Sol_TP2_MNIST_convol-full.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine learning avec les modules Python tensorflow2/keras "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les concepts de base du *Machine Learning* (ML) peuvent être consultés si besoin dans le notebook du TP1 :  `TP1_MNIST_dense.ipynb`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entraînement / exploitation d'un réseau de neurones convolutionnel pour la reconnaissance de chiffres manuscrits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'objectif du TP2 est de comprendre le fonctionnement d'un réseau de neurones convolutionnel et de mettre en oeuvre sa construction à l'aide du module **keras**, utilisé comme interface *high level* du module **tensorflow**. <br />\n",
    "\n",
    "Le séquencement reste le même que celui du TP1 :\n",
    "- Rappels sur le fonctionnement des réseaux convolutionels\n",
    "- Chargement des images depuis la banque MNIST.<br>\n",
    "Dans ce TP les images ne sont pas applaties en vecteurs car un réseau convolutionnel permet de garder la strcuture 2D des images.\n",
    "- Construction du modèle avec *keras*.\n",
    "- *one-hot encoding* des labels des images pour les rendre compatibles avec les sorties du réseau de neurones.\n",
    "- Entraînement & analyse des résultats."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A/ Les Réseaux de Neurones Convolutionnels (RNC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Principes généraux\n",
    "\n",
    "Les Réseaux de Neurones Convolutionnels (RNC, en anglais *CNN* :*Convolutionnal Neural Network*) proposent des structures particulièrement efficaces pour l'analyse du contenu des images. Pour cela les RNC mettent en oeuvre des traitements et une architecture et bien spécifiques :\n",
    "- l'extraction des caractéristiques des images (*features*) à l'aide de filtres convolutifs,\n",
    "- la réduction de la quantité d'information générée par la convolution avec des filtres de *pooling*,\n",
    "- une architecture qui empile des couches \"convolution > activation > pooling...\" chargées d'extraire les caractéristiques de l'image (*features*) qui sont au final applaties et envoyées en entrée d'un réseau dense chargé de l'étape de classification.\n",
    "\n",
    "Dans la suite du TP, nous construirons un RNC inspiré du réseau `LeNet5`, un des premiers RNC proposé par Yann LeCun *et al.* dans les années 90 pour la reconnaisannce des images MNIST :\n",
    "\n",
    "<p style=\"text-align:center; font-style:italic; font-size:12px;\">\n",
    "    <img src=\"img/LeNet5.png\" ><br>\n",
    "    [Lecun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. (1998). \"Gradient-based learning applied to document recognition\". Proceedings of the IEEE. 86 (11): 2278–2324. doi:10.1109/5.726791.]\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extraction des caractéristiques d'une image avec un filtre de convolution\n",
    "\n",
    "La convolution d'une image par un filtre (aussi appelé noyau, *kernel*) revient à déplacer une _petite fenêtre 2D_ ( 3x3, 5x5 ....) sur l'image et à calculer à chaque fois le produit tensoriel contracté entre les élements du filtre et les pixels de l'image délimités par le filtre (somme des produits terme à terme).<br>\n",
    "\n",
    "L'animation ci-dessous illustre la convolution d'une image 5x5 par un filtre 3x3 sans *padding* sur les bords : on obtient une nouvelle image plus petite de 3x3 pixels<br>\n",
    "<p style=\"text-align:center; font-style:italic; font-size:12px;\">\n",
    "    <img src=\"img/filter_3x3.png\" width=\"80\" style=\"display:inline-block;\">\n",
    "    <img src=\"img/Convolution_schematic.gif\" width=\"300\" style=\"display:inline-block;\"><br>\n",
    "    [crédit image : <a href=\"http://deeplearning.stanford.edu/tutorial\">Stanford deep learning tutorial</A>]\n",
    "</p>\n",
    "\n",
    "Pour conserver la taille de l'image d'entrée, on peut faire appel à une technique de *padding* pour créer de nouvelles données sur les bords de l'image (par dupplication des données sur les bords, ou ajout de lignes et colonnes de 0... par exemple) : \n",
    "\n",
    "<p style=\"text-align:center; font-style:italic; font-size:12px;\">\n",
    "    <img src=\"img/padding.gif\" width=\"350\"><br>\n",
    "    [crédit image : <a href=\"https://towardsdatascience.com/applied-deep-learning-part-4-convolutional-neural-networks-584bc134c1e2\"> Arden Dertat</a> ]\n",
    "</p>\n",
    "\n",
    "Le but de la convolution est d'extraire des caractéristiques particulières présentes dans l'image source : on parle de \"carte des caratéristiques\" (*feature map*) pour désigner l'image produite par l'opération de convolution. L'état de l'art conduit à utiliser plusieurs filtres convolutifs pour extraire des caractéristiques différentes : on peut avoir jusqu'à plusieurs dizaines de filtres convolutifs dans un même couche du réseau qui génèrent autant de _feature map_, d'où l'augmentation des données crées par ces opérations de convolution...\n",
    "\n",
    "#### Exemples d'extraction de caractéristiques avec des filtres convolutifs connus (filtre de [Prewitt](https://fr.wikipedia.org/wiki/Filtre_de_Prewitt)):\n",
    "\n",
    "À titre d'exemple, la figure ci-dessous montre les *features maps* obtenues en convoluant une image MNIST (un chiffre 7) avec 4 filtres 3x3 connus en traitement d'image (filtres de Prewitt pour l'extraction de contours):\n",
    "\n",
    "<p style=\"text-align:center; font-style:italic; font-size:12px;\">\n",
    "    <img src=\"img/7_mnist_4_filtres.png\" width=\"500\"><br>\n",
    "    [crédit image : JLC]\n",
    "</p>\n",
    "\n",
    "On voit que ces filtres agissent comme des filtres de détection de contour : dans les images de sortie, les pixels les plus blancs constituent ce que les filtres ont détecté :\n",
    "- les filtre (a) et (c) détectent des contours horizontaux inférieurs et supérieurs,\n",
    "- les filtre (b) et (d) détectent des contours verticaux droite et gauche.\n",
    "\n",
    "Ces exemples très simples permettent de comprendre comment fonctionne l'extraction des *features* d'une image par filtrage convolutif."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Cas général : images RGB traitée par plusieurs filtres de convolution\n",
    "\n",
    "Dans le cas général où les images correpondent à des tableaux 3D (la troisième dimension étant pour les 3 couleurs R(ed), G(reeen) &  B(lue)), le filtre de convolution est lui aussi un tableau 3D. L'opération reste identique au cas 1D : pour une position du filtre 3D sur l'image, le produit tensoriel contracté du filtre avec le sous-tableau 3D correspondant dans l'image fournit un nombre scalaire, et le balayage du procédé sur toute l'image donne la matrice des caractéristiques (*feature map*) de l'image. \n",
    "\n",
    "Par exemple si l'on utilise 10 filtres de convolution 5x5 (10 tableaux de dimensions (5,5,3)) pour traiter (avec  _padding_) une image RGB de 32x32 pixels (tableau de dimensions (32,32,3), on obtient une *feature maps*  de dimensions (32,32,10), soit 10240 termes alors que l'image source n'en a que 1024 !\n",
    "\n",
    "<p style=\"text-align:center; font-style:italic; font-size:12px;\">\n",
    "    <img src=\"img/conv_3D_10.png\" width=\"350\"><br>\n",
    "    [crédit image : <a href=\"https://towardsdatascience.com/applied-deep-learning-part-4-convolutional-neural-networks-584bc134c1e2\"> Arden Dertat</a> ]\n",
    "</p>\n",
    "\n",
    "$\\leadsto$ Pour réduire la quantité d'information générée par les filtres de convolution sans perdre trop d'information, la convolution est toujours suivie d'une opération de *pooling*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Du filtre convolutif à la couche de neurones convolutifs\n",
    "\n",
    "L'intégration du filtrage convolutif dans la structure du réseau de neurones donne l'organisation des calculs  suivante :\n",
    "\n",
    "- Chaque filtre convolutif posssède les mêmes coefficients pour les 3 couleurs : pour le réseau LeNet5 par exemple, chacun des 6 filtres 5x5x3 de la première couche possède seulement 25 coefficients, identiques pour les couleurs R, G & B.\n",
    "\n",
    "- Chaque unité (neurone convolutif) d'une *feature map* de la couche C1 reçoit 75 pixels (25 pixels rouges $R_i$, 25 pixels vert $G_i$ et 25 pixels bleus $B_i$) délimités par la position du filtre convolutif dans l'image source.\n",
    "\n",
    "- Le neurone $k$ d'une *feature map* calcule une sortie $y_k = F_a(\\sum_{i=1}^{25}{\\omega_i(R_i + G_i + B_i) - b_k})$, où $b_k$ est le biais du neurone $k$ et $F_a$ la fonction d'activation (très souvent `relu`).\n",
    "\n",
    "- pour les 6 filtres convolutrifs de la couche C1, on a donc  6 x (25 + 1) paramètres, soit 156 paramètres inconnues pour cette couche qui seront déterminé par entraînement du réseau.\n",
    "\n",
    "Le même schéma est utilisé dans toutes les couches convoltionnelles.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Le *pooling*\n",
    "\n",
    "Le *pooling* vise à réduire quantité de données à traiter. Comme pour l'opération de convolution, on déplace un filtre sur les éléments du tableau *feature map*  et à chaque position du filtre sur le tableau, on calcule un nombre représentant tous les éléments sélectionnés dans le filtre (par exemple la valeur maximale, ou la moyenne....). Mais contrairement à la convolution, on déplace le filtre sans recouvrement.<br>\n",
    "Dans l'exemple simplifié ci-dessous, le filtre *max spool* transforme la matrice 8x8 en une matrice 4x4 qui décrit \"à peu près\" la même information mais avec moins de données :\n",
    "<p style=\"text-align:center; font-style:italic; font-size:12px;\">\n",
    "    <img src=\"img/max_pool_2x2.png\" width=\"350\"><br>\n",
    "    [crédit image : JLC</a> ]\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# C/ Travail à faire"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<span style=\"color:brown;font-family:arial;font-size:normal\"> \n",
    "L'état de l'art actuel des projets de machine learning sous Python préconise l'utilisation d'un <span style=\"font-weight:bold;\">environnement virtuel Python</span> qui permet de maîtriser pour chaque projet les versions de l'interpréteur et des modules Python \"sensibles\" (comme tensorflow par exemple).\n",
    "\n",
    "Dans le cas d'un démarrage de l'ordinateur avec une clef USB Ubuntu, on peut considérer que la clef fournit un environnement Python dédié, à condition de ne pas faire de mises à jour des paquets Python avec <span style=\"font-style:italic\">pip install...</span>\n",
    "    \n",
    "Dans le cas contraire, la <A href=\"https://learn.e.ros4.pro/fr/faq/python3/venv/\">FAQ Python</A> explique comment créer et utiliser un EVP basé sur **miniconda3** pour utiliser numpy et tensorflow2 avec la bibliothèque optimisée <A href=\"https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onemkl.html\">MKL</A>.\n",
