model_creation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fab2489e",
   "metadata": {},
   "source": [
    "# Emotion Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fcabf64",
   "metadata": {},
   "source": [
    "**Importing Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e74b3e0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow version: 2.12.0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from glob import glob\n",
    "import cv2\n",
    "import random\n",
    "import os\n",
    "%matplotlib inline\n",
    "\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.layers import Dense, Input, Dropout, Flatten, Conv2D\n",
    "from tensorflow.keras.layers import BatchNormalization, Activation, MaxPooling2D\n",
    "from tensorflow.keras.models import Model, Sequential\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "from IPython.display import SVG, Image\n",
    "import tensorflow as tf\n",
    "print(\"Tensorflow version:\", tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c82a0b1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow\n",
      "  Using cached tensorflow-2.12.0-cp310-cp310-win_amd64.whl (1.9 kB)\n",
      "Collecting tensorflow-intel==2.12.0\n",
      "  Using cached tensorflow_intel-2.12.0-cp310-cp310-win_amd64.whl (272.8 MB)\n",
      "Requirement already satisfied: numpy<1.24,>=1.22 in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow) (1.23.5)\n",
      "Collecting tensorboard<2.13,>=2.12\n",
      "  Using cached tensorboard-2.12.3-py3-none-any.whl (5.6 MB)\n",
      "Collecting astunparse>=1.6.0\n",
      "  Using cached astunparse-1.6.3-py2.py3-none-any.whl (12 kB)\n",
      "Collecting tensorflow-estimator<2.13,>=2.12.0\n",
      "  Using cached tensorflow_estimator-2.12.0-py2.py3-none-any.whl (440 kB)\n",
      "Collecting keras<2.13,>=2.12.0\n",
      "  Using cached keras-2.12.0-py2.py3-none-any.whl (1.7 MB)\n",
      "Collecting google-pasta>=0.1.1\n",
      "  Using cached google_pasta-0.2.0-py3-none-any.whl (57 kB)\n",
      "Requirement already satisfied: wrapt<1.15,>=1.11.0 in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow) (1.14.1)\n",
      "Collecting absl-py>=1.0.0\n",
      "  Using cached absl_py-1.4.0-py3-none-any.whl (126 kB)\n",
      "Collecting gast<=0.4.0,>=0.2.1\n",
      "  Using cached gast-0.4.0-py3-none-any.whl (9.8 kB)\n",
      "Collecting libclang>=13.0.0\n",
      "  Using cached libclang-16.0.0-py2.py3-none-win_amd64.whl (24.4 MB)\n",
      "Collecting jax>=0.3.15\n",
      "  Using cached jax-0.4.12.tar.gz (1.3 MB)\n",
      "  Installing build dependencies: started\n",
      "  Installing build dependencies: finished with status 'done'\n",
      "  Getting requirements to build wheel: started\n",
      "  Getting requirements to build wheel: finished with status 'done'\n",
      "  Preparing metadata (pyproject.toml): started\n",
      "  Preparing metadata (pyproject.toml): finished with status 'done'\n",
      "Collecting opt-einsum>=2.3.2\n",
      "  Using cached opt_einsum-3.3.0-py3-none-any.whl (65 kB)\n",
      "Collecting flatbuffers>=2.0\n",
      "  Using cached flatbuffers-23.5.26-py2.py3-none-any.whl (26 kB)\n",
      "Collecting grpcio<2.0,>=1.24.3\n",
      "  Using cached grpcio-1.54.2-cp310-cp310-win_amd64.whl (4.1 MB)\n",
      "Requirement already satisfied: packaging in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow) (22.0)\n",
      "Collecting termcolor>=1.1.0\n",
      "  Using cached termcolor-2.3.0-py3-none-any.whl (6.9 kB)\n",
      "Requirement already satisfied: h5py>=2.9.0 in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow) (3.7.0)\n",
      "Collecting protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3\n",
      "  Using cached protobuf-4.23.2-cp310-abi3-win_amd64.whl (422 kB)\n",
      "Requirement already satisfied: six>=1.12.0 in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow) (1.16.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow) (4.4.0)\n",
      "Collecting tensorflow-io-gcs-filesystem>=0.23.1\n",
      "  Using cached tensorflow_io_gcs_filesystem-0.31.0-cp310-cp310-win_amd64.whl (1.5 MB)\n",
      "Requirement already satisfied: setuptools in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow) (65.6.3)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.12.0->tensorflow) (0.38.4)\n",
