{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
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  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.9.5 64-bit (windows store)"
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  "metadata": {
   "interpreter": {
    "hash": "241a3087cce17afe2d3bd9b072a200bdbe084ac2f3f20c4fa80011e0c0777b07"
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  "interpreter": {
   "hash": "241a3087cce17afe2d3bd9b072a200bdbe084ac2f3f20c4fa80011e0c0777b07"
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 },
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model used: exp903\n"
     ]
    }
   ],
   "source": [
    "#@title Imports\n",
    "#%load_ext autoreload  #Need to uncomment for import sometime, dont understand\n",
    "\n",
    "#Tensorflow :\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import datasets, layers, models, losses\n",
    "import tensorflow_datasets as tfds\n",
    "#from google.colab import files\n",
    "\n",
    "#Others :\n",
    "from matplotlib import image\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import random as rd\n",
    "import cv2\n",
    "import csv\n",
    "\n",
    "#Data loaders :\n",
    "from loadFer2013DS import *\n",
    "from loadRavdessDS import *\n",
    "from loadExpWDS import *\n",
    "from loadAffwildDS import *\n",
    "\n",
    "#Others\n",
    "from utils import *\n",
    "from config import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Array loading...\n",
      "Concatenation...\n",
      "X et Y chargés\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "#Chargement des données\n",
    "\n",
    "# print(\"Array loading...\")\n",
    "# Xf = np.load(\"data/array/Xf.npy\")\n",
    "# Xe = np.load(\"data/array/Xe.npy\")\n",
    "# Xa = np.load(\"data/array/Xa.npy\")\n",
    "# Xr = np.load(\"data/array/Xr.npy\")\n",
    "\n",
    "# Yf = np.load(\"data/array/Yf.npy\")\n",
    "# Ye = np.load(\"data/array/Ye.npy\")\n",
    "# Ya = np.load(\"data/array/Ya.npy\")\n",
    "# Yr = np.load(\"data/array/Yr.npy\")\n",
    "\n",
    "# print(\"Concatenation...\")\n",
    "# X = np.concatenate([Xf, Xa, Xe, Xr])\n",
    "# Y = np.concatenate([Yf, Xa, Xe, Yr])\n",
    "\n",
    "\n",
    "\n",
    "print(\"Array loading...\")\n",
    "Xf = np.load(\"data/array/Xf.npy\")\n",
    "Xe = np.load(\"data/array/Xe.npy\")\n",
    "\n",
    "Yf = np.load(\"data/array/Yf.npy\")\n",
    "Ye = np.load(\"data/array/Ye.npy\")\n",
    "\n",
    "print(\"Concatenation...\")\n",
    "X = np.concatenate([Xf, Xe])\n",
    "Y = np.concatenate([Yf, Ye])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#Enregistre X et Y directement, à faire si assez de ram\n",
    "np.save(\"data/array/X\", X)\n",
    "np.save(\"data/array/Y\", Y)\n",
    "\n",
    "\n",
    "#Chargment des données\n",
    "X = np.load(\"data/array/X.npy\")\n",
    "Y = np.load(\"data/array/Y.npy\")\n",
    "print(\"X et Y chargés\")\n",
    "\n",
    "print(\"Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "X et Y chargés\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Chargment des données\n",
    "X = np.load(\"data/array/X.npy\")\n",
    "Y = np.load(\"data/array/Y.npy\")\n",
    "print(\"X et Y chargés\")"
   ]
  },
  {
   "source": [
    "def loadData():\n",
    "    return np.load(\"data/array/X.npy\"), np.load(\"data/array/Y.npy\")\n",
    "X, Y = loadData()"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'Xr' is not defined",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-27f8e461a14b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#@title Visualisation de chaque dataset\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mX_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mXf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXa\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mYf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYa\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"fer2013\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"ravdess\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"expW\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"affwild\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[0mN\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mM\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Dataset:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'Xr' is not defined"
