diff --git a/.vscode/settings.json b/.vscode/settings.json
index c2b79dbdc852dcac7855087a52d6f36603de7e7d..e6434a613891bcd7cc24c16d18c1d15bdd084d96 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -1,3 +1,3 @@
 {
-    "python.pythonPath": "C:\\Users\\timot\\AppData\\Local\\Microsoft\\WindowsApps\\python.exe"
+    "python.pythonPath": "C:\\Users\\timot\\AppData\\Local\\Microsoft\\WindowsApps\\python3.9.exe"
 }
\ No newline at end of file
diff --git a/buildEmotionModel2.ipynb b/build/buildEmotionModel2.ipynb
similarity index 100%
rename from buildEmotionModel2.ipynb
rename to build/buildEmotionModel2.ipynb
diff --git a/buildEmotionModel.ipynb b/buildEmotionModel.ipynb
index 50ffec723037ccb8d3cac0c504e826507fac67d1..0cd664ae6fe4da9c7480ebc815213454fb519cf9 100644
--- a/buildEmotionModel.ipynb
+++ b/buildEmotionModel.ipynb
@@ -10,16 +10,16 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.4"
+   "version": "3.9.5"
   },
   "orig_nbformat": 2,
   "kernelspec": {
-   "name": "python394jvsc74a57bd0d55a872fb12b64c3eb6a530d12935ddebcb38da0925d2cc3bd9c2ebc1d370b0d",
-   "display_name": "Python 3.9.4 64-bit"
+   "name": "python395jvsc74a57bd0241a3087cce17afe2d3bd9b072a200bdbe084ac2f3f20c4fa80011e0c0777b07",
+   "display_name": "Python 3.9.5 64-bit (windows store)"
   },
   "metadata": {
    "interpreter": {
-    "hash": "d55a872fb12b64c3eb6a530d12935ddebcb38da0925d2cc3bd9c2ebc1d370b0d"
+    "hash": "241a3087cce17afe2d3bd9b072a200bdbe084ac2f3f20c4fa80011e0c0777b07"
    }
   }
  },
@@ -49,6 +49,7 @@
     "#%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",
@@ -78,15 +79,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "Array loading...\n",
-      "Concatenation...\n"
+      "Array loading...\n"
      ]
     }
    ],
@@ -105,16 +105,25 @@
     "\n",
     "print(\"Concatenation...\")\n",
     "X = np.concatenate([Xf, Xa, Xe, Xr])\n",
-    "Y = np.concatenate([Yf, Ya, Ye, Yr])\n",
+    "Y = np.concatenate([Yf, Xa, Xe, Yr])\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)"
+    "# 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"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,21 +134,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "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-4-27f8e461a14b>\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"
-     ]
-    }
-   ],
+   "outputs": [],
    "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",
@@ -163,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -177,8 +174,8 @@
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 25 Axes>",
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\n"
      },
      "metadata": {}
     }
@@ -205,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -217,14 +214,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "X (152252, 48, 48, 1)\nY (152252, 7)\n"
+      "X (53631, 48, 48, 1)\nY (53631, 7)\n"
      ]
     }
    ],
@@ -238,9 +235,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Model: \"my_model\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d (Conv2D)              (None, 46, 46, 32)        320       \n_________________________________________________________________\nmax_pooling2d (MaxPooling2D) (None, 23, 23, 32)        0         \n_________________________________________________________________\nbatch_normalization (BatchNo (None, 23, 23, 32)        128       \n_________________________________________________________________\nconv2d_1 (Conv2D)            (None, 21, 21, 64)        18496     \n_________________________________________________________________\nmax_pooling2d_1 (MaxPooling2 (None, 10, 10, 64)        0         \n_________________________________________________________________\nbatch_normalization_1 (Batch (None, 10, 10, 64)        256       \n_________________________________________________________________\nconv2d_2 (Conv2D)            (None, 8, 8, 128)         73856     \n_________________________________________________________________\nmax_pooling2d_2 (MaxPooling2 (None, 4, 4, 128)         0         \n_________________________________________________________________\nbatch_normalization_2 (Batch (None, 4, 4, 128)         512       \n_________________________________________________________________\nconv2d_3 (Conv2D)            (None, 2, 2, 256)         295168    \n_________________________________________________________________\nmax_pooling2d_3 (MaxPooling2 (None, 1, 1, 256)         0         \n_________________________________________________________________\nbatch_normalization_3 (Batch (None, 1, 1, 256)         1024      \n_________________________________________________________________\nflatten (Flatten)            (None, 256)               0         \n_________________________________________________________________\ndense (Dense)                (None, 128)               32896     \n_________________________________________________________________\ndropout (Dropout)            (None, 128)               0         \n_________________________________________________________________\ndense_1 (Dense)              (None, 64)                8256      \n_________________________________________________________________\ndropout_1 (Dropout)          (None, 64)                0         \n_________________________________________________________________\ndense_2 (Dense)              (None, 7)                 455       \n=================================================================\nTotal params: 431,367\nTrainable params: 430,407\nNon-trainable params: 960\n_________________________________________________________________\n"
+     ]
+    }
+   ],
    "source": [
     "#MODELE\n",
     "class MyModel(keras.Sequential):\n",
@@ -248,8 +253,8 @@
     "    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",
+    "        # 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",
@@ -274,9 +279,9 @@
     "        #1*1 *256\n",
     "        self.add(keras.layers.Flatten())\n",
     "        self.add(keras.layers.Dense(128, activation = 'relu'))\n",
-    "        self.add(keras.layers.Dropout(0.2))\n",
-    "        self.add(keras.layers.Dense(64, activation = 'relu'))\n",
-    "        self.add(keras.layers.Dropout(0.2))\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",
@@ -288,26 +293,27 @@
     "        self.compile(optimizer = 'adam', loss=losses.categorical_crossentropy, metrics = ['accuracy'])\n",
     "\n",
     "myModel = MyModel(input_shape)\n",
-    "myModel.compile_o()"
+    "myModel.compile_o()\n",
+    "myModel.summary()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "[0.1456086  0.144747   0.14145526 0.14557326 0.141776   0.13994008\n 0.1408998 ]\n"
+      "[0.1431607  0.14394405 0.1389222  0.14134979 0.14626577 0.14387079\n 0.14248675]\n"
