emotion_detection_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary libraries\n",
    "import os\n",
    "import cv2\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Flatten, Dense, GlobalAvgPool2D, GlobalMaxPool2D\n",
    "from tensorflow.keras.callbacks import Callback, EarlyStopping, ReduceLROnPlateau\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.applications.mobilenet import MobileNet\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "from keras.utils import np_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cca07848",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading input path\n",
    "INPUT_PATH = r\"C:/Users/suhas/Downloads/FER 2013 dataset/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry has 2832 number of images\n",
      "disgust has 380 number of images\n",
      "fear has 2535 number of images\n",
      "Happy has 6834 number of images\n",
      "Neutral has 4295 number of images\n",
      "\n",
      "total images are 16876\n"
     ]
    }
   ],
   "source": [
    "# count of emotions.\n",
    "total_images = 0\n",
    "for category in os.listdir(INPUT_PATH):\n",
    "    count = 0\n",
    "    for f in os.listdir(INPUT_PATH + category + \"/\"):\n",
    "        count += 1\n",
    "        total_images += 1\n",
    "    print(f\"{category} has {count} number of images\")\n",
    "    \n",
    "print(f\"\\ntotal images are {total_images}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16496"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing disgust emotion\n",
    "TOP_EMOTIONS = [\"fear\", \"Happy\", \"Neutral\", \"Angry\"]\n",
    "total_images -= 380\n",
    "total_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded all Angry images to numpy arrays\n",
      "loaded all fear images to numpy arrays\n",
      "loaded all Happy images to numpy arrays\n",
      "loaded all Neutral images to numpy arrays\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((16496, 48, 48, 3), array([0., 0., 0., ..., 3., 3., 3.]))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading images into an array\n",
    "img_arr = np.empty(shape=(total_images,48,48,3))\n",
    "img_label = np.empty(shape=(total_images))\n",
    "label_to_text = {}\n",
    "\n",
    "i = 0\n",
    "e = 0\n",
    "for category in os.listdir(INPUT_PATH):\n",
    "    if category in TOP_EMOTIONS:\n",
    "        label_to_text[e] = category\n",
    "        for f in os.listdir(INPUT_PATH + category + \"/\"):\n",
    "            img_arr[i] = cv2.imread(INPUT_PATH + category + \"/\" + f)\n",
    "            img_label[i] = e\n",
    "            i += 1\n",
    "        print(f\"loaded all {category} images to numpy arrays\")\n",
    "        e += 1\n",
    "\n",
    "img_arr.shape, img_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'Angry', 1: 'fear', 2: 'Happy', 3: 'Neutral'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Labels are converted to textual format\n",
    "label_to_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot of emotions\n",
    "fig = pyplot.figure(1, (8,8))\n",
    "\n",
    "idx = 0\n",
    "for k in label_to_text:\n",
    "    sample_indices = np.random.choice(np.where(img_label==k)[0], size=4, replace=False)\n",
    "    sample_images = img_arr[sample_indices]\n",
    "    for img in sample_images:\n",
    "        idx += 1\n",
    "        ax = pyplot.subplot(4,4,idx)\n",
    "        ax.imshow(img[:,:,0], cmap='gray')\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "        ax.set_title(label_to_text[k])\n",
    "        pyplot.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16496, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Converting class vector to binary class matrix\n",
    "img_label = np_utils.to_categorical(img_label)\n",
    "img_label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalizing the data\n",
    "img_arr = img_arr / 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((14846, 48, 48, 3), (1650, 48, 48, 3), (14846, 4), (1650, 4))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting Data into test and train\n",
    "X_train, X_test, y_train, y_test = train_test_split(img_arr, img_label,\n",
    "                                                    shuffle=True, stratify=img_label,\n",
    "                                                    train_size=0.9, random_state=42)\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining width, height, depth, and number of classes\n",
    "img_width = X_train.shape[1]\n",
    "img_height = X_train.shape[2]\n",
    "img_depth = X_train.shape[3]\n",