     ]
    }
   ],
   "source": [
    "#@title Visualisation de chaque dataset\n",
    "for X_, Y_, name in zip([Xf, Xr, Xe, Xa], [Yf, Yr, Ye, Ya], [\"fer2013\", \"ravdess\", \"expW\", \"affwild\"]):\n",
    "    N=5\n",
    "    M=5\n",
    "    print(\"Dataset:\", name)\n",
    "    print(\"Images:\", X_.shape, \"Labels:\", Y_.shape)\n",
    "    plt.figure()\n",
    "    for i in range(N*M):\n",
    "        if X_.shape[0] == 0: continue\n",
    "        k = rd.randrange(X_.shape[0])\n",
    "        plt.subplot(N, M, i+1)\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.grid(False)\n",
    "\n",
    "        afficher(X_[k])\n",
    "        plt.title(emotions[int(Y_[k])])\n",
    "    plt.show()"
   ]
  },
  {
   "source": [
    "#Visualisation du dataset global\n",
    "print(\"X:\", X.shape)\n",
    "print(\"Y:\", Y.shape)\n",
    "\n",
    "N=3\n",
    "M=3\n",
    "plt.figure()\n",
    "for i in range(N*M):\n",
    "    k = rd.randrange(X.shape[0])\n",
    "    plt.subplot(N, M, i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "\n",
    "    afficher(X[k])\n",
    "    plt.title(emotions[int(Y[k])])\n",
    "plt.show()"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 33,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "X: (125887, 48, 48, 1)\nY: (125887,)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "dico créé\nAngry, 8609 images, exemple:\nDisgust, 4522 images, exemple:\nFear, 6185 images, exemple:\nHappy, 39296 images, exemple:\nSad, 16609 images, exemple:\nSurprise, 9854 images, exemple:\nNeutral, 40812 images, exemple:\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<BarContainer object of 7 artists>"
      ]
     },
     "metadata": {},
     "execution_count": 35
    },
    {
     "output_type": "display_data",
     "data": {
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     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "#Visualisation emotion par emotion\n",
    "dicoEmotion = {emotion:[] for emotion in emotions}\n",
    "for k in range(len(X)):\n",
    "    emotion = emotions[int(Y[k])]\n",
    "    dicoEmotion[emotion].append(k)\n",
    "print(\"dico créé\")\n",
    "#print(dicoEmotion)\n",
    "\n",
    "i=1\n",
    "for emotion in dicoEmotion:\n",
    "    print(f\"{emotion}, {len(dicoEmotion[emotion])} images, exemple:\")q\n",
    "    i+=1\n",
    "    try:\n",
    "        label = rd.choice(dicoEmotion[emotion])\n",
    "        afficher(X[int(label)])\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "plt.bar(range(7), [len(dicoEmotion[emotion]) for emotion in dicoEmotion])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#@title Hyperparamètres\n",
    "epochs = 2\n",
    "batch_size = 128\n",
    "validation_size = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "X (152252, 48, 48, 1)\nY (152252, 7)\n"
     ]
    }
   ],
   "source": [
    "#Labels catégoriques\n",
    "Ycat = keras.utils.to_categorical(Y)\n",
    "\n",
    "print(\"X\", X.shape)\n",
