS0HSUUjwib9vYRzIFKGutU8Cj0g483I6ftiqaDmdWKjbCQ/dAQHTjNTzgX8ATrOLwiAnmcbXdzGO541Q29LH+j1Rq+mKJOWwa2Q47WS6HGS67GS67GQ47dhtGppmfNk0Dv436IAe0tExDv3TdaMquS8QxOsL4vYF8foCeHxBfIGo5f3jKi3L6cTC/s3wjx/DZV8He0SceT/wfxgno0pNLBMSfOJDA74HfNvs4oHmPjbulcCTjHIyHRTkZJCd6TgYZA59GcHGYbYoHyOhkI7HfyQYHfkK0Dvoo3fAR6wrLEUtpyOJBmOzex08+nP46FfAFlHw4DJgEGMzeWLuLJKYHKMdHzcBvzK7sL+5l017OyzujjCTk+mgMDeDglwXhTkZFOa6cDqSt2JKKKTTO+iju99Hz4CX7n7vpAPSstklzKoKG/m4++Fn1xp7XMTYLDsDPngTaKY3J7/B+EyQD9shZOQTe+8DfmF2YX9TL5v2SeBJhFQLNGZsNo3C3IyDyQFGRu/QgNTd76VnwEvPgG/MmZNTSiNKChrldOIceO54bRfrmrtZ29jN/u5BZhRkceAL7zF97PN7W/nn9kbWNnWzuaUXbzDEy1efwpkzx76vU/te1E2iAPzgrIV88/T5APT7Anz5v1t4fKeR6fehBVX87Pwl5LiGf1w+tr2Rjz+2lq2fOZuZm14Fpwsu/ozZj78RGMBIPJIAdJAEn9haDvwdk0SOfY09bN7faX2P0pTNplFWkEllcTaVxdlkutR8qZsFpEAwRFu3m+auQVo6B/H6zWd8CnJcOM3K6VhwVPYtL22jOMvJ0VWFdHtGrhv34OY6/ra5niXl+Swsy2ND8/gL7f71g8eYtt/6yg72dg1w8fzKw21fe34rf9tSzzdOnQfAHa/vxmHTuOui5Ycf0+Pxc+Mzm7jtrIXMLDwYwNc+D84MuOA6s6f6GkYAum3cnVeUmu/IxKjGOO8j4lZyf3OvBB4LuJw2KouMYFNWmBXX9Zlk5rDbqCrJoaokB13X6er30tw5SHPnIH2DRz7o508zqWDd1Qy12+Pex703ncfsIuOtsuR3L9Lviz7Suv3sRfzhfSvIcNj52Zu7JxR8Pr5sWkRbfa+b/d0DHFtdyLKKI/uc/rWjkS+fNIdbTjNGQt5AiHvW1wwLPl97YStVuZl8/oQ5w3/o20+BMxPOudKsG9/HWAMy3e+XbiT4xEYOxuayqeEXWroG2SxrPHGTl+00RjdF2RTlZUR+mKY5TdMozsukOC+TRTOKGfD4Dwei8iKTcjoW7e05FHjGYkp+fApH37++hpAO16+cMazd7Q9SnOU6/P/FWS4GhgTH12s7uG99De9cfwZ2m8nr7bVHjRHQ6R8xe9qfATXAozH5JVKYBJ/JswMPAkeHX+gd8LFmZ6tM8sZYbpaTGRV5VBVnk5MlWynGIyfTyZzqAuZUm1Q0AEum3JKBruvcv6GWbKedy5cOv2c8aVoxv19zgDNmlKKjc/ea/Zw8rQQAXzDEDU+u54snzmVlVWH0J3jpQXBlwIkXm139M7Ad2BqjXyclSfCZvB9h5PQP4/EFeVuKhMaMBlSWZDOrMp8yswKYYvK6W8GdHnt7Xtrfzv7uQa5ZMZ38jOE3ML98z1IufuhtVvzhZQDmFefwywuWAnD7qp34giFuPXPB6E/y7H3GCOiY88Ov5ACPA8cB3ZP9XVKVBJ/J+R/gK+GNwWCId7e3yLEIMZDhtDOzMo8ZFXlkZcjLNa4Ky+FL9xj1y1Y/A417E92juLln/QEArgubcgOYX5rH1s+cw7Y2IxAvKsvDabexra2XH72xm/9ccSJZTju/W72P363eT58vwCVHVfKT85aQ5QzLoHzqD5CdDwtPDH+auRgzJheTpnuA5N08cecCvzO7sG53G139Xou7o5bSgkxmVuZTVZyNzWxeXcSHMwNWnmN8NeyG1c8awci8jExK6nL7eGx7EwtKczl1eonpY5x2G8srj0xN6rrODU9u4PIlUzh3djn/2FLPl5/bwr2XrGRafhbXPLGOoA6/e+/y4T9IDxnnAZVOgbKIpIeLMCqgfCeGv17KSM90oMlbhLFgGLFRZNuBThrl6OsJcdg1ZlXlc/bKKZyypIoppTkSeBJpyjz4wOeM0dD510Bx5ajfkgoe2FSHNxgyHfVEc/ea/ezu6OfO843pt3vX1/DhhdVcsXQap80o5RunHsX9G2oImW2w8nng7z8yTkaN9G3gAxP6RVKcBJ/xK8dIqY5Ysa1p6WN3w/jTQNOd3aZx1NQCzj92Gstml5CX7Rr9m4R1svPg5PfD534LH/qCMT2Xwu5dX4PTpvGJ5dPH9PiGXjffeHEbv7xgKSUHX5v1vR6mDcnCm5afhScQon0wygixoxH+ZXqUFxg14MawiKQWCT7j48Iolz4r/EJbj1vqtY2TBsyszOPcY6aycEZxylUcSDuazSgjc+Nv4MLrISdKxlwC9Xj87Gjvo33QfNp7TWMXG1t6uXh+JeU5Yzv14LNPb+TkacVcsfTItFl1XiabW3sP///m1l5cdhulI9047VoDLz9kdiUPIwHBpMKrumTNZ3y+B5wc3tjv9rF6h5xCOh5TSnNYML2IXEmVTj0OJ5zwXlhxNrz1b3jzcWNqaRz+urGWmh7jPJy2QR++YIgfrNoJwIyCLK4aMirZ1NLDv3c2A/BGXcfB76/j9Vpj4/bnjp9NQabxOnpsRyPXPrGe754xn1vPXBjxvPeuqwHg+pUzx9TPf25r4IV9bWz5zNnD2j++bCqffGI9X3h2E1Pzs7ht1U6uWDoV22j7zFY9AtVzYP7x4VfmY4yAPkSaJCBI8Bm7szBKZAzj8wd5e1sL/gSVw081ZYVZLJpRZH54mUgtGVlw5qVw3AXGxsrVz465Jty962t4tWb45utvv2xUVjhjRsmw4LOuqfvwtUPu21B7+L8/vmzq4eAzErc/yENb6pman8V75o4+ddjj8fO5ZzYPL6Fz0NXLp9PU5+XuNfsZ8Af4wIIqfnUwHXtEug7/+hXc8BMjCWG49wPfJE1K8EhV67EpBjYBw14toZDOm1ub6egd311fOirMdbFoRrHs0VFZdyu8/HfY9AoyDTCKsqlw/U+MAD6cjpF+/R/rO2UtCT6j04CHgYhaGdsOdEqCwSiyMhwsmVlMtVn1ZKGmlhp47n7YuzHRPUluC0+ESyMmUwB6MDag7ra2Q9aS4DO6a4H7whvbe9y8saU5Ad1JHTMr81g8szhtC3ymvXUvwH/vB69sPYjq7Cuj1YDbBhyPUQlbSRJ8RjYX2EBYpWpfIMjL6xvw+OR0XDNZGQ5Wzi2VKTYBPe