    "</span>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modules Python tensorflow/keras\n",
    "\n",
    "Le module **keras** qui permet une manipulation de haut niveau des objets **tensorflow** est intégré dans le module **tensorflow** (tf) depuis la version 2. <br>\n",
    "La documentation du module **tf.keras** à consulter pour ce TP est ici : https://www.tensorflow.org/api_docs/python/tf/keras. \n",
    "\n",
    "Versions des modules Python validées pour ce TP sous Ubuntu 20 / Python3.8.5 :\n",
    "- tensorflow 2.4.0 incluant tensorflow.keras 2.4.0\n",
    "- OpenCV >= 4.2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python    : 3.8.5\n",
      "tensorflow: 2.4.0 incluant keras 2.4.0\n",
      "OpenCV    : 4.5.1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import sys, cv2\n",
    "print(f\"Python    : {sys.version.split()[0]}\")\n",
    "print(f\"tensorflow: {tf.__version__} incluant keras {keras.__version__}\")\n",
    "print(f\"OpenCV    : {cv2.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Incrustation des tracés matplotlib dans le cahier IPython et import de modules utiles :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Affectation des graines (*seed*) des générateurs pseudo-aléatoires :\n",
    "\n",
    "Le TP1 donne plus de détails sur la reproductibilité des générateurs pseudo-aléatoires utilisés."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 123\n",
    "np.random.seed(SEED)      \n",
    "tf.random.set_seed(1234)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Récupération et mise en forme des données MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consulter la documentation de la fonction `load_data` sur la page [tf.keras.datasets.mnist.load_data](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist/load_data) puis compléter la cellule ci-dessous pour charger les données du MNIST en nommant les données renvoyées :<br>\n",
    "- `im_train`, `im_test` pour les images d'entraînement et de test,\n",
    "- `lab_train`, `lab_test` pour les labels des images d'entraînement et de test.\n",
    "\n",
    "(En cas de message d'erreur de type _\"SSL error....\"_ pour téléchager les données du MNIST, voir [Python SSL Certification Problems in Tensorflow](https://stackoverflow.com/questions/46858630/python-ssl-certification-problems-in-tensorflow))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "(im_train, lab_train), (im_test, lab_test) = tf.keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La cellule ci-dessous affiche les attributs `shape` et `dtype` des tableaux numpy obtenus : les valeurs son-elles cohérentes ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "im_train -> shape: (60000, 28, 28) , dtype: uint8\n",
      "im_test  -> shape: (10000, 28, 28) , dtype: uint8\n",
      "lab_train-> shape: (60000,) , dtype: uint8\n",
      "lab_test -> shape: (10000,) , dtype: uint8\n"
     ]
    }
   ],
   "source": [
    "print(\"im_train -> shape:\", im_train.shape, \", dtype:\", im_train.dtype,)\n",
    "print(\"im_test  -> shape:\", im_test.shape,  \", dtype:\", im_test.dtype,)\n",
    "print(\"lab_train-> shape:\", lab_train.shape,  \", dtype:\", lab_train.dtype)\n",
    "print(\"lab_test -> shape:\", lab_test.shape,  \", dtype:\", lab_test.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mise en forme des données d'entrée"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les couches convolutionnelles du module keras attendent par défaut des tableaux multidimentionnels de la forme `(batch_size, height, width, depth)` :\n",
    "- `batch_size` : nombre d'image en entrée,\n",
    "- `height` et `width` : hauteur et largeur des images (en pixels),\n",
    "- `depth` : profondeur des tableaux (`3` pour une image RGB, `1` pour une image en ton de gris).\n",
    "\n",
    "La forme des images MNIST est :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28), (10000, 28, 28))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im_train.shape, im_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il faut donc rajouter une dimension (égale à `1`) après la troisième dimension `28`, par exemple avec la méthode `reshape` des tableaux `ndarray` de numpy.\n",
    "\n",
    "Compléter la cellule suivante pour définir `x_train` et `x_test` obtenus :\n",
    "- en ajoutant une quatrième dimension égale à 1 aux tableaux `im_train` et `im_test` \n",
    "- et en normalisant les valeurs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# avec  la méthode reshape des tableaux ndarray de numpy :\n",
    "x_train = im_train.reshape(im_train.shape + (1,))\n",
    "x_test  = im_test.reshape(im_test.shape + (1,))\n",
    "x_train = x_train/ 255.\n",
    "x_test  = x_test/255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vérification :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28), (60000, 28, 28, 1), (10000, 28, 28), (10000, 28, 28, 1))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im_train.shape, x_train.shape, im_test.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mise au format *one-hot* des labels MNIST\n",
    "\n",
    "Il faut mettre les labels MNIST au format *one-hot* qui permet de calculer l'erreur entre un label et la sortie de la couche de classification du réseau (voir le notebook `TP1_MNIST_dense.ipynb` pour les détails sur le format *one-hot*).<br>\n",
    "Consulter la page [tf.keras.utils.to_categorical](https://www.tensorflow.org/api_docs/python/tf/keras/utils/to_categorical) sur la fonction `to_categorical` et en déduire comment transformer les tableaux `lab_train` et `lab_test` en tableaux `y_train` et `y_test` contenant des vecteurs encodés *hot-one* :\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "# 'one-hot' encoding' des labels :\n",
    "y_train = to_categorical(lab_train)\n",
    "y_test  = to_categorical(lab_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Construction du réseau de neurones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On va maintenant construire dans la cellule ci-dessous le réseau de neurones **convolutionnel** à l'aide du module **keras**.\n",
    "\n",
    "Comme dans le TP1, on crée un objet `model` instance de la classe `Sequential` (cf [tf.keras.Sequential](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential)), puis on complète `model` de façon  incrémentale en ajoutant chaque couche avec la méthode `add` :\n",
    "\n",
    "- La couche d'entrée de type `InputLayer` (cf [tf.keras.layers.InputLayer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/InputLayer)) sert à spécifier la forme des données d'entrée.<br>\n",
    "La forme attendue par keras pour des images en entrée est (height,widt,depth) : on pourra l'obtenir par exemple avec l'attribut `shape` de n'importe quelle image du tableau `x_train`.<br><br>\n",
    "\n",
    "- Les couches convolutionnelles sont de type `Conv2D` (cf [tf.keras.layers.Conv2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D)) :\n",
    "    - les 2 premiers arguments (positionnels) sont : \n",
    "        - le nombre de filtres de la couche\n",
    "        - la forme du filtre : on peut spécifier  `N` ou `(N,N)` pour spécifier un filtre N x N\n",
    "    - les autres arguments (nommés) utilisés sont :\n",
    "        - `stride` : le pas du déplacement du filtre de convolution, valeur par défaut :  `stride=1` (équivalent à `(1, 1)`))\n",
    "        - `padding=valid` : pas de padding, ou `padding=same` : sortie de même dimensions que l'entrée (défaut : `valid`)\n",
    "        - `activation` : choix de la fonction d'activation (`'relu'`, '`tanh'`...)<br><br>\n",
    "        \n",
    "- Les couches de *pooling* du réseau LeNet5 historique utilisent un filtre *average pool* qui correspond à la classe `AveragePooling2D`  (cf [tf.keras.layers.AveragePooling2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D)), mais on aura de meilleurs résultats avec un filtrage *max pool* qui retient la valeur max des pixels dans la fenêtre du filtre (voir la page [tf.keras.layers.MaxPool2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool2D)). Principaux arguments à utiliser avec `MaxPool2D` :\n",
    "    - `pool_size` :  `N` ou `(N,N)` pour spécifier un filtre N x N (défaut : `(2,2)`)\n",
    "    - `strides` : int, tuple de 2 int, ou None. Si None (valeur par défaut), prend la même valeur que `pool_size`\n",
    "    - `padding` : comme pour la classe `Conv2D`<br><br>\n",
    "\n",
    "- Pour applatir les 16 *feature maps* 5x5 en un vecteur de 16 * 5 * 5 = 635 éléments, on peut utiliser une couche  `Flatten` (cf [tf.keras.layers.Flatten](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Flatten))<br><br>\n",
    "\n",
    "En s'inspirant de la structure du réseau `LeNet5` et des spécification ci-dessus, on obtient :\n",
    "- couche d'entrée : `Input(shape=x_train[0].shape)` : on utilise l'attribut `shape` de la 1ère image qui vaut (28,28,1).\n",
    "- couche C1 : `Conv2D(6, 5, padding='same', activation='relu', name='C1')`\n",
    "- couche S2 : `MaxPool2D(pool_size=2, name='S2')`\n",
    "- couche C3 : `Conv2D(16, 5, padding='valid', activation='relu', name='C3')`\n",
    "- couche S4 : `MaxPool2D(pool_size=2, name='S4')`\n",
    "- couche d'applissement : `Flatten()`\n",
    "- couche C5 : `Dense(200, activation='relu', name='C5')`\n",
    "- couche F5 : `Dense(84, activation='relu', name='F6'`\n",
    "- couche OUTPUT : `Dense(nb_classe, activation='softmax', name='Output')`\n",
    "\n",
    "Une fois construit, le réseau doit être compilé (au sens de tensorflow) avec la méthode `compile` en utilisant par exemple les :\n",
    "- `loss='categorical_crossentropy'` : choix de la fonction d'erreur (cf [tf.keras.categorical_crossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/losses/categorical_crossentropy))\n",
    "- `optimizer='adam'` : choix de l'optimiseur Adam (cf page [tf.keras.optimizers.Adam](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam))\n",
    "- `metrics=['accuracy']` pour obtenir les données permettant de tracer les courbes de performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Input, Dense, Conv2D,  MaxPool2D, Flatten\n",
    "\n",
    "nb_classe = 10\n",
    "tf.random.set_seed(SEED)\n",
    "\n",
    "model = Sequential(name='lenet')\n",
    "model.add(Input(shape=x_train[0].shape))\n",
    "model.add(Conv2D(6, 5, padding='same', activation='relu', name='C1'))\n",
    "model.add(MaxPool2D(pool_size=2, name='S2'))\n",
    "model.add(Conv2D(16, 5, padding='valid', activation='relu', name='C3'))\n",
    "model.add(MaxPool2D(pool_size=2, name='S4'))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(200, activation='relu', name='C5'))\n",
    "model.add(Dense(84, activation='relu', name='F6'))\n",
