diff --git a/__pycache__/game.cpython-39.pyc b/__pycache__/game.cpython-39.pyc
index 7d02876c56ba2785b4c28a3cabca083181f115e8..651c4e36d4d2ec98605dc25a7d090f585a74c840 100644
Binary files a/__pycache__/game.cpython-39.pyc and b/__pycache__/game.cpython-39.pyc differ
diff --git a/__pycache__/imageProcess.cpython-39.pyc b/__pycache__/imageProcess.cpython-39.pyc
index e7d8303a2280ab8e835ca610cec3303e6107676d..1eeed32b9d0c3883312050b81eb3a19e15ef7ce7 100644
Binary files a/__pycache__/imageProcess.cpython-39.pyc and b/__pycache__/imageProcess.cpython-39.pyc differ
diff --git a/buildEmotionModel.ipynb b/buildEmotionModel.ipynb
index 3c9282adecb544b83eb156d162c1be61d2739981..6cdbce3d11a1e06364014e94b9329360e3989d41 100644
--- a/buildEmotionModel.ipynb
+++ b/buildEmotionModel.ipynb
@@ -10,12 +10,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.9.7"
   },
   "orig_nbformat": 2,
   "kernelspec": {
    "name": "python3",
-   "display_name": "Python 3.9.5 64-bit (windows store)"
+   "display_name": "Python 3.9.7 64-bit (windows store)"
   },
   "metadata": {
    "interpreter": {
@@ -31,118 +31,123 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 1,
+   "source": [
+    "#@title Imports\r\n",
+    "#%load_ext autoreload  #Need to uncomment for import sometime, dont understand\r\n",
+    "\r\n",
+    "#Tensorflow :\r\n",
+    "from sklearn.metrics import confusion_matrix\r\n",
+    "import tensorflow as tf\r\n",
+    "from tensorflow import keras\r\n",
+    "from tensorflow.keras import datasets, layers, models, losses\r\n",
+    "import tensorflow_datasets as tfds\r\n",
+    "#from google.colab import files\r\n",
+    "\r\n",
+    "#Others :\r\n",
+    "from matplotlib import image\r\n",
+    "import os\r\n",
+    "import numpy as np\r\n",
+    "import matplotlib.pyplot as plt\r\n",
+    "import matplotlib\r\n",
+    "import random as rd\r\n",
+    "import cv2\r\n",
+    "import csv\r\n",
+    "\r\n",
+    "#Data loaders :\r\n",
+    "from loadFer2013DS import *\r\n",
+    "from loadRavdessDS import *\r\n",
+    "from loadExpWDS import *\r\n",
+    "from loadAffwildDS import *\r\n",
+    "\r\n",
+    "#Others\r\n",
+    "from utils import *\r\n",
+    "from config import *"
+   ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
+      "Chargement du modèle...\n",
       "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 *"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 2,
+   "source": [
+    "#Chargement des données\r\n",
+    "\r\n",
+    "# print(\"Array loading...\")\r\n",
+    "# Xf = np.load(\"data/array/Xf.npy\")\r\n",
+    "# Xe = np.load(\"data/array/Xe.npy\")\r\n",
+    "# Xa = np.load(\"data/array/Xa.npy\")\r\n",
+    "# Xr = np.load(\"data/array/Xr.npy\")\r\n",
+    "\r\n",
+    "# Yf = np.load(\"data/array/Yf.npy\")\r\n",
+    "# Ye = np.load(\"data/array/Ye.npy\")\r\n",
+    "# Ya = np.load(\"data/array/Ya.npy\")\r\n",
+    "# Yr = np.load(\"data/array/Yr.npy\")\r\n",
+    "\r\n",
+    "# print(\"Concatenation...\")\r\n",
+    "# X = np.concatenate([Xf, Xa, Xe, Xr])\r\n",
+    "# Y = np.concatenate([Yf, Xa, Xe, Yr])\r\n",
+    "\r\n",
+    "\r\n",
+    "\r\n",
+    "# print(\"Array loading...\")\r\n",
+    "# Xf = np.load(\"data/array/Xf.npy\")\r\n",
+    "# Xe = np.load(\"data/array/Xe.npy\")\r\n",
+    "\r\n",
+    "# Yf = np.load(\"data/array/Yf.npy\")\r\n",
+    "# Ye = np.load(\"data/array/Ye.npy\")\r\n",
+    "\r\n",
+    "# print(\"Concatenation...\")\r\n",
+    "# X = np.concatenate([Xf, Xe])\r\n",
+    "# Y = np.concatenate([Yf, Ye])\r\n",
+    "\r\n",
+    "\r\n",
+    "\r\n",
+    "\r\n",
+    "\r\n",
+    "# #Enregistre X et Y directement, à faire si assez de ram\r\n",
+    "# np.save(\"data/array/X\", X)\r\n",
+    "# np.save(\"data/array/Y\", Y)\r\n",