3w5O9gz/pE9yQ5aTa44pswL6I0JBjngn3J4h5ZRoJPdE7gDYzh7zCrd7TS2KHsDcmkyGhHmJJRUHSZOfA/PzPbxKsDpwBvWd+p+JPgE90PMDJPhqlt6WP9HtnPE05GO2JUMgqKrnw6/M9PwRGxT2gHsBJQLqtJgo+504FXMJINDhtw+3llY4NUqg4jox0xLjIKMnfKB+C8q82u3AHcYnFv4k6CT6RCjLTqYVUAQ7rO65uapGDoEDLaERMmo6BINhtc9yOjpt5wQeAEYK31nYofCT7DacBDwKXhF7bXdLGrPm2P3ohQXZLDynmlMtoRk/Pu0/DsvRCSTdqAMf32qZ+BPWLT7CaM9WdlyovLJ8dwV2ESeDp6PBJ4hlg4vYjjFpRL4BGTd/xFcNWtRvFSAa21sMr0hO1lwNct7k1cycjniCqMxb1hxf38gRAvb2iQg+Ewjjw4el4ZVSWyYVTEWFczPHSH8eGb7uwOo/xOZUT9Yj9wDLDZ+k7Fnty6HvEzTKrKbtzbLoEHyM5wcNrSagk8Ij6KKo31jgURBTfTTzAAT/wGQhH7CJ0YG96VqMkpwcdwFnBFeGNdaz8N7bKfp7Qgk9OXV5OfI+fsiDjKyDLKzZz+0UT3JPGa9sEbj5ldORZFNp7KtJtxRs9Gwg5z8vqDvLiuPu2rVc+qzGPJrBI5UVRYa+sb8Pivwa/M+vr4OZzwqZ8bRUiH8wLLgZ3Wdyp2ZORj3EVEnCK47UBnWgceTYPlc0pYNqdUAo+w3uJT4JN3QEFponuSOAG/Mf2mR3wOZWBMv6X06YvpHnxmAN8Jb+zs9VDb2p+A7iQHl8PGyYurmFmZVgcrimRTNRtu+ClMjzwULm3U74S3nzK7cjLwWYt7E1PpHnx+CQzbIanrOhv3dkR5uPoyXXZOXVpFaUFmorsiBOQWwlXfhbmmhTfTw0sPQmeT2ZU7gGqLexMz6Rx8zgE+EN64r6mX3sH0nGfOynBwypIq8kY6h14Iqzkz4LKvp28mnN8H//6t2ZVs4HsW9yZm0jX42DHKlQ/j8QXYUduVgO4kXnamg1OXVJGbNfpxxEJYzuGEj34VFp+c6J4kxoGtxjHlkT4JLLG4NzGRrsHnOiDiwPWtBzrTsmhobpaTU5dUkZ2pxPYBoSq7Az78JVh+ZqJ7khgv/Q3cEWvRNuAnCejNpKVj8CnAOC5hmM4+D/Vt6benJzfLySlLKsnKkMAjUoDNDu+/MT0DkLsPXjMtvXMhcK7FvZm0dAw+3wTKwhu37OtMQFcSKzvDwcmLK8l0SeARKeRQAFp8SqJ7Yr13n4auFrMrj2LsWUwZ6RZ85gCfD2+sb+tPu6MSslx2GfGI1GWzw4e+kH5JCAE/vPiA2ZUC4PcW92ZS0i343E7Y3UEwGGLbgfQa9WS67Jy8pIrsTEkuECnM7oCPfCX90rDb6sBverP8CYzzyFJCOpXXmQ9sJ+x00p11XeyoTZ/jEhx2G6cvk3RqoRC/Fx74PtRsS3RP4quwHM66DJadAVrUccOTwCUW9mrC0in43AdcO7TB4wvwwtp6gqG0+TvgpEUVlBdlJ7obQsTWQA/86avQ3ZbonsReTgGc/hE49j1mh8yF8wLFQNKfUZ4u024zMA6KG2ZPQ09aBZ4ls4ol8Ag15RTAZd8wNqSqIiMLzrwMPn83nPC+sQQeMOq+fSbOPYuJdBn53AXcOLTB5w/y3Jq6tAk+08tzWTkvIslPCLVsewseTsltL0c4nHDsBcZoJ3vk+oq6rqNpEYV/24HZQF+cehgT6TDyqQCuD2/c29ibNoGnKC+DZXPSuDqwSB+LTjJGC6lIs8GKs+DG38AFnxwx8Oi6TmP7AK9ubMRkAFEK3BTPrsZCOuTZfgEYViUzEAyxv6k3Qd2xVqbLzvELyrHLsQgiXZzxUWitMUZBqWLB8XD2lVA+fcSH6bpOe4+Htbta8fqNoxbq2vqZXp4X/tCvAL8FkjabSvXgU4RJ2fH9Tb34g+qf1WOzaZywsEI2kYr0otngA5+DjiZoOZDo3oxsxmI49yqYNn/Eh+m6Ts+Aj7W7Wul3B4Zd21nXzdSyXGzDp98KMdZ+fhjrLseK6ms+3wJuG9oQDIV4fk09Xn/E+ejKOeaoMqaW5Sa6G0CnIgMAACAASURBVEIkRncr/PGrMJiEsxyVs+Ccj8O8kfco6brOgCfAut1tdPVF3wi/fE6J2flb9cAsIBD5HYmncvDJAWqAkqGN+5p62bxP/fN65k0tYNGM4kR3Q4jEqtkKf/kuhJLkZrO4Es66ApaeNupDPb4AG/e209zpHvWxWRkOzjtmqlnywUcxSu8kHZXnY24gLPCEQjp7GnoS1B3rVBRlsXB6UaK7IUTizVgMF90ATyW48kxukbEWdfR5RmWGEfgDIbYe6KCmZeynKbu9AZo7B6kqyQm/9Dkk+FgqA/hqeGN9Wz9ub1KOQGPG5bCxcl6Z2R2QEOnp2PfA7rWwc7X1z52ZDad8EE68eNQ9SMFgiF0N3eyqm9gN8r6mXrPgczqwDNg0oR8aR6oGn08QdrysruvsToNRz7I5pWQ47YnuhhDJ5X3/C7Xbzc7DiQ+HC46/CE77EGRFZKINEwrpHGjpZfMkK+u393joHfCRnxNROutzGDNBSUXFNR8HsAOjgvVhDe0DrNnZmpgeWaS6JIfjFpQnuhtCJKdNr8K/fhnf57DZYMU5cObHIH/kvXWH9uqs29NGKEbJtzMr81geuafPDUwFkqqCsoojn4sJCzwAu+uTNt09JlwOG8vmlIz+QCHS1bIzYOsb8Zt+W3SSsVendMqID9N1nbZuY6+OLxDbLR91rf0smlGE0zFs9iML4/Tmn8b0ySZJxeBzTXhDS9cgPQO+BHTFOjLdJsQYxGP6bfYyI216yrwRH6brOt39xl6dAU981p6DIZ2aln7mTikIv/QZ4OdAkqT9qRd8KoD3hjfubUzCPP8Yqi7JYUppxEKjECJcXjFceH1spt+q5xhBZ86KER+m6zr9Hj/rdrXR3R//m+D9zb3Mqc4PTzqaCbwPeCLuHRgj1YLPFcCw23+3N0Bb9+h58qnK5ZTpNiHGZbLTbyXVxvTa4pNHfajbG2DD3nZau6z7DBr0BGjpclNZHFHB/nNI8Imba8Ib6lotym5JkOWzZbpNiHGbyPRbfgmc8TFYeY5xjPcIfP4gW/d3UtuWmM+f/U29ZsHnHGARkBSn7qkUfFZg5LMPU5egf3wrVJfkUC3TbUKM33im37Jy4dQPGanTo+zVCQRD7KzrTvhm9tZuN/1uP7lZEWcAfRaTepeJoFLwuSa8obPXQ7/bn4CuxJ9ktwkxScvOgC2vw6415tedGXDCe43AkznyTV4opLOvqZetB5Inm3l/Uy9LZ0d8RlwN3AIkfNOjKsHHBVwZ3lir8JTbUdMKZbpNiMl6z7WwZx3DNtrY7HD0ucYUW97I9RF1Xae+rZ8Ne9tjtlcnVmpb+1g4owiHfdixbTkYa+N3J6ZXR6gSfC7COEDpsGAwREP7QIK6E1/ZGQ6zCrZCiPEqqYaV58La50DTYPEpcPYVUFw14rfpuk5rl5u1u9vwx3ivTqwEgjq1rf3Mror4rPgIEnxi5prwhqbOQQKKntmzYHqRHA4nRKyceSn0dxknoFbNHvGhuq7T1edlzc423L7krxNZ29JnFnzOAMqANut7dIQKwacck709qk655WU7mVomSQZCxExeMVx+y4gP0XWdPrexVyeVNqz3DPgY8PjJyRyWeGAH3g/ck5heGWyjPyTpXUFYEFV5b8+iGcVSsVoICw16/by5tZmX1zekVOA5pKlj0Kz5w1b3I5wKI59rwhtUTa8uzsswy90XQsSB1x9k876OlF87bmwfMCu3cy5QBHRZ3yNDqgefFcDy8EZVN5YumiknkwoRb4FgiB21XcqU5erq9+L2BsjKGPZx7wAuAf6SmF6l/rTbJ8IbVN3bU1mcTUl+ZqK7IYTSevq9/OftGmUCzyGNHaajt4ROvaV68EmbRAM5FluI+MvLcZlVBUh5UYLPe4CE7dlI5eAzCzhqaIOu6zSZ/yWntGlluWanEwohYsymaUre6HX2evFEpoa7MLmBt0oqB5/3hDd09/tifjhTMjhqWmGiuyBE2qguzSE/W73RT5Sst49Y3Y9DlAo+rd2mf7kprbwoS8lpACGS2czIjZkpL8rU24UYJXcsl6rBx4lRHnwYK8/MsMosKaMjhOWmleXisKu1n66jx4PXH3GQaRZGALJcqgafk4C8oQ3+QJCuPm+CuhMfWRkOKoqyEt0NIdKOw25jWlluorsRUzrJNfWWqsHngvCGtm4PeiJ6EkezKvOkmoEQCaLi1FuUhKz3YYyALJWqwcdkvUetKTebBtPL80Z/oBAiLvKzXcrtrWvrceMLREy95QDHW92XVAw+FcDR4Y2tXWolG1SX5pDhkvN6hEikWVVq3QDqetS18VOt7ksqBp/zwhv6Bn24fRHRPKVJooEQiVdVnKPcoY2dvR6z5lOs7kcqBp+I9Z4WxbLc8nNcFCs23BciFdlsGjMq1Br9dJonZp2McdSCZVIt+NiA88MbVVvvmVWp1otdiFQ2U7H3Y++Az+z01QJgsZX9SLXgsxLjBL7DgsEQHebDyJTksGtMVSzFU4hUlpXhoEqho0x0oKsv8VNvqRZ8IrLc2ns9hELqJFlPKc3FYU+1fxYh1DZdsam3DvOpN0uTDlLtUy6yqoFiU25yWJwQyaesIBO7TZ09d1GSDiT4RGEDjglvVOm4bLtNo6xAEg2ESDZ2u42yQnWqjXT1eQnpETNG04FpVvUhlYLPHIxFscMCwRB9g+ocHFdemIVdptyESEoqzUoEQzo9/T6zS5at+6TSJ13EqKfb/C8vZan04hZCNRVFar0/O82TDiybekul4HNseEN3v1qFRFV7cQuhkkyXnaK8jER3I2aiZAnLyMeE0sGnKC9DyukIkeRUmp3o7DX9/FxG2PJGvKRK8DFNNlAp+Ki0j0AIVakUfLz+IP3uiDVzG3CiFc+fKsHnKGDYzkt/IMSAJ+JM8pSl0otaCFXlZ7vIznQkuhsxk8g6b6kSfJaFN3QPqDPqycl0kJftSnQ3hBBjoNKNYpQ6b0uteO5UCT5Lwht6B9TJdFPpxSyE6lR6v0b5HF1oxXOnbvAZVCf4SJabEKmjJC8Th12Nagd9kWs+YOypdMb7uVMl+EQMA/sG1NlcqlL6phCqs9k0inLVeM8GgiE8voi1cwcwN97PnQrBJxsjEg/T51Zj5JOX5ZRCokKkmAJFgg9EHf0siPfzpsKn3kJg2Bh30OMnEFSjkrVKL2Ih0kVhrjoJQv3mJcrivu6TCsHHZL1HnSk3lV7EQqSLwhx1bhpN9vqAjHwAkwjcp1CygUovYiHSRU6WE6ci0+Uy7RbdlPCGfo86I58CGfkIkZJUmbUYMA8+s+L9vKkQfKrCGzy+YCL6EXO5kmwgRMpSZb3W7Q2Yne1TSlhVmVhLhU++6vAGryLBR5U7JyHSkSrvXx0jAJmYGc/nTYXgo+zIp1CROych0pFK67WD5nUy4zr1luzBJwsoHNoQ0nW8fkWCj0IvXiHSjUpJB4PmI5+0Dj4Rox5VptxAkg2ESHWqTL3JyCdSZPBRZNSTk+mQZAMhUlx+jiLBx2t9xluyf/pFBB93ZB2ilJTpUudMECHSlSrv4yjTbhXxfM5kDz7KZrplypHZQqQ8Vd7H/kDIrDk/ns+Z7MFH2Uw3VV60QqQzVd7HUWplSvAZSp3go8ZwXYh0psr7WEY+kSKm3UzOnkhJGYrcMQmRztQZ+ZgGnzziGCOSPfgom+2myotWiHTmsNuw29Q41dQkAGnEscROygUfZabdnBJ8hFCBKjeSUUY/cZt6S+bg4wRKhjbouq5Qtpsac8VCpDtV3stWr/skc/CJqD0TDOmocH6p3abhdCTzX70QYqzUGflYm/GWzJ+AEf+ikVW/U5MqL1YhhDrvZ79Mux0WMZbVFYk+qgzThRDqZK4GzKfdCuL1fMkcfCJHPonoRRy4nMn81y6EGI8MhyLBR0Y+h5lMu6kRfmyaGqmZQgiwKZJqLdNuRyi75qNJ8BFCGaq8n2Xkc4Sy026K3CgJIVDn/SzZbkcoO+2myp2SEEKd93PQfOSTHa/nS7Hgk4huxJ4ir1UhBOq8n6OsXXni9nzx+sExYJJCokb0UeS1KoQANEXe0RJ8jlB25KPIryGEANSou0K0AqkSfECh4KPI7yGEgJAib+goW0Ak+IA6CQeq/B5CCHVuJmXkc4SyqdYhVX4RIYQyN5Oy5nOEwtNuivwiQghlPpdk5HOEO7zBYVcjqyQkQx8hlBFU5P0sI58jOsIbnIoU8PMochS4EAK8iryfJfgcERF8XIocwKbKaaxCCHXez3bN9PM1LYOPBxgc2mCzaUpMvXl8gUR3QQgRI6q8n2XkM1x7eIPLmfpTbyEdfIoM1YVIdx5VRj4SfIZRdupN1n2EUIMqwUdGPsOZBJ/UH/mAOi9YIdKdKu9lGfkMFxl8FDmC2qvIPLEQ6cwXCCpTXsdhN/1sjdjyEivJ/kkuIx8hRNJSJdPNpmlkukyLyjTH7Tnj9YNjxCThINm7PDYSfIRIfaq8j7MyTG/qmwBfvJ4z2T/JFR75yLSbEKlOneDjMGuujedzpl7wkZGPECJJqHITmW0efGri+ZzJ/kmu7MjH7VXjRStEOlPlfSwjn0jK7vNx+4Ky0VSIFNczELclEUtJ8IlkMu2mxsgHoFuRF64Q6UjXdWWCj0y7RYpI88t02THfC5V6evq9ie6CEGKC+tx+ZY5TkJFPpEGgYWiDpmnkZDoT1J3Y6pbgI0TKUunmMUqqdVoHH4Bd4Q05WaoEHzWG7EKkI1XevxlOG3ZbRCjoA7rj+bwpGXxyFQk+g96AJB0IkaJUmbkYYcotrnOKEnwSTJIOhEg9KiUbJGK9B1I0+Kiy5gNqzRsLkS5USjZIRKYbpGjwyc0y/ctKSaoM3YVIJyrdNMrIJ7r9wLCFkUyXA6cim01VWbQUIp2o9L6NsowhwQfwYzL6yc92JaArsSdJB0KkHpVmLApyMsyat8T7eVMh+ABsCm/Iz1Zn3aezT50XshCqCwRDyiQKZbrsZuf4+IBt8X7uVAk+m8Mb8nPUGPkANHcOJroLQogxau/xEFIk2aDA/HN0C8aMU1ylSvAxGfmoFXx0RY7iFUJ1Kt0sRplyW2fFc6dK8FF65OP1B5VawBRCVbquqxV8ck0/R9db8dypEnxqMMo9HOaw26Llp6cklV7QQqiqu9+LV6EEoSjTbhJ8htAxGf1EidopqblzINFdEEKMQqWbRKfDZrZhX8dkmSMeUiX4AGwIbyjNz0xEP+Kid9DPoCfua3xCiElQKfgU55mu92wHLLkTTqXg82p4Q2lBViL6ETcqvbCFUM2Ax0/voDo3iEV5pjfvb1v1/KkUfF4Jb8jPcSlzrDZI8BEimbUo9v6MMvJ5y6rnT6VP7lZga3hjaYE6U2/tvR78gVCiuyGEMKHazWGRBJ9xeTm8QaWpN12H1m61XuBCqMAfCNLe60l0N2ImP9uFwx7x8d+DseZjiVQLPq+EN6g08gFo6pDgI0Syaelyo9I+8OJ801HPO4BlUy+pFnwikg7ysl1kOE3PH09JTR0DSu0jEEIFNS19oz8ohSR6vQdSL/i0Y5KDrtLoJ6RDrWIvdCFSWd+gj/YedabcAMoKs82aJfiMwmTdR53gA3CguU9qvQmRJPY3q3UzWJSbYVbJ2gu8bmU/FAk+6iQdgHHGT2uXO9HdECLtBYIh6lrVCj6VxaajnhexaHPpIakYfFZhlIA4LDfLaRbJU9r+5t5Ed0GItFff1k8gqNYsREWx6c36U1b3IxWDTxcmhe9UG/20dLkZkHI7QiTU/ia1Rj1ZGY5oxyhI8Bkj5dd9AGoUm2sWIpV09nroHVTrqJPKItOb9A1AncVdkeCTzGpa+giGpOKBEImg4tR3hfl6z5NW9wNSN/i8BgzbDJOT6VTqdFMAXyBEY7tsOhXCal5fkMZ2tY45cdi1aMsTEnzGoReTnPSpZTkJ6Ep8qXj3JUSyq23tI6RWngFlBVnYbVp4czOwNgHdSdngA/CP8IYppeoFn64+L9393kR3Q4i0EQrpyu3tgahTbk9hYUmdoVI5+DxC2F9adqYzWqXWlLa9tivRXRAibdS09uH2BhLdjZirKEqe9R5I7eDTgrExapipCo5+WrvctPfIplMh4i0QDLGztjvR3Yi5KFUNPMALCegOkNrBB+Bv4Q3VpblEzGoqYFuNjH6EiLd9jb1KFvaNUtXgJSBhGU2pHnwew6hJdFimy05poVobTsFY+2nqUCv7Rohk4vMH2d2g3qgHkivF+pBUDz49wNPhjSpOvQFsr+mSgqNCxMnuhh7lSukA5GU7Kcgx3YZieVWDoVI9+AA8FN5QVZKDTVNv8q3P7aeutT/R3RBCOW5vgH2Nam5rmFmRZ9a8Fqi3uCvDqBB8ngKGfSI7HTYqzMtIpLwdtV1S9UCIGNtR10VIwVkFm01janmu2aX7rO5LOBWCjxt4PLxxioIbTgHcvqByxQ6FSKS+QR+1LWrOKFSX5OByRGS5uYEHE9CdYVQIPmAy9VZZlI3Drt7UG8Cu+m78ARn9CBEL2xXOJI0y5fYPjPXyhFIl+DwPdAxtsNtt0dILU54/EGJPQ8JfO0KkvK4+D02datZPzMtyUmJecPmPVvfFjCrBx49R8WCYKaWmc51K2NvYQ79bzvsRYqJCus7mfZ2J7kbcTDcf9WwB3ra4K6ZUCT5gMvVWUZRFdoYjEX2Ju2BIZ8Oedkm9FmKC9jb00KVo3USbpjHdPNHgT4SdBJ0oKgWf1wlLHdQ0jdnV+QnqTvx19HrY36RmeqgQ8dQ36GOHgmV0DqkqycblNC2n80ACumNKpeATAu4Ob5xRkYfDrtKvOdy2mi6ZfhNiHEK6zvrd7UqmVh8yo9J0yu0RIGnmGVX7VP49YbWKHHYbM83/IZQg029CjI/K020AOZkOyswPjfuT1X0ZiWrBpxO4P7xxdlU+ChY8OKyj18M+mX4TYlSqT7dB1FHPDoyliaShWvAB+CVhC2pZGQ4lD5obartMvwkxonSYbrNpML3cNPj8kSRJNDhExeCzB5OKB3OqCxLQFevI9JsQI1N9ug2gsiSHjMhEAx/w1wR0Z0QqBh+AO8MbCnMzKMk33XClDJl+E8JcOky3AcybYnqT/U+g3eKujErV4PMm8E5441zzfxilyPSbEMOlw3QbGAfGFeZmmF1KiooG4VQNPjomo5/K4mxys5wJ6I51giGdtbtaCQal9psQADtqupSfbgOYP63QrPkd4FWLuzImqgYfME45PRDeOEfhTaeHdPf7WL8n6UbZQliuvq2f3WlQB3GEUc+tJFmiwSEqB58A8KvwxmllubgcKv/ahob2AXbXqz/HLUQ03f3etLkJWxB91PNfi7syZqp/Ct9LWOlwu93GzCr1Rz9gVD9oVrRirxAj8fgCvLO9hVAoKW/6Y6qqOJsC81HPd0nSUQ+oH3z6MFlsm12Zj92m8K7TIdbuaqVv0JfobghhmWAoxLs7WvH4gonuiiWirPW8DTxncVfGRfXgA/BrjCm4wzJcdmanyegnENR5Z3sLPn96vBGF2LS3g64+9RMMIHVHPZAewace+Ht447yphTjTYO0HYMATYM3OVuVTTYXY29hDbauaR2KbmT/ddNTzFsYBm0ktPT594fvAsFt/p8PGUVNN/+GU1NbjYev+pCloK0TMtXYNsiWNXuNVxdkU5KRWhttQ6RJ8dmOy9jOrKp8sRQ+bM7OvqZealr5Ed0OImOt3+1m9sy3R3bDU/OlFZs1vkgKjHkif4APG6GdY6pfdprHAfNiqrI1722nrdie6G0LEjNcX5J3tLQTSaGN1VUk2BTkus0u3kgKjHkiv4NMM/Dy8cVpZLvnZalc9GErX4Z3tLXT0ehLdFSEmzecP8ubW5rQrKTV/mumo5w3gBYu7MmHpFHwAfgp0DG3QNI3FM0sS1J3ECIZ03t7WnDYZQUJN/kCIt7Y105tmWwmmluWm/KgH0i/49AK3hTeWF2VRUWR68p+yAkGdt7Y2050GNa+EegJBI/B096dX4HHYNRbPjDrqedHi7kxKugUfMI7a3h/euGRWidKnnZrxB0O8tbWZ3oH0egOL1BYIhnh7W0tajtznTysi02WaJPVtUmjUA+kZfLzAV8Ibc7OcabPxdChfIMQbW5pkBCRSwqHAk45rlnnZTmabF0Z+FHjZ4u5MWjoGHzAqXkf8Y82fVkSGM/3+SnyBEG9ukTUgkdz8ASO5IB0DD8DS2SXYIqdnBoEvJ6A7k5Z+n7QGHfgCMCw30+mwsWB6cWJ6lGD+YIg3tzal7RtbJDefP8gbaXyDVF2aQ1mB6br07UCtxd2JiXQNPgCbMNl4OqMil8Jc00wS5R1KQpB9QCKZeH1B3tjSRE+ark067BpLZpreFO/B5NDMVJHOwQfgO4QduaBpGivnlpEmRa8jHErDrpVKCCIJ9A74WLWpkd7B9NrHM9TC6UXRKrF8HmMNOyWle/Bpw8iNHyY/x8UC89IVaSGkw/o97WzZ3yHFSEXCNHUMsGpTI4PewOgPVlRRbgazzBOh/g08bXF3YkrT5cPFCbwLrBjaqOs6r21qSouz30dSVpjFsfPLcDnsie6KSCM767rZUduV6G4klKbBmcunkB+5oXQQWILJlpFUku4jHwA/cPXBPw/TNI2jjypNm0PnomnrdrNqY6McSCcsEQiGWL2jNe0DD8C8KQVmgQeMPT0pHXhAgs8hm4DvhTfmZrlYOCN9p98OGfAEWLWpkRY5klvE0aA3wOubm2jsGEh0VxIuJ9PBUeYnlK7FOCAz5cm02xEOjHLkx4VfeH2zpCAfsmhmEfOmpFclcBF/Hb0e3t3Rgs+fPpWpR3Lykkqz1OogxufTeut7FHsy8jkigDH9FrHIs3JeKQ57ek+/HbLtQBdrd7URDMmHhIiNmpY+3tjSJIHnoLlTCqLt6fk5igQekOATbjvwzfDGnEwni8zz7NNSfVs/b2xuZtCTvumvYvKCoRCb9nWwYU87MgFjKMrLYKF5pu1+TDJzU5lMu0WyA68Ap4ZfeFM2YA5jt2ksnlnMzMo8tHSryiompbPPw/rd7Wl3Ds9InA4bZy6fQnam6Z6e80mRE0rHSoKPuTkYSQjZQxvd3gAvrW9IqxMTx6K0IJOVc0vJzkyfQ/nExARDIXbUdrOnoWf0B6eZ4xeUU1WSY3bpp8DNFncn7iT4RPcZ4LfhjbUtfazf056A7iQ3GQWJ0choJ7rZVfksnW16qOXbwOmEbQVRgQSf6GzAc8A54Rfe2d5Cs6Qdm5JRkAgno52RFea6OG1pNbbIPYXdwErggOWdsoAEn5FNB7YAeUMbff4gr25M77IfI5FRkDhERjsjc9g1zlw+hZws05u1D2Ec/6IkCT6j+yRwb3jjoYKHwZD8/UUjo6D0JaOdsTl2fhlTSnPNLt0F3GRxdywlwWd0GvAk8N7wC40dA6ze0TquH+YeHOCpR//May88SWtTA06Xi+ppszj/kss4+8IPDxsprH3rFR7/+5+o278b9+AAJWWVHHfKOXzwihsoLC4b9bkef+geVr/xIo11++jr7SEvv4Ap0+fwvo9czYlnvCeiX/f/5oe889pzAJx0xnu45rO3kJk1LOeCt1/9L7+47Uv8+q//paJq6qh9sNs05k0tZE51Pg67ZPang8aOAbbXdMloZxQzKvJYMbfU7NI64GRSuGL1WNhvvfXWRPchFTwHXAoM29qfl+1ChzFXPwiFQnz3C1fxyrOPccyJZ3Lu+z7G/CUrqdmzg2ceewCf18OK44wM7+f+/XfuvPUm8vILufCDV3LcKefgcDh49om/8ebLz3DexZficI48onjykfvJLyzmuFPO4ZSzLmL2vMXs37Odf//jXjSbjSUrTjj82Pt+/QNefe4JPnD59Ry1eAXP/OtBenu6OOakMw8/ZqC/lx/cfD0f/cRnhrWPRNehvcdDbUs/dptGQY5LpuIU1dbjZu3ONvY29uILSEboSPKznRy/oNxsnacPOA+j4r7SZOQzdssxyu9kh18YawLCji3r+Pr/foSLP3Yt19307cPtfr+PG684j76+bv727EYAPnP5ObgHB/jDw6/iysg4/NgH/3gnj/zfb/n6D3/PiaefP+5fIhgI8OXrLqG5sY4Hn92A3W5Uq772/Sdw/iWXc/l1XwDgoXt/wfNPPsx9j791+Hvv/uk32bNjMz/542OHv2+8cjIdLJhexJTSHAlCiuju97K9potW2QM3JnabxhnLq8nLNi0aejnwd4u7lBAyDzJ2G4FrzC4cc1QZeeYLhsO4B/oBKC6tGNbudLrILywiM/NIXHMP9pObVzAs8AAUlZYDkJllWn5jVHaHg+KyCryeQYKBIwkTXq+HvPwjA7vcvEI8niMBddvG1bz4n0f57NfumHDgAaNI6dpdbby6sZHWLskYTGX9bj9rdrYa/5YSeMZs2ZySaIHnT6RJ4AGjmKYYu0eAHwDfGtrosNs4fmEFqzY14h9humHewuXk5Obz2N/+SHnlVI5atByfz8tLTz/K3p1b+N+v/ODwY1ccfzovP/NP7rvrds67+FIys7LZs2MTj/zlNyxecQJLjz55zJ3u6+0mFAzS29PFmy8/zfp3VrHk6BOHBbYFi4/m2Sf+xuIVJ6Cj8+zjD7JgydGAMTL73U9u4eKPXcvsoxaP+XlH0jPg461tLZQWZLJoRhFFeZkx+bki/jy+ALvqujnQ0idlccZp3pQCppfnmV3aAnzB4u4klEy7jZ8NeBy4OPxCa5ebt7c1M9Lf6NaN7/LbH32Dxrojx3FkZefy+W/9bNg02kB/L7/98S28veq/hILBw+3nXPQRPn3z7TgcY88gu+q9x9DXY5yPYrc7OOG08/jUl79PQdGRTW0Ntfu4/ebraaw/AED11Jl88yf3MGX6bB669xe88t8n+PVfnyUjIz5Boqokm4XTi6LdEYok4AsE2dvQy97GHsnynICpZbkcc5RpopAbOBbYZm2Pe3BntQAAF51JREFUEkuCz8TkY+w8Xhh+YU9DD1sPdEb9xn27tvLwX35DZfV0Fiw5mr6+bp751wPU1+zllh/9gRXHnQaA1+Pmb/f8grbmBo475RwyMjNZ/+5rvPifRzjnvR/ls1+7Y8yd3brhXXw+L51tzbzx8tPYbDau+/x3qJoyY9jjAgE/dfv3ADBt1lwcDid1+3fzxU9ezLd/cg/LjzuVp//1V5597EHcg/0cd+q5