jup.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-23 10:12:07.795813: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2021-11-23 10:12:07.795853: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Dropout,Activation,Flatten,BatchNormalization\n",
    "from keras.layers import Conv2D,MaxPooling2D\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_classes=7\n",
    "img_rows,img_cols=48,48\n",
    "batch_size=32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28709\n",
      "3589\n"
     ]
    }
   ],
   "source": [
    "# format the dataset so that x contains the image matrix and x contains the y the label\n",
    "\n",
    "emotion_data = pd.read_csv('fer2013.csv')\n",
    "\n",
    "x_train = []\n",
    "y_train = []\n",
    "x_test = []\n",
    "y_test = []\n",
    "for index, row in emotion_data.iterrows():\n",
    "    k = row['pixels'].split(\" \")\n",
    "    if row['Usage'] == 'Training':\n",
    "        x_train.append(np.array(k))\n",
    "        y_train.append(row['emotion'])\n",
    "    elif row['Usage'] == 'PublicTest':\n",
    "        x_test.append(np.array(k))\n",
    "        y_test.append(row['emotion'])\n",
    "\n",
    "print(len(x_train))\n",
    "print(len(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform the List object to nparray\n",
    "\n",
    "x_train = np.array(x_train)\n",
    "y_train = np.array(y_train)\n",
    "x_test = np.array(x_test)\n",
    "y_test = np.array(y_test)\n",
    "\n",
    "# Format the array to a 48*48 matrix\n",
    "x_train = x_train.reshape(x_train.shape[0], 48, 48, 1)\n",
    "x_test = x_test.reshape(x_test.shape[0], 48, 48, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "#Block-1\n",
    "model.add(Conv2D(32,(3,3),padding='same',kernel_initializer='he_normal', input_shape=(48, 48, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Conv2D(32,(3,3),padding='same',kernel_initializer='he_normal'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Dropout(0.2))\n",
    "#Block-2\n",
    "model.add(Conv2D(64,(3,3),padding='same',kernel_initializer='he_normal'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Conv2D(64,(3,3),padding='same',kernel_initializer='he_normal'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Dropout(0.2))\n",
    "# #Block-3\n",
    "model.add(Conv2D(128,(3,3),padding='same',kernel_initializer='he_normal'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Conv2D(128,(3,3),padding='same',kernel_initializer='he_normal'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Dropout(0.2))\n",
    "# #Block-4\n",
    "model.add(Conv2D(256,(3,3),padding='same',kernel_initializer='he_normal'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Conv2D(256,(3,3),padding='same',kernel_initializer='he_normal'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Dropout(0.2))\n",
    "# #Block-5\n",
    "model.add(Flatten())\n",
    "model.add(Dense(64,kernel_initializer='he_normal'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.5))\n",
    "# #Block-6\n",
    "model.add(Dense(64,kernel_initializer='he_normal'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.5))\n",
    "# #Block-7\n",
    "model.add(Dense(7, kernel_initializer='he_normal'))\n",
    "model.add(Activation('softmax'))\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
    "\n",
    "checkpoint = ModelCheckpoint('EmotionDetectionModel.h5',\n",
    "                             monitor='val_loss',\n",
    "                             mode='min',\n",
    "                             save_best_only=True,\n",
    "                             verbose=1)\n",
    "earlystop = EarlyStopping(monitor='val_loss',\n",
    "                          min_delta=0,\n",
    "                          patience=3,\n",
    "                          verbose=1,\n",
    "                          restore_best_weights=True\n",
    "                          )\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss',\n",
    "                              factor=0.2,\n",
    "                              patience=3,\n",
    "                              verbose=1,\n",
    "                              min_delta=0.0001)\n",
    "callbacks = [earlystop,checkpoint,reduce_lr]\n",
    "\n",
    "model.compile(\n",
    "    loss='categorical_crossentropy',\n",
    "    optimizer='adam',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "# print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "WARNING:tensorflow:Model was constructed with shape (None, 48, 48, 3) for input KerasTensor(type_spec=TensorSpec(shape=(None, 48, 48, 3), dtype=tf.float32, name='conv2d_8_input'), name='conv2d_8_input', description=\"created by layer 'conv2d_8_input'\"), but it was called on an input with incompatible shape (None, 48, 48, 1).\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"/home/sidis/dev/projet_ia/venv/lib/python3.8/site-packages/keras/engine/training.py\", line 878, in train_function  *\n        return step_function(self, iterator)\n    File \"/home/sidis/dev/projet_ia/venv/lib/python3.8/site-packages/keras/engine/training.py\", line 867, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/home/sidis/dev/projet_ia/venv/lib/python3.8/site-packages/keras/engine/training.py\", line 860, in run_step  **\n        outputs = model.train_step(data)\n    File \"/home/sidis/dev/projet_ia/venv/lib/python3.8/site-packages/keras/engine/training.py\", line 808, in train_step\n        y_pred = self(x, training=True)\n    File \"/home/sidis/dev/projet_ia/venv/lib/python3.8/site-packages/keras/utils/traceback_utils.py\", line 67, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/home/sidis/dev/projet_ia/venv/lib/python3.8/site-packages/keras/engine/input_spec.py\", line 247, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Exception encountered when calling layer \"sequential_1\" (type Sequential).\n    \n    Input 0 of layer \"conv2d_8\" is incompatible with the layer: expected axis -1of input shape to have value 3, but received input with shape (None, 48, 48, 1)\n    \n    Call arguments received:\n      • inputs=tf.Tensor(shape=(None, 48, 48, 1), dtype=string)\n      • training=True\n      • mask=None\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1972085/964968733.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/dev/projet_ia/venv/lib/python3.8/site-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/dev/projet_ia/venv/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mautograph_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1127\u001b[0m           \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint:disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1128\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ag_error_metadata\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1129\u001b[0;31m               \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1130\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1131\u001b[0m               \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    File \"/home/sidis/dev/projet_ia/venv/lib/python3.8/site-packages/keras/engine/training.py\", line 878, in train_function  *\n        return step_function(self, iterator)\n    File \"/home/sidis/dev/projet_ia/venv/lib/python3.8/site-packages/keras/engine/training.py\", line 867, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/home/sidis/dev/projet_ia/venv/lib/python3.8/site-packages/keras/engine/training.py\", line 860, in run_step  **\n        outputs = model.train_step(data)\n    File \"/home/sidis/dev/projet_ia/venv/lib/python3.8/site-packages/keras/engine/training.py\", line 808, in train_step\n        y_pred = self(x, training=True)\n    File \"/home/sidis/dev/projet_ia/venv/lib/python3.8/site-packages/keras/utils/traceback_utils.py\", line 67, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/home/sidis/dev/projet_ia/venv/lib/python3.8/site-packages/keras/engine/input_spec.py\", line 247, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Exception encountered when calling layer \"sequential_1\" (type Sequential).\n    \n    Input 0 of layer \"conv2d_8\" is incompatible with the layer: expected axis -1of input shape to have value 3, but received input with shape (None, 48, 48, 1)\n    \n    Call arguments received:\n      • inputs=tf.Tensor(shape=(None, 48, 48, 1), dtype=string)\n      • training=True\n      • mask=None\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "model.fit(x_train, y_train, batch_size=32, epochs=50, callbacks=callbacks, )\n"
   ]
  }
 ],
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