+    "\r\n",
+    "\r\n",
+    "#Chargment des données\r\n",
+    "X = np.load(\"data/array/X.npy\")\r\n",
+    "Y = np.load(\"data/array/Y.npy\")\r\n",
+    "print(\"X et Y chargés\")\r\n",
+    "\r\n",
+    "print(\"Done\")"
+   ],
    "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\")"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "source": [
+    "\r\n",
+    "#Chargment des données\r\n",
+    "X = np.load(\"data/array/X.npy\")\r\n",
+    "Y = np.load(\"data/array/Y.npy\")\r\n",
+    "print(\"X et Y chargés\")"
+   ],
    "outputs": [
     {
      "output_type": "stream",
@@ -152,115 +157,125 @@
      ]
     }
    ],
-   "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\")"
-   ]
+   "metadata": {}
   },
   {
+   "cell_type": "code",
+   "execution_count": 2,
    "source": [
-    "def loadData():\n",
-    "    return np.load(\"data/array/X.npy\"), np.load(\"data/array/Y.npy\")\n",
+    "def loadData():\r\n",
+    "    return np.load(\"data/array/X.npy\"), np.load(\"data/array/Y.npy\")\r\n",
     "X, Y = loadData()"
    ],
-   "cell_type": "markdown",
+   "outputs": [],
    "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"
-     ]
-    }
-   ],
+   "execution_count": null,
    "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",
+    "#@title Visualisation de chaque dataset\r\n",
+    "for X_, Y_, name in zip([Xf, Xr, Xe, Xa], [Yf, Yr, Ye, Ya], [\"fer2013\", \"ravdess\", \"expW\", \"affwild\"]):\r\n",
+    "    N=5\r\n",
+    "    M=5\r\n",
+    "    print(\"Dataset:\", name)\r\n",
+    "    print(\"Images:\", X_.shape, \"Labels:\", Y_.shape)\r\n",
+    "    plt.figure()\r\n",
+    "    for i in range(N*M):\r\n",
+    "        if X_.shape[0] == 0: continue\r\n",
+    "        k = rd.randrange(X_.shape[0])\r\n",
+    "        plt.subplot(N, M, i+1)\r\n",
+    "        plt.xticks([])\r\n",
+    "        plt.yticks([])\r\n",
+    "        plt.grid(False)\r\n",
+    "\r\n",
+    "        afficher(X_[k])\r\n",
+    "        plt.title(emotions[int(Y_[k])])\r\n",
     "    plt.show()"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
+   "cell_type": "code",
+   "execution_count": 3,
    "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",
+    "#Visualisation du dataset global\r\n",
+    "print(\"X:\", X.shape)\r\n",
+    "print(\"Y:\", Y.shape)\r\n",
+    "\r\n",
+    "N=3\r\n",
+    "M=4\r\n",
+    "plt.figure()\r\n",
+    "for i in range(N*M):\r\n",
+    "    k = rd.randrange(X.shape[0])\r\n",
+    "    plt.subplot(N, M, i+1)\r\n",
+    "    plt.xticks([])\r\n",
+    "    plt.yticks([])\r\n",
+    "    plt.grid(False)\r\n",
+    "\r\n",
+    "    afficher(X[k])\r\n",
+    "    plt.title(emotions[int(Y[k])])\r\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"
+      "X: (125887, 48, 48, 1)\n",
+      "Y: (125887,)\n"
      ]
     },
     {
      "output_type": "display_data",
      "data": {
-      "text/plain": "<Figure size 432x288 with 9 Axes>",
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\n"
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+      "text/plain": [
+       "<Figure size 432x288 with 12 Axes>"
+      ]
      },
      "metadata": {}
     }
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 12,
+   "source": [
+    "#Visualisation emotion par emotion\r\n",
+    "dicoEmotion = {emotion:[] for emotion in emotions}\r\n",
+    "for k in range(len(X)):\r\n",
+    "    emotion = emotions[int(Y[k])]\r\n",
+    "    dicoEmotion[emotion].append(k)\r\n",
+    "print(\"dico créé\")\r\n",
+    "#print(dicoEmotion)\r\n",
+    "\r\n",
+    "for emotion in dicoEmotion:\r\n",
+    "    print(f\"{emotion}, {len(dicoEmotion[emotion])} images, exemple:\")\r\n",
+    "    try:\r\n",
+    "        label = rd.choice(dicoEmotion[emotion])\r\n",