    "model.add(Dense(nb_classe, activation='softmax', name='Output'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avec la méthode `summary` de l'objet `model`, faire afficher la description du modèle : noter les valeurs des paramètres..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"lenet\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "C1 (Conv2D)                  (None, 28, 28, 6)         156       \n",
      "_________________________________________________________________\n",
      "S2 (MaxPooling2D)            (None, 14, 14, 6)         0         \n",
      "_________________________________________________________________\n",
      "C3 (Conv2D)                  (None, 10, 10, 16)        2416      \n",
      "_________________________________________________________________\n",
      "S4 (MaxPooling2D)            (None, 5, 5, 16)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 400)               0         \n",
      "_________________________________________________________________\n",
      "C5 (Dense)                   (None, 200)               80200     \n",
      "_________________________________________________________________\n",
      "F6 (Dense)                   (None, 84)                16884     \n",
      "_________________________________________________________________\n",
      "Output (Dense)               (None, 10)                850       \n",
      "=================================================================\n",
      "Total params: 100,506\n",
      "Trainable params: 100,506\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La fonction `tf.keras.utils.plot_model` permet aussi de dessiner la structure du réseau (voir la page [tf.keras.utils.plot_model](https://www.tensorflow.org/api_docs/python/tf/keras/utils/plot_model)).<br>\n",
    "Faire tracer la structure du modèle en ajoutant l'option `show_shapes=True` à l'appel de `model` :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(model, show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sauvegarde de l'état initial du  réseau"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut sauvegarder l'état initial des poids du réseau non-entraîné (valeurs aléatoires) avec la méthode `Model.save_weights`. <br>\n",
    "Ce sera utile plus loin pour remettre le réseau à son état initial avant de relancer d'autres entraînements :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weights/conv_initial.data-00000-of-00001\n",
      "weights/conv_initial.index\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# vérifier que le dossier 'weights' existe et sinon le créer:\n",
    "if not os.path.exists(\"weights\"): os.mkdir(\"weights\")\n",
    "\n",
    "# sauvegarde des poinds du réseau initial:\n",
    "key = 'conv_initial'\n",
    "model.save_weights('weights/'+key)\n",
    "\n",
    "# afficher les fichiers créés:\n",
    "files=[os.path.join(\"weights\",f) for f in os.listdir(\"weights\") if f.startswith(key)]\n",
    "for f in files: print(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remarque : la méthode `save_weights` utilise la partie `key` du chemin passé en argument pour préfixer les fichiers créés.<br>\n",
    "Lors de la lecture ultérieure des poids du réseau avec la méthode `load_weights` de la classe `Sequential`, il suffira de donner la même information pour retrouver les bons fichiers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Entraînement du réseau avec test à chaque `epoch`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consulter au besoin la documentation de la méthode `fit` dans la page [tf.keras.Sequential](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential). \n",
    "\n",
    "Compléter la cellule ci-dessous pour entraîner le réseau en utilisant la méthode `fit` de l'objet `model` avec les arguments :\n",
    "- `x_train` : les 60000 images \n",
    "- `y_train` : les 60000 labels encodés *one-hot*.\n",
    "- `epochs=10` : faire 10 fois l'entraînement complet.\n",
    "- `batch_size=64` : découper le jeu des données d'entrée (les 60000 images) en \"lots\" (*batch*) de taille `batch_size`.<br>\n",
    "La mise à jour des poids du réseau est faite au bout de `batch_size` échantillons d'entrée. La valeur de `batch_size` (par défaut est 32) est un paramètre qui influe beaucoup sur la qualité de l'apprentissage : on peut essayer d'autres valeurs (64, 128 ...) et observer comment évoluent les performances d'entraînement).\n",
    "\n",
    "\n",
    "\n",
    "Pour avoir un indicateur réaliste de la qualité du réseau entraîné on teste à chaque `epoch` la précison du réseau entraîné en utilisant les données de test : il faut passer l'agument `validation_data` à la méthode `fit`, en lui affectant le tuple des données de test `(x_test, y_test)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "938/938 [==============================] - 22s 23ms/step - loss: 0.4446 - accuracy: 0.8681 - val_loss: 0.0745 - val_accuracy: 0.9764\n",
      "Epoch 2/10\n",
      "938/938 [==============================] - 21s 23ms/step - loss: 0.0678 - accuracy: 0.9786 - val_loss: 0.0423 - val_accuracy: 0.9860\n",
      "Epoch 3/10\n",
      "938/938 [==============================] - 21s 23ms/step - loss: 0.0409 - accuracy: 0.9872 - val_loss: 0.0340 - val_accuracy: 0.9885\n",
      "Epoch 4/10\n",
      "938/938 [==============================] - 22s 23ms/step - loss: 0.0346 - accuracy: 0.9890 - val_loss: 0.0328 - val_accuracy: 0.9893\n",
      "Epoch 5/10\n",
      "938/938 [==============================] - 20s 21ms/step - loss: 0.0257 - accuracy: 0.9920 - val_loss: 0.0322 - val_accuracy: 0.9890\n",
      "Epoch 6/10\n",
      "938/938 [==============================] - 22s 23ms/step - loss: 0.0218 - accuracy: 0.9929 - val_loss: 0.0362 - val_accuracy: 0.9877\n",
      "Epoch 7/10\n",
      "938/938 [==============================] - 22s 23ms/step - loss: 0.0199 - accuracy: 0.9938 - val_loss: 0.0355 - val_accuracy: 0.9886\n",
      "Epoch 8/10\n",
      "938/938 [==============================] - 22s 24ms/step - loss: 0.0138 - accuracy: 0.9952 - val_loss: 0.0358 - val_accuracy: 0.9885\n",
      "Epoch 9/10\n",
      "938/938 [==============================] - 21s 23ms/step - loss: 0.0144 - accuracy: 0.9952 - val_loss: 0.0366 - val_accuracy: 0.9890\n",
      "Epoch 10/10\n",
      "938/938 [==============================] - 22s 23ms/step - loss: 0.0114 - accuracy: 0.9960 - val_loss: 0.0414 - val_accuracy: 0.9884\n"
     ]
    }
   ],
   "source": [
    "# au cas on on exécute plusieurs fois cette cellule, il faut ré-initialiser \n",
    "# les poids du réseau à leur valeur initiale si on veut comparer les entraînements...\n",
    "key = 'conv_initial'\n",
    "model.load_weights('weights/'+key)  \n",
    "tf.random.set_seed(SEED)\n",
    "\n",
    "hist = model.fit(x_train, y_train, \n",
    "                    validation_data=(x_test, y_test),\n",
    "                    batch_size=64,\n",
    "                    epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'objet `hist` retourné par la méthode `fit` possède un attribut `history` de type dictionnaire dont les clefs `'loss'`, `'accuracy'` contiennent l'évaluation de la fonction de cout et de la précision du réseau à la fin de chaque (*epoch*) avec les données d'entraînement. Les clefs `'val_loss'` et `'val_accuracy'` sont associées aux données de test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.20154736936092377, 0.06202346459031105, 0.04410078004002571, 0.03482348471879959, 0.027362942695617676, 0.021863402798771858, 0.01954936608672142, 0.01585736684501171, 0.015268944203853607, 0.011375262401998043]\n",
      "[0.9404333233833313, 0.980733335018158, 0.9859833121299744, 0.9889000058174133, 0.991516649723053, 0.9932166934013367, 0.9937333464622498, 0.9948333501815796, 0.9951000213623047, 0.9962166547775269]\n",
      "[0.07453861087560654, 0.042307041585445404, 0.03400133177638054, 0.03276378661394119, 0.032218676060438156, 0.03619859740138054, 0.03545724228024483, 0.03581038489937782, 0.03659989312291145, 0.041386425495147705]\n",
      "[0.9764000177383423, 0.9860000014305115, 0.9884999990463257, 0.989300012588501, 0.9890000224113464, 0.9876999855041504, 0.9886000156402588, 0.9884999990463257, 0.9890000224113464, 0.9883999824523926]\n"
     ]
    }
   ],
   "source": [
    "print(hist.history['loss'])\n",
    "print(hist.history['accuracy'])\n",
    "print(hist.history['val_loss'])\n",
    "print(hist.history['val_accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tracé des courbes `accuracy` et `loss`  de l'entraînement et des test :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La fonction `plot_loss_accuracy` du module `utils.tools` (présent dans le répertoire du notebook) permet de tracer les courbes de précision et de perte en utilisant les données stockées dans l'objet `hist`. Faire tracer ces courbes :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utils.tools import plot_loss_accuracy\n",
    "plot_loss_accuracy(hist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arrêter automatiquement l'entraînement avant *over-fit*\n",
    "\n",
    "Keras propose des outils pour arrêter automatiquement l'apprentissage en surveillant par exemple la croissance de `val_accuracy` ou la décroisance de `val_losss` d'une `epoch` à l'autre (cf le callback  [EarlyStopping](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping)).\n",
    "\n",
    "On peut ainsi définir une liste de *callback* que l'on peut passer en argument à la fonction `fit` avec l'agument nommé  `callbacks` :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "5000/5000 [==============================] - 38s 8ms/step - loss: 0.1150 - accuracy: 0.9640 - val_loss: 0.0434 - val_accuracy: 0.9860\n",
      "Epoch 2/15\n",
      "5000/5000 [==============================] - 38s 8ms/step - loss: 0.0458 - accuracy: 0.9861 - val_loss: 0.0350 - val_accuracy: 0.9897\n",
      "Epoch 3/15\n",
      "5000/5000 [==============================] - 36s 7ms/step - loss: 0.0347 - accuracy: 0.9894 - val_loss: 0.0410 - val_accuracy: 0.9872\n",
      "Epoch 4/15\n",
      "5000/5000 [==============================] - 47s 9ms/step - loss: 0.0284 - accuracy: 0.9914 - val_loss: 0.0439 - val_accuracy: 0.9867\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00004: early stopping\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "\n",
    "callbacks_list = [\n",
    "    EarlyStopping(monitor='val_loss',  # la grandeur à surveiller\n",
    "                  patience=2,              # on accepte que 'val_accuracy' puisse diminuer 2 fois de suite\n",
    "                  restore_best_weights=True,\n",
    "                  verbose=1)\n",
    "]\n",
    "\n",
    "# recharger l'état initial du réseau:\n",
    "key = 'conv_initial'\n",
    "model.load_weights('weights/'+key)  \n",
    "tf.random.set_seed(SEED)\n",
    "\n",
    "hist = model.fit(x_train, y_train,\n",
    "                    validation_data=(x_test, y_test),\n",
    "                    epochs=15, \n",
    "                    batch_size=12, \n",
