emotion-classification-cnn-using-keras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import os\n",
    "\n",
    "# Importing Deep Learning Libraries\n",
    "\n",
    "from keras.preprocessing.image import load_img, img_to_array\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.layers import Dense,Input,Dropout,GlobalAveragePooling2D,Flatten,Conv2D,BatchNormalization,Activation,MaxPooling2D\n",
    "from keras.models import Model,Sequential\n",
    "from keras.optimizers import Adam,SGD,RMSprop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Displaying Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "picture_size = 48\n",
    "folder_path = \"../input/face-expression-recognition-dataset/images/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "expression = 'disgust'\n",
    "\n",
    "plt.figure(figsize= (12,12))\n",
    "for i in range(1, 10, 1):\n",
    "    plt.subplot(3,3,i)\n",
    "    img = load_img(folder_path+\"train/\"+expression+\"/\"+\n",
    "                  os.listdir(folder_path + \"train/\" + expression)[i], target_size=(picture_size, picture_size))\n",
    "    plt.imshow(img)   \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making Training and Validation Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 28821 images belonging to 7 classes.\n",
      "Found 7066 images belonging to 7 classes.\n"
     ]
    }
   ],
   "source": [
    "batch_size  = 128\n",
    "\n",
    "datagen_train  = ImageDataGenerator()\n",
    "datagen_val = ImageDataGenerator()\n",
    "\n",
    "train_set = datagen_train.flow_from_directory(folder_path+\"train\",\n",
    "                                              target_size = (picture_size,picture_size),\n",
    "                                              color_mode = \"grayscale\",\n",
    "                                              batch_size=batch_size,\n",
    "                                              class_mode='categorical',\n",
    "                                              shuffle=True)\n",
    "\n",
    "\n",
    "test_set = datagen_val.flow_from_directory(folder_path+\"validation\",\n",
    "                                              target_size = (picture_size,picture_size),\n",
    "                                              color_mode = \"grayscale\",\n",
    "                                              batch_size=batch_size,\n",
    "                                              class_mode='categorical',\n",
    "                                              shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 48, 48, 64)        640       \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 48, 48, 64)        256       \n",
      "_________________________________________________________________\n",
      "activation (Activation)      (None, 48, 48, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 24, 24, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 24, 24, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 24, 24, 128)       204928    \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 24, 24, 128)       512       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 24, 24, 128)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 128)       0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 12, 12, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 12, 12, 512)       590336    \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 12, 12, 512)       2048      \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 12, 12, 512)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 512)         0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 6, 6, 512)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 6, 6, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 6, 6, 512)         2048      \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 6, 6, 512)         0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 3, 3, 512)         0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 3, 3, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               1179904   \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 256)               1024      \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               131584    \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 512)               2048      \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 7)                 3591      \n",
      "=================================================================\n",
      "Total params: 4,478,727\n",
      "Trainable params: 4,474,759\n",
      "Non-trainable params: 3,968\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.optimizers import Adam,SGD,RMSprop\n",
    "\n",
    "\n",
    "no_of_classes = 7\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "#1st CNN layer\n",
    "model.add(Conv2D(64,(3,3),padding = 'same',input_shape = (48,48,1)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size = (2,2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "#2nd CNN layer\n",
    "model.add(Conv2D(128,(5,5),padding = 'same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size = (2,2)))\n",
    "model.add(Dropout (0.25))\n",
    "\n",
    "#3rd CNN layer\n",