      "Requirement already satisfied: scipy>=1.7 in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from jax>=0.3.15->tensorflow-intel==2.12.0->tensorflow) (1.10.0)\n",
      "Collecting ml-dtypes>=0.1.0\n",
      "  Using cached ml_dtypes-0.2.0-cp310-cp310-win_amd64.whl (938 kB)\n",
      "Collecting google-auth-oauthlib<1.1,>=0.5\n",
      "  Using cached google_auth_oauthlib-1.0.0-py2.py3-none-any.whl (18 kB)\n",
      "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (3.4.1)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (2.28.1)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (2.2.2)\n",
      "Collecting google-auth<3,>=1.6.3\n",
      "  Using cached google_auth-2.19.1-py2.py3-none-any.whl (181 kB)\n",
      "Collecting tensorboard-data-server<0.8.0,>=0.7.0\n",
      "  Using cached tensorboard_data_server-0.7.0-py3-none-any.whl (2.4 kB)\n",
      "Collecting cachetools<6.0,>=2.0.0\n",
      "  Using cached cachetools-5.3.1-py3-none-any.whl (9.3 kB)\n",
      "Collecting rsa<5,>=3.1.4\n",
      "  Using cached rsa-4.9-py3-none-any.whl (34 kB)\n",
      "Requirement already satisfied: urllib3<2.0 in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (1.26.14)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (0.2.8)\n",
      "Collecting requests-oauthlib>=0.7.0\n",
      "  Using cached requests_oauthlib-1.3.1-py2.py3-none-any.whl (23 kB)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (2022.12.7)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (3.4)\n",
      "Requirement already satisfied: MarkupSafe>=2.1.1 in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (2.1.1)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\users\\sahil\\anaconda3\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (0.4.8)\n",
      "Collecting oauthlib>=3.0.0\n",
      "  Using cached oauthlib-3.2.2-py3-none-any.whl (151 kB)\n",
      "Building wheels for collected packages: jax\n",
      "  Building wheel for jax (pyproject.toml): started\n",
      "  Building wheel for jax (pyproject.toml): finished with status 'done'\n",
      "  Created wheel for jax: filename=jax-0.4.12-py3-none-any.whl size=1498562 sha256=f0bd01a439042f2aaa68ecc93e2a98ff0d4a059c349e610dbb02b90326499f0b\n",
      "  Stored in directory: c:\\users\\sahil\\appdata\\local\\pip\\cache\\wheels\\e2\\32\\99\\2f648da8966cd72055d750fe028e6b83e848cd525462584c16\n",
      "Successfully built jax\n",
      "Installing collected packages: libclang, flatbuffers, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, rsa, protobuf, opt-einsum, oauthlib, ml-dtypes, keras, grpcio, google-pasta, gast, cachetools, astunparse, absl-py, requests-oauthlib, jax, google-auth, google-auth-oauthlib, tensorboard, tensorflow-intel, tensorflow\n",
      "Successfully installed absl-py-1.4.0 astunparse-1.6.3 cachetools-5.3.1 flatbuffers-23.5.26 gast-0.4.0 google-auth-2.19.1 google-auth-oauthlib-1.0.0 google-pasta-0.2.0 grpcio-1.54.2 jax-0.4.12 keras-2.12.0 libclang-16.0.0 ml-dtypes-0.2.0 oauthlib-3.2.2 opt-einsum-3.3.0 protobuf-4.23.2 requests-oauthlib-1.3.1 rsa-4.9 tensorboard-2.12.3 tensorboard-data-server-0.7.0 tensorflow-2.12.0 tensorflow-estimator-2.12.0 tensorflow-intel-2.12.0 tensorflow-io-gcs-filesystem-0.31.0 termcolor-2.3.0\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9091ab7d",
   "metadata": {},
   "source": [
    "**Exploring Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b575611d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "images = glob(\"train/**/**\")\n",
    "for i in range(9):\n",
    "    image = random.choice(images)\n",
    "    plt.figure(figsize=(12,12))\n",
    "    plt.subplot(331+i)\n",
    "    plt.imshow(cv2.imread(image));plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba16325a",
   "metadata": {},
   "source": [
    "**Preparing Data for Training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "45320a47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 26425 images belonging to 7 classes.\n",
      "Found 7178 images belonging to 7 classes.\n"
     ]
    }
   ],
   "source": [
    "img_size = 48\n",
    "batch_size = 64\n",
    "datagen_train = ImageDataGenerator()\n",
    "train_generator = datagen_train.flow_from_directory(\"train/\",\n",
    "                                                   target_size = (img_size,img_size),\n",
    "                                                   color_mode = \"grayscale\",\n",
