diff --git a/buildEmotionModel.ipynb b/buildEmotionModel.ipynb
index 0cd664ae6fe4da9c7480ebc815213454fb519cf9..4d85c29e5471998163244ec72bba7910b3fd9f46 100644
--- a/buildEmotionModel.ipynb
+++ b/buildEmotionModel.ipynb
@@ -86,26 +86,44 @@
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "Array loading...\n"
+      "Array loading...\n",
+      "Concatenation...\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",
-    "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",
+    "X = np.concatenate([Xf, Xe])\n",
+    "Y = np.concatenate([Yf, Ye])\n",
+    "\n",
+    "\n",
     "\n",
     "\n",
     "\n",
@@ -114,29 +132,61 @@
     "# np.save(\"data/array/Y\", Y)\n",
     "\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"
+    "# print(\"X et Y chargés\")\n",
+    "\n",
+    "print(\"Done\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "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": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "NameError",
+     "evalue": "name 'Xf' is not defined",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-2-27f8e461a14b>\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 'Xf' 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",
@@ -160,7 +210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -174,8 +224,8 @@
      "output_type": "display_data",
      "data": {
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\n"
      },
      "metadata": {}
     }
@@ -202,7 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,14 +264,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "X (53631, 48, 48, 1)\nY (53631, 7)\n"
+      "X (152252, 48, 48, 1)\nY (152252, 7)\n"
      ]
     }
    ],
@@ -235,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -299,21 +349,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "[0.1431607  0.14394405 0.1389222  0.14134979 0.14626577 0.14387079\n 0.14248675]\n"
+      "[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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\n"
      },
      "metadata": {
@@ -329,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -337,13 +387,11 @@
      "name": "stdout",
      "text": [
       "Epoch 1/5\n",
-      "399/399 [==============================] - 56s 135ms/step - loss: 1.4518 - accuracy: 0.4442 - val_loss: 1.6893 - val_accuracy: 0.3210\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",
-      "399/399 [==============================] - 55s 139ms/step - loss: 1.0833 - accuracy: 0.5951 - val_loss: 1.8123 - val_accuracy: 0.5011\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",
-      "399/399 [==============================] - 54s 135ms/step - loss: 0.9446 - accuracy: 0.6483 - val_loss: 1.9944 - val_accuracy: 0.4933\n",
-      "Epoch 4/5\n",
-      " 19/399 [>.............................] - ETA: 50s - loss: 0.9010 - accuracy: 0.6706"
+      " 175/1130 [===>..........................] - ETA: 2:00 - loss: 1.0493 - accuracy: 0.6291"
      ]
     },
     {
@@ -353,7 +401,7 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-7-528c4d211510>\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<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",
@@ -378,19 +426,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "INFO:tensorflow:Assets written to: exp905\\assets\n"
+      "INFO:tensorflow:Assets written to: exp906\\assets\n"
      ]
     }
    ],
    "source": [
-    "myModel.save('exp905')"
+    "myModel.save('exp906')"
    ]
   },
   {
@@ -401,53 +449,93 @@
     {
      "output_type": "error",
      "ename": "ValueError",
-     "evalue": "Mix of label input types (string and number)",
+     "evalue": "Training data contains 1000 samples, which is not sufficient to split it into a validation and training set as specified by `validation_split=1`. Either provide more data, or a different value for the `validation_split` argument.",
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-10-bc0ddd3965dc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mcm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\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     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\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\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\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[0mkwargs\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     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\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\\sklearn\\metrics\\_classification.py\u001b[0m in \u001b[0;36mconfusion_matrix\u001b[1;34m(y_true, y_pred, labels, sample_weight, normalize)\u001b[0m\n\u001b[0;32m    302\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    303\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlabels\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--> 304\u001b[1;33m         \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0munique_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\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    305\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    306\u001b[0m         \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\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\\sklearn\\utils\\multiclass.py\u001b[0m in \u001b[0;36munique_labels\u001b[1;34m(*ys)\u001b[0m\n\u001b[0;32m    102\u001b[0m     \u001b[1;31m# Check that we don't mix string type with number type\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    103\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mys_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\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--> 104\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Mix of label input types (string and number)\"\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    105\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    106\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mys_labels\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;31mValueError\u001b[0m: Mix of label input types (string and number)"
+      "\u001b[1;32m<ipython-input-10-b659350fff8c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mmonModele\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"models/exp906\"\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[0mmonModele\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[1;33m:\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYcat\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_split\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\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~\\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   1117\u001b[0m       \u001b[1;31m# `Tensor` and `NumPy` input.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1118\u001b[0m       (x, y, sample_weight), validation_data = (\n\u001b[1;32m-> 1119\u001b[1;33m           data_adapter.train_validation_split(\n\u001b[0m\u001b[0;32m   1120\u001b[0m               (x, y, sample_weight), validation_split=validation_split))\n\u001b[0;32m   1121\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\\data_adapter.py\u001b[0m in \u001b[0;36mtrain_validation_split\u001b[1;34m(arrays, validation_split)\u001b[0m\n\u001b[0;32m   1474\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1475\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0msplit_at\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0msplit_at\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mbatch_dim\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1476\u001b[1;33m     raise ValueError(\n\u001b[0m\u001b[0;32m   1477\u001b[0m         \u001b[1;34m\"Training data contains {batch_dim} samples, which is not sufficient \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1478\u001b[0m         \u001b[1;34m\"to split it into a validation and training set as specified by \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Training data contains 1000 samples, which is not sufficient to split it into a validation and training set as specified by `validation_split=1`. Either provide more data, or a different value for the `validation_split` argument."
      ]
     }
    ],
    "source": [
-    "print()\n",
-    "modelCM = keras.models.load_model('models/exp905')\n",
+    "monModele = keras.models.load_model(\"models/exp906\")\n",
+    "monModele.fit(X[:1000], Ycat[:1000], 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",
-    "# À remplir\n",
-    "y_pred = modelCM(Xf)\n",
-    "y_true = Yf\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",
-    "\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, class_names)"
+    "show_confusion_matrix(cm, emotions+[\"A\"])"
    ]
   },
   {
diff --git a/game.py b/game.py
index 3f3a995b7727fdaa8d3e8fc21689f4cb2934d9eb..9ae95912cc87e57466ff45c1074d2c4ea46613c1 100644
--- a/game.py
+++ b/game.py
@@ -7,7 +7,6 @@ from config import emotions
 