+    "        afficher(X[int(label)])\r\n",
+    "    except:\r\n",
+    "        pass\r\n",
+    "print([len(dicoEmotion[emotion]) for emotion in dicoEmotion])\r\n",
+    "plt.bar(range(7), [len(dicoEmotion[emotion]) for emotion in dicoEmotion])"
+   ],
    "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"
+      "dico créé\n",
+      "Angry, 8609 images, exemple:\n",
+      "Disgust, 4522 images, exemple:\n",
+      "Fear, 6185 images, exemple:\n",
+      "Happy, 39296 images, exemple:\n",
+      "Sad, 16609 images, exemple:\n",
+      "Surprise, 9854 images, exemple:\n",
+      "Neutral, 40812 images, exemple:\n",
+      "[8609, 4522, 6185, 39296, 16609, 9854, 40812]\n"
      ]
     },
     {
@@ -271,379 +286,296 @@
       ]
      },
      "metadata": {},
-     "execution_count": 35
+     "execution_count": 12
     },
     {
      "output_type": "display_data",
      "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
      },
      "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])"
-   ]
+   "metadata": {
+    "tags": []
+   }
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
    "source": [
-    "#@title Hyperparamètres\n",
-    "epochs = 2\n",
-    "batch_size = 128\n",
+    "#@title Hyperparamètres\r\n",
+    "epochs = 2\r\n",
+    "batch_size = 128\r\n",
     "validation_size = 0.1"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "source": [
+    "#Labels catégoriques\r\n",
+    "Ycat = keras.utils.to_categorical(Y)\r\n",
+    "\r\n",
+    "print(\"X\", X.shape)\r\n",
+    "print(\"Y\", Ycat.shape)"
+   ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "X (152252, 48, 48, 1)\nY (152252, 7)\n"
+      "X (125887, 48, 48, 1)\n",
+      "Y (125887, 7)\n"
      ]
     }
    ],
-   "source": [
-    "#Labels catégoriques\n",
-    "Ycat = keras.utils.to_categorical(Y)\n",
-    "\n",
-    "print(\"X\", X.shape)\n",
-    "print(\"Y\", Ycat.shape)"
-   ]
+   "metadata": {}
   },
   {
    "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"
-     ]
-    }
-   ],
+   "execution_count": 8,
    "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()"
-   ]
+    "#MODELE\r\n",
+    "class MyModel(keras.Sequential):\r\n",
+    "\r\n",
+    "    def __init__(self, input_shape):\r\n",
+    "        super(MyModel, self).__init__()\r\n",
+    "        #Pre processing\r\n",
+    "        self.add(keras.layers.experimental.preprocessing.RandomContrast(factor=(0.5,0.5)))\r\n",
+    "        self.add(keras.layers.experimental.preprocessing.RandomFlip(mode=\"horizontal\"))\r\n",
+    "        \r\n",
+    "        #48*48 *1\r\n",
+    "        self.add(keras.layers.Conv2D(32, kernel_size = (3, 3), activation = 'relu', input_shape = input_shape))        \r\n",
+    "        self.add(keras.layers.MaxPooling2D(pool_size = 2))\r\n",
+    "        self.add(keras.layers.BatchNormalization())\r\n",
+    "\r\n",
+    "        #23*23 *32\r\n",
+    "        self.add(keras.layers.Conv2D(64, kernel_size = (3, 3), activation = 'relu'))\r\n",
+    "        self.add(keras.layers.MaxPooling2D(pool_size = 2))\r\n",
+    "        self.add(keras.layers.BatchNormalization())\r\n",
+    "\r\n",
+    "        #10*10 *64\r\n",
+    "        self.add(keras.layers.Conv2D(128, kernel_size = (3, 3), activation = 'relu'))\r\n",
+    "        self.add(keras.layers.MaxPooling2D(pool_size = 2))\r\n",
+    "        self.add(keras.layers.BatchNormalization())\r\n",
+    "\r\n",
+    "        #4*4 *128\r\n",
+    "        self.add(keras.layers.Conv2D(256, kernel_size = (3, 3), activation = 'relu'))\r\n",
+    "        self.add(keras.layers.MaxPooling2D(pool_size = 2))\r\n",
+    "        self.add(keras.layers.BatchNormalization())\r\n",
+    "        \r\n",
+    "        #1*1 *256\r\n",
+    "        self.add(keras.layers.Flatten())\r\n",
