main_model.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import cv2 \n",
    "from tqdm import tqdm\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_STATE = 42  #setting the random variable value for reobataining the variable parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_data_train = os.path.join('dataset/'+'eyes_only/train')  #defining the path where dataset is stored\n",
    "image_data_test = os.path.join('dataset/'+'eyes_only/test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = ImageDataGenerator(rescale=1/255,\n",
    "                                zoom_range=0.2,\n",
    "                                shear_range=0.4,\n",
    "                                horizontal_flip=True,\n",
    "                                rotation_range=40,\n",
    "                                fill_mode='nearest',\n",
    "                                brightness_range=[0.2,1])\n",
    "\n",
    "test_gen = ImageDataGenerator(rescale=1/255)      #data augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Found 1234 images belonging to 2 classes.\nFound 218 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_set = train_gen.flow_from_directory(image_data_train,\n",
    "                                          target_size=(128,128),\n",
    "                                          shuffle=True,\n",
    "                                          batch_size=25,\n",
    "                                          class_mode='binary')\n",
    "test_set = test_gen.flow_from_directory(image_data_test,\n",
    "                                        class_mode='binary',\n",
    "                                        shuffle=False,\n",
    "                                        target_size=(128,128),\n",
    "                                        batch_size=15)   \n",
    "# Date Preprocessing                                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential,Model\n",
    "from keras.layers import Conv2D, MaxPooling2D,Flatten,BatchNormalization,Dropout,Dense\n",
    "from keras.callbacks import ModelCheckpoint,EarlyStopping\n",
    "from keras.applications import VGG16,ResNet50V2,xception,inception_v3\n",
    "from livelossplot import PlotLossesKeras\n",
    "import pydot\n",
    "from keras.utils.vis_utils import plot_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def call_model():\n",
    "    model_cnn = Sequential()\n",
    "    model_cnn.add(Conv2D(64,(3,3),input_shape=(128,128,3),padding='same',activation='relu'))\n",
    "    model_cnn.add(MaxPooling2D(pool_size=(3,3)))\n",
    "    model_cnn.add(Conv2D(64,(3,3),activation='relu',padding='valid'))\n",
    "    model_cnn.add(MaxPooling2D(pool_size=(2,2)))\n",
    "    model_cnn.add(BatchNormalization())\n",
    "    model_cnn.add(Conv2D(32,(3,3),activation='relu',padding='valid'))\n",
    "    model_cnn.add(MaxPooling2D(2,2))\n",
    "    model_cnn.add(Conv2D(32,(3,3),activation='relu',padding='valid'))\n",
    "    model_cnn.add(MaxPooling2D(2,2))\n",
    "    model_cnn.add(BatchNormalization())\n",
    "    model_cnn.add(Flatten())\n",
    "\n",
    "    model_cnn.add(Dense(512,activation='relu'))\n",
    "    model_cnn.add(Dense(64,activation='relu'))\n",
    "    model_cnn.add(Dense(1,activation='sigmoid'))\n",
    "\n",
    "\n",
    "    model_cnn.compile(optimizer='adam',metrics=['accuracy'],loss='binary_crossentropy')\n",
    "    \n",
    "\n",
    "    return model_cnn\n",
    "\n",
    "    #defining a function that will return model (sequential) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential_1\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d_1 (Conv2D)            (None, 128, 128, 64)      1792      \n_________________________________________________________________\nmax_pooling2d_1 (MaxPooling2 (None, 42, 42, 64)        0         \n_________________________________________________________________\nconv2d_2 (Conv2D)            (None, 40, 40, 64)        36928     \n_________________________________________________________________\nmax_pooling2d_2 (MaxPooling2 (None, 20, 20, 64)        0         \n_________________________________________________________________\nbatch_normalization_1 (Batch (None, 20, 20, 64)        256       \n_________________________________________________________________\nconv2d_3 (Conv2D)            (None, 18, 18, 32)        18464     \n_________________________________________________________________\nmax_pooling2d_3 (MaxPooling2 (None, 9, 9, 32)          0         \n_________________________________________________________________\nconv2d_4 (Conv2D)            (None, 7, 7, 32)          9248      \n_________________________________________________________________\nmax_pooling2d_4 (MaxPooling2 (None, 3, 3, 32)          0         \n_________________________________________________________________\nbatch_normalization_2 (Batch (None, 3, 3, 32)          128       \n_________________________________________________________________\nflatten_1 (Flatten)          (None, 288)               0         \n_________________________________________________________________\ndense_1 (Dense)              (None, 512)               147968    \n_________________________________________________________________\ndense_2 (Dense)              (None, 64)                32832     \n_________________________________________________________________\ndense_3 (Dense)              (None, 1)                 65        \n=================================================================\nTotal params: 247,681\nTrainable params: 247,489\nNon-trainable params: 192\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = call_model()\n",
    "#plot_model(model,to_file='model_cnn_1.png',show_shapes=True,show_layer_names=True)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "callback = [EarlyStopping(monitor='val_accuracy',\n",
    "                            patience=4),\n",
    "            PlotLossesKeras()] #defining certain monitoring libraries while the model is training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "accuracy\n\tvalidation       \t (min:    0.695, max:    0.956, cur:    0.941)\n\ttraining         \t (min:    0.897, max:    0.979, cur:    0.978)\nLoss\n\tvalidation       \t (min:    0.002, max:    1.117, cur:    0.066)\n\ttraining         \t (min:    0.055, max:    0.239, cur:    0.055)\n"
     ]
    }
   ],
   "source": [
    "h = model.fit(train_set,\n",
    "              steps_per_epoch=train_set.n//25,\n",
    "              epochs=10,\n",
    "              validation_steps=test_set.n//15,\n",
    "              validation_data=test_set,\n",
    "              callbacks=callback)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'Closed': 0, 'Open': 1}"
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "train_set.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "OPEN\nOPEN\nCLOSED\nOPEN\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 4 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "test_imagepath = os.path.join('dataset/'+'eyes_only/validation/')\n",
    "\n",
    "for i in range(1,5):\n",
    "    image = cv2.imread(test_imagepath+str(i)+'.jpg')\n",
    "    image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)\n",
    "    plt.subplot(4,1,i)\n",
    "    plt.imshow(image)\n",
    "    image = cv2.resize(image,(128,128))\n",
    "    image = image.reshape(1,128,128,3)\n",
    "    prediction = model.predict(image)\n",
    "\n",
    "    if prediction == 0:\n",
    "        print('CLOSED')\n",
    "    else:\n",
    "        print('OPEN')\n",
    "\n",
    "\n",
    "    #checking the model prediction against arbitrary input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.save('models/model_cnn_1.h5') #saving the trained model\n",
    "model.save('models/model_cnn_2.h5') #saving the 2nd trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}