    "                                                   batch_size = batch_size,\n",
    "                                                   class_mode = \"categorical\",\n",
    "                                                   shuffle = True)\n",
    "\n",
    "datagen_validation = ImageDataGenerator()\n",
    "validation_generator = datagen_train.flow_from_directory(\"test/\",\n",
    "                                                   target_size = (img_size,img_size),\n",
    "                                                   color_mode = \"grayscale\",\n",
    "                                                   batch_size = batch_size,\n",
    "                                                   class_mode = \"categorical\",\n",
    "                                                   shuffle = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b045f6dd",
   "metadata": {},
   "source": [
    "**Defining Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f13c2f43",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Convolution(input_tensor, filters, kernel_size):\n",
    "    x = Conv2D(filters = filters, kernel_size = kernel_size, padding = \"same\")(input_tensor)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('relu')(x)\n",
    "    x = MaxPooling2D(pool_size = (2,2))(x)\n",
    "    x = Dropout(0.25)(x)\n",
    "    \n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "116cbf31",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Dense_f(input_tensor, nodes):\n",
    "    x = Dense(nodes)(input_tensor)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('relu')(x)\n",
    "    x = Dropout(0.25)(x)\n",
    "    \n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cbf2be45",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_fer(input_shape):\n",
    "    inputs = Input(input_shape)\n",
    "    conv_1 = Convolution(inputs,32,(3,3))\n",
    "    conv_2 = Convolution(conv_1,64,(5,5))\n",
    "    conv_3 = Convolution(conv_2,128,(3,3))\n",
    "\n",
    "    flatten = Flatten()(conv_3)\n",
    "    \n",
    "    dense_1 = Dense_f(flatten,256)\n",
    "\n",
    "    output  = Dense(7, activation=\"softmax\")(dense_1)\n",
    "    model  =  Model(inputs=[inputs],outputs = [output])\n",
    "    \n",
    "    model.compile(loss=['categorical_crossentropy'] , optimizer = 'adam' , metrics =[\"accuracy\"])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7cc1245b",
   "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, 1)]       0         \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 48, 48, 32)        320       \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 48, 48, 32)       128       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " activation (Activation)     (None, 48, 48, 32)        0         \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 24, 24, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 24, 24, 32)        0         \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 24, 24, 64)        51264     \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 24, 24, 64)       256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " activation_1 (Activation)   (None, 24, 24, 64)        0         \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 12, 12, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 12, 12, 64)        0         \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 12, 12, 128)       73856     \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 12, 12, 128)      512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " activation_2 (Activation)   (None, 12, 12, 128)       0         \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 6, 6, 128)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 6, 6, 128)         0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 4608)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 256)               1179904   \n",
      "                                                                 \n",
      " batch_normalization_3 (Batc  (None, 256)              1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " activation_3 (Activation)   (None, 256)               0         \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 256)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 7)                 1799      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,309,063\n",