XP2Zr8c0IJUVZDKrKp+K4mxssiaUFLr7vRxo7qO+rV+CzgSVFWRy4qJKswQDMLZz3G9xlxJOgs/EzcM4fjvicJu1u9qob+uP+IYDe3dw8w0f5JM3fYsLPnDl4Xavx81NV11ASA/x+3+8gqZpfOtzlxMMBvnR3Y8MW5j/v7t/zL8e/APf+8X/sfy4iNqnY3Lnd29iy4Z3uOuvz5GbXxD1cbqu843PfIyqqTP5/Dd/yusvPsWvb/8qn/36jyitqOLXt9/MyuNP43+/ctuE+jGSTJedmZV5zKjII9Mls8NWC4ZCNLYPsr+5l64+pTN+4y4/x8WpS6pwOkyX2H8BfMniLiUFSTiYuN3AZUDEIs+KuSUU5kZOHz35j/vw+bycfNZFw9ozMrM45uSzaGtuoLW5nu2b1rBt42pOOuOCiIywQ9+7ZcO7E+74WRd+mK6ONt5a9eyIj3vmsQdoqjvAtTfeAsDzTz3MSWdewBnnv5/Fy4/nI1d9mpeefpRQHM728fiC7Kjt5rk1daze2Up7jyxoW2HA42frgU6eW13Hut1tEngmKSvDwUmLKqIFnoeBr1jcpaQht5ST81/gaxhVZw+z22wcv6CC1zY14vYdWa/paG8BIBQKEi4UDBz8M0hHW/MIjwsOe/xE+LzGvqT+3ui7zzvamnngDz/lU1++jfyCosNtc+cvOfyY0vIqfD4vvT2dFBaZbpabNF2HxvYBGtsHyMtyMqsqn6lludHezGICdF2npcvNgeZeWrokyMeK02HjpEUV0UbuqzAOr0zbDVHyDp68O4EHwxuzMhycvKSKTNeRtORpM+cC8NLT/xz22P6+Xt557QVy8wqorJ7OtJnzAFj13BMEAsN3ib/0zKMAzF247HDbQH8v9TV76e0+stbkcQ/iHhyI6GwwGOTpf/0VgKMWr4j6S/3h599lwZJjOOP89x9uKy4pp2bfzsP/X7N3Jw6ni/wCaw7a63P72bSvg/+urmXDnnZaOgflRNVJ6O73sqO2ixfW1vPO9hYJPDFk0zSOX1ARLYFmG/ABYGy70xUlFQ5i4xngAqB6aKPLaaeiKIuGjgGCIZ2pM+byyrOPsebNl2hqqKGnq531777G3T/9Jh2tTVxz4y3MX7ySopIyavbtZMv6t3n3tRdwuwfYfzBRYdXz/2b+4pV84tNfx2Yz7h1ee+FJbv3iJ3BlZLL06BMBqD2wmy9cfSGN9QdoqN1Hfc1eVr/xIn+489vs2bGJsy78MBd/9FrTX+bNV57h3/+4l2/99D5y8/IPt4f0EE88dA/9fT3U7tvFw3+5i1POvogTT3+P6c+JF103UrXr2wfY29hLd7+XYEgnM8MhJXxGEAyFaOt2s7exh41729nb2EtHrwe/nE8Vc8fML6eyOGI/OkAjcBbQYm2Pko8kHMTONGA1UBF+oWfAxxtbmvAHQjQ11PDw/Xexae2bdHe248rIZNa8hVz8sWs56YwLDn+P3+/jyYfvZ9VzT9BYt5+QrlNeUc1JZ13IRz/x2WE11158+lHu+uHNXHrtTYcrFPR2d/LQvb9k+6Y1tLc24R4cIDs3j9nzFnH2RR/m9PPeb1phYKC/lxuvPJ8PXnEDl1x63bBruq7zzwfu5tnHHsTrcXPsyWdzwxe/S3aO6b6FhCjOy6CyOJvK4mxJ2wa8/iAtXYM0dw7S2uWWbDULLJlVzJxq00SePuA0jA3raU+CT2wtxTiCO2IeqrvfyxtbmuUUVAvlZDoOB6KivAzsNvVHRbqu0+/209w5SHPXIJ29kjBgpTnV+SyZZXoonB+4EHjR2h4lLwk+sXcMxgss4tans9fDW9uaCQTl79xqmgb52S4Kcl0U5mRQmJtBfo4zpQOSrusMeAJ093sPfvnoGfDK6ytBqktzOG5+ebTLH8dkbTidSfCJjxOB54GIgzrae9y8va1Fpj+SQCoFJAk0ya26NIdj5pVF20T6deDHFncp6UnwiZ/TgWeBiAqgrd1u3tnWQkj+7pOOpkFelpOsDAeZLgeZLjuZLjsZLjtZLgcZTuO/Y119wecP4vEd/PIH8PiCeH1B3D7jv/sGfRJoktT08lxWzC2NVqX9t8DnYMSqW2lJgk98nQs8BWSEX2juHOTdHS1SmDFFZTiPBKVMpx2bTcOmaWgaaJqG7eCfum6MWkK6PuS/DwabgwHH6wsgA+HUNLsqn6WzTdd4wKgB+REgcsOekOBjgYswXoQRlUAbOwZYs7NVApAQKeioqQUsnBF1j9vLGKcfy+apKJJvcls9T2Ocghpx91NdksPR80Y/DlsIkVwWzSgaKfD8Bwk8o5LgY43HMLJdIvKsD5VZN1+nFEIkm2WzS5g3NaKe8CGPAB9CAs+oZNrNWlcDfza70Nbt5t0dLbKoLESS0oAVc0uZXhF1U/X9wA3IGs+YSPCx3qeA35td6O738va2Frx+ee0KkUw0DY49qpzq0pxoD7kL4yRS2UU+RhJ8EuMm4FdmFwY8ft7e1kK/2292WQhhMbtN47gF5VQUmdZqA/gh8C0knXpcJPgkzrXAnwB7+AWvP8g721ro6pfSKEIkksOuccLCCkoLIrbrHfIN4EcWdkkZEnwS6yKMBcqIW6pAMMS6XW00dQ5a3yshBJkuOycsrKAwN2Kb3iE3YmwiFRMgwSfxjsdIzTQ9jW3bgU52N0Q/9E0IEXtFeRkcv6A82kFwIeCTwF+s7ZVaJPgkh3kYpXhmm12sbelj49522QUvhAWmleeyfE4pdvP9D37gCuBRa3ulHgk+yaMCYwR0jNnF9h4Pq3e04AtIMo0Q8aABi2YWM3eK6Vk8YJw8+mGMjeNikiT4JJdcjLLrl5hdHHD7eXu7ZMIJEWsOu41j55eNlNHWCHwQeNe6XqlNgk/ysQN3AF81uxgIhti4t536tgFreyWEovJzXBw3v5zcrIjyi4e8ixF4Gq3rlfok+CSv6zA2o5queNa29rF5X4dURBBiEqaV57J8dgl2e9RKY38F/gdjyk3EkASf5HYW8E+gyOxiv9vPmp2t9Az4rO2VECnOpmksnV3CzMqopXJCwNeAO5HNo3ExruCjaVoR0IRxPs1Vuq4/EK+OicPmYZwJdJTZxVBIZ1tNJ3sbe63tlRApKvv/27v34Div8o7j32dXq7tk62pZkh3bsYPjOCGxcULNtJCUkhCGpkChZWiIE5NyaaEdoDTwT6BcBpihDem0lDbp0ELSpkDaMk0znZTUhJnS0DaBOFAcx7aCncSyfJGs+0ra0z+ed621vLtaWdpXkv37zLx5492j1WvJ2p/ec55zTlUFOza3F5u/cxr4TeDR+K7q4jPXVa3fBVQCh/BuISm