    "print(\"Y\", Ycat.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"my_model\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d (Conv2D)              (None, 46, 46, 32)        320       \n_________________________________________________________________\nmax_pooling2d (MaxPooling2D) (None, 23, 23, 32)        0         \n_________________________________________________________________\nbatch_normalization (BatchNo (None, 23, 23, 32)        128       \n_________________________________________________________________\nconv2d_1 (Conv2D)            (None, 21, 21, 64)        18496     \n_________________________________________________________________\nmax_pooling2d_1 (MaxPooling2 (None, 10, 10, 64)        0         \n_________________________________________________________________\nbatch_normalization_1 (Batch (None, 10, 10, 64)        256       \n_________________________________________________________________\nconv2d_2 (Conv2D)            (None, 8, 8, 128)         73856     \n_________________________________________________________________\nmax_pooling2d_2 (MaxPooling2 (None, 4, 4, 128)         0         \n_________________________________________________________________\nbatch_normalization_2 (Batch (None, 4, 4, 128)         512       \n_________________________________________________________________\nconv2d_3 (Conv2D)            (None, 2, 2, 256)         295168    \n_________________________________________________________________\nmax_pooling2d_3 (MaxPooling2 (None, 1, 1, 256)         0         \n_________________________________________________________________\nbatch_normalization_3 (Batch (None, 1, 1, 256)         1024      \n_________________________________________________________________\nflatten (Flatten)            (None, 256)               0         \n_________________________________________________________________\ndense (Dense)                (None, 128)               32896     \n_________________________________________________________________\ndropout (Dropout)            (None, 128)               0         \n_________________________________________________________________\ndense_1 (Dense)              (None, 64)                8256      \n_________________________________________________________________\ndropout_1 (Dropout)          (None, 64)                0         \n_________________________________________________________________\ndense_2 (Dense)              (None, 7)                 455       \n=================================================================\nTotal params: 431,367\nTrainable params: 430,407\nNon-trainable params: 960\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#MODELE\n",
    "class MyModel(keras.Sequential):\n",
    "\n",
    "    def __init__(self, input_shape):\n",
    "        super(MyModel, self).__init__()\n",
    "        #Pre processing\n",
    "        # self.add(keras.layers.experimental.preprocessing.RandomContrast(factor=(0.5,0.5)))\n",
    "        # self.add(keras.layers.experimental.preprocessing.RandomFlip(mode=\"horizontal\"))\n",
    "        \n",
    "        #48*48 *1\n",
    "        self.add(keras.layers.Conv2D(32, kernel_size = (3, 3), activation = 'relu', input_shape = input_shape))        \n",
    "        self.add(keras.layers.MaxPooling2D(pool_size = 2))\n",
    "        self.add(keras.layers.BatchNormalization())\n",
    "\n",
    "        #23*23 *32\n",
    "        self.add(keras.layers.Conv2D(64, kernel_size = (3, 3), activation = 'relu'))\n",
    "        self.add(keras.layers.MaxPooling2D(pool_size = 2))\n",
    "        self.add(keras.layers.BatchNormalization())\n",
    "\n",
    "        #10*10 *64\n",
    "        self.add(keras.layers.Conv2D(128, kernel_size = (3, 3), activation = 'relu'))\n",
    "        self.add(keras.layers.MaxPooling2D(pool_size = 2))\n",
    "        self.add(keras.layers.BatchNormalization())\n",
    "\n",
    "        #4*4 *128\n",
    "        self.add(keras.layers.Conv2D(256, kernel_size = (3, 3), activation = 'relu'))\n",
    "        self.add(keras.layers.MaxPooling2D(pool_size = 2))\n",
    "        self.add(keras.layers.BatchNormalization())\n",
    "\n",
    "        #1*1 *256\n",
    "        self.add(keras.layers.Flatten())\n",
    "        self.add(keras.layers.Dense(128, activation = 'relu'))\n",
    "        self.add(keras.layers.Dropout(0.3))\n",
    "        self.add(keras.layers.Dense(64,  activation = 'relu'))\n",
    "        self.add(keras.layers.Dropout(0.3))\n",