 cap = cv2.VideoCapture(0)   #0 means we capture the first camera, your webcam probably
 score = 0
-t = 0
 N = 15
 
 def smileyRandom(emotionToDodge):
@@ -25,22 +24,22 @@ while cap.isOpened():		 #or while 1. cap.isOpened() is false if there is a probl
     if not ret: break
     
     emotionsList = ip.imageProcess(frame, returnEmotion=True)
-    if len(emotionsList)==1:
-        if emotionsList[0] == emotion: #If emotion recognized, increase score, reset smiley to mimick and write "GG!"
-            score += 1
-            cv2.putText(smiley, "Emotion reconnue !", (50,50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)
-            cv2.imshow("Smiley", smiley)         
-            smiley, emotion = smileyRandom(emotion)
+    
+    if emotion in emotionsList: #If emotion recognized, increase score, reset smiley to mimick and write "GG!"
+        score += 1
+        cv2.putText(smiley, "Emotion reconnue !", (50,50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)
+        cv2.imshow("Smiley", smiley)         
+        smiley, emotion = smileyRandom(emotion)
 
 
     cv2.imshow("Caméra", frame)  			#Show you making emotional faces
-    cv2.putText(smiley, "Score: "+str(score), (20,20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
+    cv2.putText(smiley, "Score: "+str(score), (40,40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
     cv2.imshow("Smiley", smiley)            #Show the smiley to mimic
 
     if cv2.waitKey(1) & 0xFF == ord('q'):			#If you press Q, stop the while and so the capture
-        break
+        break   
 
-    if cv2.waitKey(1) & 0xFF == ord('p'):			#If you press P, pass the smiley but lower your score
+    elif cv2.waitKey(1) & 0xFF == ord('p'):			#If you press P, pass the smiley but lower your score
         score -= 1
         smiley, emotion = smileyRandom(emotion)
 
diff --git a/models/exp906/keras_metadata.pb b/models/exp906/keras_metadata.pb
new file mode 100644
index 0000000000000000000000000000000000000000..bcaed56d1aaf5c93ecb85881e3bbeb5954ea6fc8
--- /dev/null
+++ b/models/exp906/keras_metadata.pb
@@ -0,0 +1,25 @@
+
+�
root"_tf_keras_sequential*ː
{"name": "my_model", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "MyModel", "config": {"name": "my_model", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 48, 48, 1]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "conv2d_input"}}, {"class_name": "Conv2D", "config": {"name": "conv2d", "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": 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+�root.layer-4"_tf_keras_layer*�{"name": "max_pooling2d_1", "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_1", "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": 55}}2