    "                    callbacks = callbacks_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tracer les courbes `loss` et `accuracy` :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utils.tools import plot_loss_accuracy\n",
    "plot_loss_accuracy(hist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le réseau convolutionnel tend vers une meilleure précision voisine de 99%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sauvegarder les poids du  réseau entraîné"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La méthode `save_weights` de la classe `Sequential`permet d'enregistrer les **poids** du réseau entraïné dans un fichier :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weights/conv_trained.index\n",
      "weights/conv_trained.data-00000-of-00001\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "# vérifier que le dossier 'weights' existe et sinon le créer:\n",
    "if not os.path.exists(\"weights\"): os.mkdir(\"weights\")\n",
    "\n",
    "# sauvegarde des poids du réseau entrainé:\n",
    "key = 'conv_trained'\n",
    "model.save_weights('weights/'+key)\n",
    "\n",
    "# afficher les fichiers créés:\n",
    "files=[os.path.join(\"weights\",f) for f in os.listdir(\"weights\") if f.startswith(key)]\n",
    "for f in files: print(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sauvegarder la structure du réseau et ses poids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La méthode `save` de la classe `Sequential` permet d'enregistrer **toute la structure et les poids** du réseau entraïné dans un fichier.<br />\n",
    "Ceci permet de recréer plus tard *from scratch* le réseau entrainé pour passer en phase exploitation du réseau par exemple, en utilisant la fonction`tf.keras.models.load_model` :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: models/conv1/assets\n",
      "models/conv1\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "# vérifier que le dossier 'weights' existe et sinon le créer:\n",
    "if not os.path.exists(\"models\"): os.mkdir(\"models\")\n",
    "\n",
    "# sauvegarder structure réseau + poids :\n",
    "key = 'conv1'\n",
    "model.save('models/'+key) \n",
    "\n",
    "# afficher les fichiers créés:\n",
    "files=[os.path.join(\"models\",f) for f in os.listdir(\"models\") if f.startswith(key)]\n",
    "for f in files: print(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6/ Exploitation du réseau avec le jeu de test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La méthode `predict` de l'objet `model` permet de calculer les inférences du réseau pour une ou plusieurs entrées (voir la méthode `predict`dans la page [tf.keras.Sequential](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential)).\n",
    "\n",
    "La cellule ci-dessous montre la mise en oeuvre de la méthode `predict`, et comment exploiter la représentation  *one-hot* renvoyée par `fit` en utilisant la méthode `argmax` des tableaux de numpy :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sortie du réseau pour l'image de rang 100 :\n",
      "[3.6815929e-06 2.8430456e-06 7.4637916e-07 1.6630321e-07 1.6036262e-07\n",
      " 5.4625507e-06 9.9998283e-01 2.3382853e-09 4.1234830e-06 1.5995952e-09]\n",
      "\n",
      "sortie réseau arrondie à 2 chiffre : [0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00]\n",
      "rep[0].argmax() donne : 6\n",
      "\n",
      "La bonne réponse est 6 soit en 'one-hot' : [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utils.tools import plot_images\n",
    "\n",
    "i = 100   # numéro image de test\n",
    "rep = model.predict(x_test[i:i+1]) # Attention: x doit être un tableau de matrices...\n",
    "                                   # => x[i] ne convient pas !\n",
    "\n",
    "print(f\"sortie du réseau pour l'image de rang {i} :\\n{rep[0]}\")\n",
    "\n",
    "# limiter l'affichage des composantes des tableaux numpy à 1 chiffre :    \n",
    "with np.printoptions(formatter={'float':'{:.2f}'.format}):    \n",
    "    print(f\"\\nsortie réseau arrondie à 2 chiffre : {rep[0]}\")\n",
    "    \n",
    "print(f\"rep[0].argmax() donne : {rep[0].argmax()}\")\n",
    "\n",
    "print(f\"\\nLa bonne réponse est {lab_test[i]} soit en 'one-hot' : {y_test[i]}\")\n",
    "\n",
    "plot_images(im_test,i,1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Utilité de la méthode numpy `ndarray.argmax` pour décoder le tableau de vecteurs *one-hot* renvoyé par la méthode `predict`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quand on calcule l'inférence du réseau `model` avec les données de test par exemple, on obtient un résultat qui est un tableau de vecteurs codés *one-hot*, comme le détaille la cellue suivante :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "forme du tableau 'results': (10000, 10)\n",
      "allure des vecteurs du tableau 'result', par exemple :\n",
      "results[0]  : [0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000]\n",
      "results[-1] : [0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000]\n"
     ]
    }
   ],
   "source": [
    "results = model.predict(x_test)\n",
    "\n",
    "print(\"forme du tableau 'results':\", results.shape)\n",
    "print(\"allure des vecteurs du tableau 'result', par exemple :\")\n",
    "with np.printoptions(formatter={'float':'{:.4f}'.format}): \n",
    "    print(\"results[0]  :\", results[0])\n",
    "    print(\"results[-1] :\", results[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En écrivant `results.argmax(axe=-1)`, on obtient le tableau des `argmax` de chaque vecteur -> c'est directement le tableau des chiffres reconnus par le réseau :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chiffres_reconnus -> shape: (10000,) , dtype: int64\n",
      "contenu de chiffres_reconnus : [7 2 1 ... 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "chiffres_reconnus = results.argmax(axis=-1)\n",
    "\n",
    "print(\"chiffres_reconnus -> shape:\", chiffres_reconnus.shape, \", dtype:\", chiffres_reconnus.dtype)\n",
    "print(f\"contenu de chiffres_reconnus : {chiffres_reconnus}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrouver le taux de réussite du réseau entrainé en utilisant les inférences du réseau pour les entrées `x_test` et les labels `lab_test` :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "taux de réussite : 98.97 %\n"
     ]
    }
   ],
   "source": [
    "results = model.predict(x_test)\n",
    "chiffres_reconnus = results.argmax(axis=-1)\n",
    "\n",
    "success = 0\n",
    "for chiffre, label in zip(chiffres_reconnus, lab_test):\n",
    "    success += (chiffre == label)\n",
    "print(f\"taux de réussite : {success/len(x_test)*100:.2f} %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Afficher la matrice de confusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La cellule suivante définie la fonction `show_cm_mnist` qui affiche la **matrice de confusion**.\n",
    "\n",
    "La matrice de confusion permet de visualiser :\n",
    "- sur la diagonale : les bonnes réponses du réseau, avec dans chaque case le nombre de bonnes réponses\n",
    "- hors diagonale : les erreurs du réseau, avec dans chaque case la fréquence d'apparition de l'erreur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from seaborn import heatmap\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def show_cm_mnist(target, results, classes):\n",
    "    # target  : the actual labels (one-hot format)\n",
    "    # results : the labels computed by the trained network (one-hot format)\n",
    "    # classes : list of possible label values\n",
    "    predicted = np.argmax(results, axis=-1) # tableau d'entiers entre 0 et 9 \n",
    "    cm = confusion_matrix(target, predicted)\n",
    "    df_cm = pd.DataFrame(cm, index=classes, columns=classes)\n",
    "    plt.figure(figsize=(11,9))\n",
    "    heatmap(df_cm, annot=True, cbar=False, fmt=\"3d\")\n",
    "    plt.xlabel('actual label')\n",
    "    plt.ylabel('predicted label')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Faire afficher la matrice de confusion en lui passant les labels attendus `lab_test` et les labels calculés par le model :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApMAAAIWCAYAAAALawaWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAABI70lEQVR4nO3deXwU9f3H8dc3CchNQkS5VBAPikdF8KrUYq1XtXigVms9wNYD1NartT+1ra1HtV71bL1axRNvUUFUPEA8QAUVxKMWlAiCQLg1Er6/PxKptgbilNkJmdfz8eDhZja7+/44s5t3ZnY2IcaIJEmSlERR1gEkSZK09rJMSpIkKTHLpCRJkhKzTEqSJCkxy6QkSZISs0xKkiQpsZKsA9Rl2Ygrc/eZRa33uyjrCJIkSf9leVVFqOs690xKkiQpMcukJEmSErNMSpIkKTHLpCRJkhKzTEqSJCkxy6QkSZISs0xKkiQpMcukJEmSErNMSpIkKTHLpCRJkhKzTEqSJCkxy6QkSZISs0xKkiQpMcukJEmSErNMSpIkKTHLpCRJkhKzTEqSJCkxy6QkSZISs0xKkiQpsVyWydufncSAP93JgX+6g9uemQTAr/7xOIdcfBeHXHwXe597K4dcfNfK73/no0848vJ7OfBPd3DQRXfy2efLs4qeqsEnHM3UKWNZXlVBeXlZ1nEKIo8zA3TtugHjxg5n6pSx3HH7dTRp0iTrSKnL48yQz7nzOHNeX8vyOHdD3L5zVybfmzmX+1+Ywm2nHsSwMw5lzJRpfDCnkouP3pNhvzqUYb86lB98e2N227o7AMurV3DW0Cc565B+3H/mT7jxxAMoKW6c/9vGvTCePfc+lGnTPsw6SsHkcWaACy84iyuuvIEePfsyf/4CBg08LOtIqcvjzJDPufM4c15fy/I4d0PcvlNrRSGEHiGEX4cQrqz99+sQwrfSerz6ev/j+Wy10fo0b9qEkuIienfvxFOvv7/y+hgjoyb+k716bwrAC29/wKadytm887oAlLZsRnFR4yyTEydOZvr0GVnHKKg8zgywa7+due++RwEYOvQe9uu/Z8aJ0pfHmSGfc+dx5ry+luVx7oa4fafSikIIvwbuAgLwcu2/ANwZQjgzjcesr006tOPV9z+icsmnLKv6nLFTpvNx5eKV17/6/kzKWzdno/alAEyfvYAQ4ITrHubQS+7m70+9mlFyac0oLy+jsnIB1dXVAMyomEmnzh0yTpWuPM4M+Zw7jzMrPxrq9l2S0v0eA2wRY/z8ywtDCJcBk4E/fd2NQgjHAscCXHXSjzlm7++s8WAbd2jHwN225YTrHqZ50xI277wuRSGsvH7kK++w17abrvy6esUKXnt/JrefejDNmpZw3DUP0XOD9uyw2QZrPJskSdLaJq3jtSuATl+zvGPtdV8rxnh9jLFPjLFPGkXyCwfs2JM7Tz+Em08+kNYt1mGj9UqBmvdHPvX6++zZ699lcv3SVmzbvRNlrZrTvGkT+vbciLdmzEktm5S2uXPnU1raluLiYgC6dO7IRxWzMk6VrjzODPmcO48zKz8a6vadVpn8JfBUCGFECOH62n8jgaeAX6T0mPU2b9FSAGbOX8To199n7203A+Cldz6k2/plrF/aauX3fqfHBrw3cy7Lqj5nefUKXvnnR2y8frtMcktryjPPjmPAgH0AOOKIg3l4+KiME6UvjzNDPufO48zKj4a4fYcYYzp3HEIRsD3QuXZRBTA+xlhdn9svG3FlOsGAgVfez4Iln1JSXMRp+++88pD1Obc/xdZd1+fgnbf8yvc/OuFtbnryFQKBvj034pT+6ew1bb3fRancb32dOGQQp582mA4d2jN79ieMGDma444/I9NMacvjzADdum3IHbddS1lZKRMnTebIo06iqqoq61ipyuPMkM+58zhzXl/L8jh3Vtv38qqKUNd1qZXJ/1WaZbKhyrpMSpIkfZ1VlcnG+Rk3kiRJKgjLpCRJkhKzTEqSJCkxy6QkSZISs0xKkiQpMcukJEmSErNMSpIkKTHLpCRJkhKzTEqSJCkxy6QkSZISs0xKkiQpMcukJEmSErNMSpIkKTHLpCRJkhKzTEqSJCkxy6QkSZISs0xKkiQpMcukJEmSErNMSpIkKbEQY8w6w9cqadq5YQZL0bKPxmQdoeCad/pu1hEkSdJqLK+qCHVd555JSZIkJWaZlCRJUmKWSUmSJCVmmZQkSVJilklJkiQlZpmUJElSYpZJSZIkJWaZlCRJUmKWSUmSJCVmmZQkSVJilklJkiQlZpmUJElSYpZJSZIkJWaZlCRJUmKWSUmSJCVmmZQkSVJilklJkiQlZpmUJElSYpZJSZIkJWaZlCRJUmKWyTp07boB48YOZ+qUsdxx+3U0adIk60jf2NkXXMYu+xzK/j89fuWyx0ePYb/Dj2Orvj/kzbfeWbn8jSlvM+CoIQw4aggHHjWYJ599fpX301g0hvWcxK23XMXkN59j4mtPccP1l1JSUpJ1pNTldV3nce7BJxzN1CljWV5VQXl5WdZxCiKPM0M+t++GOLNlsg4XXnAWV1x5Az169mX+/AUMGnhY1pG+sf1/uDt/vey8ryzbZOONuOKCc+i9zZb/tfzum67kvluu4W+XnscfLr6K5cur67yfxqIxrOck7rzzAbbYche26bUbzZs345hBP8k6Uuryuq7zOPe4F8az596HMm3ah1lHKZg8zgz53L4b4syWyTrs2m9n7rvvUQCGDr2H/frvmXGib67PNlvRtk3rryzr3nVDum3U5b++t3mzZpSUFAPwWVUVhLDK+2ksGsN6TmLEyNErL48fP5EuXTpmmKYw8rqu8zj3xImTmT59RtYxCiqPM0M+t++GOHPBy2QIYWChH/ObKi8vo7JyAdXVNXvmZlTMpFPnDhmnSt/rk6ey3+HHccCRJ/DbM05cWS4bq7yu5y8rKSnh8MMH8PjjT2cdJVV