+    "        self.add(keras.layers.Dense(256, activation = 'relu'))\r\n",
+    "        self.add(keras.layers.Dense(128, activation = 'relu'))\r\n",
+    "        self.add(keras.layers.Dense(64,  activation = 'relu'))\r\n",
+    "        self.add(keras.layers.Dense(20,  activation = 'relu'))\r\n",
+    "        self.add(keras.layers.Dense(7, activation = 'softmax'))\r\n",
+    "        #7        \r\n",
+    "\r\n",
+    "        #Compile\r\n",
+    "        self.compile(optimizer = 'adam', loss=losses.categorical_crossentropy, metrics = ['accuracy'])\r\n",
+    "\r\n",
+    "    \r\n",
+    "    def predir(self, monImage):\r\n",
+    "        return self.predict(np.array([monImage]))[0,:]\r\n",
+    "\r\n",
+    "myModel = MyModel(input_shape)\r\n",
+    "#myModel.summary()"
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 28,
+   "source": [
+    "theImage = X[0]\r\n",
+    "afficher(theImage)\r\n",
+    "print(predir(myModel, theImage))"
+   ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "[0.14478482 0.13992985 0.1444098  0.14547563 0.14638613 0.14012544\n 0.1388883 ]\n"
+      "tf.Tensor(\n",
+      "[0.14272237 0.14253289 0.14222175 0.13957562 0.14415951 0.14358862\n",
+      " 0.14519916], shape=(7,), dtype=float32)\n"
      ]
     },
     {
      "output_type": "display_data",
      "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"250.052344pt\" version=\"1.1\" viewBox=\"0 0 251.565 250.052344\" width=\"251.565pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2021-06-09T14:11:17.946663</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.4.1, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  </rdf:RDF>\r\n </metadata>\r\n <defs>\r\n  <style 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\n"
+      "image/png": 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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=",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
      },
      "metadata": {
       "needs_background": "light"
      }
     }
    ],
-   "source": [
-    "theImage = X[0]\n",
-    "afficher(theImage)\n",
-    "print(predir(myModel, theImage))"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 29,
+   "source": [
+    "history = myModel.fit(X, Ycat, epochs=5, batch_size=128, validation_split=0.05)\r\n",
+    "\r\n",
+    "#Affichage de l'historique de l'apprentissage\r\n",
+    "plt.plot(history.history['accuracy'], label='accuracy')\r\n",
+    "plt.plot(history.history['val_accuracy'], label='val_accuracy')\r\n",
+    "plt.legend()\r\n",
+    "plt.ylim([min(history.history['val_accuracy']+history.history['accuracy']), 1])\r\n",
+    "plt.show()"
+   ],
    "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",
+      "935/935 [==============================] - 117s 124ms/step - loss: 1.2981 - accuracy: 0.5278 - val_loss: 1.0284 - val_accuracy: 0.6559\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",
+      "935/935 [==============================] - 113s 121ms/step - loss: 1.1026 - accuracy: 0.6055 - val_loss: 1.0460 - val_accuracy: 0.6426\n",
       "Epoch 3/5\n",
-      " 175/1130 [===>..........................] - ETA: 2:00 - loss: 1.0493 - accuracy: 0.6291"
+      "935/935 [==============================] - 112s 120ms/step - loss: 1.0359 - accuracy: 0.6284 - val_loss: 1.0453 - val_accuracy: 0.6321\n",
+      "Epoch 4/5\n",
+      "935/935 [==============================] - 113s 121ms/step - loss: 0.9919 - accuracy: 0.6447 - val_loss: 1.0190 - val_accuracy: 0.6537\n",
+      "Epoch 5/5\n",
+      "935/935 [==============================] - 113s 121ms/step - loss: 0.9512 - accuracy: 0.6589 - val_loss: 1.0040 - val_accuracy: 0.6408\n"
      ]
     },
     {
-     "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: "
