CNN_Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Face Mask Detection Model\n",
    "\n",
    "Face mask detection system using machine learning, specifically a Convolutional Neural Network (CNN) model. This system will analyze live video streams or recorded video to identify whether individuals are wearing face masks or not. By leveraging the power of deep learning, it aims to enhance safety and compliance with mask-wearing protocols in various environments, such as public spaces and healthcare settings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Requirements\n",
    "\n",
    "- Numpy\n",
    "- Matplotlib\n",
    "- Scikit-Learn\n",
    "- TensorFlow\n",
    "- Tkinter\n",
    "- Keras\n",
    "- ImUtils\n",
    "- OpenCV-Python\n",
    "\n",
    "*Additional* - Download and Install CUDA and cuDNN for better performance in tensorflow training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import itertools\n",
    "from enum import Enum\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_auc_score, auc\n",
    "from sklearn.metrics import roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.preprocessing.image import load_img\n",
    "from tensorflow.keras.preprocessing.image import img_to_array\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN Architecture Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# different types of CNN architecture\n",
    "class CNN_TYPE(Enum):\n",
    "    VGGNET = 1\n",
    "    GOOGLENET = 2\n",
    "    RESNET = 3\n",
    "    MOBILENET = 4  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the type to RESNET\n",
    "current_cnn_type = CNN_TYPE.RESNET"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Function for Retrieving CNN Models and Preprocessing Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to initialize and configure a CNN model and its associated preprocessing function \n",
    "def get_cnn_mode(type, image_shape):\n",
    "    preprocess_input = []\n",
    "    abc_model = []\n",
    "    t2wi_model = []\n",
    "    if type==CNN_TYPE.VGGNET:\n",
    "        preprocess_input = tf.keras.applications.vgg16.preprocess_input\n",
    "        model = tf.keras.applications.VGG16(input_shape=image_shape,\n",
    "                                               include_top=False,\n",
    "                                               weights='imagenet')\n",
    "    elif type==CNN_TYPE.GOOGLENET:    \n",
    "        preprocess_input = tf.keras.applications.inception_v3.preprocess_input\n",
    "        model = tf.keras.applications.InceptionV3(input_shape=image_shape,\n",
    "                                               include_top=False,\n",
    "                                               weights='imagenet')\n",
    "    elif type==CNN_TYPE.RESNET:    \n",
    "        preprocess_input = tf.keras.applications.resnet50.preprocess_input\n",
    "        model = tf.keras.applications.ResNet50(input_shape=image_shape,\n",
    "                                               include_top=False,\n",
    "                                               weights='imagenet')\n",
    "    elif type==CNN_TYPE.MOBILENET:    \n",
    "        preprocess_input = tf.keras.applications.mobilenet_v2.preprocess_input\n",
    "        model = tf.keras.applications.MobileNetV2(input_shape=image_shape,\n",
    "                                               include_top=False,\n",
    "                                               weights='imagenet')\n",
    "\n",
    "    else:\n",
    "        preprocess_input = tf.keras.applications.vgg19.preprocess_input\n",
    "        model = tf.keras.applications.MobileNetV2(input_shape=image_shape,\n",
    "                                               include_top=False,\n",
    "                                               weights='imagenet')\n",
    "    return preprocess_input, model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the shape of an image with a width of 224 pixels, a height of 224 pixels, and 3 color channels (typically red, green, and blue)\n",
    "img_shape = (224, 224, 3)\n",
    "preprocess_input, base_model = get_cnn_mode(current_cnn_type, img_shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading and Preprocessing Images for Face Mask Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize the initial learning rate, number of epochs to train for and batch size\n",
    "lr = 1e-4\n",
    "epochs = 10\n",
    "batch_size = 32\n",
    "input_path = \"FaceMask_Dataset\"\n",
    "clasess = [\"WithMask\", \"WithoutMask\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] loading images...\n"
     ]
    }
   ],
   "source": [
    "# grab the list of images in our dataset directory, then initialize the list of data (i.e., images) and class images\n",
    "print(\"[INFO] loading images...\")\n",
    "\n",
    "images = []\n",
    "labels = []\n",
    "for each_cls in clasess:\n",
    "    path = os.path.join(input_path, each_cls)\n",
    "    for img in os.listdir(path):\n",
    "        img_path = os.path.join(path, img)\n",
    "        image = load_img(img_path, target_size=(224, 224))\n",
    "        image = img_to_array(image)\n",
    "        image = preprocess_input(image)\n",
    "        images.append(image)\n",
    "        labels.append(each_cls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-Hot Encoding and Data Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# perform one-hot encoding on the labels\n",
    "lb = LabelBinarizer()\n",
    "labels = lb.fit_transform(labels)\n",
    "labels = to_categorical(labels)\n",
    "\n",
    "images = np.array(images, dtype=\"float32\")\n",
    "labels = np.array(labels)\n",
    "\n",
    "(x_train, x_test, y_train, y_test) = train_test_split(images, labels,test_size=0.20, stratify=labels, random_state=42)\n",
    "\n",
    "# construct the training image generator for data augmentation\n",
    "data_augumentation = ImageDataGenerator(\n",
    "\trotation_range=20,\n",
    "\tzoom_range=0.15,\n",
    "\twidth_shift_range=0.2,\n",
    "\theight_shift_range=0.2,\n",
    "\tshear_range=0.15,\n",
    "\thorizontal_flip=True,\n",
    "\tfill_mode=\"nearest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1600, 224, 224, 3)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of x_train\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating and Compile the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# construct the head of the model that will be placed on top of the base model\n",
    "model = base_model.output\n",
    "#model = AveragePooling2D(pool_size=(7, 7))(model)\n",
    "\n",
    "model=tf.keras.layers.GlobalAveragePooling2D()(model)\n",
    "model = Flatten(name=\"flatten\")(model)\n",
    "model = Dense(128, activation=\"relu\")(model)\n",
    "model = Dropout(0.5)(model)\n",
    "model = Dense(2, activation=\"softmax\")(model)\n",
    "\n",
    "model = Model(inputs=base_model.input, outputs=model)\n",
    "\n",
    "for layer in base_model.layers:\n",
    "\tlayer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compile our model\n",