//cAO4O/yPZlIGFvXt/DqLauoSmmRcpFiVrfU8tqrO4sFzzN4xamCp8zmeufzNHAS+GfgHmBTCOFAma5tQZhZQwhhcLGvYwEY8G58RnXe1Q3H0lM8tb+Pvn6t5i6Sq7IiwVWXttDVWl+s2d8AHwC0rEgMSv5V2cy2AVfj36AH8MlWtxdoe72ZPWJmJ8xszMwOmtn9ZtY6o93bzOw/zKzfzEbMbJ+Z3WtmldHzu8wsmNnr8nyOPWbWM+Oxnujxa8zs38xsAP9NBjNrMLPPmNmTZnbczMbN7Hkz+7yZnVNfae7OqP1QdOw1sz+Knn9rdG3vKfA1+En0+gu1U0/Av/bbgKfzNaiuTLLzig62rGtiwT6ryDLX2VLLDdu6iwVPGi8quB0FT2zm0k+zGxgGvh1COIFPiLzNzM56DTN7L/Bd4CrgK8AH8bDaDnTntPssPku4HfgTfDnyf8LXOytYbF+CtcDjwAt4ufKfRo93Ae8B/gf4NPBh4CngY/hmbzN9HfhL/E3/s9FrPY7vyQ7wHeAoebofzezVwBbgr8PCV3Q8B/wC8MeFGmzqWskvXtlJXXXeQjmRi0JVKsGOV7SzY/MqqlLnrN+b1QPsxBf5VWFBjErqdjOzarzG/TshhF3RY7cQhUUI4dHosW7gQHTsDCH0z3idRAghY2bXAk/i+5zfHEIYy2ljACGEYGa78A2arg8h7JnxWnuAdSGEdTmP9QCXAHeGEO6b0b4yetmJGY9/Gl8O/boQwg+jx94BPAR8A7gthJDJaZ/I/tnMPoevantFCOGnOW3+Cv8tam0IoZxzA27GN6fLuxf35FSGZw6c4HDfUBkvQWTp6Wqt48oNLcVCB3z44A58KEFiVuqdz1vxct/chfQeAY7h37yst+MFCZ+aGTwAOW/i74rOH88NnqhNmOfdwkk8sGZ+7nQ2eMyswsyaom7Af4+aXJfTPHt9H80Nnhl/B5j+benM3Y+Z1QG/ATxa5uABX+bjlUz/Hc5SkUyw7bI2dl7RUWwCncgFoyqVZMfmdl71ivZiwXMS/xl/CwqeRVNq+OwG+oAjZrbRzDYC64DHgF/NGcvZFJ3zjknk2IS/af94bpdbkgMhhLxbgZrZB8zsGWAc/0fXB+yJns6dS7MJeDmE0FvsE4UQDuFv/LeaWfbd/R1AA3BfwQ9cWC8DN+JzEibzNWhbWcP1V3ex5ZKmQosliix73W113HBNF50tBXcbBe9ivwJ4EHWzLapZw8fM1uOTHdvw8Yb9OUe29Pq3ss2j82zfVCuhzWyvU2hAI++AoZl9GK8UexnfyvpNwK8Au6ImuV+LUq8PfFyojen12HbjY0GPlPjxCyEDfBF4DXAwX4NEwtjUvZJf3tZNZ8t8htRElpaqVJJrN7ez/bJ2Kgvf7ZzA5+68Df/5lEVWyoj07fib8Z3AOV1pwGfwN9x7gH3RY9fg4VTIPuAmvCih2EJ92Vvi5jzPrccr7kp1Kz64+MYZYzg3Fbi+W8xs1Wx3P3i/8TFgt5k9iwfAF0IIee9CyuyH+Nf+z5nuOjxLTVUFOzav4lj/KHsPntAipbKsrWmrZ+uGZiorio7tfAv4HfznVJaIonc+USXbLmBvCOG+EMK3Zh745MetZrYD/yangbvNrDHP62XvjB6Mzp8zs3Nme+W0ey46v37G8+8EOkv6G06bwu9mzvQ7mVkFcFeetg9E5y/mqeY7q98qGkf6Gt71dXf08P1zvLaFdBq/E30zPhk4r/aoK+7yteqKk+WndUU1r31lJ9suaysWPH14N/jbUfAsOUWr3aK7gkeBT4YQPlWgzVZgL/DVEML7zOz9ePfWYeBv8ZLnLuAW4I4Qwo+ij/s8Pk7xE7yy7Ch+N/PrwLXZggUzewy4Ae/e+hE+1+gtwCCQylPt1hNCeF2e67wLXy36MeBhoBHfFGoCeBVeJPHJnPZ/jxcO/AAvqz6FL3FzYwhh64zX3ogHpQHfy/f5F0kNXkr+cXxJpLxGxid59uAJbdktS15jbYot65qLbX2Q9RA+zaOv/Fcl52O28PkmHgZXhRD2Fmm3D98MbXUIYdTM3oDPi7kWf9N7CZ/7c1c0Ryj7ce/E90G/Cr8LO4zv6PmxEEI6atOBz9W5MWrzfXyOzlfIX2pdKHyS+BvxbmANHnYP4ZVxP+Xc8EkA74/ab8bvnA7h85zOCWIz+y4eku8OIXy90NdqkVwKfBkf5yqo99QIew+eYHhsMXoMRQqrqUyyeW0Ta9rrmWXe9jF8lYJvx3Nlcr60qvUCMbN/xSd/doYQlur6Nm8G7sUrFfOaygQOvjTA8y8OaNdUWXSpZIJN3SvY0NlIMjFrfdSDwO8Bx8t/ZTJfCp8FEHW77QP+LITwocW+nlnU4uNcf4hXKuY1OZWh5+ggz784wPhE3sp1kbJJmLF+dQOXda8sVsGW9b94r8bj5b8yWSgKn3kws+uAy4EPRefLQwg9i3pRpduI3wW9sVijqakMPb0eQmNphZCUX3dbHZevbaZ29uWhDgGfAP4Bn24gy4jCZx7M7Gv4StMH8XGqhxf3iubM8EKQL+Nr4hU0lQn8vHeQ/S8OMDquMSFZeG0ra9hySVOx7Q6yTuDrM/4FPmFcliGFj4B3xX0C+ChFquLAN6873DfEc0f6GVFhgsxTwqCrtZ4NnY2lhM4YvgjxF4CBsl+clJXCR3J14gH0PrxMu6BMCBzpG2L/kQFNVJU5q0olWNfRyLqORqorZx3TyeBz6e4GjpT72iQeCh/Jpx34CD4rvOhCWSEEXjw+zHOH+xlUCMksGusquXR1I11t9aVObn4EL5B5trxXJnFT+Egxrfg+Sx/EJ+UWdax/lJ6jpzl6cgT9s5JcHc21XNrZSOuKojfUuf4br2DbU7aLkkWl8JFSNOEVfb8PrJyt8Vh6kp/3DtHTO6jihItYRdJY297Ahs5G6qpL3tLjB8CX8FVI9OZ0AVP4yFw04l1xHwFaZmscQojuhgbpPaW7oYtFXXUF61c3sra9gVRFSbu2TALfxKsunyzrxcmSofCR81GPFyX8AT4+NKvxiSmO9A1x+NgQA8Ppsl6cxK8qlaCrtZ6utjqaG6pL/bCTwFfxVdhVSHCRUfjIfNTiW238Lj5ptSSnh9McjoJIqycsXxVJY3VzHd1tdbStrJltzbVc/4dvwfINCuy/JRc+hY8shAS+qOp7gV+jtH2iznTLHT05Qu+pUY0PLQMVSWNVUy2rW+roaKohmSx1M2TAt32/B9/9V288FzmFjyy0DnwDwt+myAKm+ZweTtN7yoPo5OCYxoiWiFRFgtXNHjhtK2vmuv/TCD5H516mN5sUUfhI2STwbcrvwJfwmXX6eq6JyQx9/aNnwkjdc/FqqE3RuqKG1c21tKyoJlF6l1rWj/FutfvxvbBEzqLwkTg04btJ3gbsPJ8X6B8ap/eUh9GpQS3ntZAMn/zZsqKa1sZqWhqrS1lJOp8efFuDB/FNIkUKUvhI3Dbii7Heyhy75bLSE1Mc6x/l+MAYA8PjnB5Ok9E/45KZwcr6Kg+aFdU0N1SXWhKdz3F8VekH8Dk6+k5ISRQ+slgS+E63N0fH9vN9oUwmcHokzcBwmv6hcQaG0gyMpMkokQBIJoymhipaorua5oaquRYKzNQL/CO+W+j38K3oReZE4SNLRQdwEx5EbwBWzOfFMiEwODLBwNA4/VEonR5OM3UBB1JF0qivSZ1zNNRUkphbkUA+L+Bh8zDwX/i28iLnTeEjS1EK35I8e1d05UK8aAiBwdEJBobSjIxPMDo+xVh6ktHxKUbTk0wsk23Da6sqzg6Y2hQNNSmqK0uqcC/VCPCfwPeBfwGeRl1qsoAUPrIcrMF3XH0T8Hp8cuuCm5rKMJqeDiQ/T571WDmq7lLJBKmKs4/KiqT/f/RcVSpJfU2KupoKkol5dZkVchoPmiei4ylAS1FI2Sh8ZLmpAn4JeA0+TrQN34coFiEEpjKBqanonMlE50AmE8iEcGZ+UvZnK2T/Y3mCJpmYy8oAC+k400HzBPAM6kqTGCl85ELQgYfQdqYDac2iXtHS8xJeHPBEdP4Z6kaTRaTwkQtVO3AN02G0nfMs7V5GMsAhfCWBn0Xn7NGLwkaWEIWPXExa8CDaCHTnOcoyllQGpzg7WLJBcwDQDFxZFhQ+Is7w8u58oZR7zKsEvIAhPFD6o/OpAn9+AQ+a4+guRpY5hY/I3KSAGvwuqSbnyP65Ap9Aa9E59wCvKssNl340SVMuQgofERGJXVkmDIiIiBSj8BERkdgpfEREJHYKHxERiZ3CR0REYqfwERGR2Cl8REQkdgofERGJncJHRERip/AREZHYKXxERCR2Ch8REYmdwkdERGKn8BERkdgpfEREJHYKHxERiZ3CR0REYqfwERGR2Cl8REQkdgofERGJncJHRERip/AREZHYKXxERCR2Ch8REYmdwkdERGKn8BERkdgpfEREJHYKHxERiZ3CR0REYqfwERGR2Cl8REQkdgofERGJncJHRERip/AREZHY/T9OYKvd2bcDlgAAAABJRU5ErkJggg==\n",
      "text/plain": [
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       "<Figure size 432x432 with 1 Axes>"
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     "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": [
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       "            font-size:  20pt;\n",
       "        }    #T_65f76824_414d_11ea_86c3_492c802c4115row0_col1 {\n",
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       "            color:  #000000;\n",
       "            font-size:  20pt;\n",
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       "            background-color:  #fff6e5;\n",
       "            color:  #000000;\n",
       "            font-size:  20pt;\n",
       "        }    #T_65f76824_414d_11ea_86c3_492c802c4115row1_col1 {\n",
       "            background-color:  #ffa500;\n",
       "            color:  #000000;\n",
       "            font-size:  20pt;\n",
       "        }</style><table id=\"T_65f76824_414d_11ea_86c3_492c802c4115\" ><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_65f76824_414d_11ea_86c3_492c802c4115level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_65f76824_414d_11ea_86c3_492c802c4115row0_col0\" class=\"data row0 col0\" >0.89</td>\n",
       "                        <td id=\"T_65f76824_414d_11ea_86c3_492c802c4115row0_col1\" class=\"data row0 col1\" >0.11</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65f76824_414d_11ea_86c3_492c802c4115level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_65f76824_414d_11ea_86c3_492c802c4115row1_col0\" class=\"data row1 col0\" >0.12</td>\n",
       "                        <td id=\"T_65f76824_414d_11ea_86c3_492c802c4115row1_col1\" class=\"data row1 col1\" >0.88</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9bc836a9d0>"
      ]
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Jean-Luc Parouty committed
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
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    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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