-     ]
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
     }
    ],
-   "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()"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 24,
+   "source": [
+    "modelName = \"exp908\"\r\n",
+    "myModel.save(modelName)"
+   ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "INFO:tensorflow:Assets written to: exp906\\assets\n"
+      "INFO:tensorflow:Assets written to: exp907\\assets\n"
      ]
     }
    ],
-   "source": [
-    "myModel.save('exp906')"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
+   "execution_count": 26,
+   "source": [
+    "monModele = keras.models.load_model(\"models/\"+modelName)\r\n",
+    "#monModele.summary()\r\n",
+    "monModele.fit(X[:10000], Ycat[:10000], validation_split = 0.9)"
+   ],
    "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"
+      "32/32 [==============================] - 5s 128ms/step - loss: 0.8691 - accuracy: 0.6737 - val_loss: 0.8754 - val_accuracy: 0.6761\n"
      ]
     },
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<tensorflow.python.keras.callbacks.History at 0x1f28d21ea60>"
+       "<tensorflow.python.keras.callbacks.History at 0x23f23a0ab80>"
       ]
      },
      "metadata": {},
-     "execution_count": 28
+     "execution_count": 26
     }
    ],
-   "source": [
-    "monModele = keras.models.load_model(\"models/exp903\")\n",
-    "monModele.summary()\n",
-    "monModele.fit(X[:10000], Ycat[:10000], validation_split = 0.9)"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
+   "execution_count": 30,
+   "source": [
+    "print(\"Chargement du modèle...\")\r\n",
+    "modelCM = keras.models.load_model('models/'+modelName)\r\n",
+    "\r\n",
+    "print(\"Predictions...\")\r\n",
+    "Nmax = 10000\r\n",
+    "y_pred = modelCM(X[:Nmax])\r\n",
+    "y_true = np.array([int(nbr) for nbr in Y[:Nmax]])\r\n",
+    "\r\n",
+    "y_pred = np.argmax(y_pred, axis=-1)\r\n",
+    "\r\n",
+    "print(\"Calcul de la CM...\")\r\n",
+    "cm = confusion_matrix(y_true, y_pred)\r\n",
+    "\r\n",
+    "print(\"Affichage...\")\r\n",
+    "def show_confusion_matrix(matrix, labels):\r\n",
+    "    fig, ax = plt.subplots(figsize=(10,10))\r\n",
+    "    im = ax.imshow(matrix)\r\n",
+    "    \r\n",
+    "    N = len(labels)\r\n",
+    "\r\n",
+    "    # We want to show all ticks...\r\n",
+    "    ax.set_xticks(np.arange(N))\r\n",
+    "    ax.set_yticks(np.arange(N))\r\n",
+    "    # ... and label them with the respective list entries\r\n",
+    "    ax.set_xticklabels(labels)\r\n",
+    "    ax.set_yticklabels(labels)\r\n",
+    "\r\n",
+    "    # Rotate the tick labels and set their alignment.\r\n",
+    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\r\n",
+    "             rotation_mode=\"anchor\")\r\n",
+    "\r\n",
+    "    # Loop over data dimensions and create text annotations.\r\n",
+    "    for i in range(N):\r\n",
+    "        for j in range(N):\r\n",
+    "            text = ax.text(j, i, cm[i, j],\r\n",
+    "                           ha=\"center\", va=\"center\", color=\"w\")\r\n",
+    "\r\n",
+    "    ax.set_title(\"Matrice de confusion\")\r\n",
+    "    fig.tight_layout()\r\n",
+    "    plt.show()\r\n",
+    "show_confusion_matrix(cm, emotions)"
+   ],
    "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"
+      "Predictions...\n"
      ]
     }
    ],
-   "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": []
+   "metadata": {}
   }
  ]
 }
\ No newline at end of file
diff --git a/game.py b/game.py
index 2c292a212a1ff4cc107c09a1c676ac3b01250c8c..c387bc93ac2583f8fae390b26493aba79b094945 100644
--- a/game.py
+++ b/game.py
@@ -49,7 +49,7 @@ def game(playTime = 30, invincibleFrame=0.5, dt_required=0.5, n_photos=None):
 