    "        #self.add(keras.layers.BatchNormalization())\n",
    "        self.add(keras.layers.Dense(7, activation = 'softmax'))\n",
    "        #7\n",
    "    \n",
    "    def predir(self, monImage):\n",
    "        return self.predict(np.array([monImage]))[0,:]\n",
    "\n",
    "    def compile_o(self):\n",
    "        self.compile(optimizer = 'adam', loss=losses.categorical_crossentropy, metrics = ['accuracy'])\n",
    "\n",
    "myModel = MyModel(input_shape)\n",
    "myModel.compile_o()\n",
    "myModel.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[0.14478482 0.13992985 0.1444098  0.14547563 0.14638613 0.14012544\n 0.1388883 ]\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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2Z6f1wBq+jYsSbUOAkZSLWtkqJ1ykSQyBTgbnrCVGBtOYEAzb+vOwfIKPJw/o+ZglhYQAUzo+uGlG1NDiV1blgSWW+JOc5HOKelpoa4tSUUlLH6e9qq+/W+PiX1bTQqfsc/9qQ/f+bB7j5ayse91t8Ps4rhPjIHWtwzUdZLS6nRsF49nPWNlqQ3wdqqpkxmFkWWjrsDLIymizh15F9Ho7iyw4/2Z3nJzgi91xcoIvdsfJCaP12QOgch1EQILsoQ7oRIvYCKopxfzAVuDN7DgPtogMv34x5n5kzwgGaYlknqinK9VYARpjx/n5xg5on51SbpNVdOLHoQ/xKjCTL+nrGFvgQSSWbyd9xE7d8JmN68iK/Hzlsg5YmR3jra6eOLRD2Vz+f/m4vKD1iVhoFiHW+kAQySDz79SVatIqv9jqK8arb2gfWSKetZEII7tfBSsJSpWb1jYyYEcGb1moxJwzuPD+ze44OcEXu+PkBF/sjpMTfLE7Tk4YvUAn4k+oNfjzRoptlvgmK9VMFIxgFCH0SVEP0BlU3WktCE2W+bFfPGRUiuno/RoFKcBsUTa1l7hod+QW3UeNblpk41OFurJJi1wlsoQ2KSw1t2mbzpRWieI5Psc9MyeVzVsneT/25/73XmUz/r0X2Dg0dLlrupz3lTv6S1uVjaQyr+e81OPXVj5pCJZW+zURaBMZyXOUyh0HZ6IZ7QGN+Rhz9FLSjuMMwhe74+QEX+yOkxNG6rNTBhQawv8WPntmtG2SwS9W+yfZ7mi2tKJsKsLhaqusHGClwoNoTrS1zyx9/+1zi8rmyLPaH8/KfL/Fv68rqsy3+Jymp44rG9nGamWXTsRZ2yECkSaM3uOikm0oGf55VesaW6a4b/3emf3K5uYq98e/VvhFZdO7hgfaJAvaZ5c09ug5bn8L71e/8P1tykYGrBRWrDZOhq8tKr4GK1lGkA3hj0dG8FhalJVijbUgg67kfLy6rOM4vtgdJyf4YnecnDBwsRPR5UT0EBE9TURPEdGn+9uniegBInqh/39dncBxnE3DMAJdD8BvhBAeJaIagB8R0QMA/gmAB0MIXyCiuwDcBeCzZzpQlAKlZRlYwj9vzJZMwsZqNyQDbaQYBwBTBS6IFYz+S+2MZ7CVjJZEJztctNta1f2fwrVqE157lfcEbx+TdbWBIASxk6e0QBgn/B4WJ9rKZrzCA3+qRnWftUl+rVYlY2u/qya5aLitcErZ7E24QLr31gPK5snZXWxcntf3IyvwSd1407N6koKJ9+uAqqcOCDHQKCNuBR7J4JfzCWphxzW+ZrOiOL9xKvXqn0XLqIHf7CGEIyGER/v/bgB4BsAOAB8FcG/f7F4AHxv6rI7jjJyz8tmJaDeAdwB4GMBcCOFI/0dHAcxtsM+niGgfEe3rtvWfwxzHGQ1DL3YiGgfwZwA+E0Jgv7eGEAI2SJsPIdwdQrghhHBDoaSLHjqOMxqGCqohogLWF/ofhxC+0d98jIi2hRCOENE2APODjhN1Mowf5EkUJ9/MPwBSo3pMT/r1mf6Mkn5818g0KIvMj1qsK8WUE+6jTiU68OXVRPjeRnDONeP6dqzM8tKoh5p1ZdNKpR89WJ+wKunOlXmAylisNYzjbX7vl7u6Ko6VUDRb5L+hzST6N7ZaxO/JP9v+HWXzH1u/xOezTesTU1X+jF4xqtROlvgcb597Qtk8/eQVbGy1Xpb6EWAHtiibIVowDVN1RrZ1TqUPDygffaiWUaePP8iAiAjAPQCeCSH87ut+9E0Ad/T/fQeA+4c+q+M4I2eYb/b3APg1AE8Q0eP9bf8WwBcAfI2I7gTwCoB/dEFm6DjOG8LAxR5C+C42jrj9wBs7HcdxLhQeQec4OWGkWW9ZMUJjFw+cSMdEWx5DkGq3+TS7Zf0ZJcWlaqIDNGSATCXSwSgF0Vh9R0FXYSkKoW810+Wma5EW/7qBX8feMS3iNVJ+He0w+BHNGgJZKn4ZW0m1+NYTIqZVuWe6oAXK1ZRfbyvTtZMLxI+9t6Cz927b9jQbf7d4lbI5tjL4LzjX1LkY+day7hGVNPg7Qz0dUBW3tdg1xO3XFWVIv5/SxhLW5DarhZhElZ/2UtKO4/hid5yc4IvdcXLCSH32tAQsXSUCZMqi8kZbB8MkwiVeWh1TNuNFHjSyluqWTAsd7v+VZalbAKn4/KtD+6zS17d89kam55iJ7AfpnwPaR7cSeuS2yIjYSIU/3ky1X52oRCB9z1ZSfW0dEUS0mGp9pBV49ZiaEfhzZYlrFu1p/TrObuF6hHWtZeLPsUr6nslKulYCSaxfB41V8VW7/3o3cbrI2EcG8FiBODJWTLWVOsMc/JvdcXKCL3bHyQm+2B0nJ/hid5ycMNr2T9D92eM2Fxgyo4JILx0cXLDU4mKXFSDSGaLGr8yWOxXpTCxZmWUm1kEtHSPrTop2saHAjEc6y2wQTSOoRVbcOdHV1yGFtq5xf1Z7+tiTIhOuFbSwt5RxBcr6VqnHTTbeU1pQNoc60/zcRqbi1uIiG9cM4TUS7Z+s9ku6jZOR9WaVdxaBLVHXyMoUK83KaIvE+YfJpjujIiePP7yp4ziXMr7YHScn+GJ3nJzgi91xcsJIBbpAgArkkglDXaOcr/hMygzBbrXFD3wqNiLYRC8vq+STjHKzOBzX2XhrUZeSrsVaaJuMeTSeJexJrOg8Jeyl+loPCmHrsFECS5a3kuW/LBsAKItQMyuj7qiIvNsa6wxDSc0QJ7cVFtm4oELhgHrEhb6Wob5lCb+OrDBYIAOASGbCGa+HzFaz+qqHSO5o2Mj30TzOgN5z3uvNcRxf7I6TE3yxO05OGG1QDQGZjr/gJoY/TqJne0b6IN0CD+Lo9Ize68T9SOl7AjqIJDHSk9bSqhjrwJPt5UW1TVaP2W60TZIZXBbSjz/SmVQ2L6/yctdHGjVls9bm854abyqbuYrumT6e8Kyy2UTblEUq2GQ0OKDJqu5TFRmGEXQgkmzj1TCCjFRQjREcY1QER9waHLVyNuWcf7aP3iZjmobJelPag/vsjuP4YnecnOCL3XFygi92x8kJI896U0h9oWcE1Yge3dTRNr0WF+06ZS10kQgQaRi9zQAe2GFZ1ItcSKonWtiyykmpMxlqZSbKEFvZc00h0FkZbYeXJ9h4pamvZE6UYK4U9Jy3jmnxTZaXltlrAFAX6Y2TkS4JfVnMj72QahFRinYnUn2cg10uRlplsmTLPkuMy2SACoC0zLcVGlqMy4qiTLVRckrGJg0Ru2XaqIpbUsTzUtKO4/hid5yc4IvdcXLCyH32EAmnQvocZgUROTZ8K1HhptPVl1YqcD9SVrcBgLYIxpkd08kqstVUUZbfAVDIBtcXXoL2tUuiyorVZ35JJL5YSS5Ly/zY03V9HVdN8pZMVqUa69pkIo6VnLIqkmraQWsolyd828tdnfQjy3ZLvQIAlkUFoMdXrlA2Y8dFIozhn1v0Svw6hmnJNFSQjRHUIyvlWD67nHcsz+VBNY7j+GJ3nJzgi91xcoIvdsfJCSPPetNZO2Jc1qk+SpAzsoFkME6nrS+tVeDbqiUdRJKKaiEto2ecrN5yvK0DPWSZZgCoimotk4nO8pJVZ6webUfaPMvt4GJd2WSinHE50SJaQwhitYKuJrPaG9zrbaE4oWyKQlV9RZ8euxKd9afmmHEx1CpbHYsX4q8PXalsJpr8mRm31c5Ek+9n0ajm0xZls41S0lHvHARCQ8STUTNW9t5G+De74+QEX+yOkxMGLnYiKhPRD4nox0T0FBH9dn/7HiJ6mIj2E9FXicj4xchxnM3CMD57G8CtIYQVIioA+C4R/TmAXwfwpRDCfUT0nwHcCeAPBx5NfryIIBsrYCarcJ+IWvozSvrsVhupbolfbifWgS/1MZ4IYwWaSD/eqlI7DMPst9zTlWOPrfGEESuACCt826GjU8rk4NplbJws6WuNW0bS0Th3bh++RgexvGmat3JaaGldoybaSN08fUDZ7CrywB8ryUUGHrWe15V7JlPpkA/3zKQfbwXMyCQX2Q4K0G3OAg2TCTNMcM5gk9MMPGNY53T4VaH/XwBwK4A/7W+/F8DHhj+t4zijZiifnYhiInocwDyABwC8CGAxhHBaYz0EYMcFmaHjOG8IQy32EEIaQng7gJ0AbgRw7bAnIKJPEdE+ItqXrq4O3sFxnAvCWanxIYRFAA8BuBlAnYhOO4Y7ARzeYJ+7Qwg3hBBuiKs68cNxnNEwUKAjossAdEMIi0Q0BuCDAL6I9UX/cQD3AbgDwP2DjhVIZ/IojcoIbIAQ7WTlGgCADNowAhtkoE2SaIGum3KxJ46sCQ3WNa1WQoWIC3uW+BcJRehURwt0cr+tdd1+6vCLXBCb/oH+Y8nEizyop/Cq7o+OTF9/56qtbLx09YyyeSHi26wglgO3cIHu/TPPK5uGCjLSQT6PL+9k48n9+lwyiMUKoDGrvMiOTEbWm2zBZAa6SKFvGO3NSpyU8xGv0Jk032HU+G0A7iWiGOu/CXwthPAtInoawH1E9DsAHgNwzxDHchznIjFwsYcQfgLgHcb2A1j33x3HuQTwCDrHyQkjTYShDIhUroX0d4z9ZMXZc4thQejxz7ZezwiYEZVqqkWdLCODYazgGCsRpp1yJyw1Wkv1ROsi69jbKktiH30dh3fzwJLV4zqoJUtEgMqbdimbwqp2bssnuUCSrGkHdHk3v9fRzTrp5T1zXNNtGm2bGqIddDvo+7pv/2423rao56wqvMhWzNigMoxor2QlsMj2ZJEK4AGyTGoGZ98yChiuUs5G+De74+QEX+yOkxN8sTtOTvDF7jg5YbSVaoLRJ1u2drL2E+2eLCElJGcveKQ9faC2yCArGJlxRbFNVq4BNhLtuJDWMy5E7jdZbCmb2SIvC902xMBrtvIAmYO36FLOC8d5RCMZmYLJip5j6QQPbGlP6evf9a6DbPxrO/5G2Ty0+GY2tgS6kihT/VqrrmyqT8lAG/3MZKUYK6jFeoPkY7QEMurJtDfjOGI/o/o2pPZoBdUM0zZqI/yb3XFygi92x8kJvtgdJyeMPKhGdjeWvkyvYlQCkUE1ln8+QAsAgNARQTXG5XcSHhDRNJw7ElVprUALy4+3KtVKyrFo/2QEzCx2eTBM27Bpp/zaeqn+XI/HuFMY14w205fpTSuz3LcuGvv1RPunRqYTeqT2YFXSHReaxV+9dJWymdvPr6NXtgJf+DizgreM5CWF9fUoKsqEyKikJFs2G4eRPvqwLaqGxb/ZHScn+GJ3nJzgi91xcoIvdsfJCSPvzy6R7b+NRDAVSBB1jWohwibTrdf1PoZK01oRWWcVQ3wT1WtKib6NY4kOYlGiVUdXXamJ9kJHVnVrpZUW388SA1UbqzUtfgVRASjtGK2VikY7LiGQWhV/Vtp8jk+t6nqk20uLbLxklM2e7/DrL/9QZ+9FXfESGQLdMEE1pmo2BMmKEGzL+n1IS1xEtcRAVfHG+CpWQp8K8tlY1PNvdsfJCb7YHScn+GJ3nJww2qCaAIiuxcovKcgAGgAyPyIrGs6VrN5p+fVyk1W9UwSf9AraZ5UVaNs9fRsLhvjQ7PIL6Rj7HWtzm+WG9mOVb2kFiMgEDqOtVrnKfc1SQWdnyMQgAOiSqOZT1kE1MlnouaUt2maKn29bcUnZ3PPku9l47mWjuk+FP7PEqEITdfm2zrgRZNQZ3Npp8Uqta5y4rs7GE6/od6Z2gAcQdSe1XtMVfr1VoVj66OqdPkMcjn+zO05O8MXuODnBF7vj5ARf7I6TE0YbVJNZZYcHZ/b0hN5hltMVfd5jo4d7Jsr3ZiWjB5A49DB93kNZ1cc2s9W6IqhmZU2LNJ3W4EciW3uTESGSFLiQ1e7qoJrWKt/WK+l7dvXccbVNBvHIYCEASIXyGhv9lmRVHqtSTfkRXk1HBdBAZ4cZt14pWcWGns/iXn3vl6/n4mNtVjcnffSd/4Of3+hh9g/3/z02PnXPHmUzfpifKy1a2XMiw05WWT9D5p5/sztOTvDF7jg5wRe74+QEX+yOkxMuQlmqM6cWWSWFBkXdrR+b79eN9HmU/jNEP24Y5aaliNYsamHJKiXdE5F3VilriDLNpePaprAixg19IWOnuEgUWdFhQswJRvbeob271TZ5/+XzAYDGjbz3+/SUFrYgEvq+u6BLTk2KiLnemL4fMqNNRssBwOpWfu9PvlWLaP/9I7+vtn3p8IfYeP/Xr1E237xuio33FueVzRd3fYONv/KZ9yibv7jnZjYuLek59kr8vUpaQnQ+Qykr/2Z3nJzgi91xcoIvdsfJCSP12aM0KD8k7oo+3rrAC7rjIiBiWdtI/1P21V7fxschNjLjhihXknW5/9cx+rwnsfa3mm0xASMYJp7jvm66WlE2tVf5uHzSaFHV4DcyXtGZaVGDl2nOarq8T/WADmLpzopS1lM6E6w1y7P1mm/R5y+JMkUvP7Fd2exa4Tadmr7XMoNt7R8sKpvfuf5+Nr6pvKBsXjOyEI+KSkFGpy18/p5/zMaVo/q5yiI8rVlD09kh9pPRUwDGD/Nn3ZqSZZz0/Ib4keM4P0/4YnecnDD0YieimIgeI6Jv9cd7iOhhItpPRF8lIv33J8dxNg1n883+aQDPvG78RQBfCiFcDeAUgDvfyIk5jvPGMpRAR0Q7AdwO4D8A+HUiIgC3AviVvsm9AP49gD8844GCDoAorHLBIUu0AJOtiQ1W3IDYZol4ulaTkRkne8QZ55I2HSm8AYgNgU4yOa57r8+NN9i4vktePHDy3Vq0kySi3PWJNb3P8SVeltkqCT0zroW1neM8aET2nQeAWRFUdHxNl4A+vFZn4+knLFGVb7NKNc2/j4uRn7/2QWXzTIuXsv7M9z+hbKpPaoFybJ6fr9rRz7W0zO9baUE/1840P3ZpyciKrIrAsJoyUVlvU8/w5onx2sbv3bDf7F8G8Jv4WaW3GQCLIYTTUukhALowuOM4m4aBi52IPgJgPoTwo3M5ARF9ioj2EdG+bscImXQcZyQM82v8ewD8MhF9GEAZ6xHNvwegTkRJ/9t9J4DD1s4hhLsB3A0Atcmd59hzw3Gc82XgYg8hfA7A5wCAiN4H4F+HEH6ViL4O4OMA7gNwB4D7NzrGzw4GRMLnidd