      "Trainable params: 1,308,103\n",
      "Non-trainable params: 960\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = model_fer((48,48,1))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0175ace",
   "metadata": {},
   "source": [
    "**Initializing the Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "358ef08b",
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 20\n",
    "steps_per_epoch = train_generator.n//train_generator.batch_size\n",
    "validation_steps = validation_generator.n//validation_generator.batch_size\n",
    "\n",
    "checkpoint = ModelCheckpoint(\"model_weights.h5\", monitor  ='val_accuracy', save_weights_only = True, mode = 'max', verbose =1)\n",
    "callbacks = [checkpoint]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edeadc08",
   "metadata": {},
   "source": [
    "# Traning the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d8021a8",
   "metadata": {},
   "source": [
    "**The next cell takes time to complete**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e40b7418",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.6894 - accuracy: 0.7431\n",
      "Epoch 1: saving model to model_weights.h5\n",
      "412/412 [==============================] - 357s 868ms/step - loss: 0.6894 - accuracy: 0.7431 - val_loss: 1.2957 - val_accuracy: 0.5678\n",
      "Epoch 2/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.6538 - accuracy: 0.7590\n",
      "Epoch 2: saving model to model_weights.h5\n",
      "412/412 [==============================] - 84s 205ms/step - loss: 0.6538 - accuracy: 0.7590 - val_loss: 1.2487 - val_accuracy: 0.5749\n",
      "Epoch 3/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.6172 - accuracy: 0.7717\n",
      "Epoch 3: saving model to model_weights.h5\n",
      "412/412 [==============================] - 76s 185ms/step - loss: 0.6172 - accuracy: 0.7717 - val_loss: 1.1777 - val_accuracy: 0.6060\n",
      "Epoch 4/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.5996 - accuracy: 0.7806\n",
      "Epoch 4: saving model to model_weights.h5\n",
      "412/412 [==============================] - 82s 200ms/step - loss: 0.5996 - accuracy: 0.7806 - val_loss: 1.1742 - val_accuracy: 0.5974\n",
      "Epoch 5/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.5692 - accuracy: 0.7949\n",
      "Epoch 5: saving model to model_weights.h5\n",
      "412/412 [==============================] - 84s 205ms/step - loss: 0.5692 - accuracy: 0.7949 - val_loss: 1.2580 - val_accuracy: 0.5767\n",
      "Epoch 6/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.5457 - accuracy: 0.8013\n",
      "Epoch 6: saving model to model_weights.h5\n",
      "412/412 [==============================] - 81s 196ms/step - loss: 0.5457 - accuracy: 0.8013 - val_loss: 1.2189 - val_accuracy: 0.5935\n",
      "Epoch 7/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.5178 - accuracy: 0.8102\n",
      "Epoch 7: saving model to model_weights.h5\n",
      "412/412 [==============================] - 82s 200ms/step - loss: 0.5178 - accuracy: 0.8102 - val_loss: 1.2078 - val_accuracy: 0.6104\n",
      "Epoch 8/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.4981 - accuracy: 0.8204\n",
      "Epoch 8: saving model to model_weights.h5\n",
      "412/412 [==============================] - 84s 205ms/step - loss: 0.4981 - accuracy: 0.8204 - val_loss: 1.2567 - val_accuracy: 0.6060\n",
      "Epoch 9/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.4809 - accuracy: 0.8229\n",
      "Epoch 9: saving model to model_weights.h5\n",
      "412/412 [==============================] - 83s 202ms/step - loss: 0.4809 - accuracy: 0.8229 - val_loss: 1.2690 - val_accuracy: 0.6021\n",
      "Epoch 10/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.4616 - accuracy: 0.8311\n",
      "Epoch 10: saving model to model_weights.h5\n",
      "412/412 [==============================] - 83s 200ms/step - loss: 0.4616 - accuracy: 0.8311 - val_loss: 1.2679 - val_accuracy: 0.6144\n",
      "Epoch 11/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.4487 - accuracy: 0.8345\n",
      "Epoch 11: saving model to model_weights.h5\n",
      "412/412 [==============================] - 82s 198ms/step - loss: 0.4487 - accuracy: 0.8345 - val_loss: 1.2760 - val_accuracy: 0.6084\n",
      "Epoch 12/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.4244 - accuracy: 0.8460\n",
      "Epoch 12: saving model to model_weights.h5\n",