    "opt = tf.keras.optimizers.Adam(learning_rate=lr)\n",
    "model.compile(loss=\"binary_crossentropy\", optimizer=opt,metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)        [(None, 224, 224, 3)]        0         []                            \n",
      "                                                                                                  \n",
      " conv1_pad (ZeroPadding2D)   (None, 230, 230, 3)          0         ['input_1[0][0]']             \n",
      "                                                                                                  \n",
      " conv1_conv (Conv2D)         (None, 112, 112, 64)         9472      ['conv1_pad[0][0]']           \n",
      "                                                                                                  \n",
      " conv1_bn (BatchNormalizati  (None, 112, 112, 64)         256       ['conv1_conv[0][0]']          \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv1_relu (Activation)     (None, 112, 112, 64)         0         ['conv1_bn[0][0]']            \n",
      "                                                                                                  \n",
      " pool1_pad (ZeroPadding2D)   (None, 114, 114, 64)         0         ['conv1_relu[0][0]']          \n",
      "                                                                                                  \n",
      " pool1_pool (MaxPooling2D)   (None, 56, 56, 64)           0         ['pool1_pad[0][0]']           \n",
      "                                                                                                  \n",
      " conv2_block1_1_conv (Conv2  (None, 56, 56, 64)           4160      ['pool1_pool[0][0]']          \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_1_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block1_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block1_1_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block1_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block1_2_conv (Conv2  (None, 56, 56, 64)           36928     ['conv2_block1_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_2_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block1_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block1_2_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block1_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block1_0_conv (Conv2  (None, 56, 56, 256)          16640     ['pool1_pool[0][0]']          \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_3_conv (Conv2  (None, 56, 56, 256)          16640     ['conv2_block1_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_0_bn (BatchNo  (None, 56, 56, 256)          1024      ['conv2_block1_0_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block1_3_bn (BatchNo  (None, 56, 56, 256)          1024      ['conv2_block1_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block1_add (Add)      (None, 56, 56, 256)          0         ['conv2_block1_0_bn[0][0]',   \n",
      "                                                                     'conv2_block1_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv2_block1_out (Activati  (None, 56, 56, 256)          0         ['conv2_block1_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv2_block2_1_conv (Conv2  (None, 56, 56, 64)           16448     ['conv2_block1_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_1_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block2_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block2_1_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block2_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block2_2_conv (Conv2  (None, 56, 56, 64)           36928     ['conv2_block2_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_2_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block2_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block2_2_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block2_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block2_3_conv (Conv2  (None, 56, 56, 256)          16640     ['conv2_block2_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_3_bn (BatchNo  (None, 56, 56, 256)          1024      ['conv2_block2_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block2_add (Add)      (None, 56, 56, 256)          0         ['conv2_block1_out[0][0]',    \n",
      "                                                                     'conv2_block2_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv2_block2_out (Activati  (None, 56, 56, 256)          0         ['conv2_block2_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv2_block3_1_conv (Conv2  (None, 56, 56, 64)           16448     ['conv2_block2_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_1_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block3_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block3_1_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block3_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block3_2_conv (Conv2  (None, 56, 56, 64)           36928     ['conv2_block3_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_2_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block3_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block3_2_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block3_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block3_3_conv (Conv2  (None, 56, 56, 256)          16640     ['conv2_block3_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_3_bn (BatchNo  (None, 56, 56, 256)          1024      ['conv2_block3_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block3_add (Add)      (None, 56, 56, 256)          0         ['conv2_block2_out[0][0]',    \n",
      "                                                                     'conv2_block3_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv2_block3_out (Activati  (None, 56, 56, 256)          0         ['conv2_block3_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv3_block1_1_conv (Conv2  (None, 28, 28, 128)          32896     ['conv2_block3_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_1_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block1_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block1_1_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block1_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block1_2_conv (Conv2  (None, 28, 28, 128)          147584    ['conv3_block1_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_2_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block1_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block1_2_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block1_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block1_0_conv (Conv2  (None, 28, 28, 512)          131584    ['conv2_block3_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_3_conv (Conv2  (None, 28, 28, 512)          66048     ['conv3_block1_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_0_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block1_0_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block1_3_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block1_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block1_add (Add)      (None, 28, 28, 512)          0         ['conv3_block1_0_bn[0][0]',   \n",