    "num_classes = y_train.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
     ]
    }
   ],
   "source": [
    "#Defining transfer learning model\n",
    "mobile_net = MobileNet(\n",
    "    input_shape = (img_width, img_height, img_depth),\n",
    "    include_top = False,\n",
    "    weights = \"imagenet\",\n",
    "    classes = num_classes\n",
    ")\n",
    "\n",
    "x = mobile_net.layers[-14].output\n",
    "global_pool = GlobalMaxPool2D(name=\"global_pool\")(x)\n",
    "out = Dense(num_classes, activation=\"softmax\", name=\"out_layer\")(global_pool)\n",
    "\n",
    "model = Model(inputs=mobile_net.input, outputs=out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 48, 48, 3)]       0         \n",
      "                                                                 \n",
      " conv1 (Conv2D)              (None, 24, 24, 32)        864       \n",
      "                                                                 \n",
      " conv1_bn (BatchNormalizatio  (None, 24, 24, 32)       128       \n",
      " n)                                                              \n",
      "                                                                 \n",
      " conv1_relu (ReLU)           (None, 24, 24, 32)        0         \n",
      "                                                                 \n",
      " conv_dw_1 (DepthwiseConv2D)  (None, 24, 24, 32)       288       \n",
      "                                                                 \n",
      " conv_dw_1_bn (BatchNormaliz  (None, 24, 24, 32)       128       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_1_relu (ReLU)       (None, 24, 24, 32)        0         \n",
      "                                                                 \n",
      " conv_pw_1 (Conv2D)          (None, 24, 24, 64)        2048      \n",
      "                                                                 \n",
      " conv_pw_1_bn (BatchNormaliz  (None, 24, 24, 64)       256       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_1_relu (ReLU)       (None, 24, 24, 64)        0         \n",
      "                                                                 \n",
      " conv_pad_2 (ZeroPadding2D)  (None, 25, 25, 64)        0         \n",
      "                                                                 \n",
      " conv_dw_2 (DepthwiseConv2D)  (None, 12, 12, 64)       576       \n",
      "                                                                 \n",
      " conv_dw_2_bn (BatchNormaliz  (None, 12, 12, 64)       256       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_2_relu (ReLU)       (None, 12, 12, 64)        0         \n",
      "                                                                 \n",
      " conv_pw_2 (Conv2D)          (None, 12, 12, 128)       8192      \n",
      "                                                                 \n",
      " conv_pw_2_bn (BatchNormaliz  (None, 12, 12, 128)      512       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_2_relu (ReLU)       (None, 12, 12, 128)       0         \n",
      "                                                                 \n",
      " conv_dw_3 (DepthwiseConv2D)  (None, 12, 12, 128)      1152      \n",
      "                                                                 \n",
      " conv_dw_3_bn (BatchNormaliz  (None, 12, 12, 128)      512       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_3_relu (ReLU)       (None, 12, 12, 128)       0         \n",
      "                                                                 \n",
      " conv_pw_3 (Conv2D)          (None, 12, 12, 128)       16384     \n",
      "                                                                 \n",
      " conv_pw_3_bn (BatchNormaliz  (None, 12, 12, 128)      512       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_3_relu (ReLU)       (None, 12, 12, 128)       0         \n",
      "                                                                 \n",
      " conv_pad_4 (ZeroPadding2D)  (None, 13, 13, 128)       0         \n",
      "                                                                 \n",
      " conv_dw_4 (DepthwiseConv2D)  (None, 6, 6, 128)        1152      \n",
      "                                                                 \n",
      " conv_dw_4_bn (BatchNormaliz  (None, 6, 6, 128)        512       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_4_relu (ReLU)       (None, 6, 6, 128)         0         \n",
      "                                                                 \n",
