Mini_project2022.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "3a350b97",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import cv2\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "93adfca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_array = cv2.imread(\"Test_Dataset/Closed_Eyes/s0002_00131_0_0_0_0_1_01.png\",cv2.IMREAD_GRAYSCALE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "b76e9a17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x21a0d496310>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img_array,cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "71ec7f54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(107, 107)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "d5e8a620",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Datadirectory = \"Test_Dataset\"\n",
    "Classes = [\"Closed_Eyes\",\"Open_Eyes\"]\n",
    "for category in Classes:\n",
    "    path = os.path.join(Datadirectory , category)\n",
    "    for img in os.listdir(path):\n",
    "        img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE)\n",
    "        backtorgb = cv2.cvtColor(img_array,cv2.COLOR_GRAY2RGB)\n",
    "        plt.imshow(img_array,cmap = \"gray\")\n",
    "        plt.show()\n",
    "        break\n",
    "    break\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "ee61063f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_size = 224\n",
    "new_array = cv2.resize(backtorgb, (img_size,img_size))\n",
    "plt.imshow(new_array, cmap = \"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5678c888",
   "metadata": {},
   "source": [
    "#reading all the images and coverting them into an array for data and labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "be27ec91",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_Data=[]\n",
    "\n",
    "def create_training_Data():\n",
    "    for category in Classes:\n",
    "        path=os.path.join(Datadirectory,category)\n",
    "        class_num=Classes.index(category)\n",
    "        for img in os.listdir(path):\n",
    "            try:\n",
    "                img_array=cv2.imread(os.path.join(path,img),cv2.IMREAD_GRAYSCALE)\n",
    "                backtorgb=cv2.cvtColor(img_array,cv2.COLOR_GRAY2RGB)\n",
    "                new_array=cv2.resize(backtorgb,(img_size,img_size))\n",
    "                training_Data.append([new_array,class_num])\n",
    "            except Exception as e:\n",
    "                pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "d17b4e2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "create_training_Data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "43df9d72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2119\n"
     ]
    }
   ],
   "source": [
    "print(len(training_Data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "3f1674f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "random.shuffle(training_Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "fad013a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=[]\n",
    "y=[]\n",
    "for features,label in training_Data:\n",
    "    X.append(features)\n",
    "    y.append(label)\n",
    "    \n",
    "X=np.array(X).reshape( -1 , img_size , img_size,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "ac28bed3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2119, 224, 224, 3)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "fa00e5b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X/255.0;\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "7401c0ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "b5f5c87e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle_out=open(\"X.pickle\",\"wb\")\n",
    "pickle.dump(X,pickle_out)\n",
    "pickle_out.close()\n",
    "\n",
    "pickle_out=open(\"y.pickle\",\"wb\")\n",
    "pickle.dump(y,pickle_out)\n",
    "pickle_out.close()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "8f0fb926",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_in=open(\"X.pickle\",\"rb\")\n",
    "X=pickle.load(pickle_in)\n",
    "\n",
    "pickle_in=open(\"y.pickle\",\"rb\")\n",
    "y=pickle.load(pickle_in)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1636fcbd",
   "metadata": {},
   "source": [
    "#deep learning model for training - Training Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "6740df7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "caa61da9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"mobilenet_1.00_224\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_4 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
      "                                                                 \n",
      " conv1 (Conv2D)              (None, 112, 112, 32)      864       \n",
      "                                                                 \n",
      " conv1_bn (BatchNormalizatio  (None, 112, 112, 32)     128       \n",
      " n)                                                              \n",
      "                                                                 \n",
      " conv1_relu (ReLU)           (None, 112, 112, 32)      0         \n",
      "                                                                 \n",
      " conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)     288       \n",
      "                                                                 \n",
      " conv_dw_1_bn (BatchNormaliz  (None, 112, 112, 32)     128       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_1_relu (ReLU)       (None, 112, 112, 32)      0         \n",
      "                                                                 \n",
      " conv_pw_1 (Conv2D)          (None, 112, 112, 64)      2048      \n",
      "                                                                 \n",
      " conv_pw_1_bn (BatchNormaliz  (None, 112, 112, 64)     256       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_1_relu (ReLU)       (None, 112, 112, 64)      0         \n",
      "                                                                 \n",
      " conv_pad_2 (ZeroPadding2D)  (None, 113, 113, 64)      0         \n",
      "                                                                 \n",
      " conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)       576       \n",
      "                                                                 \n",
      " conv_dw_2_bn (BatchNormaliz  (None, 56, 56, 64)       256       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_2_relu (ReLU)       (None, 56, 56, 64)        0         \n",
      "                                                                 \n",
      " conv_pw_2 (Conv2D)          (None, 56, 56, 128)       8192      \n",
      "                                                                 \n",
      " conv_pw_2_bn (BatchNormaliz  (None, 56, 56, 128)      512       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_2_relu (ReLU)       (None, 56, 56, 128)       0         \n",
      "                                                                 \n",
      " conv_dw_3 (DepthwiseConv2D)  (None, 56, 56, 128)      1152      \n",
      "                                                                 \n",
      " conv_dw_3_bn (BatchNormaliz  (None, 56, 56, 128)      512       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_3_relu (ReLU)       (None, 56, 56, 128)       0         \n",
      "                                                                 \n",
      " conv_pw_3 (Conv2D)          (None, 56, 56, 128)       16384     \n",
      "                                                                 \n",
      " conv_pw_3_bn (BatchNormaliz  (None, 56, 56, 128)      512       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_3_relu (ReLU)       (None, 56, 56, 128)       0         \n",
      "                                                                 \n",
      " conv_pad_4 (ZeroPadding2D)  (None, 57, 57, 128)       0         \n",
      "                                                                 \n",
      " conv_dw_4 (DepthwiseConv2D)  (None, 28, 28, 128)      1152      \n",
      "                                                                 \n",
      " conv_dw_4_bn (BatchNormaliz  (None, 28, 28, 128)      512       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_4_relu (ReLU)       (None, 28, 28, 128)       0         \n",
      "                                                                 \n",
      " conv_pw_4 (Conv2D)          (None, 28, 28, 256)       32768     \n",
      "                                                                 \n",
      " conv_pw_4_bn (BatchNormaliz  (None, 28, 28, 256)      1024      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_4_relu (ReLU)       (None, 28, 28, 256)       0         \n",
      "                                                                 \n",
      " conv_dw_5 (DepthwiseConv2D)  (None, 28, 28, 256)      2304      \n",
      "                                                                 \n",
      " conv_dw_5_bn (BatchNormaliz  (None, 28, 28, 256)      1024      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_5_relu (ReLU)       (None, 28, 28, 256)       0         \n",
      "                                                                 \n",
      " conv_pw_5 (Conv2D)          (None, 28, 28, 256)       65536     \n",
      "                                                                 \n",
      " conv_pw_5_bn (BatchNormaliz  (None, 28, 28, 256)      1024      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_5_relu (ReLU)       (None, 28, 28, 256)       0         \n",
      "                                                                 \n",
      " conv_pad_6 (ZeroPadding2D)  (None, 29, 29, 256)       0         \n",
      "                                                                 \n",
      " conv_dw_6 (DepthwiseConv2D)  (None, 14, 14, 256)      2304      \n",
      "                                                                 \n",
      " conv_dw_6_bn (BatchNormaliz  (None, 14, 14, 256)      1024      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_6_relu (ReLU)       (None, 14, 14, 256)       0         \n",
      "                                                                 \n",
      " conv_pw_6 (Conv2D)          (None, 14, 14, 512)       131072    \n",
      "                                                                 \n",
      " conv_pw_6_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_6_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_dw_7 (DepthwiseConv2D)  (None, 14, 14, 512)      4608      \n",
      "                                                                 \n",
      " conv_dw_7_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_7_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_pw_7 (Conv2D)          (None, 14, 14, 512)       262144    \n",
      "                                                                 \n",
      " conv_pw_7_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_7_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_dw_8 (DepthwiseConv2D)  (None, 14, 14, 512)      4608      \n",
      "                                                                 \n",
      " conv_dw_8_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_8_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_pw_8 (Conv2D)          (None, 14, 14, 512)       262144    \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                 \n",
      " conv_pw_8_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_8_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_dw_9 (DepthwiseConv2D)  (None, 14, 14, 512)      4608      \n",
      "                                                                 \n",
      " conv_dw_9_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_9_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_pw_9 (Conv2D)          (None, 14, 14, 512)       262144    \n",
      "                                                                 \n",
      " conv_pw_9_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_9_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_dw_10 (DepthwiseConv2D  (None, 14, 14, 512)      4608      \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv_dw_10_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_dw_10_relu (ReLU)      (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_pw_10 (Conv2D)         (None, 14, 14, 512)       262144    \n",