 
 
-    while cap.isOpened():		 #or while 1. cap.isOpened() is false if there is a problem
+    while cap.isOpened():		
         ret, frame = cap.read()  #Read next video frame, stop if frame not well read
         if not ret: break
         
@@ -113,4 +113,7 @@ def game(playTime = 30, invincibleFrame=0.5, dt_required=0.5, n_photos=None):
             plt.imshow(photo)
             plt.xticks([])
             plt.yticks([])
-            plt.show()
\ No newline at end of file
+            plt.show()
+
+if __name__ == "__main__":
+    game()
diff --git a/imageProcess.py b/imageProcess.py
index 0234f78b612312258d355ad927cb34ef68f293b3..c98cd8042eb83cf535eb84aa52a41ac6f828d99e 100644
--- a/imageProcess.py
+++ b/imageProcess.py
@@ -57,6 +57,7 @@ def selectFace(image):
         face = image[y:y+h, x:x+w]
         return face
 
+#Some tests here.
 # image = cv2.imread("cagnol.jpg", 1)  #Load Cagnol colored image
 # imageProcess(image)
 # cv2.imshow("Cagnol", image)
diff --git a/main.py b/main.py
index 3620ab465569f7c12b1e51298f1ebb4abcbaf660..da14caa777a43b6ee6c88de0cbf10f2a56dd6406 100644
--- a/main.py
+++ b/main.py
@@ -1,6 +1,6 @@
 from game import *
 from videoCapture import *
 