      " conv_pw_4 (Conv2D)          (None, 6, 6, 256)         32768     \n",
      "                                                                 \n",
      " conv_pw_4_bn (BatchNormaliz  (None, 6, 6, 256)        1024      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_4_relu (ReLU)       (None, 6, 6, 256)         0         \n",
      "                                                                 \n",
      " conv_dw_5 (DepthwiseConv2D)  (None, 6, 6, 256)        2304      \n",
      "                                                                 \n",
      " conv_dw_5_bn (BatchNormaliz  (None, 6, 6, 256)        1024      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_5_relu (ReLU)       (None, 6, 6, 256)         0         \n",
      "                                                                 \n",
      " conv_pw_5 (Conv2D)          (None, 6, 6, 256)         65536     \n",
      "                                                                 \n",
      " conv_pw_5_bn (BatchNormaliz  (None, 6, 6, 256)        1024      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_5_relu (ReLU)       (None, 6, 6, 256)         0         \n",
      "                                                                 \n",
      " conv_pad_6 (ZeroPadding2D)  (None, 7, 7, 256)         0         \n",
      "                                                                 \n",
      " conv_dw_6 (DepthwiseConv2D)  (None, 3, 3, 256)        2304      \n",
      "                                                                 \n",
      " conv_dw_6_bn (BatchNormaliz  (None, 3, 3, 256)        1024      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_6_relu (ReLU)       (None, 3, 3, 256)         0         \n",
      "                                                                 \n",
      " conv_pw_6 (Conv2D)          (None, 3, 3, 512)         131072    \n",
      "                                                                 \n",
      " conv_pw_6_bn (BatchNormaliz  (None, 3, 3, 512)        2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_6_relu (ReLU)       (None, 3, 3, 512)         0         \n",
      "                                                                 \n",
      " conv_dw_7 (DepthwiseConv2D)  (None, 3, 3, 512)        4608      \n",
      "                                                                 \n",
      " conv_dw_7_bn (BatchNormaliz  (None, 3, 3, 512)        2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_7_relu (ReLU)       (None, 3, 3, 512)         0         \n",
      "                                                                 \n",
      " conv_pw_7 (Conv2D)          (None, 3, 3, 512)         262144    \n",
      "                                                                 \n",
      " conv_pw_7_bn (BatchNormaliz  (None, 3, 3, 512)        2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_7_relu (ReLU)       (None, 3, 3, 512)         0         \n",
      "                                                                 \n",
      " conv_dw_8 (DepthwiseConv2D)  (None, 3, 3, 512)        4608      \n",
      "                                                                 \n",
      " conv_dw_8_bn (BatchNormaliz  (None, 3, 3, 512)        2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_8_relu (ReLU)       (None, 3, 3, 512)         0         \n",
      "                                                                 \n",
      " conv_pw_8 (Conv2D)          (None, 3, 3, 512)         262144    \n",
      "                                                                 \n",
      " conv_pw_8_bn (BatchNormaliz  (None, 3, 3, 512)        2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_8_relu (ReLU)       (None, 3, 3, 512)         0         \n",
      "                                                                 \n",
      " conv_dw_9 (DepthwiseConv2D)  (None, 3, 3, 512)        4608      \n",
      "                                                                 \n",
      " conv_dw_9_bn (BatchNormaliz  (None, 3, 3, 512)        2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_9_relu (ReLU)       (None, 3, 3, 512)         0         \n",
      "                                                                 \n",
      " conv_pw_9 (Conv2D)          (None, 3, 3, 512)         262144    \n",
      "                                                                 \n",
      " conv_pw_9_bn (BatchNormaliz  (None, 3, 3, 512)        2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_9_relu (ReLU)       (None, 3, 3, 512)         0         \n",