      "                                                                     'conv3_block1_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv3_block1_out (Activati  (None, 28, 28, 512)          0         ['conv3_block1_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv3_block2_1_conv (Conv2  (None, 28, 28, 128)          65664     ['conv3_block1_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_1_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block2_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block2_1_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block2_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block2_2_conv (Conv2  (None, 28, 28, 128)          147584    ['conv3_block2_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_2_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block2_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block2_2_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block2_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block2_3_conv (Conv2  (None, 28, 28, 512)          66048     ['conv3_block2_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_3_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block2_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block2_add (Add)      (None, 28, 28, 512)          0         ['conv3_block1_out[0][0]',    \n",
      "                                                                     'conv3_block2_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv3_block2_out (Activati  (None, 28, 28, 512)          0         ['conv3_block2_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv3_block3_1_conv (Conv2  (None, 28, 28, 128)          65664     ['conv3_block2_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_1_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block3_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block3_1_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block3_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block3_2_conv (Conv2  (None, 28, 28, 128)          147584    ['conv3_block3_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_2_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block3_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block3_2_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block3_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block3_3_conv (Conv2  (None, 28, 28, 512)          66048     ['conv3_block3_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_3_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block3_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block3_add (Add)      (None, 28, 28, 512)          0         ['conv3_block2_out[0][0]',    \n",
      "                                                                     'conv3_block3_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv3_block3_out (Activati  (None, 28, 28, 512)          0         ['conv3_block3_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv3_block4_1_conv (Conv2  (None, 28, 28, 128)          65664     ['conv3_block3_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_1_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block4_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block4_1_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block4_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block4_2_conv (Conv2  (None, 28, 28, 128)          147584    ['conv3_block4_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_2_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block4_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block4_2_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block4_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block4_3_conv (Conv2  (None, 28, 28, 512)          66048     ['conv3_block4_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_3_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block4_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block4_add (Add)      (None, 28, 28, 512)          0         ['conv3_block3_out[0][0]',    \n",
      "                                                                     'conv3_block4_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv3_block4_out (Activati  (None, 28, 28, 512)          0         ['conv3_block4_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block1_1_conv (Conv2  (None, 14, 14, 256)          131328    ['conv3_block4_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block1_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block1_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block1_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block1_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block1_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block1_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block1_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block1_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block1_0_conv (Conv2  (None, 14, 14, 1024)         525312    ['conv3_block4_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block1_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_0_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block1_0_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block1_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block1_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block1_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block1_0_bn[0][0]',   \n",
      "                                                                     'conv4_block1_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block1_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block1_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block2_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block1_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block2_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block2_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block2_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block2_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block2_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block2_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block2_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block2_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block2_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block2_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block2_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block2_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block1_out[0][0]',    \n",