      "412/412 [==============================] - 87s 212ms/step - loss: 0.4244 - accuracy: 0.8460 - val_loss: 1.3735 - val_accuracy: 0.5684\n",
      "Epoch 13/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.4100 - accuracy: 0.8519\n",
      "Epoch 13: saving model to model_weights.h5\n",
      "412/412 [==============================] - 86s 209ms/step - loss: 0.4100 - accuracy: 0.8519 - val_loss: 1.3323 - val_accuracy: 0.6037\n",
      "Epoch 14/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.3980 - accuracy: 0.8549\n",
      "Epoch 14: saving model to model_weights.h5\n",
      "412/412 [==============================] - 70s 171ms/step - loss: 0.3980 - accuracy: 0.8549 - val_loss: 1.2736 - val_accuracy: 0.6154\n",
      "Epoch 15/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.3793 - accuracy: 0.8646\n",
      "Epoch 15: saving model to model_weights.h5\n",
      "412/412 [==============================] - 69s 167ms/step - loss: 0.3793 - accuracy: 0.8646 - val_loss: 1.2916 - val_accuracy: 0.6120\n",
      "Epoch 16/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.3706 - accuracy: 0.8643\n",
      "Epoch 16: saving model to model_weights.h5\n",
      "412/412 [==============================] - 69s 166ms/step - loss: 0.3706 - accuracy: 0.8643 - val_loss: 1.2954 - val_accuracy: 0.6071\n",
      "Epoch 17/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.3629 - accuracy: 0.8716\n",
      "Epoch 17: saving model to model_weights.h5\n",
      "412/412 [==============================] - 69s 168ms/step - loss: 0.3629 - accuracy: 0.8716 - val_loss: 1.3456 - val_accuracy: 0.5991\n",
      "Epoch 18/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.3535 - accuracy: 0.8716\n",
      "Epoch 18: saving model to model_weights.h5\n",
      "412/412 [==============================] - 69s 166ms/step - loss: 0.3535 - accuracy: 0.8716 - val_loss: 1.2988 - val_accuracy: 0.6045\n",
      "Epoch 19/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.3390 - accuracy: 0.8756\n",
      "Epoch 19: saving model to model_weights.h5\n",
      "412/412 [==============================] - 68s 164ms/step - loss: 0.3390 - accuracy: 0.8756 - val_loss: 1.3196 - val_accuracy: 0.6060\n",
      "Epoch 20/20\n",
      "412/412 [==============================] - ETA: 0s - loss: 0.3360 - accuracy: 0.8788\n",
      "Epoch 20: saving model to model_weights.h5\n",
      "412/412 [==============================] - 65s 158ms/step - loss: 0.3360 - accuracy: 0.8788 - val_loss: 1.3648 - val_accuracy: 0.6109\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "x = train_generator,\n",
    "steps_per_epoch = steps_per_epoch,\n",
    "epochs = epochs,\n",
    "validation_data = validation_generator,\n",
    "validation_steps = validation_steps,\n",
    "callbacks = callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b9a3d19",
   "metadata": {},
   "source": [
    "**Model Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a46371ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113/113 [==============================] - 8s 71ms/step - loss: 1.3644 - accuracy: 0.6112\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.3643845319747925, 0.6111730337142944]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(validation_generator)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7793c450",
   "metadata": {},
   "source": [
    "**Plotting Loss**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "17d82f94",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.legend(['Train','Validation'],loc = 'upper left')\n",
    "plt.subplots_adjust(top=1.0,bottom=0.0,right =0.95,left=0.0,hspace=0.25,wspace=0.35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c51087b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.legend(['Train','Validation'],loc = 'upper left')\n",
    "plt.subplots_adjust(top=1.0,bottom=0.0,right =0.95,left=0.0,hspace=0.25,wspace=0.35)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c304077",
   "metadata": {},
   "source": [
    "**Saving Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "506b54cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_json = model.to_json()\n",
    "with open(\"model_a.json\",\"w\") as json_file:\n",
    "    json_file.write(model_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99ca4471",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}