      "                                                                 \n",
      " conv_dw_10 (DepthwiseConv2D  (None, 3, 3, 512)        4608      \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv_dw_10_bn (BatchNormali  (None, 3, 3, 512)        2048      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_dw_10_relu (ReLU)      (None, 3, 3, 512)         0         \n",
      "                                                                 \n",
      " conv_pw_10 (Conv2D)         (None, 3, 3, 512)         262144    \n",
      "                                                                 \n",
      " conv_pw_10_bn (BatchNormali  (None, 3, 3, 512)        2048      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_pw_10_relu (ReLU)      (None, 3, 3, 512)         0         \n",
      "                                                                 \n",
      " conv_dw_11 (DepthwiseConv2D  (None, 3, 3, 512)        4608      \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv_dw_11_bn (BatchNormali  (None, 3, 3, 512)        2048      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_dw_11_relu (ReLU)      (None, 3, 3, 512)         0         \n",
      "                                                                 \n",
      " conv_pw_11 (Conv2D)         (None, 3, 3, 512)         262144    \n",
      "                                                                 \n",
      " conv_pw_11_bn (BatchNormali  (None, 3, 3, 512)        2048      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_pw_11_relu (ReLU)      (None, 3, 3, 512)         0         \n",
      "                                                                 \n",
      " global_pool (GlobalMaxPooli  (None, 512)              0         \n",
      " ng2D)                                                           \n",
      "                                                                 \n",
      " out_layer (Dense)           (None, 4)                 2052      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,629,892\n",
      "Trainable params: 1,615,172\n",
      "Non-trainable params: 14,720\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in model.layers[:15]:\n",
    "    layer.trainable = False\n",
    "#Augumenting Training images\n",
    "datagen = ImageDataGenerator(\n",
    "    rotation_range=15,\n",
    "    width_shift_range=0.15,\n",
    "    height_shift_range=0.15,\n",
    "    shear_range=0.15,\n",
    "    zoom_range=0.15,\n",
    "    horizontal_flip=True,\n",
    "    zca_whitening=False,\n",
    ")\n",
    "datagen.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Using two callbacks one is `early stopping` for avoiding overfitting of training data\n",
    "and other `ReduceLROnPlateau` for learning rate.\n",
    "\"\"\"\n",
    "early_stopping = EarlyStopping(\n",
    "    monitor='val_accuracy',\n",
    "    min_delta=0.00008,\n",
    "    patience=11,\n",
    "    verbose=1,\n",
    "    restore_best_weights=True,\n",
    ")\n",
    "\n",
    "lr_scheduler = ReduceLROnPlateau(\n",
    "    monitor='val_accuracy',\n",
    "    min_delta=0.0001,\n",
    "    factor=0.25,\n",
    "    patience=4,\n",
    "    min_lr=1e-7,\n",
    "    verbose=1,\n",
    ")\n",
    "callbacks = [\n",
    "    early_stopping,\n",
    "    lr_scheduler]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "463/463 [==============================] - 68s 143ms/step - loss: 0.7453 - accuracy: 0.7076 - val_loss: 0.9405 - val_accuracy: 0.6570 - lr: 0.0100\n",
      "Epoch 2/40\n",
      "463/463 [==============================] - 71s 153ms/step - loss: 0.7316 - accuracy: 0.7141 - val_loss: 2.1329 - val_accuracy: 0.5206 - lr: 0.0100\n",
      "Epoch 3/40\n",
      "463/463 [==============================] - 73s 156ms/step - loss: 0.7306 - accuracy: 0.7155 - val_loss: 0.8233 - val_accuracy: 0.6582 - lr: 0.0100\n",
      "Epoch 4/40\n",
      "463/463 [==============================] - 73s 158ms/step - loss: 0.7166 - accuracy: 0.7193 - val_loss: 1.0212 - val_accuracy: 0.6297 - lr: 0.0100\n",
      "Epoch 5/40\n",
      "463/463 [==============================] - 68s 146ms/step - loss: 0.7092 - accuracy: 0.7205 - val_loss: 0.8193 - val_accuracy: 0.6758 - lr: 0.0100\n",
      "Epoch 6/40\n",
      "463/463 [==============================] - 68s 146ms/step - loss: 0.7008 - accuracy: 0.7259 - val_loss: 0.9339 - val_accuracy: 0.6382 - lr: 0.0100\n",
      "Epoch 7/40\n",