      "                                                                     'conv4_block2_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block2_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block2_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block3_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block2_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block3_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block3_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block3_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block3_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block3_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block3_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block3_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block3_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block3_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block3_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block3_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block3_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block2_out[0][0]',    \n",
      "                                                                     'conv4_block3_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block3_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block3_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block4_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block3_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block4_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block4_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block4_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block4_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block4_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block4_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block4_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block4_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block4_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block4_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block4_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block4_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block3_out[0][0]',    \n",
      "                                                                     'conv4_block4_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block4_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block4_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block5_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block4_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block5_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block5_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block5_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block5_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block5_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block5_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block5_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block5_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block5_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block5_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block5_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block5_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block4_out[0][0]',    \n",
      "                                                                     'conv4_block5_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block5_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block5_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block6_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block5_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block6_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block6_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block6_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block6_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block6_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block6_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block6_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block6_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block6_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block6_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block6_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block6_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block5_out[0][0]',    \n",
      "                                                                     'conv4_block6_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block6_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block6_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv5_block1_1_conv (Conv2  (None, 7, 7, 512)            524800    ['conv4_block6_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_1_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block1_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block1_1_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block1_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block1_2_conv (Conv2  (None, 7, 7, 512)            2359808   ['conv5_block1_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_2_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block1_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block1_2_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block1_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block1_0_conv (Conv2  (None, 7, 7, 2048)           2099200   ['conv4_block6_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_3_conv (Conv2  (None, 7, 7, 2048)           1050624   ['conv5_block1_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_0_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block1_0_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block1_3_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block1_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block1_add (Add)      (None, 7, 7, 2048)           0         ['conv5_block1_0_bn[0][0]',   \n",
      "                                                                     'conv5_block1_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv5_block1_out (Activati  (None, 7, 7, 2048)           0         ['conv5_block1_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv5_block2_1_conv (Conv2  (None, 7, 7, 512)            1049088   ['conv5_block1_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_1_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block2_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block2_1_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block2_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block2_2_conv (Conv2  (None, 7, 7, 512)            2359808   ['conv5_block2_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_2_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block2_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block2_2_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block2_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block2_3_conv (Conv2  (None, 7, 7, 2048)           1050624   ['conv5_block2_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_3_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block2_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block2_add (Add)      (None, 7, 7, 2048)           0         ['conv5_block1_out[0][0]',    \n",