+�	root.layer_with_weights-3"_tf_keras_layer*�{"name": "batch_normalization_1", "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_1", "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": 56}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 10, 10, 64]}}2


+�	root.layer_with_weights-4"_tf_keras_layer*�	{"name": "conv2d_2", "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_2", "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": 57}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 10, 10, 64]}}2


+�root.layer-7"_tf_keras_layer*�{"name": "max_pooling2d_2", "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_2", "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": 58}}2


+�		root.layer_with_weights-5"_tf_keras_layer*�{"name": "batch_normalization_2", "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_2", "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": 59}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 4, 4, 128]}}2


+�	
+root.layer_with_weights-6"_tf_keras_layer*�	{"name": "conv2d_3", "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_3", "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": 60}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 4, 4, 128]}}2


+�
root.layer-10"_tf_keras_layer*�{"name": "max_pooling2d_3", "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_3", "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": 61}}2


+�	root.layer_with_weights-7"_tf_keras_layer*�{"name": "batch_normalization_3", "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_3", "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": 62}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 1, 1, 256]}}2


+�

root.layer-12"_tf_keras_layer*�{"name": "flatten", "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", "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": 63}}2


+�root.layer_with_weights-8"_tf_keras_layer*�{"name": "dense", "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", "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": 64}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 256]}}2


+�
root.layer-14"_tf_keras_layer*�{"name": "dropout", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout", "trainable": true, "dtype": "float32", "rate": 0.3, "noise_shape": null, "seed": null}, "shared_object_id": 41}2


+�root.layer_with_weights-9"_tf_keras_layer*�{"name": "dense_1", "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_1", "trainable": true, "dtype": "float32", "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 42}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 43}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 44, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 128}}, "shared_object_id": 65}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 128]}}2


+�
root.layer-16"_tf_keras_layer*�{"name": "dropout_1", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "dtype": "float32", "rate": 0.3, "noise_shape": null, "seed": null}, "shared_object_id": 45}2


+�root.layer_with_weights-10"_tf_keras_layer*�{"name": "dense_2", "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_2", "trainable": true, "dtype": "float32", "units": 7, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 46}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 47}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 48, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 64}}, "shared_object_id": 66}, "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": 67}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": 51}2


\ No newline at end of file
diff --git a/models/exp906/saved_model.pb b/models/exp906/saved_model.pb
new file mode 100644
index 0000000000000000000000000000000000000000..91a757b67e62a81dd144fd846b5d901d3e99f200
Binary files /dev/null and b/models/exp906/saved_model.pb differ
diff --git a/models/exp906/variables/variables.data-00000-of-00001 b/models/exp906/variables/variables.data-00000-of-00001
new file mode 100644
index 0000000000000000000000000000000000000000..01e3b74ad9b42b581d0916589338fc2bdad09368
Binary files /dev/null and b/models/exp906/variables/variables.data-00000-of-00001 differ
diff --git a/models/exp906/variables/variables.index b/models/exp906/variables/variables.index
new file mode 100644
index 0000000000000000000000000000000000000000..49c461765caae8d27f721756cacd5f18e90ad62b
Binary files /dev/null and b/models/exp906/variables/variables.index differ
diff --git a/test.py b/test.py
index 8fb1880cb8d6411300b0593e9921ae3caa75ccd6..62e3c9c16a93b8b1ae516d758d4a7b0a5a639ff0 100644
--- a/test.py
+++ b/test.py
@@ -1,7 +1,11 @@
-import numpy as np
-A = np.array([ [[1,2],[3,4]] , [[5,6],[7,8]] , [[1,2],[3,4]]])
-B = np.array([ [[1,2],[3,4]] , [[5,6],[7,8]] ])
-A = A.tolist()
-B = B.tolist()
-C = np.stack(A+B, axis = 0)
-print(C.shape)
\ No newline at end of file
+
+import cv2
+while 1:
+    if cv2.waitKey(0) & 0xFF== ord('q'):			#If you press Q, stop the while and so the capture
+        break
+
+    if cv2.waitKey(1) & 0xFF == ord('p'):			#If you press P, pass the smiley but lower your score
+        score -= 1
+        smiley, emotion = smileyRandom(emotion)
+    print(1)
+    cv2.waitKey(2)
\ No newline at end of file