      "463/463 [==============================] - 68s 146ms/step - loss: 0.6882 - accuracy: 0.7322 - val_loss: 0.7795 - val_accuracy: 0.6964 - lr: 0.0100\n",
      "Epoch 8/40\n",
      "463/463 [==============================] - 70s 151ms/step - loss: 0.6791 - accuracy: 0.7374 - val_loss: 0.8415 - val_accuracy: 0.6570 - lr: 0.0100\n",
      "Epoch 9/40\n",
      "463/463 [==============================] - 71s 152ms/step - loss: 0.6817 - accuracy: 0.7399 - val_loss: 0.8499 - val_accuracy: 0.6624 - lr: 0.0100\n",
      "Epoch 10/40\n",
      "463/463 [==============================] - 72s 154ms/step - loss: 0.6689 - accuracy: 0.7405 - val_loss: 0.8389 - val_accuracy: 0.6867 - lr: 0.0100\n",
      "Epoch 11/40\n",
      "464/463 [==============================] - ETA: 0s - loss: 0.6653 - accuracy: 0.7425\n",
      "Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.0024999999441206455.\n",
      "463/463 [==============================] - 71s 153ms/step - loss: 0.6653 - accuracy: 0.7425 - val_loss: 0.8968 - val_accuracy: 0.6861 - lr: 0.0100\n",
      "Epoch 12/40\n",
      "463/463 [==============================] - 72s 154ms/step - loss: 0.6175 - accuracy: 0.7602 - val_loss: 0.7346 - val_accuracy: 0.7127 - lr: 0.0025\n",
      "Epoch 13/40\n",
      "463/463 [==============================] - 75s 161ms/step - loss: 0.6005 - accuracy: 0.7643 - val_loss: 0.7198 - val_accuracy: 0.7218 - lr: 0.0025\n",
      "Epoch 14/40\n",
      "463/463 [==============================] - 75s 162ms/step - loss: 0.5879 - accuracy: 0.7698 - val_loss: 0.6965 - val_accuracy: 0.7267 - lr: 0.0025\n",
      "Epoch 15/40\n",
      "463/463 [==============================] - 75s 162ms/step - loss: 0.5826 - accuracy: 0.7768 - val_loss: 0.7083 - val_accuracy: 0.7200 - lr: 0.0025\n",
      "Epoch 16/40\n",
      "463/463 [==============================] - 75s 162ms/step - loss: 0.5732 - accuracy: 0.7788 - val_loss: 0.7031 - val_accuracy: 0.7291 - lr: 0.0025\n",
      "Epoch 17/40\n",
      "463/463 [==============================] - 74s 160ms/step - loss: 0.5648 - accuracy: 0.7828 - val_loss: 0.7109 - val_accuracy: 0.7333 - lr: 0.0025\n",
      "Epoch 18/40\n",
      "463/463 [==============================] - 74s 159ms/step - loss: 0.5608 - accuracy: 0.7816 - val_loss: 0.6950 - val_accuracy: 0.7273 - lr: 0.0025\n",
      "Epoch 19/40\n",
      "463/463 [==============================] - 72s 155ms/step - loss: 0.5651 - accuracy: 0.7833 - val_loss: 0.6897 - val_accuracy: 0.7297 - lr: 0.0025\n",
      "Epoch 20/40\n",
      "463/463 [==============================] - 74s 160ms/step - loss: 0.5563 - accuracy: 0.7883 - val_loss: 0.6849 - val_accuracy: 0.7376 - lr: 0.0025\n",
      "Epoch 21/40\n",
      "463/463 [==============================] - 74s 160ms/step - loss: 0.5549 - accuracy: 0.7845 - val_loss: 0.6989 - val_accuracy: 0.7279 - lr: 0.0025\n",
      "Epoch 22/40\n",
      "463/463 [==============================] - 73s 157ms/step - loss: 0.5518 - accuracy: 0.7904 - val_loss: 0.7166 - val_accuracy: 0.7309 - lr: 0.0025\n",
      "Epoch 23/40\n",
      "463/463 [==============================] - 71s 154ms/step - loss: 0.5487 - accuracy: 0.7912 - val_loss: 0.6796 - val_accuracy: 0.7406 - lr: 0.0025\n",
      "Epoch 24/40\n",
      "463/463 [==============================] - 68s 146ms/step - loss: 0.5436 - accuracy: 0.7898 - val_loss: 0.7041 - val_accuracy: 0.7255 - lr: 0.0025\n",
      "Epoch 25/40\n",
      "463/463 [==============================] - 73s 158ms/step - loss: 0.5321 - accuracy: 0.7985 - val_loss: 0.7214 - val_accuracy: 0.7261 - lr: 0.0025\n",
      "Epoch 26/40\n",
      "463/463 [==============================] - 72s 156ms/step - loss: 0.5423 - accuracy: 0.7933 - val_loss: 0.6799 - val_accuracy: 0.7479 - lr: 0.0025\n",
      "Epoch 27/40\n",
      "463/463 [==============================] - 72s 156ms/step - loss: 0.5400 - accuracy: 0.7940 - val_loss: 0.6864 - val_accuracy: 0.7364 - lr: 0.0025\n",
      "Epoch 28/40\n",
      "463/463 [==============================] - 72s 156ms/step - loss: 0.5340 - accuracy: 0.7958 - val_loss: 0.6861 - val_accuracy: 0.7376 - lr: 0.0025\n",
      "Epoch 29/40\n",
      "463/463 [==============================] - 70s 150ms/step - loss: 0.5267 - accuracy: 0.7985 - val_loss: 0.7096 - val_accuracy: 0.7309 - lr: 0.0025\n",
      "Epoch 30/40\n",
      "464/463 [==============================] - ETA: 0s - loss: 0.5248 - accuracy: 0.8006\n",
      "Epoch 00030: ReduceLROnPlateau reducing learning rate to 0.0006249999860301614.\n",
      "463/463 [==============================] - 69s 149ms/step - loss: 0.5248 - accuracy: 0.8006 - val_loss: 0.6991 - val_accuracy: 0.7236 - lr: 0.0025\n",