-game(playTime=60, invincibleFrame=1, dt_required=0.3, n_photos=5)
+#game(playTime=40, invincibleFrame=1, dt_required=0.3, n_photos=5)
 
-#videoCapture()
\ No newline at end of file
+videoCapture()
\ No newline at end of file
diff --git a/models/exp907/keras_metadata.pb b/models/exp907/keras_metadata.pb
new file mode 100644
index 0000000000000000000000000000000000000000..485f760336ff9fd5535e0329f315131e2cb9c68c
--- /dev/null
+++ b/models/exp907/keras_metadata.pb
@@ -0,0 +1,23 @@
+
+��
root"_tf_keras_sequential*�
{"name": "my_model_1", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "MyModel", "config": {"name": "my_model_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 48, 48, 1]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "conv2d_4_input"}}, {"class_name": "Conv2D", "config": {"name": "conv2d_4", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 48, 48, 1]}, "dtype": "float32", "filters": 32, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_4", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_4", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_5", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_5", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_5", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_6", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_6", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_6", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_7", "trainable": true, "dtype": "float32", "filters": 256, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_7", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_7", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}}, {"class_name": "Flatten", "config": {"name": "flatten_1", "trainable": true, "dtype": "float32", "data_format": "channels_last"}}, {"class_name": "Dense", "config": {"name": "dense_3", "trainable": true, "dtype": "float32", "units": 128, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_4", "trainable": true, "dtype": "float32", "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_5", "trainable": true, "dtype": "float32", "units": 7, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "shared_object_id": 47, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 1}}, "shared_object_id": 48}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 48, 48, 1]}, "is_graph_network": true, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 48, 48, 1]}, "float32", "conv2d_4_input"]}, "keras_version": "2.5.0", "backend": "tensorflow", "model_config": {"class_name": "MyModel", "config": {"name": "my_model_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 48, 48, 1]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "conv2d_4_input"}, "shared_object_id": 0}, {"class_name": "Conv2D", "config": {"name": "conv2d_4", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 48, 48, 1]}, "dtype": "float32", "filters": 32, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 2}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 3}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_4", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "shared_object_id": 4}, {"class_name": 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"gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 15}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 16}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 17}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "shared_object_id": 18}, {"class_name": "Conv2D", "config": {"name": "conv2d_6", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 19}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 20}, 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root.layer_with_weights-0"_tf_keras_layer*�
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+�root.layer-1"_tf_keras_layer*�{"name": "max_pooling2d_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_4", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "shared_object_id": 4, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 50}}2


+�	root.layer_with_weights-1"_tf_keras_layer*�{"name": "batch_normalization_4", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_4", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 5}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 6}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 7}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 8}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "shared_object_id": 9, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 32}}, "shared_object_id": 51}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 23, 23, 32]}}2


+�	root.layer_with_weights-2"_tf_keras_layer*�	{"name": "conv2d_5", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_5", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 11}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 12, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 32}}, "shared_object_id": 52}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 23, 23, 32]}}2


+�root.layer-4"_tf_keras_layer*�{"name": "max_pooling2d_5", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_5", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "shared_object_id": 13, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 53}}2


+�	root.layer_with_weights-3"_tf_keras_layer*�{"name": "batch_normalization_5", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_5", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 14}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 15}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 16}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 17}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "shared_object_id": 18, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 64}}, "shared_object_id": 54}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 10, 10, 64]}}2


+�	root.layer_with_weights-4"_tf_keras_layer*�	{"name": "conv2d_6", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_6", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 19}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 20}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 21, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 64}}, "shared_object_id": 55}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 10, 10, 64]}}2


+�root.layer-7"_tf_keras_layer*�{"name": "max_pooling2d_6", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_6", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "shared_object_id": 22, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 56}}2


+�		root.layer_with_weights-5"_tf_keras_layer*�{"name": "batch_normalization_6", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_6", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 23}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 24}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 25}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 26}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "shared_object_id": 27, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 128}}, "shared_object_id": 57}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 4, 4, 128]}}2