      "                                                                     'conv5_block2_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv5_block2_out (Activati  (None, 7, 7, 2048)           0         ['conv5_block2_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv5_block3_1_conv (Conv2  (None, 7, 7, 512)            1049088   ['conv5_block2_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_1_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block3_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block3_1_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block3_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block3_2_conv (Conv2  (None, 7, 7, 512)            2359808   ['conv5_block3_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_2_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block3_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block3_2_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block3_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block3_3_conv (Conv2  (None, 7, 7, 2048)           1050624   ['conv5_block3_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_3_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block3_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block3_add (Add)      (None, 7, 7, 2048)           0         ['conv5_block2_out[0][0]',    \n",
      "                                                                     'conv5_block3_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv5_block3_out (Activati  (None, 7, 7, 2048)           0         ['conv5_block3_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " global_average_pooling2d (  (None, 2048)                 0         ['conv5_block3_out[0][0]']    \n",
      " GlobalAveragePooling2D)                                                                          \n",
      "                                                                                                  \n",
      " flatten (Flatten)           (None, 2048)                 0         ['global_average_pooling2d[0][\n",
      "                                                                    0]']                          \n",
      "                                                                                                  \n",
      " dense (Dense)               (None, 128)                  262272    ['flatten[0][0]']             \n",
      "                                                                                                  \n",
      " dropout (Dropout)           (None, 128)                  0         ['dense[0][0]']               \n",
      "                                                                                                  \n",
      " dense_1 (Dense)             (None, 2)                    258       ['dropout[0][0]']             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 23850242 (90.98 MB)\n",
      "Trainable params: 262530 (1.00 MB)\n",
      "Non-trainable params: 23587712 (89.98 MB)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# summary of the architecture\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "50/50 [==============================] - 127s 2s/step - loss: 0.3909 - accuracy: 0.8706 - val_loss: 0.0905 - val_accuracy: 0.9950\n",
      "Epoch 2/100\n",
      "50/50 [==============================] - 126s 3s/step - loss: 0.1276 - accuracy: 0.9669 - val_loss: 0.0431 - val_accuracy: 0.9975\n",
      "Epoch 3/100\n",
      "50/50 [==============================] - 117s 2s/step - loss: 0.0788 - accuracy: 0.9825 - val_loss: 0.0261 - val_accuracy: 0.9950\n",
      "Epoch 4/100\n",
      "50/50 [==============================] - 121s 2s/step - loss: 0.0683 - accuracy: 0.9825 - val_loss: 0.0218 - val_accuracy: 0.9975\n",
      "Epoch 5/100\n",
      "50/50 [==============================] - 119s 2s/step - loss: 0.0551 - accuracy: 0.9837 - val_loss: 0.0170 - val_accuracy: 0.9975\n",
      "Epoch 6/100\n",
      "50/50 [==============================] - 119s 2s/step - loss: 0.0491 - accuracy: 0.9869 - val_loss: 0.0154 - val_accuracy: 0.9975\n",
      "Epoch 7/100\n",
      "50/50 [==============================] - 118s 2s/step - loss: 0.0387 - accuracy: 0.9912 - val_loss: 0.0143 - val_accuracy: 0.9975\n",
      "Epoch 8/100\n",
      "50/50 [==============================] - 121s 2s/step - loss: 0.0420 - accuracy: 0.9894 - val_loss: 0.0120 - val_accuracy: 0.9975\n",
      "Epoch 9/100\n",
      "50/50 [==============================] - 115s 2s/step - loss: 0.0296 - accuracy: 0.9925 - val_loss: 0.0106 - val_accuracy: 1.0000\n",
      "Epoch 10/100\n",
      "50/50 [==============================] - 112s 2s/step - loss: 0.0282 - accuracy: 0.9937 - val_loss: 0.0095 - val_accuracy: 0.9975\n",
      "Epoch 11/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0270 - accuracy: 0.9925 - val_loss: 0.0098 - val_accuracy: 0.9975\n",
      "Epoch 12/100\n",
      "50/50 [==============================] - 102s 2s/step - loss: 0.0238 - accuracy: 0.9900 - val_loss: 0.0102 - val_accuracy: 0.9975\n",
      "Epoch 13/100\n",
      "50/50 [==============================] - 94s 2s/step - loss: 0.0217 - accuracy: 0.9937 - val_loss: 0.0085 - val_accuracy: 0.9975\n",
      "Epoch 14/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0208 - accuracy: 0.9944 - val_loss: 0.0087 - val_accuracy: 0.9975\n",
      "Epoch 15/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0178 - accuracy: 0.9962 - val_loss: 0.0088 - val_accuracy: 0.9975\n",
      "Epoch 16/100\n",
      "50/50 [==============================] - 96s 2s/step - loss: 0.0286 - accuracy: 0.9894 - val_loss: 0.0077 - val_accuracy: 0.9975\n",
      "Epoch 17/100\n",
      "50/50 [==============================] - 96s 2s/step - loss: 0.0169 - accuracy: 0.9962 - val_loss: 0.0071 - val_accuracy: 0.9975\n",
      "Epoch 18/100\n",
      "50/50 [==============================] - 94s 2s/step - loss: 0.0207 - accuracy: 0.9944 - val_loss: 0.0072 - val_accuracy: 0.9975\n",
      "Epoch 19/100\n",
      "50/50 [==============================] - 95s 2s/step - loss: 0.0167 - accuracy: 0.9956 - val_loss: 0.0072 - val_accuracy: 0.9975\n",
      "Epoch 20/100\n",
      "50/50 [==============================] - 95s 2s/step - loss: 0.0131 - accuracy: 0.9962 - val_loss: 0.0070 - val_accuracy: 0.9975\n",
      "Epoch 21/100\n",
      "50/50 [==============================] - 101s 2s/step - loss: 0.0162 - accuracy: 0.9937 - val_loss: 0.0071 - val_accuracy: 0.9975\n",
      "Epoch 22/100\n",
      "50/50 [==============================] - 94s 2s/step - loss: 0.0183 - accuracy: 0.9950 - val_loss: 0.0068 - val_accuracy: 0.9975\n",
      "Epoch 23/100\n",
      "50/50 [==============================] - 96s 2s/step - loss: 0.0145 - accuracy: 0.9950 - val_loss: 0.0069 - val_accuracy: 0.9975\n",