      "Epoch 31/40\n",
      "463/463 [==============================] - 70s 150ms/step - loss: 0.5124 - accuracy: 0.8024 - val_loss: 0.6733 - val_accuracy: 0.7442 - lr: 6.2500e-04\n",
      "Epoch 32/40\n",
      "463/463 [==============================] - 69s 149ms/step - loss: 0.5075 - accuracy: 0.8081 - val_loss: 0.6968 - val_accuracy: 0.7279 - lr: 6.2500e-04\n",
      "Epoch 33/40\n",
      "463/463 [==============================] - 68s 147ms/step - loss: 0.4942 - accuracy: 0.8071 - val_loss: 0.7044 - val_accuracy: 0.7248 - lr: 6.2500e-04\n",
      "Epoch 34/40\n",
      "464/463 [==============================] - ETA: 0s - loss: 0.4993 - accuracy: 0.8079\n",
      "Epoch 00034: ReduceLROnPlateau reducing learning rate to 0.00015624999650754035.\n",
      "463/463 [==============================] - 70s 151ms/step - loss: 0.4993 - accuracy: 0.8079 - val_loss: 0.7099 - val_accuracy: 0.7291 - lr: 6.2500e-04\n",
      "Epoch 35/40\n",
      "463/463 [==============================] - 69s 149ms/step - loss: 0.4964 - accuracy: 0.8111 - val_loss: 0.6974 - val_accuracy: 0.7303 - lr: 1.5625e-04\n",
      "Epoch 36/40\n",
      "463/463 [==============================] - 72s 154ms/step - loss: 0.4862 - accuracy: 0.8155 - val_loss: 0.6988 - val_accuracy: 0.7339 - lr: 1.5625e-04\n",
      "Epoch 37/40\n",
      "464/463 [==============================] - ETA: 0s - loss: 0.4834 - accuracy: 0.8184Restoring model weights from the end of the best epoch: 26.\n",
      "463/463 [==============================] - 67s 145ms/step - loss: 0.4834 - accuracy: 0.8184 - val_loss: 0.7101 - val_accuracy: 0.7267 - lr: 1.5625e-04\n",
      "Epoch 00037: early stopping\n"
     ]
    }
   ],
   "source": [
    "batch_size = 32\n",
    "epochs = 40\n",
    "\n",
    "optimizer = optimizers.Adam(0.01)\n",
    "model.compile(\n",
    "        loss='categorical_crossentropy',\n",
    "        optimizer=optimizer,\n",
    "        metrics=['accuracy']\n",
    ")\n",
    "\n",
    "history = model.fit(\n",
    "    datagen.flow(X_train, y_train, batch_size=batch_size),\n",
    "    validation_data=datagen.flow(X_test, y_test,batch_size= batch_size),\n",
    "    steps_per_epoch=len(X_train) / batch_size,\n",
    "    epochs=epochs,\n",
    "    callbacks=callbacks\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.6728285551071167\n",
      "Test accuracy: 0.7539393901824951\n",
      "(1, 48, 48, 3)\n",
      "Train Loss: 0.4814305305480957\n",
      "Train accuracy: 0.8146302103996277\n",
      "(1, 48, 48, 3)\n"
     ]
    }
   ],
   "source": [
    "#Fetching accuracy and loss of test and train dataset\n",
    "score = model.evaluate(X_test, y_test,verbose=0)\n",
    "print('Test Loss:', score[0])\n",
    "print('Test accuracy:', score[1])\n",
    "test_image = X_test[0:1]\n",
    "print (test_image.shape)\n",
    "\n",
    "score = model.evaluate(X_train, y_train,verbose=0) \n",
    "print('Train Loss:', score[0])\n",
    "print('Train accuracy:', score[1])\n",
    "train_image = X_train[0:1]\n",
    "print (train_image.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved in disk\n"
     ]
    }
   ],
   "source": [
    "model.save(\"model.h5\")\n",
    "print('Model saved in disk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'Angry', 1: 'fear', 2: 'Happy', 3: 'Neutral'}\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.63      0.65       283\n",
      "           1       0.66      0.56      0.60       253\n",
      "           2       0.89      0.87      0.88       684\n",
      "           3       0.66      0.78      0.71       430\n",
      "\n",
      "    accuracy                           0.75      1650\n",
      "   macro avg       0.72      0.71      0.71      1650\n",
      "weighted avg       0.76      0.75      0.75      1650\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scikitplot\n",
    "yhat_test = np.argmax(model.predict(X_test), axis=1)\n",
    "ytest = np.argmax(y_test,axis=1)\n",
    "print(label_to_text)\n",
    "scikitplot.metrics.plot_confusion_matrix(ytest, yhat_test,figsize=(7,7))\n",
    "pyplot.savefig(\"model_confusion_matrix.jpg\")\n",
    "print(classification_report(ytest, yhat_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confusion matrix indicates that the model performs better on Happy and Neutral emotion labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "90205406ddd71c9b52b696f2bf58c5648ba0240343acd3a46e448f8390b9ab9c"
  },
  "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": 2
}