5Xdd5nVv5kMftu6HOnMWeyXPruiKEcGwIYUIIYcKKFUsKmUnA1lv04KHb/8ZdN/6FG4cO47PPqrKOpJRdfdUFjBnzEmOffznrKJKktVQq77oPIbxe11XA+nXdLsZ4PXA9QEnTzjGFaPUyd+58SkvbUlxcTHV1NV06d+SjillZxSm47l03pEXz5rz7/jS2/NZmWcdJTd7X8zlnn0L79uWcMPhnWUdJXV7XdV7nVj7kcftuqDOntWdyfeBI4Edf829uSo+5Rj3z7DgGDNgHgCOOOJiHh4/KOFG6Znw0a+UJNx/N+ph/Tf+Qzh3r7P2NRt7W8xcGDTyMPXbvx+E/HUKMmf3eVlB5Xdd5nVv5kMftuyHOHNL4QRJCuAn4e4xx7Ndcd0eMcbWnjma5ZxKgW7cNueO2aykrK2XipMkcedRJVFWle9h32Udj1uj9nfG7PzH+tdeprFxIebtSBh9zBG3btOLCy69jXuUCWrdqRY9NN+b6y8/n4ZFPcdPQYZSUlFBUFDh+4E/YbZfv1Hk/A360Zt7w27zTd9fI/SSVxXpuCD5dOp3p02ewaHHN20kefPAxzjv/imxDpSyv6zqPc584ZBCnnzaYDh3aM3v2J4wYOZrjjj8j61ipyuPMkM/tO6uZl1dVhLquS6VMrglZl8ksrOkyuTbIukxKkqTVW1WZ9KOBJEmSlJhlUpIkSYlZJiVJkpSYZVKSJEmJWSYlSZKUmGVSkiRJiVkmJUmSlJhlUpIkSYlZJiVJkpSYZVKSJEmJWSYlSZKUmGVSkiRJiVkmJUmSlJhlUpIkSYlZJiVJkpSYZVKSJEmJWSYlSZKUmGVSkiRJiVkmJUmSlFiIMWad4WuVNO3cMINpjVr83GVZR8hEq11OzTqCpDUoZB0gI/6gzo/lVRV1bubumZQkSVJilklJkiQlZpmUJElSYpZJSZIkJWaZlCRJUmKWSUmSJCVmmZQkSVJilklJkiQlZpmUJElSYpZJSZIkJWaZlCRJUmKWSUmSJCVmmZQkSVJilklJkiQlZpmUJElSYpZJSZIkJWaZlCRJUmKWSUmSJCVmmZQkSVJilklJkiQlZpmsQ9euGzBu7HCmThnLHbdfR5MmTbKOlLrBJxzN1CljWV5VQXl5WdZxEvntjQ/Q78SLOPD/rl65bMHipRx38T/40a+u4LiL/8HCJcsAeHTcJA466xoGnHU1R/7xBt7+YBYAn1V9zk9+/zcOPvsaDvjNVVx7/+hMZklTHrfvPM4M+Zw7jzPfdOPlvPP2C0wYP4oJ40fx7W9vkXWkgrj+b5fwyoQnePWVJ7j7rutp2bJF1pFS1xC3b8tkHS684CyuuPIGevTsy/z5Cxg08LCsI6Vu3Avj2XPvQ5k27cOsoyS2X99eXHf6EV9ZdvOjY9i+58YMv/iXbN9zY256ZAwAnduXcfP/DeK+80/k2P7f4w9/fwiApk1KuPHMo7nnvCEM++Ngnn/jXV5/b+39f/J18rh953FmyOfceZwZ4MzfnEef7fagz3Z7MGnS5KzjFMRpp/+e3n12Z9veu/PhBxUMGTww60ipa4jbd2plMoTQI4SwWwih1X8s3yutx1yTdu23M/fd9ygAQ4few37998w4UfomTpzM9Okzso7xP+ndoyttWjb/yrKnX51K/769AOjftxdPv/oWANtsuuHK7916kw34eN5CAEIItGi2DgDLq6tZXr0CQqEmKIw8bt95nBnyOXceZ86rRYsWr7zcrHkzYowZpimMhrh9p1ImQwgnAw8BJwFvhhD2+9LVF6TxmGtSeXkZlZULqK6uBmBGxUw6de6QcSolNW/hEtqXtgZg3batmLdwyX99zwPPvkLfrTdd+XX1ihUccs617HrSxey4RXe27r5BwfKmLY/bdx5nhnzOnceZv/CHP/yaV195gkv+/HuaNm2adZyCufGGy6j4cCI9Nt+Eq6+5Oes4qWqo23daeyZ/DvSOMe4P9APOCSH8ova6OvfxhBCODSFMCCFMWLHiv3/gS/+rEP5783v5rfd54LlX+eWP91i5rLioiGF/HMyoy0/jzfdn8O6MjwsZU5K+kbPOvpAtt9yFHXfah3btSjnjjMFZRyqYn/38VDbYaFvemvouhxzcP+s4uZRWmSyKMS4GiDFOo6ZQ7h1CuIxVlMkY4/Uxxj4xxj5FRS1TirZ6c+fOp7S0LcXFxQB06dyRjypmZZZH/5t2bVoyp3IRAHMqF9Guzb+3rXc+mMW5Nz3EFb/8CaWt/vuN221aNme7b3Vj3OvvFixv2vK4fedxZsjn3HmcGWDWrNkAVFVV8Y9b7ma7Pr0yTlRYK1asYNiwhzjwgH2yjpKqhrp9p1UmPw4hbPPFF7XFcl9gXWCrlB5zjXrm2XEMGFCzUR5xxME8PHxUxomUVL9ePXh47GsAPDz2NXbdtgcAM+dWcupVd3H+cQPo2mHdld8/b+GSlWd8f1r1OS9O/iddO7UvfPAU5XH7zuPMkM+58zhzhw7rrby8X/+9mDxlaoZpCqd7964rL/9o3z14++33sgtTIA1x+w5pvFk1hNAFWB5j/K+6HELYOcb4/Oruo6Rp50zfRdut24bccdu1lJWVMnHSZI486iSqqqqyjJS6E4cM4vTTBtOhQ3tmz/6EESNHc9zxZ6T6mIufu2yN3t+vr72HCVP/ReXipbRr04oTDtiV7/f+Fmdcczez5i6gY3kpfx5yCG1bteD3Nz3IkxOm0GndUqDm0Pad5x7POx/M4uwb7mfFisiKGNlj+y04fv9d12jOVrucukbv75vK4/adx5khn3NnMXPW5+iNenwY7du3gxB4fdJkBg85kyVLlqb+uFn+oA4h8OzTD9C6TStCCLz++hSGnPibr5yU0xhl9ZxeXlVR99sUG+qZT1mXSRXGmi6Ta4usy6SkNSvrMpkVf1Dnx6rKpJ8zKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMRCjDHrDF+rpGnnhhlMWgMWPXl+1hEKrvUPzso6giQpoeVVFaGu69wzKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMQsk3Xo2nUDxo0dztQpY7nj9uto0qRJ1pFS58xr78y/+8ej7HrqXxjwuxtWLluwZBnHXXYnPzrrrxx32Z0sXLIMgBgjF905ih/933Uc/PsbeWv6LADGT53OIefetPLf9idczOjX3slknjQ0lnX9TQw+4WimThnL8qoKysvLso5TMHlc13mcGfK5jTfEmS2TdbjwgrO44sob6NGzL/PnL2DQwMOyjpQ6Z157Z+7/na249hc//sqym0e8wA7f6srw849nh2915eYRLwIw9s1/8sHs+Tx8/vGcc8TenH/7SAC267ERw353DMN+dww3nP4TmjVtwk49uxV8lrQ0lnX9TYx7YTx77n0o06Z9mHWUgsrjus7jzJDPbbwhzmyZrMOu/XbmvvseBWDo0HvYr/+eGSdKnzOvvTP33mxD2rRs9pVlz0x8lx/ttBUAP9ppK56e+M7K5fvuuCUhBLbu3plFSz9jTuXir9z2iVemsvOWG9N8ncazd6OxrOtvYuLEyUyfPiPrGAWXx3Wdx5khn9t4Q5w5tTIZQtg+hLBd7eWeIYRTQwg/TOvx1qTy8jIqKxdQXV0NwIyKmXTq3CHjVOly5sY389yFS2hf2gqAddu2ZO7CJQDMnr+IDu3arPy+9ctaM7ty0Vdu+/jLb7H39j0LFzZljX1d69/yuK7zOLMalpI07jSE8Dtgb6AkhPAEsAPwNHBmCKFXjPH8NB5X0tcLIRBCqNf3zqlczHsVs9lpi41TTiVJagxSKZPAQcA2wDrALKBLjHFhCOES4CXga8tkCOFY4FiAUNyWoqKWKcVbtblz51Na2pbi4mKqq6vp0rkjH1XMyiRLoThz45u5vE1L5lQupn1pK+ZULqZd6xYArFfWmlnzFq78vo/nL2K90tYrvx414S127bU5TUqKC545LY19Xevf8riu8zizGpa0DnMvjzFWxxiXAv+MMS4EiDEuA1bUdaMY4/Uxxj4xxj5ZFckvPPPsOAYM2AeAI444mIeHj8o0TyE4c+Oa+Xvf3pThL7wBwPAX3qDfNpuuXP7Ii28SY+T1f1bQqvk6Kw+HA4x8eUqjOsT9hca8rvVVeVzXeZxZDUeIMa75Ow3hJWDXGOPSEEJRjHFF7fK2wNMxxm1Xdx8lTTuv+WDfQLduG3LHbddSVlbKxEmTOfKok6iqqsoyUuqcuXAzL3pyzb7T48zrH2TCOx9QuXgZ7Vq35IT+32XXXpvyq789yMx5C+lU3paLj9ufti2bE2PkwjtGMW7y+zRr2oRzj96HLbp2BKDik0qOvmgoj190IkVF9TssXl+tf3DWGr2/byqP2/eJQwZx+mmD6dChPbNnf8KIkaM57vgzso6Vujyu6zzODPncxrOaeXlVRZ0/FNIqk+vEGD/7muXrAh1jjG+s7j6yLpNSmtZ0mVwbZF0mJUnJrapMpvKeya8rkrXLPwE+SeMxJUmSVHh+zqQkSZISs0xKkiQpMcukJEmSErNMSpIkKTHLpCRJkhKzTEqSJCkxy6QkSZISs0xKkiQpMcukJEmSErNMSpIkKTHLpCRJkhKzTEqSJCkxy6QkSZISs0xKkiQpMcukJEmSErNMSpIkKTHLpCRJkhKzTEqSJCkxy6QkSZISCzHGrDN8rZKmnRtmMEmJLLr/tKwjZKL1gZdmHUHSGlQUQtYRMlH12Yw6B3fPpCRJkhKzTEqSJCkxy6QkSZISs0xKkiQpMcukJEmSErNMSpIkKTHLpCRJkhKzTEqSJCkxy6QkSZISs0xKkiQpMcukJEmSErNMSpIkKTHLpCRJkhIrqeuKEMIbQPy6q4AYY9w6tVSSJElaK9RZJoF9C5ZCkiRJa6U6y2SMcfoXl0MIGwGbxhifDCE0X9XtJEmSlB+rfc9kCOHnwL3A32oXdQEeTDGTJEmS1hL1OQFnCLAzsBAgxvgusF6aoSRJkrR2qE+Z/CzGWPXFFyGEEr7+xBxJkiTlTH3K5LMhhP8DmocQdgfuAYanGyt7XbtuwLixw5k6ZSx33H4dTZo0yTpS6gafcDRTp4xleVUF5eVlWccpiDzODI1v+779uTcY8OdhHHjxMG577vWVy+8c8yb7/+luDrx4GJcPfxGAinmL2OHXN3LIpfdyyKX3ct69z2UVuyAa27qujzw+r/O4nqHxr+s/nPsr/vney8yb+/ZXlv/85z/l1VeeZPzLj/P06Pv5Vo9NM0pYoz5l8kxgDvAGcBzwGHB2mqEaggsvOIsrrryBHj37Mn/+AgYNPCzrSKkb98J49tz7UKZN+zDrKAWTx5mhcW3f782cx/0vvcVtvziAYacdxJgpH/DBJwsY/14Fz0yexrDTD+L+Xx3CUf2+vfI2XdZtw7DTDmLYaQdx9kG7ZJg+fY1pXddXHp/XeVzP0PjX9SOPPsnOff/7w3XuuutBtu39A7bbfk8uvew6Lv7z7zJI92+rLZMxxhXALcAfgXOBW2KMjf4w9679dua++x4FYOjQe9iv/54ZJ0rfxImTmT59RtYxCiqPM0Pj2r7fnz2frTZcj+ZNm1BSXETv7h156vV/MWzcFAZ+fxualhQD0K5184yTZqMxrev6yuPzOo/rGRr/un755VeZNWv2fy1ftGjxysstW7Qg61q22o/4CSHsA/wV+Cc1H1jeLYRwXIxxxDd5oBDCrTHGI5PFLKzy8jIqKxdQXV0NwIyKmXTq3CHjVNKa0di27006tOPqx8ZTueRT1mlSzNi3PqBnl/ZMn7OAV9+fydUjxrNOSTGn/GhHttyw5tzBinmL+PGl99KqWVOG7L0d227cMeMp0tHY1rW+nus5n44//ih+8Yuf07RJU/bc68eZZqnP50VeCuwaY3wPIITQHXgUqLNMhhAe/s9FwK4hhFKAGGP/RGkl6T9svH4ZA7+/DSdc/yjNm5awead1KSoKVK9YwcKlnzH05P1588M5/Grokzz6f4fRvk0LRp59OKUtmzHlwzmc8vfHue9Xh9CqWdOsR5GkevvrX2/hr3+9hUN/vD+/OfNkjvnZKZllqU+ZXPRFkaz1PrBoNbfpAkwBbqTmzO8A9KGmmNYphHAscCxAKG5LUVHLesRb8+bOnU9paVuKi4uprq6mS+eOfFQxK5Ms0prWGLfvA3bowQE79ADgysdeYv22rZg2u5Ldtu5GCIGtNlyPohCYv+RT2rVqvvLQd88N2tNl3TZMn7OALTZon+UIqWiM61r/zfWcb3cPe4irrrog0wx1vmcyhHBgCOFAYEII4bEQwtEhhKOoOZN7/Grutw/wCnAWsCDG+AywLMb4bIzx2bpuFGO8PsbYJ8bYJ6si+YVnnh3HgAH7AHDEEQfz8PBRmeaR1qTGtn3PW7QMgJnzFzH69Wnsve0m7LplN8a/9xEA0+dU8vnyaspaNmPe4mVUr1gBwIy5C/lgzgK6lLfOLHvaGtu61tdzPefLJpt0W3n5hz/cjffe+1eGaSDU9abNEMLfV3XDGOPA1d55CF2Ay4GPgf4xxg3rG6ykaedM303arduG3HHbtZSVlTJx0mSOPOokqqqqVn/DtdiJQwZx+mmD6dChPbNnf8KIkaM57vgzso6VqjzODNls34vuPy21+x549UMsWPopJUVFnNZ/J3bYrAufL6/md3c/w9sfzaVJcTGn/mhHtt+0M0++/j7XjpxASXERRSFwwp69+d4WXVPL1vrAVR6QSZ2vZfl4XudxPUM267oohFTv/8suvOAsfvzj/enUaX0++uhj/v73O/njeZdx6aXnstv3+/L558uZP38Bv/zl2Ux5651Us1R9NqPOwessk2tS7Uk8O8cY/6++t8m6TEpas9Iskw1Z1mVS0ppVyDLZkKyqTNbnbO5mwDHAFkCzL5bHGAfVN0CM8VFqTtqRJElSI1KfDy0fCnQA9gSepebkmtWdgCNJkqQcqE+Z3CTGeA6wJMZ4C7APsEO6sSRJkrQ2qE+Z/Lz2v5UhhC2BtsB66UWSJEnS2qI+nzN5fQihDDgHeBhoBfw21VSSJElaK6y2TMYYb6y9+CywcbpxJEmStDaps0yGEE5d1Q1jjJet+TiSJElam6xqz2Tj/ZMQkiRJWiPqLJMxxnMLGUSSJElrn/qczS1JkiR9LcukJEmSErNMSpIkKTHP5pYkSVJi9Tmbe3NgO2o+sBzgR8DLaYaSJEnS2mG1Z3OHEJ4Dto0xLqr9+vfAowVJJ0mSpAatPu+ZXB+o+tLXVbXLJEmSlHP1+dvctwIvhxAeqP16f+CW1BJJkiRprVGfv819fghhBPDd2kUDY4yvpRtLkiRJa4P6fjRQC2BhjPEvwIwQQrcUM0mSJGktsdoyGUL4HfBr4De1i5oAt6UZSpIkSWuHEGNc9TeEMBHoBbwaY+xVu+z1GOPWaQYradp51cGktVjIOkAG8vqEXnj5AVlHKLg2pzyw+m+StFZZXlVR54+u+hzmroo1jTMChBBarqlgkiRJWrvVp0wOCyH8DSgNIfwceBK4Md1YkiRJWhvU52zuS0IIuwMLqflrOL+NMT6RejJJkiQ1eKstkyGEi2KMvwae+JplkiRJyrH6HObe/WuW7b2mg0iSJGntU+eeyRDCCcBgoHsI4fUvXdUaGJd2MEmSJDV8qzrMfQcwArgQOPNLyxfFGOelmkqSJElrhToPc8cYF8QYpwF/AebFGKfHGKcDy0MIOxQqoCRJkhqu+rxn8jpg8Ze+Xly7TJIkSTlXnzIZ4pf+TE6McQX1OAtckiRJjV99yuT7IYSTQwhNav/9Ang/7WCSJElq+OpTJo8HvgNUADOAHYBj0wwlSZKktUN9/gLObODQAmSRJEnSWmZVnzP5qxjjxSGEq4D4n9fHGE9ONZkkSZIavFXtmXyr9r8TChFEkiRJa586y2SMcXjtf28pXBxJkiStTVZ1mHs4X3N4+wsxxv6pJJIkSdJaY1WHuS+p/e+BQAfgttqvDwM+TjOUJEmS1g6rOsz9LEAI4dIYY58vXTU8hOD7KCVJklSvz5lsGULY+IsvQgjdgJbpRWoYbr3lKia/+RwTX3uKG66/lJKSxv9Hf7p23YBxY4czdcpY7rj9Opo0aZJ1pNTlceYv/OEPv2by5DG8/voznDhkUNZxUtcY1/Vtr01nwG3jOOi2cZw58nU+W17NSx/O5bA7X+THd7zAwHvG80HlUgBeqZjPYXe+SJ+rnuSJdxv3waXBJxzN1CljWV5VQXl5WdZxCiKPM0M+526Ir2X1KZOnAM+EEJ4JITwLPA38MtVUDcCddz7AFlvuwja9dqN582YcM+gnWUdK3YUXnMUVV95Aj559mT9/AYMGHpZ1pNTlcWaAo448hA26dGLLLXdh6637cfewh7KOlLrGtq5nL/6UOyd9wO2H7sC9P/0OK1bA4+98zAVPT+X8Pbfk7p/sxN6bd+DG8TV/sKxj62acu/sW7LV5h4yTp2/cC+PZc+9DmTbtw6yjFEweZ4Z8zt0QX8tWWyZjjCOBTYFfACcDm8cYH087WNZGjBy98vL48RPp0qVjhmkKY9d+O3PffY8CMHToPezXf8+ME6UvjzMDHHfckZx3/uXEWHOO3Zw5czNOlL7GuK6rV0Q+W76C5StW8Onyatq3XIcALKmqBmDRZ8tp33IdADq1ac5m67amKGQYuEAmTpzM9Okzso5RUHmcGfI5d0N8LVvtsdsQQgvgVGCjGOPPQwibhhA2jzE+Ut8HCSH0BbYH3owxjkoet/BKSko4/PABnHrqb7OOkqry8jIqKxdQXV3zQ2hGxUw6dW7cezDyOPMXNt64Kwcf3J/999uLOXPmcsqpv+W99/6VdazUNMZ1vV6rZhy5bVf2/vsY1ikuYqeNytlpo3J+u1tPTnr4NdYpLqJl0xJuPWT7rKNKWkMa6mtZfQ5z/x2oAnaq/boCOG9VNwghvPylyz8HrgZaA78LIZyZLGo2rr7qAsaMeYmxz7+8+m+W1hLrrNOUTz/9jB13+iE33XwHN1x/adaR9A0t/PRznnl/No8c1ZdRx+zCss+reXTqTG6f+AFX9e/F48fswn49O3HpmLezjiqpkatPmeweY7wY+BwgxrgUWN2Bki+/G/RYYPcY47nAHsDhdd0ohHBsCGFCCGHCihVL6hEtXeecfQrt25dz+hm/zzpK6ubOnU9paVuKi4sB6NK5Ix9VzMo4VbryOPMXZlTM5MEHHwPgwQdHsNVW38o4Uboa47p+6cN5dGrTnHYtmtKkuIjvd1+PiTMreWfOIrbq0BaAPTZbn0kzF2ScVNKa0lBfy+pTJqtCCM2p/QDzEEJ34LPV3W8IoSyEUA6EGOMcgBjjEmB5XTeKMV4fY+wTY+xTVJTtCeODBh7GHrv34/CfDln5vrLG7plnxzFgwD4AHHHEwTw8fK16R0IieZwZ4OGHR9Lve98BYJddduLdd9/POFH6Gtu67tC6GW/MWsCyz6uJMfLyh/PYuF1LFlctZ/r8ml/GX/xgHt3aNfoP35BypSG+loXVFaUQwu7A2UBPYBSwM3B0jPGZVdxmGrCCmj2YEdg5xjgzhNAKGBtj3GZ1wUqads60wX26dDrTp89g0eKaF+UHH3yM886/IstIqevWbUPuuO1ayspKmThpMkcedRJVVVVZx0pVVjNnfQ5E27ZtuPWWq9lww04sXryUISeeyeuvT0n1MbP+lSyrdb3w8gNSu+/rXvwno96dRXEI9Gjfht/u1pOx0z/huhf/SQjQZp0m/P4HPenStgWTP17AqY9MYuFnn7NOSTHlLZpy30+/k0quNqc8kMr91teJQwZx+mmD6dChPbNnf8KIkaM57vgzMs2UtjzODPmcO6vXsuVVFXX+6FplmQwhFAEHAU8BO1LzM/DFGOMnSYLUnsyzfoxxte/0z7pMSmnKukxmIa9P6DTLZEOVdZmUtOatqkyu8mzuGOOKEMKvYozDgEf/1yC177dsvKeMSpIk5Ux93jP5ZAjh9BDCBiGEdl/8Sz2ZJEmSGrz6/I3AH9f+d8iXlkVg46/5XkmSJOXIastkjLFbIYJIkiRp7VOfv4DTDBgM9KVmj+QY4K8xxk9TziZJkqQGrj6HuW8FFgFX1X79E2AocHBaoSRJkrR2qE+Z3DLG2PNLXz8dQkj3A+kkSZK0VqjP2dyvhhB2/OKLEMIOwIT0IkmSJGltUZ89k72BcSGED2q/3hB4O4TwBhBjjFunlk6SJEkNWn3K5F6pp5AkSdJaqT4fDTS9EEEkSZK09qnPeyYlSZKkr2WZlCRJUmKWSUmSJCVmmZQkSVJilklJkiQlZpmUJElSYpZJSZIkJWaZlCRJUmKWSUmSJCUWYoxZZ/haTZp2bpjBUpS7gSU1SotuOy7rCAXX+qd/yzqClKrlVRWhruvcMylJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJN1eHr0/UwYP4oJ40cxfdor3HvvTVlHKpjLL/sDlfPeyTpGQXTtugHjxg5n6pSx3HH7dTRp0iTrSAUx+ISjmTplLMurKigvL8s6TkHcestVTH7zOSa+9hQ3XH8pJSUlWUcqqMbyvL79hakMuOoRDrzyEW4bNxWAa56cxMFXP8oh1zzG8f94itkLlwKwcNlnnHLHsxx89aMc/teRvPdxZYbJ0+VrWX5eyxriurZM1mHX7x9In+32oM92e/DiS6/w4IMjso5UEL233ZqystKsYxTMhRecxRVX3kCPnn2ZP38BgwYelnWkghj3wnj23PtQpk37MOsoBXPnnQ+wxZa7sE2v3WjevBnHDPpJ1pEKprE8r9/7uJL7J7zHbcftxbAhP2TM2xV8MHcRR/XtyT0n7sOwIT9kl807c/0zbwBw47OT2bxDGfecuA/nDdiJix+bkPEE6fG1LD+vZQ1xXVsmV6N161bs2m9nHnpoZNZRUldUVMRFfzqHM39zXtZRCmbXfjtz332