+�	
+root.layer_with_weights-6"_tf_keras_layer*�	{"name": "conv2d_7", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_7", "trainable": true, "dtype": "float32", "filters": 256, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 28}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 29}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 30, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 128}}, "shared_object_id": 58}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 4, 4, 128]}}2


+�
root.layer-10"_tf_keras_layer*�{"name": "max_pooling2d_7", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_7", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "shared_object_id": 31, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 59}}2


+�	root.layer_with_weights-7"_tf_keras_layer*�{"name": "batch_normalization_7", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_7", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 32}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 33}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 34}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 35}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "shared_object_id": 36, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 256}}, "shared_object_id": 60}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 1, 1, 256]}}2


+�

root.layer-12"_tf_keras_layer*�{"name": "flatten_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Flatten", "config": {"name": "flatten_1", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "shared_object_id": 37, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 1, "axes": {}}, "shared_object_id": 61}}2


+�root.layer_with_weights-8"_tf_keras_layer*�{"name": "dense_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_3", "trainable": true, "dtype": "float32", "units": 128, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 38}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 39}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 40, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 256}}, "shared_object_id": 62}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 256]}}2


+�root.layer_with_weights-9"_tf_keras_layer*�{"name": "dense_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_4", "trainable": true, "dtype": "float32", "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 41}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 42}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 43, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 128}}, "shared_object_id": 63}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 128]}}2


+�root.layer_with_weights-10"_tf_keras_layer*�{"name": "dense_5", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_5", "trainable": true, "dtype": "float32", "units": 7, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 44}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 45}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 46, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 64}}, "shared_object_id": 64}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 64]}}2


+�
�
root.keras_api.metrics.0"_tf_keras_metric*�
{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 65}2


+�
�
root.keras_api.metrics.1"_tf_keras_metric*�
{"class_name": "MeanMetricWrapper", "name": "accuracy", "dtype": "float32", "config": {"name": "accuracy", "dtype": "float32", "fn": "categorical_accuracy"}, "shared_object_id": 49}2


\ No newline at end of file
diff --git a/models/exp907/saved_model.pb b/models/exp907/saved_model.pb
new file mode 100644
index 0000000000000000000000000000000000000000..008c3ed80ffea6f86ff8190721e4d3ed24830432
Binary files /dev/null and b/models/exp907/saved_model.pb differ
diff --git a/models/exp907/variables/variables.data-00000-of-00001 b/models/exp907/variables/variables.data-00000-of-00001
new file mode 100644
index 0000000000000000000000000000000000000000..2032fb83376e6c6ca007960b0fbaada57f36c96b
Binary files /dev/null and b/models/exp907/variables/variables.data-00000-of-00001 differ
diff --git a/models/exp907/variables/variables.index b/models/exp907/variables/variables.index
new file mode 100644
index 0000000000000000000000000000000000000000..708032fba5aa3a370cef8a5636d548dae1713d16
Binary files /dev/null and b/models/exp907/variables/variables.index differ
diff --git a/smileys/Disgust_.png b/smileys/Disgust2.png
similarity index 100%
rename from smileys/Disgust_.png
rename to smileys/Disgust2.png
diff --git a/videoCapture.py b/videoCapture.py
index 6c27ee3f87054f0368c66472c397c2f8750f1d36..ac712cf82f29b57076769d14adacd59e49001145 100644
--- a/videoCapture.py
+++ b/videoCapture.py
@@ -14,7 +14,7 @@ def videoCapture():
 
         ip.imageProcess(frame)                          #Process frame
 
-        cv2.imshow("Image traitée", frame)  			#Show processed image in a window
+        cv2.imshow("Image", frame)  			#Show processed image in a window
 
         if cv2.waitKey(1) & 0xFF == ord('q'):			#If you press Q, stop the while and so the capture
             break