40ERsVGppznGfuDtmtffh48QIjkmLg+vuysAbOQaAEPNfiNK2rjjTWNU7luvclyslOmBlrcf3W2zpUtJyPyvpZqrEfbl3b3lJ2czu4Bk1U4n+zasbtG95uD2ltkkmE641PFvcqmy+99KVbLznaX3+LOH3+vjbtfZw9a6DbPxUU3uU337pOjZOXtPPrHTSqPgjEk1a05ZgxO9RIP3MiktcVygH43tPlOhu1/W5Dt7O9yvO8/vReWXjX9bP5+/sn8W6WLcf6z78PedxLMdxLjBnFS4bQvhLAH/Z//cBADe+8VNyHOdC4BF0jpMTfLE7Tk646P3ZJcUFLdKEmAdk0JSetkwgS43gXZk9l2l9DJEQ7UJsiDYr/DMyKxlioFHuuiey5U6tGn3c5D5WRp0IfrECeI6v8BLMz87PKZt2i4uBkVHdZ2pSP4+r67y89GxpRdks9rhw9OLSrLKRZaKb23VQT6vO7/Xi39JBPm+t8B5xMpsOANKnePZaZIiaS2/S1187wO0Kxl+Pm1v5HLsVfWwZDFM8qa8jafHrj3paRFy5VmR37uFCaCief1CN4ziXOL7YHScn+GJ3nJww4uqyAYmomJLJVkqx9lFL8zxAJERVZSOrjuo2U7ptVGdC+1bS14/XBgfedI2PzFRPET0RMJRlRnCQ8Jt7bX0/eovcHy4uGhVXRcWfWLuI2HKY+3flU9pnPnXNZWrbD9/FNZTts4vKJhaJOEcf00E1ux7h/ubxt2kNI7mN6wPvmz2ibMZifrEnu/rmj4vqPlYF2vaUfh7N7SJxa1nbjC1wm7U5Q6+p8pdmqmdoQU1+/4tLWnu47Lv8OMuiXzy1L0xQjeM4lxC+2B0nJ/hid5yc4IvdcXLCSAW6tBzh1HVc3Jl95CS3qelAgniel50py4btANrbReCNIYBUhCgTdQ3xa0wG1SgTdGoiOKdpiHiG0JiKyiPBCGKhslDWUv2Ios7g0tqxyNayymbL4I+0ZLwORnJWOMaf0WuoK5usx4999f/SFXeS53i22so/v1zZ/Ks9D7PxL1afUzYt8ZD+58mblY0ULGuHtGJZParv0eo2KYgZbcVENR1Z3QYAVq7gNsfeqaO+pp7n77VVtlqWYp/9Mf/5a/o2/xT/ZnecnOCL3XFygi92x8kJI/XZswLQFAEHS9fzqie1AzqpIqvwypx0VLfuGVvi+2UzE8omK8sAHq0PgLj/Z7WQlv6f1bIq1gVGlc+elHTQRL0m2j9V9YGak3zevau1hhGJZBmrmk2ry+9HFOnjWG2sZMhKmurvjOSvJ/n4+ReUzbOfv5qNv3fLf1I2DTHv2BARHlnbzcZbCg1l09jNjzO7T79nhz80o7Y138kDunbPnVA2R5f5g135SV3ZjB/i825coUxw4np+HycO6GsticCn0gn+fshKUOxnG/7EcZyfK3yxO05O8MXuODnBF7vj5ITRZr31gLIo19vcInprT+ueN8UVvk/5hLapPsmzoehlXcY+GeNZVXFjUtts4fJTa1qXhKZMfkYaQTWqbDUQrYmKJkYQS3uMb9s7o8XISiIyB402VleM8WAlqyR0W0RtyDEArBrVUo40ufj50jPblM3e73ORjIr6Pha2cDHyr9Z0UM3lBS6IPbq2R9n8+fz1bHxwsa5sem/iQtuz/0a3o6rVT6lt//TKR9n4w7WfKJtXe1xk/i18TNk0Iv6uFZf0+9GZ5O/54puVCaKUP8fisniuVqXr0/tu/CPHcX6e8MXuODnBF7vj5ISLXl220BDtjyeNaiFbSIz1Z9TSlTxKoTKvq65MPMOrkNIhXfWktMb9r2RRVz3JKtz/XN2hW/2mJT1HWd02OqX92AbxKjSnxnW7o0iU0m32dFLFWMx1jUKk78dih2sY8rgAsNzV13Zylc9p6kl9rfELh9i4d0r7w1vu4z76703cqmxumTvAz5U0lc14oc3GZFyHDIbpKd0FOHxCazhPr3A9YmdRB9UUSQQwGRWIMvGIVq7UAVVjr/Hl2C3q6zj+Dj5u1/mz6D3vlWocJ/f4YnecnOCL3XFygi92x8kJFKw+0RfqZEQLAF4BMAvg+ADzzcalOGfg0py3z/nc2RVC0PW/MeLF/tOTEu0LIdww8hOfB5finIFLc94+5wuD/xrvODnBF7vj5ISLtdjvvkjnPR8uxTkDl+a8fc4XgIviszuOM3r813jHyQkjX+xEdBsRPUdE+4norlGffxiI6CtENE9ET75u2zQRPUBEL/T/P3WmY4waIrqciB4ioqeJ6Cki+nR/+6adNxGVieiHRPTj/px/u799DxE93H9HvkpEOvj/IkNEMRE9RkTf6o83/ZxHutiJKAbwBwD+LoDrAHySiK4b5RyG5I8A3Ca23QXgwRDCXgAP9sebiR6A3wghXAfgJgD/on9vN/O82wBuDSG8DcDbAdxGRDcB+CKAL4UQrgZwCsCdF2+KG/JpAM+8brzp5zzqb/YbAewPIRwIIXQA3AfgoyOew0BCCN8BcFJs/iiAe/v/vhcwypFcREIIR0IIj/b/3cD6i7gDm3jeYZ3TNZ0L/f8CgFsB/Gl/+6aaMwAQ0U4AtwP4r/0xYZPPGRj9Yt8B4PUNvg71t10KzIUQTufEHgUwdzEncyaIaDeAdwB4GJt83v1fhx8HMA/gAQAvAlgMIZzOAd2M78iXAfwmgNNF2mew+efsAt25ENb/hLEp/4xBROMA/gzAZ0IIrOXjZpx3CCENIbwdwE6s/+Z37cWd0Zkhoo8AmA8h/Ohiz+VsGXXxisMAXl+xYGd/26XAMSLaFkI4QkTbsP5NtKkgogLWF/ofhxC+0d+86ecNACGERSJ6CMDNAOpElPS/KTfbO/IeAL9MRB8GUAYwAeD3sLnnDGD03+yPANjbVy6LAD4B4JsjnsO58k0Ad/T/fQeA+y/iXBR9v/EeAM+EEH73dT/atPMmosuIqN7/9xiAD2Jda3gIwMf7ZptqziGEz4UQdoYQdmP9/f1/IYRfxSae808JIYz0PwAfBvA81n2z3xr1+Yec458AOAKgi3X/606s+2UPAngBwF8AmL7Y8xRzvgXrv6L/BMDj/f8+vJnnDeAXADzWn/OTAP5df/uVAH4IYD+ArwMoXey5bjD/9wH41qUyZ4+gc5yc4AKd4+QEX+yOkxN8sTtOTvDF7jg5wRe74+QEX+yOkxN8sTtOTvDF7jg54f8D1fj6aGof/fYAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "theImage = X[0]\n",
    "afficher(theImage)\n",
    "print(predir(myModel, theImage))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/5\n",
      "1130/1130 [==============================] - 148s 129ms/step - loss: 1.3903 - accuracy: 0.4971 - val_loss: 1.4602 - val_accuracy: 0.4642\n",
      "Epoch 2/5\n",
      "1130/1130 [==============================] - 142s 126ms/step - loss: 1.1473 - accuracy: 0.5998 - val_loss: 1.6280 - val_accuracy: 0.4651\n",
      "Epoch 3/5\n",
      " 175/1130 [===>..........................] - ETA: 2:00 - loss: 1.0493 - accuracy: 0.6291"
     ]
    },
    {
     "output_type": "error",