PAjB06D3s13/PjBMVxsSJk5k+fUbWMQpqxMjRKy+PHz+RLl06ZpimcBrT8/r9OQvYqsu6NG9aQklxEb27rsdTUz6gVbN/75lZVrWcQFj5/dtv3AGAbu3b8tH8JcxdvCyT7GnztSw/GuK6TqVMhhB2CCG0qb3cPIRwbghheAjhohBC2zQeMy377bcXo59+nkWLFmcdJXVDBg9k+COjmDVrdtZRCqK8vIzKygVUV1cDMKNiJp06d8g4ldJWUlLC4YcP4PHHn846SkE0puf1JuuV8ur02VQu/YxlVcsZ++5HfLyg5pD2VU9MZM8/P8Bjr0/jhN22BmCzDmU8NaVmj9UbMz5h5oIlK7+/MfG1LD8a6rpOa8/kzcAXz9i/AG2Bi2qX/T2lx0zFjw/Zj7vvfjDrGKnr2HF9DhqwL1dfc3PWUaRUXX3VBYwZ8xJjn3856yipa2zP643Xa8vA7/bkhFtGM+TW0WzeoYyiopq9kCftvg2Pn3EAP9y6K3e9WPPe0EHf3YJFn1ZxyDWPcdeLb7N5x39/v6Q1J613oBfFGJfXXu4TY9y29vLYEMLEum4UQjgWOBagqLgtRUUtU4pXP+XlZWy3XS8OOvhnmeYohF7bbEn37l15+63nAWjRojlTp4ylR8++GSdLz9y58yktbUtxcTHV1dV06dyRjypmZR1LKTrn7FNo376cEwY3/uc0NM7n9QG9N+GA3psAcOUTE1m/TYuvXP/Db3fjxKFPM3i3rWnVrAl/OHAnAGKM/PCyh+hS1rrgmdPma1l+NNR1ndaeyTdDCANrL08KIfQBCCFsBnxe141ijNfHGPvEGPtkXSQBBhy4L4899iSfffZZ1lFS99iIp+iyYS822WxHNtlsR5YuXbZW/8Cpr2eeHceAAfsAcMQRB/Pw8FEZJ1JaBg08jD1278fhPx1CjDHrOAXRGJ/X8xZ/CsDMyiWMnvIhe2/dlelzF668/pmpM+i2bhsAFi6r4vPlNYcD73/ln/TeaL2vvL+yMfG1LD8a4roOabyo1r4v8i/Ad4FPgG2BD2v/nRxjnLS6+2jStHPmr/ZPPnEPF//5GkaNeqYgj5f5wF9SOe8dStttlnWM1HXrtiF33HYtZWWlTJw0mSOPOomqqqqsY6XuxCGDOP20wXTo0J7Zsz9hxMjRHHf8GVnHStWnS6czffoMFi1eAsCDDz7GeedfkW2oAivU83rRbceldt8DbxzFgqWfUVJUxGl792aH7h047c7nmPbJQopCoGNpS87qvz3rt2nBpA/mcM79LxCA7uuV8vsDdqBN83VSydX6p39L5X7ry9ey/LyWZbWul1dV1PkekVTK5Mo7rzkJpxs1h9NnxBg/ru9tG0KZLLTcDSypUUqzTDZUWZdJKW2rKpOpfmpvjHEhsNq9kJIkSVo7+TmTkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSizEGLPO8LVKmnZumMEkSfoPi5+9JOsImWj1vdOzjqACWV5VEeq6zj2TkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyWYeuXTdg3NjhTJ0yljtuv44mTZpkHSl11//tEl6Z8ASvvvIEd991PS1btsg6UsFcftkfqJz3TtYxCiaP23ceZ4Z8zt0YZv7tTQ/S76SLOfCsa1YuW7B4Kcf9+VZ+9OsrOe7Pt7JwyTIAHh33OgedfS0Dzr6WI8+7kbc/mLXyNs+//i79z7yKfX/1F256ZEzB50jb4BOOZuqUsSyvqqC8vCzrOAXRELdvy2QdLrzgLK648gZ69OzL/PkLGDTwsKwjpe60039P7z67s23v3fnwgwqGDB6YdaSC6L3t1pSVlWYdo6DyuH3ncWbI59yNYeb9+m7Ddaf99CvLbn50LNt/qxvDLzqZ7b/VjZseHQtA5/al3Pybgdx33mCO7f89/vCP4QBUr1jBBUMf49pTD+eBC4Yw8qU3+WfF7ILPkqZxL4xnz70PZdq0D7OOUjANcfu2TNZh1347c999jwIwdOg97Nd/z4wTpW/RosUrLzdr3owYY4ZpCqOoqIiL/nQOZ/7mvKyjFFQet+88zgz5nLsxzNx78660adn8K8uefu1t+vfdBoD+fbfh6VenArDNphuu/N6tu3fh43kLAXjz/Qo2WL8dXdZrR5OSEvbaYUueee3twg1RABMnTmb69BlZxyiohrh9p1ImQwgnhxA2SOO+C6G8vIzKygVUV1cDMKNiJp06d8g4VWHceMNlVHw4kR6bb8LV19ycdZzUDRk8kOGPjGLWrMb12/qq5HH7zuPMkM+5G/PM8xYspn1pawDWbduKeQsW/9f3PPDcq/TdehMAZs9fSId2bVZet15ZGz6ev7AwYZWKhrp9p7Vn8o/ASyGEMSGEwSGE9vW5UQjh2BDChBDChBUrlqQUTavys5+fygYbbctbU9/lkIP7Zx0nVR07rs9BA/bNRWmW1LiEECCEryx7+a1/8cBzr/HLQ3bPKJXyKq0y+T7QhZpS2RuYEkIYGUI4KoTQuq4bxRivjzH2iTH2KSpqmVK01Zs7dz6lpW0pLi4GoEvnjnxUMWs1t2o8VqxYwbBhD3HgAftkHSVVvbbZku7du/L2W8/z3jsv0qJFc6ZOGZt1rNTlcfvO48yQz7kb88zt2rZiTuUiAOZULqJdm3//nHznw1mce/PDXPGLQyltVXPy5HplbZg17997ImfPX8j6ZW3Q2quhbt9plckYY1wRYxwVYzwG6ARcC+xFTdFs8J55dhwDBtSUqSOOOJiHh4/KOFH6unfvuvLyj/bdg7fffi+7MAXw2Iin6LJhLzbZbEc22WxHli5dRo+efbOOVRB53L7zODPkc+7GOnO/bTbn4bETAXh47ER27bU5ADPnVnLqVXdz/rEH0LXDuiu/f4tunfjg47nMmDOfz5cvZ+RLb/K92tto7dUQt++QxkkWIYTXYoy96riuRYxx6eruo6Rp50zP/ujWbUPuuO1ayspKmThpMkcedRJVVVVZRkpVCIFnn36A1m1aEULg9denMOTE33zlpJzGrnLeO5S22yzrGAWRt+0b8jkz5HPuLGZe/Owla/T+fn3dvUyYOo3KxUtp16YlJ+y/K9/v3YMzrrmHWfMW0LG8LX8efDBtW7Xg9zc/xJMT3qJTeVsAiouLuPP3xwEwZtI7XHzHSFasiOz/3V78vP8uazRnq++dvkbv75s6ccggTj9tMB06tGf27E8YMXI0xx1/RqaZ0pbVc3p5VUWo67q0yuRmMcb/6UP7si6TkiTV15ouk2uLrMukCmdVZTKVw9z/a5GUJEnS2sHPmZQkSVJilklJkiQlZpmUJElSYpZJSZIkJWaZlCRJUmKWSUmSJCVmmZQkSVJilklJkiQlZpmUJElSYpZJSZIkJWaZlCRJUmKWSUmSJCVmmZQkSVJilklJkiQlZpmUJElSYpZJSZIkJWaZlCRJUmKWSUmSJCUWYoxZZ/haJU07N8xgkiQJgEX3npJ1hIJrfdDlWUfIxPKqilDXde6ZlCRJUmKWSUmSJCVmmZQkSVJilklJkiQlZpmUJElSYpZJSZIkJWaZlCRJUmKWSUmSJCVmmZQkSVJilklJkiQlZpmUJElSYpZJSZIkJWaZlCRJUmKWSUmSJCVmmZQkSVJilklJkiQlZpmUJElSYpZJSZIkJWaZlCRJUmKWSUmSJCVmmazDrbdcxeQ3n2Pia09xw/WXUlJSknWk1HXtugHjxg5n6pSx3HH7dTRp0iTrSKnL43qGfM6dx+178AlHM3XKWJZXVVBeXpZ1nILJ47pujM/p28e8yYBL7uXAS+7htjFvrFx+59g32f/iYRx4yT1c/shLAFQu+ZSf/fURdjrr71z4wPNZRS6oyy/7A5Xz3sk6BmCZrNOddz7AFlvuwja9dqN582YcM+gnWUdK3YUXnMUVV95Aj559mT9/AYMGHpZ1pNTlcT1DPufO4/Y97oXx7Ln3oUyb9mHWUQoqj+u6sT2n35s1j/tfmsptJ+/PsFMGMGbKB3zwyQLGv/cRz0yezrBTB3D/6QdzVL+tAVinSTFD9uzDqfvukHHywui97daUlZVmHWOlVMpkCKFpCOHIEMIPar/+SQjh6hDCkBDCWvEr4oiRo1deHj9+Il26dMwwTWHs2m9n7rvvUQCGDr2H/frvmXGi9OVxPUM+587j9j1x4mSmT5+RdYyCy+O6bmzP6fc/rmSrDdvTvGkJJcVF9N64I0+9MY1hL0xh4K7b0LSkGIB2rZoD0LxpE3p160DTRrBHdnWKioq46E/ncOZvzss6ykpp7Zn8O7AP8IsQwlDgYOAlYDvgxpQeMxUlJSUcfvgAHn/86ayjpKq8vIzKygVUV1cDMKNiJp06d8g4VeHkZT3/p7zMnfftO0/yvq4by3N6kw5lvPqvWVQu+ZRlVcsZO/VDPl6wmOlzFvDqv2bx0ysf5JjrhvPmh3OyjlpwQwYPZPgjo5g1a3bWUVZKq8JvFWPcOoRQAlQAnWKM1SGE24BJdd0ohHAscCxAKG5LUVHLlOLV39VXXcCYMS8x9vmXs46iFOV1Ped1bqmxaizP6Y3XL2Pgrt/mhBtG0LxpCZt3KqcoFFG9IrJw2acMPWk/3vxwDr8a+iSP/uZQQghZRy6Ijh3X56AB+/L9HxyUdZSvSKtMFoUQmgItgRZAW2AesA5Q52HuGOP1wPUAJU07x5Sy1ds5Z59C+/blnDD4Z1lHSd3cufMpLW1LcXEx1dXVdOnckY8qZmUdqyDytJ6/LE9z53n7zps8r+vG9pw+YPseHLB9DwCuHDGe9du2ZNrsSnbbshshBLbacD2KQmD+kk9XHu5u7HptsyXdu3fl7bdqTjJq0aI5U6eMpUfPvpnmSusw903AVGAicBZwTwjhBmA8cFdKj7lGDRp4GHvs3o/DfzqEGDPvtQXxzLPjGDBgHwCOOOJgHh4+KuNE6cvjeoZ8zp3H7Tuv8riuG+Nzet7iZQDMnL+Y0W/8i717dWfXLTdi/D8/AmD6nEo+r15BWctmWcYsqMdGPEWXDXuxyWY7sslmO7J06bLMiyRASGujCyF0AogxfhRCKAV+AHwQY6zXvves90x+unQ606fPYNHiJQA8+OBjnHf+FVlGSl23bhtyx23XUlZWysRJkznyqJOoqqrKOlaq8rieIZ9z53H7PnHIIE4/bTAdOrRn9uxPGDFyNMcdf0bWsVKXx3Wd1XN60b2npHbfA699mAVLPqOkuIjTfrQjO2zamc+XV/O7Yc/x9kdzaVJSxKn77sD2m3QGYO8L7mTJp5/zeXU1rZuvw3U/35vu66/5j8RqfdDla/w+k6qc9w6l7TYryGMtr6qo870EqZXJ/1XWZVKSJK1ammWyoWpIZbKQVlUm/ZxJSZIkJWaZlCRJUmKWSUmSJCVmmZQkSVJilklJkiQlZpmUJElSYpZJSZIkJWaZlCRJUmKWSUmSJCVmmZQkSVJilklJkiQlZpmUJElSYpZJSZIkJWaZlCRJUmKWSUmSJCVmmZQkSVJilklJkiQlZpmUJElSYpZJSZIkJVaSdQBJktZ2IesAGWl90OVZRyi4RSN+l3WEBsc9k5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJN16Np1A8aNHc7UKWO54/braNKkSdaRUnf93y7hlQlP8OorT3D3XdfTsmWLrCOlLo/rGfI59623XMXkN59j4mtPccP1l1JSUpJ1pIK6/LI/UDnvnaxjFEQet++bbrycd95+gQnjRzFh/Ci+/e0tso5UEINPOJqpU8ayvKqC8vKyrOP8z24f/SoD/vgPDvzjP7ht9CsAvD1jNkf++Q4OOu8WTr72ARYv++wrt5k5byE7nXIltzwxPovIgGWyThdecBZXXHkDPXr2Zf78BQwaeFjWkVJ32um/p3ef3dm29+58+EEFQwYPzDpS6vK4niGfc9955wNsseUubNNrN5o3b8Yxg36SdaSC6b3t1pSVlWYdo2DyuH0DnPmb8+iz3R702W4PJk2anHWcghj3wnj23PtQpk37MOso/7P3PvqE+59/ndt+fTjD/u9IxrzxPh/Mns+5t43i5P2+y71nH8X3t9mEW56c8JXbXXrfM+zcs1tGqWukViZDCBuHEE4PIfwlhHBZCOH4EEKbtB5vTdu1387cd9+jAAwdeg/79d8z40TpW7Ro8crLzZo3I8aYYZrCyON6hnzOPWLk6JWXx4+fSJcuHTNMUzhFRUVc9KdzOPM352UdpWDyuH3n1cSJk5k+fUbWMdaI92fNZauuHWnetAklxUX03rQLT018lw9mz6f3pl0A2LHHRjz12r+PMIye+C6dytvSvWN5VrGBlMpkCOFk4K9AM2A7YB1gA+DFEEK/NB5zTSovL6OycgHV1dUAzKiYSafOHTJOVRg33nAZFR9OpMfmm3D1NTdnHSdVeV3PeZ37CyUlJRx++AAef/zprKMUxJDBAxn+yChmzZqddZSCyPP2/Yc//JpXX3mCS/78e5o2bZp1HH1Dm3Rcl1f/WUHl4mUsq/qcsZP/xcfzF7Fxx3KenvQeAE+89g6z5i8CYOmnVfzjifEc/8OdsowNpLdn8ufA3jHG84AfAFvEGM8C9gIur+tGIYRjQwgTQggTVqxYklI0rcrPfn4qG2y0LW9NfZdDDu6fdRxpjbv6qgsYM+Ylxj7/ctZRUtex4/ocNGDfRv+LoeCssy9kyy13Yced9qFdu1LOOGNw1pH0DW3csZyBu2/HCVfdy5Cr72PzLutRVFTEuUfsybDnJnHYhUNZ8mkVTUqKAfjro+M4/Pu9adEs+18c0nwHeglQTc1eyVYAMcYPQgh1vhM6xng9cD1ASdPOmR1jnTt3PqWlbSkuLqa6upounTvyUcWsrOIU3IoVKxg27CFOP20wt9w6LOs4qcnres7r3ADnnH0K7duXc8Lgn2UdpSB6bbMl3bt35e23ngegRYvmTJ0ylh49+2acLD153b6/2PNcVVXFP265m1NPOT7jRErigJ234oCdtwLgyofGsH5pa7p1KOevJx8EwPSP5zHmzX8B8Ma0WTzx2rtc8cBzLFr2GUUhsE6TEg7t16vgudPaM3kjMD6EcAPwAnANQAihPTAvpcdco555dhwDBuwDwBFHHMzDw0dlnCh93bt3XXn5R/vuwdtvv5ddmALJ43qGfM49aOBh7LF7Pw7/6ZBcvB8Y4LERT9Flw15sstmObLLZjixduqxRF8kv5HH77tBhvZWX9+u/F5OnTM0wjZKat2gpUHOG9uiJ77L3dj1WLluxInLDiJc4+LtbA/D30w5lxHk/Z8R5P+fwXbflmD23z6RIAoS0XlRDCFsA3wLejDF+4606yz2TAN26bcgdt11LWVkpEydN5sijTqKqqirLSKkKIfDs0w/Quk0rQgi8/voUhpz4m6+clNMY5W09fyGPc3+6dDrTp89g0eKat9A8+OBjnHf+FdmGKrDKee9Q2m6zrGOkLovtO6R676s36vFhtG/fDkLg9UmTGTzkTJYsWZr642b9a9mJQwZx+mmD6dChPbNnf8KIkaM57vgzUn3MRSN+l9p9D7z0LhYsWUZJcTGnDfgeO/TYiNtHv8rdz00EYLdtNuHk/b5LCF/d4q57ZBwt1mnCUbtvl1q25rsdW+dmnlqZ/F9lXSYlSaqvrMtkVvL4gzrNMtmQrapM+jmTkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMQsk5IkSUrMMilJkqTELJOSJElKzDIpSZKkxCyTkiRJSswyKUmSpMRCjDHrDA1OCOHYGOP1WecopDzODPmc25nzI49z53FmyOfcztxwuGfy6x2bdYAM5HFmyOfczpwfeZw7jzNDPud25gbCMilJkqTELJOSJElKzDL59Rrc+xEKII8zQz7ndub8yOPceZwZ8jm3MzcQnoAjSZKkxNwzKUmSpMQsk18SQtgrhPB2COG9EMKZWecphBDCzSGE2SGEN7POUighhA1CCE+HEKaEECaHEH6RdaZCCCE0CyG8HEKYVDv3uVlnKpQQQnEI4bUQwiNZZymEEMK0EMIbIYSJIYQJWecplBBCaQjh3hDC1BDCWyGEnbLOlKYQwua16/iLfwtDCL/MOlfaQgin1L6GvRlCuDOE0CzrTIUQQvhF7cyTG9p69jB3rRBCMfAOsDswAxgPHBZjnJJpsJSFEHYBFgO3xhi3zDpPIYQQOgIdY4yvhhBaA68A++dgXQegZYxxcQihCTAW+EWM8cWMo6UuhHAq0AdoE2PcN+s8aQshTAP6xBg/yTpLIYUQbgHGxBhvDCE0BVrEGCszjlUQtT/DKoAdYozTs86TlhBCZ2peu3rGGJeFEIYBj8UY/5FtsnSFELYE7gK2B6qAkcDxMcb3Mg1Wyz2T/7Y98F6M8f0YYxU1K22/jDOlLsb4HDAv6xyFFGOcGWN8tfbyIuAtoHO2qdIXayyu/bJJ7b9G/9tkCKELsA9wY9ZZlJ4QQltgF+AmgBhjVV6KZK3dgH825iL5JSVA8xBCCdAC+CjjPIXwLeClGOPSGONy4FngwIwzrWSZ/LfOwIdf+noGOSgYeRdC6Ar0Al7KOEpB1B7unQjMBp6IMeZh7iuAXwErMs5RSBEYFUJ4JYTQID/kOAXdgDnA32vf0nBjCKFl1qEK6FDgzqxDpC3GWAFcAnwAzAQWxBhHZZuqIN4EvhtCKA8htAB+CGyQcaaVLJPKrRBCK+A+4JcxxoVZ5ymEGGN1jHEboAuwfe2hk0YrhLAvMDvG+ErWWQqsb4xxW2BvYEjt21kauxJgW+C6GGMvYAmQl/e+NwX6A/dknSVtIYQyao4adgM6AS1DCD/NNlX6YoxvARcBo6g5xD0RqM4y05dZJv+tgq+2/C61y9QI1b5n8D7g9hjj/VnnKbTaw39PA3tlHCVtOwP9a99DeBfw/RDCbdlGSl/t3htijLOBB6h5G09jNwOY8aW97fdSUy7zYG/g1Rjjx1kHKYAfAP+KMc6JMX4O3A98J+NMBRFjvCnG2DvGuAswn5rzPBoEy+S/jQc2DSF0q/0t71Dg4YwzKQW1J6LcBLwVY7ws6zyFEkJoH0Iorb3cnJqTzaZmGiplMcbfxBi7xBi7UvOcHh1jbNR7MUIILWtPLKP2MO8e1Bwia9RijLOAD0MIm9cu2g1o1CfVfclh5OAQd60PgB1DCC1qX8t3o+Z9741eCGG92v9uSM37Je/INtG/lWQdoKGIMS4PIZwIPA4UAzfHGCdnHCt1IYQ7gX7AuiGEGcDvYow3ZZsqdTsDRwBv1L5/EOD/YoyPZRepIDoCt9Se9VkEDIsx5uKjcnJmfeCBmp+zlAB3xBhHZhupYE4Cbq/dIfA+MDDjPKmr/YVhd+C4rLMUQozxpRDCvcCrwHLgNRroX4VJwX0hhHLgc2BIQzrBzI8GkiRJUmIe5pYkSVJilklJkiQlZpmUJElSYpZJSZIkJWaZlCRJUmKWSUmqQwihXwjhf/pA5BDC4m+y/EvXdw0hfKPPhwwh/COEcNA3uY0k/a8sk5JUt37k5K9rSFJSlklJuRJCeDCE8EoIYXII4dgvLd8rhPBqCGFSCOGpEEJX4HjglBDCxBDCd/9zz98XexdDCK1qb/NqCOGNEMJ+3yDPqm5bEkK4PYTwVgjh3hBCi9rb9A4hPFs7x+MhhI7/6/8XSUrKDy2XlCshhHYxxnm1f1JyPPA9an6xfhXYJcb4ry99z++BxTHGS2pv+w/gkRjjvbVfL44xtgohlAAtYowLQwjrAi8Cm8YY4xff8zU5VnlbYCPgX0DfGOPzIYSbqfnzgH8BngX2izHOCSH8GNgzxjjoP/NJUiH45xQl5c3JIYQDai9vQE1xaw88F2P8F0CMcd43vM8AXBBC2AVYAXSm5s8azvofbgvwYYzx+drLtwEnAyOBLYEnav9kYjEw8xvmlaQ1xjIpKTdCCP2AHwA7xRiXhhCeAZp9g7tYTu3bg0IIRUDT2uWHU1NIe8cYPw8hTPsG97uq2/7noaNITfmcHGPc6RvklqTU+J5JSXnSFphfWyR7ADvWLn8R2CWE0A1qDoXXLl8EtP7S7acBvWsv9weafOl+Z9eWwV2pOUT9TTLVddsNQwhflMafAGOBt4H2XywPITQJIWzxDR5PktYoy6SkPBlJzUktbwF/oqZEEmOcAxwL3B9CmATcXfv9w4EDvjgBB7gB+F7t9+wELKn9vtuBPiGEN4AjganfINOqbvs2MKQ2bxlwXYyxCjgIuKg2x0Q841xShjwBR5IkSYm5Z1KSJEmJWSYlSZKUmGVSkiRJiVkmJUmSlJhlUpIkSYlZJiVJkpSYZVKSJEmJWSYlSZKU2P8DrC/QBW5rFWkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 792x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_cm_mnist(lab_test, results, range(nb_classe))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Effectivement, il y a assez peu d'erreurs hors diagonale."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# D/ Bonus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Faire reconnaître des images originales à un réseau entraîné avec les images MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plusieurs possibilités :\n",
    "- utiliser des images crées pour l'occasion... \n",