      ]
     },
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
      },
      "metadata": {
@@ -323,23 +329,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
+    {
+     "output_type": "stream",
+     "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",
+      "Epoch 2/5\n",
+      "399/399 [==============================] - 55s 139ms/step - loss: 1.0833 - accuracy: 0.5951 - val_loss: 1.8123 - val_accuracy: 0.5011\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"
+     ]
+    },
     {
      "output_type": "error",
-     "ename": "TypeError",
-     "evalue": "fit() got an unexpected keyword argument 'validation_size'",
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-10-a9853c6d5064>\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[0mY\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_size\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;31mTypeError\u001b[0m: fit() got an unexpected keyword argument 'validation_size'"
+      "\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~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1181\u001b[0m                 _r=1):\n\u001b[0;32m   1182\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1183\u001b[1;33m               \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1184\u001b[0m               \u001b[1;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1185\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    887\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    888\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 889\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    891\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    915\u001b[0m       \u001b[1;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    916\u001b[0m       \u001b[1;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 917\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=not-callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    918\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    919\u001b[0m       \u001b[1;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3021\u001b[0m       (graph_function,\n\u001b[0;32m   3022\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m-> 3023\u001b[1;33m     return graph_function._call_flat(\n\u001b[0m\u001b[0;32m   3024\u001b[0m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0;32m   3025\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1958\u001b[0m         and executing_eagerly):\n\u001b[0;32m   1959\u001b[0m       \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1960\u001b[1;33m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[0;32m   1961\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0;32m   1962\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
+      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    589\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    590\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 591\u001b[1;33m           outputs = execute.execute(\n\u001b[0m\u001b[0;32m    592\u001b[0m               \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    593\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     57\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m     60\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[0;32m     61\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
    "source": [
-    "history = myModel.fit(X, Y, epochs=5, batch_size=128, validation_split=0.05)\n",
+    "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",
@@ -351,11 +378,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "INFO:tensorflow:Assets written to: exp905\\assets\n"
+     ]
+    }
+   ],
    "source": [
-    "myModel.save('exp904')"
+    "myModel.save('exp905')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "ValueError",
+     "evalue": "Mix of label input types (string and number)",
+     "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)"
+     ]
+    }
+   ],
+   "source": [
+    "print()\n",
+    "modelCM = keras.models.load_model('models/exp905')\n",
+    "\n",
+    "# À remplir\n",
+    "y_pred = modelCM(Xf)\n",
+    "y_true = Yf\n",
+    "\n",
+    "y_pred = np.argmax(y_pred, axis=-1)\n",
+    "\n",
+    "cm = confusion_matrix(y_true, y_pred)\n",
+    "\n",
+    "\n",
+    "def show_confusion_matrix(matrix, labels):\n",
+    "    fig, ax = plt.subplots(figsize=(10,10))\n",
+    "    im = ax.imshow(matrix)\n",
+    "    N = len(labels)\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",
+    "    # Rotate the tick labels and set their alignment.\n",
+    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
+    "             rotation_mode=\"anchor\")\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",
+    "    ax.set_title(\"Matrice de confusion\")\n",
+    "    fig.tight_layout()\n",
+    "    plt.show()\n",
+    "show_confusion_matrix(cm, class_names)"
    ]
   },
   {
diff --git a/game.py b/game.py
index c5482446f4b3a17589e8d55329ea01d7cd72d2f6..3f3a995b7727fdaa8d3e8fc21689f4cb2934d9eb 100644
--- a/game.py
+++ b/game.py
@@ -34,13 +34,13 @@ while cap.isOpened():		 #or while 1. cap.isOpened() is false if there is a probl
 
 
     cv2.imshow("Caméra", frame)  			#Show you making emotional faces
-    cv2.putText(smiley, "Score: "+str(score), (0,0), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
+    cv2.putText(smiley, "Score: "+str(score), (20,20), 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
 
-    if cv2.waitKey(1) & 0xFF == ord('q'):			#If you press P, pass the smiley but lower your score
+    if 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/exp904/keras_metadata.pb b/models/exp904/keras_metadata.pb
new file mode 100644
index 0000000000000000000000000000000000000000..a003e79e3cff768333962d13b8fbf00c2be91f73
--- /dev/null
+++ b/models/exp904/keras_metadata.pb
@@ -0,0 +1,27 @@
+
+ŕ
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": "random_contrast_1_input"}}, {"class_name": "RandomContrast", "config": {"name": "random_contrast_1", "trainable": true, "dtype": "float32", "factor": {"class_name": "__tuple__", "items": [0.5, 0.5]}, "seed": null}}, {"class_name": "RandomFlip", "config": {"name": "random_flip_1", "trainable": true, "dtype": "float32", "mode": "horizontal", "seed": null}}, {"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": "Dropout", "config": {"name": "dropout_2", "trainable": true, "dtype": "float32", "rate": 0.2, "noise_shape": null, "seed": 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, 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root.layer_with_weights-0"_tf_keras_layer*�
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+�	
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