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-528c4d211510>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mhistory\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmyModel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYcat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m128\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_split\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.05\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m#Affichage de l'historique de l'apprentissage\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'accuracy'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'accuracy'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'val_accuracy'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'val_accuracy'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1181\u001b[0m                 _r=1):\n\u001b[0;32m   1182\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1183\u001b[1;33m               \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1184\u001b[0m               \u001b[1;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1185\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    887\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    888\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 889\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    891\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    915\u001b[0m       \u001b[1;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    916\u001b[0m       \u001b[1;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 917\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=not-callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    918\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    919\u001b[0m       \u001b[1;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3021\u001b[0m       (graph_function,\n\u001b[0;32m   3022\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m-> 3023\u001b[1;33m     return graph_function._call_flat(\n\u001b[0m\u001b[0;32m   3024\u001b[0m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0;32m   3025\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1958\u001b[0m         and executing_eagerly):\n\u001b[0;32m   1959\u001b[0m       \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1960\u001b[1;33m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[0;32m   1961\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0;32m   1962\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    589\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    590\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 591\u001b[1;33m           outputs = execute.execute(\n\u001b[0m\u001b[0;32m    592\u001b[0m               \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    593\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     57\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m     60\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[0;32m     61\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "history = myModel.fit(X, Ycat, epochs=5, batch_size=128, validation_split=0.05)\n",
    "\n",
    "#Affichage de l'historique de l'apprentissage\n",
    "plt.plot(history.history['accuracy'], label='accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='val_accuracy')\n",
    "plt.legend()\n",
    "plt.ylim([min(history.history['val_accuracy']+history.history['accuracy']), 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "INFO:tensorflow:Assets written to: exp906\\assets\n"
     ]
    }
   ],
   "source": [