    "- utiliser les images toutes prêtes du répertoire `chiffres`.\n",
    "\n",
    "Si tu crées tes propres images de chiffres écrits à la main, il faut :\n",
    "- les mettre au format MNIST (20x28 pixels en ton de gris, chiffre centré dans l'image) \n",
    "- les placer dans un répertoire spécifique ,\n",
    "- affecter ce nom de ce répertoire à `images_dir` dans la cellule ci-dessous :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Images du dossier <chiffres> à reconnaître :\n",
      "chiffres/0.png\n",
      "chiffres/1.png\n",
      "chiffres/2.png\n",
      "chiffres/3.png\n",
      "chiffres/4.png\n",
      "chiffres/5.png\n",
      "chiffres/6.png\n",
      "chiffres/7.png\n",
      "chiffres/8.png\n",
      "chiffres/9.png\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# changer le nom du répertoire au besoin :\n",
    "images_dir = \"chiffres\"\n",
    "\n",
    "images = [os.path.join(images_dir,f) for f in os.listdir(images_dir) if f.endswith(\".png\")]\n",
    "images.sort()\n",
    "\n",
    "print(f\"Images du dossier <{images_dir}> à reconnaître :\")\n",
    "for im in images: print(im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lecture des fichiers image avec openCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les images doivent être convertie en image en ton de gris de 28 x 28 pixels pour pouvoir être traitées par le réseau entraîné sur les images MNIST.\n",
    "\n",
    "Plusieurs fonctions du module OpenCV pourront être utilisées :\n",
    "- `cv2.imread(file_name)` : pour lire un fichier image aux formats standards (PNG, JPG,...)\n",
    "- `cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)` : pour convertir le tableau `img` renvoyé par `cv2.imread` en tons de gris\n",
    "- `cv2.resize` : pour retailler l'image.\n",
    "\n",
    "La cellule ci-dessous montre un exemple de lecture et traitement avec OpenCV des images du dossier `chiffres` qui sont déjà au format 28 x 28 pixe :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "my_images = []\n",
    "for image_path in images:\n",
    "    img = cv2.imread(image_path)                    # lecture fichier image\n",
    "    img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) # conversion en tons de gris\n",
    "    my_images.append(img_gray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualisation des images lues :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_images(my_images, 0, 1, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inversion des images pour avoir des chiffres doivent être en blanc sur fond noir :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj8AAABBCAYAAAA+G8WvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAxGUlEQVR4nO2deXRUdZbHP6/2VKUqSVX2fTFhCyGsCiLoAR0ZXBAV3LoHd51Rp0dHHW23bnta1OkRV6Btj44L9qhtK6KCGyCILIGEJCwhK5A9la0qS+01fzDvCcqSQCVVFd7nHI5IKpXfy3v13v3d+73fK/j9fmRkZGRkZGRkzhYUwV6AjIyMjIyMjMxwIgc/MjIyMjIyMmcVcvAjIyMjIyMjc1YhBz8yMjIyMjIyZxVy8CMjIyMjIyNzViEHPzIyMjIyMjJnFarBvFgQhLDui/f7/cLJvh7uxwdY/X5/3MleEO7HeKpzCPIxhgPyZzH8j1G+To8w0o8x3I+PE3wW5czPyOJgsBcgIyMDyJ9FGZlQ4bifxUFlfmTOnLS0NC688EJ27NjB/v37g72cM8ZoNBITE0NbWxv9/f3BXo6MjIyMTIihVqtRqVQYjUYUCgVKpRKfz4fP56O3txefz4ff78ftduPxeIZlTXLwM4woFAqSkpK49NJLaW1tDevgR6FQoNVqsVgspKam4na7pa95vV5cLlcQVxcYBEFAq9UiCAKCICC6obtcLunDOpIRz7HH48Hr9eLz+YK9JJkzRKFQoFAoUKvVwJFr3Ol04vV6g7wymZGKIAio1WoiIiKIj49HpVKhUqnw+Xx4vV6sVqt0f+nr68PhcOD1eof8/ioHP8OERqNh/PjxTJ48mYKCAtLS0oiOjsZut4fljScvL4+VK1ei0WjweDwolUr8fj+9vb388MMP/O53vwv2Es+YnJwcnnnmGcxmM0ajEY/HQ2dnJ8uWLaO2tpaqqqpgL3FIOe+883jiiSdYt24d27ZtY/fu3fT29gZ7WTKniUKhYNKkSeTm5vKrX/0Ko9FIREQE999/P99//32wlyczAlGr1Wg0GnJzc0lLS+OSSy4hMjISg8FAb28vDoeDjo4OKfipra2lsbGRkpISbDYbLpdryIKgYQt+9Ho9Wq0Wm82GIAhERUVhNBqJjo7G7/fj9/sRhJ90V+LfxQM/+v/dbjcul4u6urphS5GdKSqViry8PHJycqRjj4yMpLe3N6yCH0EQyM3NZfTo0RiNRlwuF729veh0OpRKJRqNhtjYWCZPnsyhQ4doa2sL9pIHhdFoxGAwYDabGTNmDJMmTQI4Zjcybtw4oqKiMBgMNDQ0YLVag7zqoSEqKoqJEyeya9cuOesTpiiVSiIiIoiNjcVisVBYWEhaWhoGg0HafcvnNnRRq9WMGjUKlUr1i2ekyNHPSI/Hw/79+4P+XBQEAYVCgcFgIDIyktTUVNLT08nJyZGefTabDYfDQWxsLHDkGanVatHpdBw+fBiAjo6OIcuyD1vwM3r0aLKzs/n2229RKBRceumlzJ49m8suuwy3243P50OhOKK/FlOzCoVCOoliZsHn89HS0sKhQ4e4/fbbaW9vH65DOCMiIyO58847SU1NRa/XEx8fT3p6Op2dnceUjEIdnU7HCy+8gNFo5Omnn6a6upoDBw4ARz6oGRkZXHTRRaxfv54HH3yQlStXBnnFg2Py5MlMnTqVa6+9lpSUFBITE/n0009ZuXIlOp0Os9nM/fffT1RUFH6/nyeffJK33nor2MseEtRqNdHR0TQ2NrJ9+3b5IRmGREVFkZeXx+233878+fNRKBTU1tby5JNPUldXx8GDB3E6ncFepswJsFgsrFq1iri4ONxut1QygiNBj8fjkQJYrVZLa2srF198MS0tLUFdt0KhQKfTkZ2dTUZGBueffz4pKSlkZGRIAVF7eztOp1P6t6SkJLKysqisrMThcFBXV8euXbtwOBxD8owc8uAnMzOTefPmkZGRQWxsLBkZGQCMGjWK7OxsvF4vVVVVtLW1UVtbKwU7CoUCQRCkrIgYGAFccMEF5OTkcOutt1JWVsaXX3451IdxRsTExBAfH49Wq0WpVOL1eunq6qKxsTGsAp/zzjuPgoICfvzxR3p6eqioqMBqteJwOIAjWpiWlhY6OzslnUy4kZmZyaRJk9i3bx+lpaX09PRQUlJCdXU1KpWKyMhIVq1ahclkwmAwkJiYyM0338zq1avDJhA/FXq9noULF5KVlcXnn39OdXW1HPiAdIM+OlvidDppbm4OOf2XmDHIzMyU7pdut5svvviC/fv3U1VVRUdHR9g1KUyYMIGZM2dKWVjxPBz9fFAoFFitVj7++OOwyqr/nMsuu4yxY8fS2NhIZWUle/fuRalUSscqHr/4O8jMzESn03Hdddexb98+vvrqq6CtXafTkZSURE5ODqNGjSI3Nxej0Uhra6u09v7+fqncpdfraW9vx+VykZCQQGZmJoIgYLVaaWtrk74vkAxp8CMIAuPHj2fp0qXSCXO73ZIg1ul0YrPZ2LJlC+Xl5axevfqUH0ZBEPjggw+YPHkyjz/+OGvWrGHdunUhfXNOSEggIyNDUrh7PB5aW1upq6sL9tIGxfz587n++utZsGAB5eXlv/i6z+ejra2N9vZ2+vr6wiqwE8nJyWHGjBk89dRTlJWVHbfkU1RUJGXvHnjgAe644w527NgxYoKfqKgoHn74YRoaGnj11VfZt29fsJd0xgQiEI+OjqawsFDqSPH5fNhsNlpaWkIu+NFqtUyfPp1JkyZxzTXX4HA46Orq4sUXX2Tv3r3BXt5pM3PmTJYuXYrL5cLr9R5TGYAj51mn01FSUsKaNWvCLrgTEQSBm2++mdmzZ/P222+zY8cO/vrXv570OpszZw75+fncd999bN68mW+++SZoz0W9Xk9mZibjxo1jwoQJjBs3Do/Hw7Zt2+jq6qKzsxO1Wo0gCNjtdnQ6HTExMYwbN460tDTOOeccIiIi6O7uRhCE8Al+8vPzSUtLY/HixWRlZUlqb6/Xy29/+1tqamqkiNXn89HU1ITNZhtQ+tXv9/Pss8+yatUqJk2ahNls5oMPPuCNN94I2QzQVVddxaRJk9iwYQMpKSnMmTPnmJ1KODGYh0g4Zn5UKhUKhYLKykqqqqpOePNwOp20trbicrnQarVhez5/zsMPP8z555+PyWSitLSUHTt20N3dHexlDQqLxUJUVBRqtZqYmBimT59OamoqGRkZqFQqBEGQdp0/72QT71UiHo8Hv98v6dmMRqO0+/Z6vRw4cIAdO3acUXfj0Z2EgWD06NFkZWWxcOFCnE4nK1asoLKykkOHDnHo0KGA/Zxg8Pnnn1NTUyPpRH+uCVUoFBQUFBAZGcmjjz7Kli1bQva5cCpqa2tJS0tj1qxZTJ48mSuvvJI333yTdevWHff1u3fvprq6mvLycnJzc/n44495/fXX+fzzz4d55T9lfjIyMsjKyqKjo4OWlhY2bNhAfX09hw4dks6Zx+PBZDKRnp6Oz+dDpVIxbdo02tvbqampoampCZVKFfAOsIAGPwaDgejoaEkQe8kll6BWq2lqapK6gjZt2nTcrMFgKCoqory8HIfDwQUXXMAtt9zCDz/8wKZNm+jr6wuZLJDYUpqXl8fYsWPZvn07Op1OesCGG6IO62RoNBo0Go1Utgw3xJ281Wqlq6vrhK/zer309fXh9XqlB6NarQ7LbBf8dK1OmzaN2bNnU1dXR1NTE83NzcFe2oARNVmJiYlYLBa0Wi1xcXHMnDmTzMxMcnJypECnt7cXl8uFw+HA5XJJGQRBENBoNFLZVrQ1EMXBbrdb8izxeDxYrdYzus5/bqNwJiiVSlQqFZmZmYwePZqEhARqa2vZtm0b5eXlYZdpPh51dXUnPQ6lUonD4WDUqFEsXrx4SDIGw0VfXx89PT1YLBbMZjPx8fGSOPh4WK1WrFYrhw8fxmQycdlll/HNN98M44p/QqlUYjKZiIqKwmQycfjwYdrb2yWdWU1NzTGvj4qKwuv1MmbMGHp6eoiLi0Or1RIVFUVkZCRarVZqOgkUAQ1+rr/+ep588kl8Pp+Ufvzuu++45557pNcEqjPG4XCwfv16DAYDCoWC6dOn43A4+Oijj0Kmwyg2Npb09HT0ej0KhYIZM2YQGxtLTEwMWq022MsbNKIa/0QBkNjOP2rUKKKiotBoNMO8wjPnlVde4Y033qCjo2NArxc7D8eMGYPH46G0tDRkgu/BEBcXR0ZGBhqNhkOHDnHDDTdQX18f7GUNilmzZrFixQopo6xSqVCr1ZjNZpqbm9m1axdff/01hw4dwuv1YrfbOXz4MDabjb6+vl+83/GCkp933Ljd7pARDCckJJCdnc2NN95IdnY2b731FhUVFWzcuDFsg/LTQalUYjAYGDNmDImJicFezmmTnp6O2WzmhhtukJ6bdrv9pN+jVCpJTU0lPj7+mMzYcKPVaomOjkav16NWq+nv78dms0lVnp/jcDhoaWmhq6uL3t5eqcuroKAAn8+H3W6noqLipBvSwXJGwY9CoSAvL4+oqCiioqI455xzcDqdmEwmAEpKSigtLR2y3aPD4aC1tZWdO3ei0WgoLCwMSorvRCQmJjJ16lQ0Gg19fX2kpKQQERFBa2trWPql7Nmzh7Vr156wDKLVapk5cybZ2dlUV1cH9EIdLux2+ylvMHDkJqPVaqUsQU9PDz09PSGn/RgoSUlJnH/++XR2dko3qXArd3V0dLBjxw6plCWWp7RaLW1tbTQ0NFBSUkJLSws+n4/+/n7a2tro7e0NWgATyOslKSmJGTNmEB0djdPppLKykoMHD0oNCWcDCoWC3NxcEhIS+Pbbb8PSSDYzM5Px48fT1NREV1cX9fX1A/4sarVaZsyYgdls5tNPP/1FhmU4ETu0j+5IO1FAJrbGw09CboVCQVxcnBREiV1ugeKM3k2lUnHjjTeSn59PYWEhBw8eZN26dVx44YXo9Xpee+21Ib/4qqureeGFF7jhhhu48sorefXVV0Omrj1hwgTuuece6uvraW5uZtasWdhsNoqKikImOzUY3n77bd5+++0Tft1kMvHwww9jt9v529/+RnV19TCubnjRarXEx8djNBpRqVRUV1eHtenhlClTuP/++1mxYgXbt28Py+C8qKiIxYsXB3sZgyZQAdDEiRO599572b9/PxUVFWzdupXOzs6AvHe4oFKpWLBgAT6fj4ULF4al4HnevHksXbqU6667btB6JZPJxL//+79TXFzMtddeG/TNmNvtHpBjs1KpRK/XS9UCv9+PVqslMzOTw4cPExkZKYnaA8UZBT9+v5/Dhw/T29vL6tWrmThxIrNnzyY+Pp6enh6qqqpoamoK1FqPiyAI0i8lFNsafT4fX331Fc3NzeTn59Pc3Mx3330XdiWFgeDz+ejq6qK6uprVq1ePCI3Bz1GpVGRkZJCXl8c//uM/4nK5eO+99wZcJgs10tLS+Nd//VcmTpwoefqEgknamaJQKBg7diwAe/fuDctS5EBJS0vjvvvuk87hxo0b2bx583FLeSOZqKgo4uLiiIyMxOFwBP3Bf7oolcpjRPcnIj4+nquvvlrS1YhNRStWrKCqqiqox+/xeOju7pbc/1UqFTqdjsTERJxOp9QZKxoex8bGMnbsWLKzs0lOTpbKZeLXMzMzKSsrC+gazyj4ETu1Ghsb+fLLLzGZTNxzzz309vZitVppamoalvZfsTYfKvOHRL2TUqnE5XKxY8cODhw4gM1mo7W1leLi4rDM/JwKn89HT08PTU1NbN26NdjLCThqtRq9Xk92djaTJ09m4cKFvP/++3z11VfHrWOHA/Hx8dx5551oNBpcLhfNzc2Su2q4InZsjRkzBp/PNyJa9U9GXFycdA7dbjc7d+5kw4YNwV7WsBMdHU1ycrLUiReuiCWg42krRbE9HJFVXHXVVcTFxWE2m9HpdLS2tjJ37tygmxx6PB56enqkLkjx3hkXF0d3dzcajUbqooyOjiYxMZGcnBxSUlKIi4sjIiICQHKJTkxMRKfTBXSNZxT8eL1eaSaM1+vF6XTS2dnJY489xvfffz8sgY/4S+7v7w+ZYZq5ubksW7aM7u5u1q9fj9VqlUydPB4ParU6LLu9ToVSqZRqtCOR+++/n3PPPZeEhAQAKisr2blzJ1u3bqWnpyfIqzs9PB4PXV1dfPHFFyxfvjyoGoFAkZGRQWZmJrfeeit2u501a9aE9cPwVHi9Xjo6Oli7di0rVqwYEefwdFiwYAGXX345f/jDH6ioqAgZIfpgOd5GXtQY3nPPPSxYsACHw0FERASjR4/mo48+4sUXX5QmIoSC31h/fz8NDQ3U1tZKLuOpqan4/X5aWlpobGyUdD1msxmz2Uxubi7JycmYTCZcLheCIGAymVAqldjt9oBno89YQdTd3Y1arSYpKQmTyYTH46GmpoY9e/YEYn2nRK/Xk5GRgdFoDHprtSAIZGdnM378eAoLC9m+fTt79uw55sHo8Xiw2+0hE6gFiszMTNLT06mqqhox5S69Xk9eXp70/wUFBYwePRqbzUZHRweVlZU0NDSEZdZHEARycnJITk6mpKSEXbt2UVJScsrvi4iIICcnh46ODhobG4d+oadBamoqhYWFYW09MBDEc5iWlkZtbS179+4d0DkcaURFRZGVlUVCQsIxHl3hiij4zc7OZsKECcCRcrs4o+3oCeii8/OZ2scEGrfbLXn7NDY2kpWVhVqtJjU1FZPJJB2HGOBERkZisVik5hG1Wi1ZiAAB1/tAgFrdY2NjWbJkCVOnTh32jMaYMWP4/e9/DxB0kaZWq+WZZ56hsLAQs9lMTU0N77zzDl6vl4SEBLRaLb29vezYsSMkynOBQhAEHn30UcaPH8+iRYuCnnINFHl5eXzxxRcolUqcTicqlYru7m6WLVtGVVUVpaWlYauN0Wq1vPDCC/j9fm666aYBf3ZycnL48MMPWbVqFU8//fQQr/L0uOKKK7j55pv54x//SElJyYgNgLRaLf/93/+N0WjkpZdekmbsnW3MmDGDt956i/fff5/nn38+rLypjofYFfXb3/4Wt9uNIAhS8PPYY4/xxBNPSK8Vh5mGGna7nb1796JQKOjo6ECj0ZCcnExOTg4qlUoqs4ulL6fTSUdHB7W1tXR2djJu3DgMBgN9fX0IgkBsbGzA7WECEvwYDAbOO+88srKy0Ol0wx4AKRSKkBG3RUREYLfbWb58ORs3bjzmwvR4PNJ4j5GC0WjEYrEQHR2NRqPB6XSOmKyWQqGQhHeiv5FCoWDWrFmMGjWK8ePH43K56OvrY/369WHT2j9z5kymTp1KdnY2LS0tOJ3OAd9AlUolkZGRIe3h5PP5cLlc1NbWcvDgwZC5NwQS0fQuMjISv99PTU3NiNQRngyDwcB1113H1KlT0ev1dHZ2Ul1dHXblLkEQSElJITY2lilTpnDBBRegUqno7OyktbVVsm9Qq9WUlJSEhXWBOMKqtbUVhUJBcnIyXV1d0hwvg8EgZbBcLhd2u52GhgYaGxvp6urCbDYTGxsrVZM8Ho+khQpU4iAgwY/JZOLCCy/EYDAAQ5OiOhHiLy/QFvGng0KhQKlU0tjYyCOPPHLMRer3+wf1kAkXzGYzo0ePls79SEPsJhSdf/V6Pddeey0ul4v+/n7sdjsdHR3s2bOH7u7uoF+DA+Gqq67irrvuQqlUDtrLR/w9BNpzI5A4HA5sNhuVlZXU1NSccshuOJyzn5OUlEReXh46nY7Ozk4qKirC4qEYSKKionjyySeJj4/H7XbT3NwcltkvpVJJbm4uEyZM4IEHHsBkMklmo7t37+aZZ54JO88tv9+Py+Wivr4eq9WKXq+noaGB/v5+oqOjiYmJAY4ESZ2dnVitVqqrq2lubqanp4ecnBzgiPTA4/FIG2pxPmYgCMgdzOfz4XQ6KS0tZfPmzUGpt27dupWSkpKgtRzfeuutXH755RQWFp7Q30alUg1rYDgcTJgwgdtuu43Vq1ezb9++sMl+DITq6mquu+46KZMpPkDdbrdk2rVkyRIuueQSnnvuOaqqqvj73/9OfX09Bw8eDObSj4vZbCYvLw+LxYLNZuP3v/89ZWVlA8rUifqSUaNGSdmwUEXUCgiCQGZmJo8//jhqtVoSWB49B0qpVJ50XlKoMmfOHBYuXIhGo6GrqyssA7gzxefz0dnZSXl5OS+//HJYDmy99dZbmT9/PgaDgdbWVu666y7JnfyWW25hxowZIZ1lPRUej4e+vj727dtHXV0dlZWVREREoNPp8Pl8eL1eqWFJdHVWqVTH6GLFkp9Op5PGZAXieg/Y9s3j8VBfX8/mzZuHJQARBEH6hcCRmS87d+4MmrfF2LFjufjii+nu7j5uACCm7IItyg4USqVS8l+YOHEiL7/8stT5N1Lo7u4+pcnY7NmzcTqdFBQUYLFYqKioQBAEbDYbPT09IaU3iYyMZMyYMWg0GlpbW1m/fv2ATUgFQcBgMEjfOxAX7GAhlidjY2NJSEjgkksuwe/3Y7fbjzFbE9tvt2zZwu7du7FarWGTmU1NTWXChAns2rWL5ubmERn8qFSqE2YYDQYDZrOZhoYGysrKwm54qVarxWw2M378eKZPn05HRwft7e188803OJ1OBEHg17/+NUlJSWG9YRY3iVarVSrliWNnRCd2h8MhmSGK2fW+vj6cTqc0Tka8FgKZcQ7IO4kW1nV1dXz77bfDovmIiIjgyiuvZOrUqQiCwN69e1m7dm3Q6r19fX00Nzdz6623UlZWdtwU9EjK/KSlpfHII48wfvx4YmNjw3p3cib813/9F6+//jqLFy9m1KhRPProoxw8eJCysjJWrlwZUl0YeXl5PPzww3z99df86U9/GtTQR5/Px969e6msrOTrr78OaQM9cff42muvST4iX3/9Ne+99x579uyRXI/j4uKYOHEi8+bN4/bbb2fx4sVhkz1wu910dXXx5JNPDjh7F26InWzipvFo75sbb7yR2NhY7rvvviE30h0KZs6cycsvv8yGDRt47LHH2LRpE1arNez0SgNF1IKKjttHy1SOHnnh8XhwOBw0NjZiNBrJzs7G6/VKImnR+DCkMj+CIODz+Yat7qxWqxk/fjxxcXHs37+f1tbWYa15KxQKYmJiiImJITs7m56eHtauXUtNTc0JfRaCOWgu0Gg0GjIyMujq6mLNmjUjpsNrsNhsNnp7eyktLcVut6PT6TAajYwdO1Yy6wq226w4c+3cc8/FZDLR1dXF/v37B/V5USgUTJgwAY1GQ11dXUhfx6KjbFNTE3a7nZKSErZv305lZSWNjY2SNYHT6USn0zF37lzi4uJCWsckEhcXR0FBASkpKQDk5+djNBqBn+4vgiDgdDrZsmVLWD1MVSoV55xzDiaTCYvFQkZGBvHx8ahUql9kzHNycjAajUycOJGsrKzjvp9Go6G6ujqkNiA6nY6ZM2eSn59Pa2urZAvT0NDwi47Lzs5O2traRkyDzECef+Jrenp66O3tlY49ZFvdBUFAq9UOa1YjIiKCyy+/nK6uLj766KNh1xmJDrLTp0/nn//5n3nqqad49tlnT/h6MTs2Ui5kjUZDeno677//fsi2PA8XXq+Xr776CoCVK1dyzz338Mwzz5CZmUl1dTUNDQ1BPe8xMTEsW7aMhIQE3G43NTU1bN++fVDvodFoeOihhzCZTKxcuZIDBw4Mm5fXYNFoNGi1Wt5++21KSkooLy8/7u+/q6uLoqIiGhsbcblcYWE/UVBQwEsvvYROp0OlUvG73/1O2nh6vV5poGtLSwuXXnrpoLJ7wcZgMHDDDTcwbtw4Zs+eDRy5b+r1egApWBcDIb/fz7Jly37xb36/H5VKRUxMDC+++CL333//MB/JiYmJieGVV16hpaWF119/nR07dlBRUfGL1/n9fqqqqvB6vSFVOh8OxBJ1V1eX1A4vbkwC+RkNqOZnuG4e119/PZMnTyY6Opra2lo2bNhAQ0PDsPxskYiICBYuXEhOTs6Ajl3skglloehAUKvV3H333ZjNZp577rmQ2lWFCt9//z0PPfQQeXl5ZGdn8/zzzwfNCPFXv/oVU6ZMIT4+nqqqKl5//XV+/PHHQb+PIAiYzWY0Gs0xbdXiv7W2toZM8PDJJ59QUVHBtm3bBrRzDoeMbFRUFP/2b//GmDFjMBqNUgdibW0tTU1NfPfddzidTtxuNxkZGZhMJh555BGp0+1vf/tbyGvy+vv7+fzzz/nxxx9Zu3YtwDEPPq/Xy6xZs7jiiit488032bNnj3TNHZ0Z8vv9KBQKtFptyJk+ilrVvr4+duzYcdKM+TfffEN6ejqPPPIIBw4c4I033hjGlQYH0eCxt7eX/v5+KZOp0WhQKpUBTbAETPMj7jqGErHteO7cucydOxelUonVamX37t1D+nNFxHS6OGxt+vTpxMTEYLfbTxmdi/OGwknzI2b0xBuL+MG95ppr8Hg8LF269Kxrrx0IpaWllJWV8cYbb1BQUMBf/vIXnE7nsJYgVCoVWq2WefPmcfHFFxMZGUlTUxP/+7//i9/vl2bnDBS9Xo/JZAKgo6MDp9NJREQE8fHx6HQ62tvbQyb42bx5M5s3bx7U94Ry8KPRaLBYLCxZsgSLxUJ/fz9utxun00ldXR179+7l3XfflUSikydPJi8vjz/+8Y/Exsai1+uprKwM+eDH5XKxbdu2437taKO/BQsWsG7durATOYuoVCr6+/uPm/E5GlHM/tJLL7F9+/azIviBn+xrRMGz+NxVqVQB9RAMWOZnOG58aWlpjB49mtTUVPr7+1myZMmwzrGZNWsWCxcuJDY2FrVaTVFREdXV1axdu/aUmhfR5yecUpgWi4Xly5cTHx8vBW4qlYqcnJywEYYGC7/fz3vvvceuXbt499132b59Ow888MCw/fxrr72WBx98kLi4OARBoL+/n8mTJ/Pll19KmY6fb1bEXZb4d/G/CoUClUpFWloaAO+++y5utxuPxyP5zNx2220hMVPodDiVD1Cwefjhh5k5cyZKpZLe3l78fj/Lly/nk08+oaenB6fTic1mk+7B+/bto7a2lrKyMubMmcNzzz2HXq/HYrHQ1dUVlqX3uLg4Fi1axIUXXkhERERYbSKPxuv1cujQoQFrJEX9iygUFsXfgz2HBoMBv98f0o0KIuJgcHE4uNhlGuimmoAKniMiIiQPkUA+5FUqFRaLhezsbAoKClAqlbS1tVFeXj7krqYRERHo9XqSkpKYMGEChYWFdHV1YbPZ2L9/PxUVFQNqFw6lqfMnQ6PRoNPpiI+PJzU1FbPZTFxcHLGxsZLOoKqqigMHDoT0bjkUqKurQ61Wk5ubGxTthc/no6am5pjPorhzEtPLRwc8giCg1+sl88Ojr1WxZdXn80mBj9im2tfXF5bXgjgTKiYmRtIWhBqCIGCxWIiPj6e5uRm9Xi/9/UQl576+Pvr6+ujo6CAjI4P+/n6SkpKYOHEi27ZtC2mbghMRERFBfn4+KpWK4uLisPUTE7Wfg7FUCERZNhSv7ZMhenAd/d9AT44ImOBZqVSSlZXFpZdeysaNG6mvrw/EWwNHMhCLFi1i2rRpXHjhhWzbto19+/YNeRlBoVCQm5vLuHHjeOKJJ4iJicFgMHDTTTexdu3aYzxDBkIoTZ4/EQkJCYwaNYp7772X7OxsXnnlFcxmMxdffDHJyckYDAZuueUWysrKwqqTJBjU1NTgcDjo6uoa9h3Xhx9+yCeffDLg61Mc4TFhwgSMRiMbNmw4pqQZERHBp59+is/n45prrjmue3m4MXPmTN555x1aW1upr68Pyays3++noqIChUJBTU0NY8eO5bbbbpP8zU5FX18fNTU1LFq0iJtuuom5c+cOm0wgkERFRTF37lzef/99Fi9eHJbXGxx5pkRGRkoi7oGg1Wolrejpbp7DIeNzNOKmTK1Wo1KppPJXIDO0Acv8aDQakpKSKCwspLi4OFBvCxxJ2U2bNo309HQ8Hg8lJSUUFRUN+QfA7/fT3t5OZWUlq1atQqfToVarqaysPK2fLUawoUxGRgb/8A//gMvl4tChQ+Tn52OxWIiLi2PXrl1UV1czY8YMMjMz+fvf/x6WKfThwmw2S5Omh5vB7i7FGnttbS0ajYaenp5jvl9MtYt2FuGs9dJoNEyaNImCggL0ej2lpaWsX79e8v8JNXbv3k1XVxdTpkzBbDZz+PBhenp6BvS9ol5oMA/bUEScs6dUKqUSUDji8/mw2+3Ex8dz5513snnz5pN2Tf7c32iko1AopI2YSqXC7XZL+jav1xt6mh9RWZ+WlsaMGTP49NNPA/G2UpRnMpmYNWsWPp8Pm83Gpk2b2LhxY0B+xsnw+/00NDTQ0NBAUVHRGb9foAVbQ8GoUaNYtGgR69ato7Kykssvv5zo6GhMJhOvvPIKb7/9NmvXrsXn87FmzRo5+DkJSUlJZGRkhIV/jCgyPJmGTix7hTt6vZ758+czYcIEANavX8/y5cuDvKoTs2XLFiorK1myZAkmk4mKiooBB2o6nY6kpCQ8Hg82my3syh8iCoVCKruHMz6fj/b2djIzM3n66ad59NFH2bt370nPy0iaDHAqxEaNiIiIYwZl2+12PB5PQH8XAbmSqqurWbx4MXPnzuXqq68ecEr2ZFx++eXMmDGD5ORkFAoFn3zyCVVVVZSWloZde3VUVBQWi0Xy4ghliouLefnllznvvPMYN24cdrudoqIiPvjgA0nkrFKpQv44QoGrrrqKcePG8eCDD1JbWxvs5Zw2mZmZZGRkYDQaw1IvcjQPPPAA5557LllZWTQ3N/PEE0+csMMolFAqlVgsFtxutzSKYyC4XC66urr44Ycf2LVrV9hNflcqlVx22WWYTCZ+/etfD2uDy1Bgs9n4z//8Ty666CIef/xxbrzxRgoKCnj++ec5fPjwL17v9/vp7+8P2zLfQBEzPmlpaSQmJnLOOeeQlJSE3++nsbGR8vJyDh8+fIyw/0wJSPAjzkDKzMzEZDJhNBqJjIyUOhMGiqjojoyMpKCggNmzZ5OSkkJbWxvLly9n9+7dAcnADDcajUZqLQ71nVdzczNbt25l/PjxqFQqDh8+THl5udRWqtPpsFqtKBQKEhMT6ejoCJqHzZliMBhQKpXY7fbA2KWrVERHR0sivfz8fPLy8ti0aVNYmc39nISEBEaPHo1Gowl65kepVBIZGXmMVf7JED1CtFotBoOB6dOnM2vWLGw2G1VVVWzcuHHYPcJOB1H0Kg6KPFVJU3SgNxqNOJ1Oqqqq2LZt2y9chEMdpVLJ2LFjAfjrX/8akrqsweByuSgqKiImJoaWlhaioqIYO3YsKSkpuN3uX2Q14uLiht0mY7hRKpXSnL3Y2FhSU1NJTEwkJiYGh8NBe3s7Bw8exGazSe3vgSCgOUSNRkNUVBRXXXUVqampvPnmm4OqTU+cOJFp06Zxxx13YDAYUKlU7Nq1iz179vDRRx+FnWjr54SDyWFzczNWq5U9e/ZIw+eOFmk7HA5uueUW8vPz+eyzz1i1ahXPPPNMEFd8+lx99dWkpKTwyiuvBCSjkZGRwdKlS4mNjcVisZCSkkJ3d3fIlzpPxfz581myZAnd3d20tbUFNYBPTEzkrrvuYvv27Xz22WenfH1kZCQTJ05k9uzZXHnllZjNZtrb21mxYgV79+6luLg4LIaZOhwOvvnmG7KysrjiiivYt2/fSTNWMTExvPrqq2RlZUkdmhs3bgz5houfo1QqufDCC/H5fLzwwgthH/yIfP/991xwwQX4fD60Wi3Lli0jLy9Pej4IgiDp9rq6usJyftmpEK1TTCYTZrOZ3NxccnNzycjI4KKLLsLlcvHxxx+zf/9+ysvLf9GBeqYENPipqalh9erVUtfI3LlzqaurO6XL5pQpU0hNTSU3N5fRo0eTlJREeXk51dXV7N+/n7q6Onp6esK21KLT6TAYDJKIK5QRtR8nu0m2t7fT3d1NUlISycnJxMfHS1bkp0NcXByFhYXAEaFuUVHRsJRXkpKSyM7OJjk5mdbW1gHpKBQKBRaLBbPZTHZ29jFfS0lJISsrC6fTSX19PdXV1cM+c24oEO0PNm3axO7du4Oa/RGzHykpKcydO5eSkpJflICMRiNGo5ExY8ZgsVjIysoiPz+flJQUioqKqK2tpby8nPr6+rAJBtxuN8XFxSgUCqZPn35SaUFKSgoZGRmkp6ejVqspLS2lubk57LIHiYmJJCYmEhUVFXYZq1PhdDppbm4Gjny+ysrKsNvtkn+RQqGQRluIZZ9wQqlUShlX8f5xtKUG/JQMiImJwWKxkJmZSXp6OgkJCdhsNmw2G83NzbS1tdHd3R3wETQBfRJ/9tlnfP755/zP//wPV1xxBZdccglr167ljjvuOOH3CILAAw88wLx5845pHX/vvfd4/fXXA7m8oGE2m0lMTCQyMjIgeqhQQKzRiv4hZ6InKCwsZPny5fh8Pvr6+rjuuusG5J10JgiCQEZGBgUFBUybNo2KiooBzbtSq9UUFBQwY8YM7r33XuAnzxzRhfbLL7/k3Xffpbi4OKzLXSKibuTPf/4z5eXlQd199/f3s3fvXmbOnMlvfvMb7r77btavX3/Ma9LT0xk9ejRPPfUU6enp2Gw2tFotWq2W1157LSydgfv6+vjggw/o6+vjrrvuQqvVnvC1M2fOZNKkSSQnJ1NZWclf/vKXkJ3DdjKmTJnCBRdcgNFopLu7O9jLGTJcLhdPPfVUsJcRUDQaDXFxccTHx2OxWEhMTJTK5mKZS5S5JCcnExkZSUxMDGazGYPBwM6dOzl8+DCVlZXU19cPiVYt4GkIn8/HO++8Q0lJCb/5zW8499xzWbp0qRSxib36R4+lz8/Pp6+vjz/96U9SR8LpzB8KVZKTk8nKykKtVo8Y1f7hw4d56KGHSElJ4frrr6elpWVAF2h2djbjx48nOzub6Oho/H4/ZrOZmpoatm3bxt69e4dlQrzf7+fjjz+mrKyM2bNnc/755zN//nzp2vz5NGGlUikNjUxJSZGE+D09PdjtdrZu3UpraysHDx6krq6O/fv3h704WEStVqPT6ejt7Q36Dryvr4/i4mKSkpKw2Wxce+21zJ49+xjHX7PZTHR0NGazmZaWFv785z/jcDhwu91h70wuBtoLFy4kLy+PNWvW0NfXR1paGgkJCaSlpZGTk0NkZCRffvklFRUVFBcXh53QGaC8vBy73c60adPCLmsVKqhUKpRK5bCbeIpdhmPHjiU7O5vU1FRUKhV2u13yBYyJiZFcnMWAqKmpCavVKo32qK2tHbL76JDUYL766ivKysq4++67SU9P58Ybb5TcZDUazTEus36/H7VaTXNzM+++++6wPPiGG9EtOVwt2Y9HW1sbK1eu5J/+6Z9YtGgRiYmJA5oXlZWVxezZs6Vhm36/H6vVyr59+/juu+9+sYsfSjZv3kxVVRXXX3+9NAZC9NQQyyFqtVoSMIs+N+Jr+vv76ezsxGq1smnTJg4cOMD27duDLgoONGIK2+VyBb2EJ4p3Dx06RHt7O9OmTUOv1x+zsRDvNUqlkqamJj788EM6OjpGRDDq9Xrp6enhvPPOY8aMGVitVtrb2yksLGTUqFEUFBRIItFVq1ZRUVFBVVVVsJd9WtTV1dHc3ExLS4t0TmUGhxhUuN3uYf39aTQaSRpQUFBAWloaCoXimEx4YmIiOp0Ou90ulfhaWlrYs2cP+/btw2q10tTUNGT30yEToFitVhYsWIBKpTpGUHg88acgCLjd7rCdDXQyBEHgoosuYs6cOfT29oatLfuJWLNmDTt37uRf/uVfePzxx+nt7ZUiezG4VSgU+P1+XC4XcXFxJCUl8Yc//IHt27fjcDjweDxSIDGcOJ1OGhsbuf3224/RYgmCcMy06JNl60RRojjsc6QFPvDTaJZQYsOGDZSXl0veWUffV8SbvFKpxOFw0NTUFBai5oGwZcsW5s2bx0033cScOXNYuHChNP9INCZdtmwZO3bsGBF6M6fTyX/8x38AhI0+K5RwuVy43e6g6/TsdrvkcdTf309vby+lpaXSmJyenh5aW1upq6ujoaGBpqYmHA7HkK57yIIft9sddiKtoSIyMhKTyURxcfGIy2y1t7fT0dFBd3f3MbPLxLLm0X9Ek0qPx8OBAwcoLy8PajpbDMhONV35bKe+vp7du3eHVLdlV1fXiNtIDASbzUZ5eTnFxcWSllCr1WK1Wunp6aGzs5OysrIh18wNF36/n7q6umAvI2wJxFyw08Hr9dLf309HRwcNDQ3YbDa8Xi/Nzc309fVJcgGxdb23t5e2tjap8aS3t3fINyzCYH4xgiCEdd7R7/efVHAzFMcnCAJvvPEGU6dO5dlnn2X//v1D6VW00+/3TznFeobkHB5dzjwVgiDgcrlOK5twqnP4/+8/oq9TGN5jVKlUqFSqgHpsBOOzOMwM6WdR1EkcL+M1XDv9ULtOhwL5GE/v+MQBvHq9XprQ7vV6aWtrw+l04nA4pJEVYknu6D8B5rifxdDuux4hiDcpp9M5Ynwqfo6ckh65DHZOmMzQ4/V6Q64UKSMj4na76e7upre3F5VKhVqtxufzSTMDxT/BLKnLwc8woFAoJO2TfMOSkZGRkRnJuN3ukB0ULCIHP0OM3+/nhRdeICoqin379gW9VVhGRkZGRuZsRw5+hoFdu3YFewkyMjIyMjIy/89ggx8rcHAoFjIMZAzgNeF8fDDyj3EgxwfyMYY6I/06hZF/jPJ1+hMj/RjD+fjgBMc4qG4vGRkZGRkZGZlwJ7zHTcvIyMjIyMjIDBI5+JGRkZGRkZE5q5CDHxkZGRkZGZmzCjn4kZGRkZGRkTmrkIMfGRkZGRkZmbMKOfiRkZGRkZGROauQgx8ZGRkZGRmZswo5+JGRkZGRkZE5q5CDHxkZGRkZGZmziv8DMeg7HOBKhQEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x72 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_images = [255 - im for im in my_images]\n",
    "plot_images(my_images, 0, 1, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut maintenant :\n",
    "- transformer des matrices 28x28 en tableaux (28,28,1) de float normalisés,\n",
    "- calculer les inférences du réseau entaîné de votre choix (`model` ou autre...) avec les images perso en entrée,\n",
    "- faire afficher la précision obtenue et la matric de confusion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_images = np.array(my_images)\n",
    "\n",
    "nb_images = my_images.shape[0]\n",
    "nb_pixel  = my_images[0].size\n",
    "\n",
    "# resize des matrices au format attendu par keras :\n",
    "my_x = my_images.reshape(my_images.shape + (1,))/255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_results = model.predict(my_x)\n",
    "print(my_results.argmax(axis=-1))\n",
    "target    = np.array([0,1,2,3,4,5,6,7,8,9])\n",
    "show_cm_mnist(target, my_results, range(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Que suggèrent les  résultats ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autres ressources intéressantes... des vidéos :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=\"https://www.youtube.com/embed/trWrEWfhTVg\" width=\"800\" height=\"450\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<iframe src=\"https://www.youtube.com/embed/trWrEWfhTVg\" width=\"800\" height=\"450\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=\"https://www.youtube.com/embed/aircAruvnKk\" width=\"800\" height=\"450\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<iframe src=\"https://www.youtube.com/embed/aircAruvnKk\" width=\"800\" height=\"450\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=\"https://www.youtube.com/embed/IHZwWFHWa-w\" width=\"800\" height=\"450\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<iframe src=\"https://www.youtube.com/embed/IHZwWFHWa-w\" width=\"800\" height=\"450\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=\"https://www.youtube.com/embed/Ilg3gGewQ5U\" width=\"800\" height=\"450\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<iframe src=\"https://www.youtube.com/embed/Ilg3gGewQ5U\" width=\"800\" height=\"450\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "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": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}