      "                                                                 \n",
      " conv_pw_10_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_pw_10_relu (ReLU)      (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_dw_11 (DepthwiseConv2D  (None, 14, 14, 512)      4608      \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv_dw_11_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_dw_11_relu (ReLU)      (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_pw_11 (Conv2D)         (None, 14, 14, 512)       262144    \n",
      "                                                                 \n",
      " conv_pw_11_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_pw_11_relu (ReLU)      (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_pad_12 (ZeroPadding2D)  (None, 15, 15, 512)      0         \n",
      "                                                                 \n",
      " conv_dw_12 (DepthwiseConv2D  (None, 7, 7, 512)        4608      \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv_dw_12_bn (BatchNormali  (None, 7, 7, 512)        2048      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_dw_12_relu (ReLU)      (None, 7, 7, 512)         0         \n",
      "                                                                 \n",
      " conv_pw_12 (Conv2D)         (None, 7, 7, 1024)        524288    \n",
      "                                                                 \n",
      " conv_pw_12_bn (BatchNormali  (None, 7, 7, 1024)       4096      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_pw_12_relu (ReLU)      (None, 7, 7, 1024)        0         \n",
      "                                                                 \n",
      " conv_dw_13 (DepthwiseConv2D  (None, 7, 7, 1024)       9216      \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv_dw_13_bn (BatchNormali  (None, 7, 7, 1024)       4096      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_dw_13_relu (ReLU)      (None, 7, 7, 1024)        0         \n",
      "                                                                 \n",
      " conv_pw_13 (Conv2D)         (None, 7, 7, 1024)        1048576   \n",
      "                                                                 \n",
      " conv_pw_13_bn (BatchNormali  (None, 7, 7, 1024)       4096      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_pw_13_relu (ReLU)      (None, 7, 7, 1024)        0         \n",
      "                                                                 \n",
      " global_average_pooling2d_3   (None, 1, 1, 1024)       0         \n",
      " (GlobalAveragePooling2D)                                        \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 1, 1, 1024)        0         \n",
      "                                                                 \n",
      " conv_preds (Conv2D)         (None, 1, 1, 1000)        1025000   \n",
      "                                                                 \n",
      " reshape_2 (Reshape)         (None, 1000)              0         \n",
      "                                                                 \n",
      " predictions (Activation)    (None, 1000)              0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4,253,864\n",
      "Trainable params: 4,231,976\n",
      "Non-trainable params: 21,888\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model=tf.keras.applications.mobilenet.MobileNet()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "ed6859b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transfer Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "9a4d3cc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_input=model.layers[0].input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "0a826dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_output=model.layers[-4].output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "2067e70b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Flat_layer=layers.Flatten()(base_output)\n",
    "final_output=layers.Dense(1)(Flat_layer)\n",
    "final_output=layers.Activation('sigmoid')(final_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "202d46be",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model=keras.Model(inputs=base_input,outputs=final_output)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "3df1e161",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_4 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
      "                                                                 \n",
      " conv1 (Conv2D)              (None, 112, 112, 32)      864       \n",
      "                                                                 \n",
      " conv1_bn (BatchNormalizatio  (None, 112, 112, 32)     128       \n",
      " n)                                                              \n",
      "                                                                 \n",
      " conv1_relu (ReLU)           (None, 112, 112, 32)      0         \n",
      "                                                                 \n",
      " conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)     288       \n",
      "                                                                 \n",
      " conv_dw_1_bn (BatchNormaliz  (None, 112, 112, 32)     128       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_1_relu (ReLU)       (None, 112, 112, 32)      0         \n",
      "                                                                 \n",
      " conv_pw_1 (Conv2D)          (None, 112, 112, 64)      2048      \n",
      "                                                                 \n",
      " conv_pw_1_bn (BatchNormaliz  (None, 112, 112, 64)     256       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_1_relu (ReLU)       (None, 112, 112, 64)      0         \n",
      "                                                                 \n",
      " conv_pad_2 (ZeroPadding2D)  (None, 113, 113, 64)      0         \n",
      "                                                                 \n",
      " conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)       576       \n",
      "                                                                 \n",
      " conv_dw_2_bn (BatchNormaliz  (None, 56, 56, 64)       256       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_2_relu (ReLU)       (None, 56, 56, 64)        0         \n",
      "                                                                 \n",
      " conv_pw_2 (Conv2D)          (None, 56, 56, 128)       8192      \n",
      "                                                                 \n",
      " conv_pw_2_bn (BatchNormaliz  (None, 56, 56, 128)      512       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_2_relu (ReLU)       (None, 56, 56, 128)       0         \n",
      "                                                                 \n",
      " conv_dw_3 (DepthwiseConv2D)  (None, 56, 56, 128)      1152      \n",
      "                                                                 \n",
      " conv_dw_3_bn (BatchNormaliz  (None, 56, 56, 128)      512       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_3_relu (ReLU)       (None, 56, 56, 128)       0         \n",
      "                                                                 \n",
      " conv_pw_3 (Conv2D)          (None, 56, 56, 128)       16384     \n",
      "                                                                 \n",
      " conv_pw_3_bn (BatchNormaliz  (None, 56, 56, 128)      512       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_3_relu (ReLU)       (None, 56, 56, 128)       0         \n",
      "                                                                 \n",
      " conv_pad_4 (ZeroPadding2D)  (None, 57, 57, 128)       0         \n",
      "                                                                 \n",
      " conv_dw_4 (DepthwiseConv2D)  (None, 28, 28, 128)      1152      \n",
      "                                                                 \n",
      " conv_dw_4_bn (BatchNormaliz  (None, 28, 28, 128)      512       \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_4_relu (ReLU)       (None, 28, 28, 128)       0         \n",
      "                                                                 \n",
      " conv_pw_4 (Conv2D)          (None, 28, 28, 256)       32768     \n",
      "                                                                 \n",
      " conv_pw_4_bn (BatchNormaliz  (None, 28, 28, 256)      1024      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_4_relu (ReLU)       (None, 28, 28, 256)       0         \n",
      "                                                                 \n",
      " conv_dw_5 (DepthwiseConv2D)  (None, 28, 28, 256)      2304      \n",
      "                                                                 \n",
      " conv_dw_5_bn (BatchNormaliz  (None, 28, 28, 256)      1024      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_5_relu (ReLU)       (None, 28, 28, 256)       0         \n",
      "                                                                 \n",
      " conv_pw_5 (Conv2D)          (None, 28, 28, 256)       65536     \n",
      "                                                                 \n",
      " conv_pw_5_bn (BatchNormaliz  (None, 28, 28, 256)      1024      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_5_relu (ReLU)       (None, 28, 28, 256)       0         \n",
      "                                                                 \n",
      " conv_pad_6 (ZeroPadding2D)  (None, 29, 29, 256)       0         \n",
      "                                                                 \n",
      " conv_dw_6 (DepthwiseConv2D)  (None, 14, 14, 256)      2304      \n",
      "                                                                 \n",
      " conv_dw_6_bn (BatchNormaliz  (None, 14, 14, 256)      1024      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_6_relu (ReLU)       (None, 14, 14, 256)       0         \n",
      "                                                                 \n",
      " conv_pw_6 (Conv2D)          (None, 14, 14, 512)       131072    \n",
      "                                                                 \n",
      " conv_pw_6_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_6_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_dw_7 (DepthwiseConv2D)  (None, 14, 14, 512)      4608      \n",
      "                                                                 \n",
      " conv_dw_7_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_7_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_pw_7 (Conv2D)          (None, 14, 14, 512)       262144    \n",
      "                                                                 \n",
      " conv_pw_7_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_7_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_dw_8 (DepthwiseConv2D)  (None, 14, 14, 512)      4608      \n",
      "                                                                 \n",
      " conv_dw_8_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_8_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_pw_8 (Conv2D)          (None, 14, 14, 512)       262144    \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                 \n",
      " conv_pw_8_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_8_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_dw_9 (DepthwiseConv2D)  (None, 14, 14, 512)      4608      \n",
      "                                                                 \n",
      " conv_dw_9_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_dw_9_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_pw_9 (Conv2D)          (None, 14, 14, 512)       262144    \n",
      "                                                                 \n",
      " conv_pw_9_bn (BatchNormaliz  (None, 14, 14, 512)      2048      \n",
      " ation)                                                          \n",
      "                                                                 \n",