      "Epoch 24/100\n",
      "50/50 [==============================] - 95s 2s/step - loss: 0.0097 - accuracy: 0.9975 - val_loss: 0.0072 - val_accuracy: 0.9975\n",
      "Epoch 25/100\n",
      "50/50 [==============================] - 95s 2s/step - loss: 0.0157 - accuracy: 0.9950 - val_loss: 0.0071 - val_accuracy: 0.9975\n",
      "Epoch 26/100\n",
      "50/50 [==============================] - 95s 2s/step - loss: 0.0177 - accuracy: 0.9962 - val_loss: 0.0063 - val_accuracy: 0.9975\n",
      "Epoch 27/100\n",
      "50/50 [==============================] - 95s 2s/step - loss: 0.0164 - accuracy: 0.9956 - val_loss: 0.0058 - val_accuracy: 0.9975\n",
      "Epoch 28/100\n",
      "50/50 [==============================] - 94s 2s/step - loss: 0.0123 - accuracy: 0.9969 - val_loss: 0.0055 - val_accuracy: 0.9975\n",
      "Epoch 29/100\n",
      "50/50 [==============================] - 93s 2s/step - loss: 0.0119 - accuracy: 0.9969 - val_loss: 0.0055 - val_accuracy: 0.9975\n",
      "Epoch 30/100\n",
      "50/50 [==============================] - 93s 2s/step - loss: 0.0094 - accuracy: 0.9969 - val_loss: 0.0055 - val_accuracy: 0.9975\n",
      "Epoch 31/100\n",
      "50/50 [==============================] - 95s 2s/step - loss: 0.0062 - accuracy: 0.9994 - val_loss: 0.0060 - val_accuracy: 0.9975\n",
      "Epoch 32/100\n",
      "50/50 [==============================] - 94s 2s/step - loss: 0.0140 - accuracy: 0.9950 - val_loss: 0.0056 - val_accuracy: 0.9975\n",
      "Epoch 33/100\n",
      "50/50 [==============================] - 93s 2s/step - loss: 0.0114 - accuracy: 0.9956 - val_loss: 0.0061 - val_accuracy: 0.9975\n",
      "Epoch 34/100\n",
      "50/50 [==============================] - 95s 2s/step - loss: 0.0078 - accuracy: 0.9969 - val_loss: 0.0067 - val_accuracy: 0.9975\n",
      "Epoch 35/100\n",
      "50/50 [==============================] - 95s 2s/step - loss: 0.0157 - accuracy: 0.9956 - val_loss: 0.0058 - val_accuracy: 0.9975\n",
      "Epoch 36/100\n",
      "50/50 [==============================] - 95s 2s/step - loss: 0.0132 - accuracy: 0.9975 - val_loss: 0.0055 - val_accuracy: 0.9975\n",
      "Epoch 37/100\n",
      "50/50 [==============================] - 94s 2s/step - loss: 0.0077 - accuracy: 0.9994 - val_loss: 0.0065 - val_accuracy: 0.9975\n",
      "Epoch 38/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0086 - accuracy: 0.9987 - val_loss: 0.0059 - val_accuracy: 0.9975\n",
      "Epoch 39/100\n",
      "50/50 [==============================] - 94s 2s/step - loss: 0.0066 - accuracy: 0.9987 - val_loss: 0.0073 - val_accuracy: 0.9975\n",
      "Epoch 40/100\n",
      "50/50 [==============================] - 94s 2s/step - loss: 0.0055 - accuracy: 0.9987 - val_loss: 0.0056 - val_accuracy: 0.9975\n",
      "Epoch 41/100\n",
      "50/50 [==============================] - 94s 2s/step - loss: 0.0066 - accuracy: 0.9994 - val_loss: 0.0068 - val_accuracy: 0.9975\n",
      "Epoch 42/100\n",
      "50/50 [==============================] - 93s 2s/step - loss: 0.0070 - accuracy: 0.9987 - val_loss: 0.0056 - val_accuracy: 0.9975\n",
      "Epoch 43/100\n",
      "50/50 [==============================] - 93s 2s/step - loss: 0.0088 - accuracy: 0.9975 - val_loss: 0.0068 - val_accuracy: 0.9975\n",
      "Epoch 44/100\n",
      "50/50 [==============================] - 93s 2s/step - loss: 0.0070 - accuracy: 0.9987 - val_loss: 0.0058 - val_accuracy: 0.9975\n",
      "Epoch 45/100\n",
      "50/50 [==============================] - 93s 2s/step - loss: 0.0057 - accuracy: 0.9994 - val_loss: 0.0067 - val_accuracy: 0.9975\n",
      "Epoch 46/100\n",
      "50/50 [==============================] - 93s 2s/step - loss: 0.0070 - accuracy: 0.9981 - val_loss: 0.0091 - val_accuracy: 0.9975\n",
      "Epoch 47/100\n",
      "50/50 [==============================] - 93s 2s/step - loss: 0.0072 - accuracy: 0.9987 - val_loss: 0.0064 - val_accuracy: 0.9975\n",
      "Epoch 48/100\n",
      "50/50 [==============================] - 93s 2s/step - loss: 0.0073 - accuracy: 0.9969 - val_loss: 0.0064 - val_accuracy: 0.9975\n",
      "Epoch 49/100\n",
      "50/50 [==============================] - 93s 2s/step - loss: 0.0062 - accuracy: 0.9981 - val_loss: 0.0071 - val_accuracy: 0.9975\n",
      "Epoch 50/100\n",
      "50/50 [==============================] - 93s 2s/step - loss: 0.0059 - accuracy: 0.9987 - val_loss: 0.0064 - val_accuracy: 0.9975\n",
      "Epoch 51/100\n",
      "50/50 [==============================] - 96s 2s/step - loss: 0.0070 - accuracy: 0.9987 - val_loss: 0.0085 - val_accuracy: 0.9975\n",
      "Epoch 52/100\n",
      "50/50 [==============================] - 92s 2s/step - loss: 0.0054 - accuracy: 0.9994 - val_loss: 0.0076 - val_accuracy: 0.9975\n",
      "Epoch 53/100\n",
      "50/50 [==============================] - 107s 2s/step - loss: 0.0053 - accuracy: 0.9994 - val_loss: 0.0089 - val_accuracy: 0.9975\n",
      "Epoch 54/100\n",
      "50/50 [==============================] - 100s 2s/step - loss: 0.0057 - accuracy: 0.9987 - val_loss: 0.0080 - val_accuracy: 0.9975\n",
      "Epoch 55/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0046 - accuracy: 0.9987 - val_loss: 0.0075 - val_accuracy: 0.9975\n",
      "Epoch 56/100\n",
      "50/50 [==============================] - 99s 2s/step - loss: 0.0076 - accuracy: 0.9981 - val_loss: 0.0062 - val_accuracy: 0.9950\n",
      "Epoch 57/100\n",
      "50/50 [==============================] - 95s 2s/step - loss: 0.0041 - accuracy: 0.9994 - val_loss: 0.0070 - val_accuracy: 0.9975\n",
      "Epoch 58/100\n",
      "50/50 [==============================] - 96s 2s/step - loss: 0.0051 - accuracy: 0.9987 - val_loss: 0.0067 - val_accuracy: 0.9950\n",
      "Epoch 59/100\n",
      "50/50 [==============================] - 96s 2s/step - loss: 0.0053 - accuracy: 0.9981 - val_loss: 0.0062 - val_accuracy: 0.9975\n",
      "Epoch 60/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0068 - accuracy: 0.9975 - val_loss: 0.0078 - val_accuracy: 0.9975\n",
      "Epoch 61/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0046 - accuracy: 0.9981 - val_loss: 0.0058 - val_accuracy: 0.9975\n",
      "Epoch 62/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0028 - accuracy: 0.9994 - val_loss: 0.0058 - val_accuracy: 0.9975\n",
      "Epoch 63/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0035 - accuracy: 0.9994 - val_loss: 0.0073 - val_accuracy: 0.9975\n",
      "Epoch 64/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0043 - accuracy: 0.9987 - val_loss: 0.0073 - val_accuracy: 0.9975\n",
      "Epoch 65/100\n",
      "50/50 [==============================] - 99s 2s/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0078 - val_accuracy: 0.9975\n",
      "Epoch 66/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0036 - accuracy: 0.9987 - val_loss: 0.0078 - val_accuracy: 0.9975\n",
      "Epoch 67/100\n",
      "50/50 [==============================] - 98s 2s/step - loss: 0.0027 - accuracy: 0.9994 - val_loss: 0.0084 - val_accuracy: 0.9975\n",