    "myModel.save('exp906')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"my_model_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_6 (Conv2D)            (None, 46, 46, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 23, 23, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 23, 23, 32)        128       \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 21, 21, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 10, 10, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 10, 10, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 8, 8, 128)         73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 4, 4, 128)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_10 (Batc (None, 4, 4, 128)         512       \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 2, 2, 256)         295168    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 1, 1, 256)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_11 (Batc (None, 1, 1, 256)         1024      \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 64)                16448     \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 7)                 455       \n",
      "=================================================================\n",
      "Total params: 406,663\n",
      "Trainable params: 405,703\n",
      "Non-trainable params: 960\n",
      "_________________________________________________________________\n",
      "32/32 [==============================] - 5s 133ms/step - loss: 0.9429 - accuracy: 0.6607 - val_loss: 0.8001 - val_accuracy: 0.7026\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1f28d21ea60>"
      ]
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "monModele = keras.models.load_model(\"models/exp903\")\n",
    "monModele.summary()\n",
    "monModele.fit(X[:10000], Ycat[:10000], validation_split = 0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Chargement du modèle...\n",
      "Predictions...\n",
      "Calcul de la CM...\n",
      "(6, 6)\n",
      "Affichage...\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "IndexError",
     "evalue": "index 6 is out of bounds for axis 1 with size 6",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-5e456c6b1639>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     39\u001b[0m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m \u001b[0mshow_confusion_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0memotions\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"A\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-19-5e456c6b1639>\u001b[0m in \u001b[0;36mshow_confusion_matrix\u001b[1;34m(matrix, labels)\u001b[0m\n\u001b[0;32m     33\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 35\u001b[1;33m             text = ax.text(j, i, cm[i, j],\n\u001b[0m\u001b[0;32m     36\u001b[0m                            ha=\"center\", va=\"center\", color=\"w\")\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 6 is out of bounds for axis 1 with size 6"
     ]
    }
   ],
   "source": [
    "print(\"Chargement du modèle...\")\n",
    "modelCM = keras.models.load_model('models/exp906')\n",
    "\n",
    "print(\"Predictions...\")\n",
    "Nmax = 10\n",
    "y_pred = modelCM(Xf[:Nmax])\n",
    "y_true = np.array([int(nbr) for nbr in Yf[:Nmax]])\n",
    "\n",
    "y_pred = np.argmax(y_pred, axis=-1)\n",
    "\n",
    "print(\"Calcul de la CM...\")\n",
    "cm = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "print(\"Affichage...\")\n",
    "def show_confusion_matrix(matrix, labels):\n",
    "    fig, ax = plt.subplots(figsize=(10,10))\n",
    "    im = ax.imshow(matrix)\n",
    "    \n",
    "    N = len(labels)\n",
    "\n",
    "    # We want to show all ticks...\n",
    "    ax.set_xticks(np.arange(N))\n",
    "    ax.set_yticks(np.arange(N))\n",
    "    # ... and label them with the respective list entries\n",
    "    ax.set_xticklabels(labels)\n",
    "    ax.set_yticklabels(labels)\n",
    "\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    for i in range(N):\n",
    "        for j in range(N):\n",
    "            text = ax.text(j, i, cm[i, j],\n",
    "                           ha=\"center\", va=\"center\", color=\"w\")\n",
    "\n",
    "    ax.set_title(\"Matrice de confusion\")\n",
    "    fig.tight_layout()\n",
    "    plt.show()\n",
    "show_confusion_matrix(cm, emotions+[\"A\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}