      " conv_pw_9_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_dw_10 (DepthwiseConv2D  (None, 14, 14, 512)      4608      \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv_dw_10_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_dw_10_relu (ReLU)      (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_pw_10 (Conv2D)         (None, 14, 14, 512)       262144    \n",
      "                                                                 \n",
      " conv_pw_10_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_pw_10_relu (ReLU)      (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_dw_11 (DepthwiseConv2D  (None, 14, 14, 512)      4608      \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv_dw_11_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_dw_11_relu (ReLU)      (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_pw_11 (Conv2D)         (None, 14, 14, 512)       262144    \n",
      "                                                                 \n",
      " conv_pw_11_bn (BatchNormali  (None, 14, 14, 512)      2048      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_pw_11_relu (ReLU)      (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " conv_pad_12 (ZeroPadding2D)  (None, 15, 15, 512)      0         \n",
      "                                                                 \n",
      " conv_dw_12 (DepthwiseConv2D  (None, 7, 7, 512)        4608      \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv_dw_12_bn (BatchNormali  (None, 7, 7, 512)        2048      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_dw_12_relu (ReLU)      (None, 7, 7, 512)         0         \n",
      "                                                                 \n",
      " conv_pw_12 (Conv2D)         (None, 7, 7, 1024)        524288    \n",
      "                                                                 \n",
      " conv_pw_12_bn (BatchNormali  (None, 7, 7, 1024)       4096      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_pw_12_relu (ReLU)      (None, 7, 7, 1024)        0         \n",
      "                                                                 \n",
      " conv_dw_13 (DepthwiseConv2D  (None, 7, 7, 1024)       9216      \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv_dw_13_bn (BatchNormali  (None, 7, 7, 1024)       4096      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_dw_13_relu (ReLU)      (None, 7, 7, 1024)        0         \n",
      "                                                                 \n",
      " conv_pw_13 (Conv2D)         (None, 7, 7, 1024)        1048576   \n",
      "                                                                 \n",
      " conv_pw_13_bn (BatchNormali  (None, 7, 7, 1024)       4096      \n",
      " zation)                                                         \n",
      "                                                                 \n",
      " conv_pw_13_relu (ReLU)      (None, 7, 7, 1024)        0         \n",
      "                                                                 \n",
      " global_average_pooling2d_3   (None, 1, 1, 1024)       0         \n",
      " (GlobalAveragePooling2D)                                        \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 1, 1, 1024)        0         \n",
      "                                                                 \n",
      " flatten_3 (Flatten)         (None, 1024)              0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 1)                 1025      \n",
      "                                                                 \n",
      " activation_3 (Activation)   (None, 1)                 0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 3,229,889\n",
      "Trainable params: 3,208,001\n",
      "Non-trainable params: 21,888\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "new_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "0937e5f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#settings for binary classification(open/closed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "38a1f2e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model.compile(loss=\"binary_crossentropy\",optimizer=\"adam\",metrics=[\"accuracy\"])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "b020b5ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60/60 [==============================] - 171s 3s/step - loss: 0.0704 - accuracy: 0.9754 - val_loss: 12.1795 - val_accuracy: 0.5519\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2198010d9a0>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_model.fit(X,Y,epochs=1,validation_split=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "126dd453",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model.save('my_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "6503169b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#checking network for predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "d977e097",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_array=cv2.imread(\"s0001_02190_0_0_1_0_0_01.png\",cv2.IMREAD_GRAYSCALE)\n",
    "backtorgb=cv2.cvtColor(img_array,cv2.COLOR_GRAY2RGB)\n",
    "new_array=cv2.resize(backtorgb,(img_size,img_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "6b73c4e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_input=np.array(new_array).reshape(1,img_size,img_size,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "13f486d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 224, 224, 3)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_input.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "f46043d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x21981de7430>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(new_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "82686f73",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_input=X_input/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "a6010eba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 20ms/step\n"
     ]
    }
   ],
   "source": [
    "prediction=new_model.predict(X_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "17769c85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.17256664e-11]], dtype=float32)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "prediction\n"
   ]
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