      "Epoch 68/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0063 - accuracy: 0.9969 - val_loss: 0.0055 - val_accuracy: 0.9950\n",
      "Epoch 69/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0045 - accuracy: 0.9987 - val_loss: 0.0100 - val_accuracy: 0.9975\n",
      "Epoch 70/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0043 - accuracy: 0.9987 - val_loss: 0.0065 - val_accuracy: 0.9950\n",
      "Epoch 71/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0058 - accuracy: 0.9981 - val_loss: 0.0076 - val_accuracy: 0.9975\n",
      "Epoch 72/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0050 - accuracy: 0.9987 - val_loss: 0.0075 - val_accuracy: 0.9975\n",
      "Epoch 73/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0070 - val_accuracy: 0.9975\n",
      "Epoch 74/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0036 - accuracy: 0.9981 - val_loss: 0.0072 - val_accuracy: 0.9975\n",
      "Epoch 75/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0035 - accuracy: 0.9994 - val_loss: 0.0095 - val_accuracy: 0.9975\n",
      "Epoch 76/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 0.0091 - val_accuracy: 0.9975\n",
      "Epoch 77/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9975\n",
      "Epoch 78/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0029 - accuracy: 0.9994 - val_loss: 0.0074 - val_accuracy: 0.9975\n",
      "Epoch 79/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0088 - val_accuracy: 0.9975\n",
      "Epoch 80/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0044 - accuracy: 0.9987 - val_loss: 0.0071 - val_accuracy: 0.9975\n",
      "Epoch 81/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0084 - val_accuracy: 0.9975\n",
      "Epoch 82/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0053 - accuracy: 0.9987 - val_loss: 0.0102 - val_accuracy: 0.9975\n",
      "Epoch 83/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.0077 - val_accuracy: 0.9975\n",
      "Epoch 84/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0033 - accuracy: 0.9987 - val_loss: 0.0082 - val_accuracy: 0.9975\n",
      "Epoch 85/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0031 - accuracy: 0.9994 - val_loss: 0.0088 - val_accuracy: 0.9975\n",
      "Epoch 86/100\n",
      "50/50 [==============================] - 97s 2s/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0109 - val_accuracy: 0.9975\n",
      "Epoch 87/100\n",
      "50/50 [==============================] - 94s 2s/step - loss: 0.0045 - accuracy: 0.9987 - val_loss: 0.0137 - val_accuracy: 0.9975\n",
      "Epoch 88/100\n",
      "50/50 [==============================] - 95s 2s/step - loss: 0.0030 - accuracy: 0.9994 - val_loss: 0.0110 - val_accuracy: 0.9975\n",
      "Epoch 89/100\n",
      "50/50 [==============================] - 94s 2s/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0088 - val_accuracy: 0.9975\n",
      "Epoch 90/100\n",
      "50/50 [==============================] - 95s 2s/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0099 - val_accuracy: 0.9975\n",
      "Epoch 91/100\n",
      "50/50 [==============================] - 94s 2s/step - loss: 0.0039 - accuracy: 0.9994 - val_loss: 0.0108 - val_accuracy: 0.9975\n",
      "Epoch 92/100\n",
      "50/50 [==============================] - 94s 2s/step - loss: 0.0053 - accuracy: 0.9994 - val_loss: 0.0099 - val_accuracy: 0.9975\n",
      "Epoch 93/100\n",
      "50/50 [==============================] - 95s 2s/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0098 - val_accuracy: 0.9975\n",
      "Epoch 94/100\n",
      "50/50 [==============================] - 95s 2s/step - loss: 0.0050 - accuracy: 0.9987 - val_loss: 0.0069 - val_accuracy: 0.9975\n",
      "Epoch 95/100\n",
      "50/50 [==============================] - 94s 2s/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0079 - val_accuracy: 0.9975\n",
      "Epoch 96/100\n",
      "50/50 [==============================] - 94s 2s/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 0.0083 - val_accuracy: 0.9975\n",
      "Epoch 97/100\n",
      "50/50 [==============================] - 94s 2s/step - loss: 0.0041 - accuracy: 0.9987 - val_loss: 0.0093 - val_accuracy: 0.9975\n",
      "Epoch 98/100\n",
      "50/50 [==============================] - 94s 2s/step - loss: 0.0029 - accuracy: 0.9994 - val_loss: 0.0076 - val_accuracy: 0.9975\n",
      "Epoch 99/100\n",
      "50/50 [==============================] - 96s 2s/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0108 - val_accuracy: 0.9975\n",
      "Epoch 100/100\n",
      "50/50 [==============================] - 94s 2s/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0123 - val_accuracy: 0.9975\n"
     ]
    }
   ],
   "source": [
    "# train the model\n",
    "history = model.fit(data_augumentation.flow(x_train, y_train, batch_size=batch_size),\n",
    "                    steps_per_epoch=len(x_train) // batch_size,validation_data=(x_test, y_test),\n",
    "\t                validation_steps=len(x_test) // batch_size,\n",
    "\t                epochs=epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizes the training and validation accuracy as well as the training and validation loss over epochs\n",
    "def train_histor_view(history, name):\n",
    "    acc = history.history['accuracy']\n",
    "    val_acc = history.history['val_accuracy']\n",
    "\n",
    "    loss = history.history['loss']\n",
    "    val_loss = history.history['val_loss']\n",
    "\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    plt.subplot(2, 1, 1)\n",
    "    plt.plot(acc, label= 'Training Accuracy')\n",
    "    plt.plot(val_acc, label='Validation Accuracy')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.ylim([min(plt.ylim()),1])\n",
    "    plt.title(name + ' ' + 'Training and Validation Accuracy')\n",
    "\n",
    "    plt.subplot(2, 1, 2)\n",
    "    plt.plot(loss, label='Training Loss')\n",
    "    plt.plot(val_loss, label='Validation Loss')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.ylabel('Cross Entropy')\n",
    "    plt.ylim([0,1.0])\n",
    "    plt.title(name + ' ' + 'Training and Validation Loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.show()\n",
    "\n",
    "train_histor_view(history, 'model accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] evaluating network...\n",
      "13/13 [==============================] - 19s 1s/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    WithMask       1.00      1.00      1.00       200\n",
      " WithoutMask       1.00      0.99      1.00       200\n",
      "\n",
      "    accuracy                           1.00       400\n",
      "   macro avg       1.00      1.00      1.00       400\n",
      "weighted avg       1.00      1.00      1.00       400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# make predictions on the testing set\n",
    "print(\"[INFO] evaluating network...\")\n",
    "predIdxs = model.predict(x_test, batch_size=batch_size)\n",
    "\n",
    "# for each image in the testing set we need to find the index of the label with corresponding largest predicted probability\n",