    "model.add(Conv2D(512,(3,3),padding = 'same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size = (2,2)))\n",
    "model.add(Dropout (0.25))\n",
    "\n",
    "#4th CNN layer\n",
    "model.add(Conv2D(512,(3,3), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "#Fully connected 1st layer\n",
    "model.add(Dense(256))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "\n",
    "# Fully connected layer 2nd layer\n",
    "model.add(Dense(512))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Dense(no_of_classes, activation='softmax'))\n",
    "\n",
    "\n",
    "\n",
    "opt = Adam(lr = 0.0001)\n",
    "model.compile(optimizer=opt,loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting the Model with Training and Validation Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.optimizers import RMSprop,SGD,Adam\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
    "\n",
    "checkpoint = ModelCheckpoint(\"./model.h5\", monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss',\n",
    "                          min_delta=0,\n",
    "                          patience=3,\n",
    "                          verbose=1,\n",
    "                          restore_best_weights=True\n",
    "                          )\n",
    "\n",
    "reduce_learningrate = ReduceLROnPlateau(monitor='val_loss',\n",
    "                              factor=0.2,\n",
    "                              patience=3,\n",
    "                              verbose=1,\n",
    "                              min_delta=0.0001)\n",
    "\n",
    "callbacks_list = [early_stopping,checkpoint,reduce_learningrate]\n",
    "\n",
    "epochs = 48\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer = Adam(lr=0.001),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/48\n",
      "225/225 [==============================] - 180s 799ms/step - loss: 1.7711 - accuracy: 0.3183 - val_loss: 1.7775 - val_accuracy: 0.3482\n",
      "Epoch 2/48\n",
      "225/225 [==============================] - 22s 99ms/step - loss: 1.4261 - accuracy: 0.4517 - val_loss: 1.3999 - val_accuracy: 0.4814\n",
      "Epoch 3/48\n",
      "225/225 [==============================] - 22s 97ms/step - loss: 1.2760 - accuracy: 0.5113 - val_loss: 1.2810 - val_accuracy: 0.5163\n",
      "Epoch 4/48\n",
      "225/225 [==============================] - 22s 100ms/step - loss: 1.1831 - accuracy: 0.5490 - val_loss: 1.2041 - val_accuracy: 0.5491\n",
      "Epoch 5/48\n",
      "225/225 [==============================] - 23s 100ms/step - loss: 1.1257 - accuracy: 0.5724 - val_loss: 1.2334 - val_accuracy: 0.5278\n",
      "Epoch 6/48\n",
      "225/225 [==============================] - 22s 98ms/step - loss: 1.0765 - accuracy: 0.5909 - val_loss: 1.2720 - val_accuracy: 0.5112\n",
      "Epoch 7/48\n",
      "225/225 [==============================] - 23s 101ms/step - loss: 1.0328 - accuracy: 0.6091 - val_loss: 1.1415 - val_accuracy: 0.5705\n",
      "Epoch 8/48\n",
      "225/225 [==============================] - 23s 103ms/step - loss: 0.9813 - accuracy: 0.6282 - val_loss: 1.3052 - val_accuracy: 0.5300\n",
      "Epoch 9/48\n",
      "225/225 [==============================] - 23s 103ms/step - loss: 0.9526 - accuracy: 0.6418 - val_loss: 1.0722 - val_accuracy: 0.6038\n",
      "Epoch 10/48\n",
      "225/225 [==============================] - 27s 120ms/step - loss: 0.9028 - accuracy: 0.6593 - val_loss: 1.0720 - val_accuracy: 0.6024\n",
      "Epoch 11/48\n",
      "225/225 [==============================] - 23s 104ms/step - loss: 0.8707 - accuracy: 0.6724 - val_loss: 1.0670 - val_accuracy: 0.6081\n",
      "Epoch 12/48\n",
      "225/225 [==============================] - 22s 98ms/step - loss: 0.8188 - accuracy: 0.6906 - val_loss: 1.1353 - val_accuracy: 0.5786\n",
      "Epoch 13/48\n",
      "225/225 [==============================] - 23s 104ms/step - loss: 0.7788 - accuracy: 0.7076 - val_loss: 1.0726 - val_accuracy: 0.6175\n",
      "Epoch 14/48\n",
      "225/225 [==============================] - ETA: 0s - loss: 0.7399 - accuracy: 0.7234Restoring model weights from the end of the best epoch.\n",
      "\n",
      "Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
      "225/225 [==============================] - 23s 102ms/step - loss: 0.7399 - accuracy: 0.7234 - val_loss: 1.0975 - val_accuracy: 0.6054\n",
      "Epoch 00014: early stopping\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(generator=train_set,\n",
    "                                steps_per_epoch=train_set.n//train_set.batch_size,\n",
    "                                epochs=epochs,\n",
    "                                validation_data = test_set,\n",
    "                                validation_steps = test_set.n//test_set.batch_size,\n",
    "                                callbacks=callbacks_list\n",
    "                                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Accuracy & Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('dark_background')\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.suptitle('Optimizer : Adam', fontsize=10)\n",
    "plt.ylabel('Loss', fontsize=16)\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.ylabel('Accuracy', fontsize=16)\n",
    "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}