    "predIdxs = np.argmax(predIdxs, axis=1)\n",
    "\n",
    "# shows a nicely formatted classification report\n",
    "print(classification_report(y_test.argmax(axis=1), predIdxs,target_names=lb.classes_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[200   0]\n",
      " [  1 199]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to generate and display a confusion matrix for a classification task\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=55)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.tight_layout()\n",
    "\n",
    "cm = confusion_matrix(y_test.argmax(axis=1), predIdxs)\n",
    "\n",
    "cm_plot_label =['With Mask', 'Without Mask']\n",
    "plot_confusion_matrix(cm, cm_plot_label, title ='Confusion Metrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_sensitivity_specificity(y_test, y_pred_test):\n",
    "    # Note: More parameters are defined than necessary. \n",
    "    # This would allow return of other measures other than sensitivity and specificity\n",
    "    \n",
    "    # Get true/false for whether a breach actually occurred\n",
    "    actual_pos = y_test == 1\n",
    "    actual_neg = y_test == 0\n",
    "    \n",
    "    # Get true and false test (true test match actual, false tests differ from actual)\n",
    "    true_pos = (y_pred_test == 1) & (actual_pos)\n",
    "    false_pos = (y_pred_test == 1) & (actual_neg)\n",
    "    true_neg = (y_pred_test == 0) & (actual_neg)\n",
    "    false_neg = (y_pred_test == 0) & (actual_pos)\n",
    "    \n",
    "    # Calculate accuracy\n",
    "    accuracy = np.mean(y_pred_test == y_test)\n",
    "    \n",
    "    # Calculate sensitivity and specificity\n",
    "    sensitivity = np.sum(true_pos) / np.sum(actual_pos)\n",
    "    specificity = np.sum(true_neg) / np.sum(actual_neg)\n",
    "    \n",
    "    return sensitivity, specificity, accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity: 0.995\n",
      "Specificity: 1.0\n",
      "Accuracy: 0.9975\n"
     ]
    }
   ],
   "source": [
    "# calculates and reports the sensitivity, specificity, and overall accuracy of a classification model's performance\n",
    "sensitivity, specificity, accuracy = calculate_sensitivity_specificity(y_test.argmax(axis=1), predIdxs)\n",
    "print ('Sensitivity:', sensitivity)\n",
    "print ('Specificity:', specificity)\n",
    "print ('Accuracy:', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizes the performance of a binary classification model using a Receiver Operating Characteristic (ROC) curve\n",
    "roc_log = roc_auc_score(y_test.argmax(axis=1), predIdxs)\n",
    "false_positive_rate, true_positive_rate, threshold = roc_curve(y_test.argmax(axis=1), predIdxs)\n",
    "area_under_curve = auc(false_positive_rate, true_positive_rate)\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'r--')\n",
    "plt.plot(false_positive_rate, true_positive_rate, label='AUC = {:.3f}'.format(area_under_curve))\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve Binary class')\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n",
    "#plt.savefig(ROC_PLOT_FILE, bbox_inches='tight')\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving and Loading The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Python3.11.5\\Lib\\site-packages\\keras\\src\\engine\\training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Saved!\n"
     ]
    }
   ],
   "source": [
    "# save the model (will save in .h5 format)\n",
    "model.save('RESNET.h5')\n",
    "print('Model Saved!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)        [(None, 224, 224, 3)]        0         []                            \n",
      "                                                                                                  \n",
      " conv1_pad (ZeroPadding2D)   (None, 230, 230, 3)          0         ['input_1[0][0]']             \n",
      "                                                                                                  \n",
      " conv1_conv (Conv2D)         (None, 112, 112, 64)         9472      ['conv1_pad[0][0]']           \n",
      "                                                                                                  \n",
      " conv1_bn (BatchNormalizati  (None, 112, 112, 64)         256       ['conv1_conv[0][0]']          \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv1_relu (Activation)     (None, 112, 112, 64)         0         ['conv1_bn[0][0]']            \n",
      "                                                                                                  \n",
      " pool1_pad (ZeroPadding2D)   (None, 114, 114, 64)         0         ['conv1_relu[0][0]']          \n",
      "                                                                                                  \n",
      " pool1_pool (MaxPooling2D)   (None, 56, 56, 64)           0         ['pool1_pad[0][0]']           \n",
      "                                                                                                  \n",
      " conv2_block1_1_conv (Conv2  (None, 56, 56, 64)           4160      ['pool1_pool[0][0]']          \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_1_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block1_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block1_1_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block1_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block1_2_conv (Conv2  (None, 56, 56, 64)           36928     ['conv2_block1_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_2_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block1_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block1_2_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block1_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block1_0_conv (Conv2  (None, 56, 56, 256)          16640     ['pool1_pool[0][0]']          \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_3_conv (Conv2  (None, 56, 56, 256)          16640     ['conv2_block1_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_0_bn (BatchNo  (None, 56, 56, 256)          1024      ['conv2_block1_0_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block1_3_bn (BatchNo  (None, 56, 56, 256)          1024      ['conv2_block1_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block1_add (Add)      (None, 56, 56, 256)          0         ['conv2_block1_0_bn[0][0]',   \n",
      "                                                                     'conv2_block1_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv2_block1_out (Activati  (None, 56, 56, 256)          0         ['conv2_block1_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv2_block2_1_conv (Conv2  (None, 56, 56, 64)           16448     ['conv2_block1_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_1_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block2_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block2_1_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block2_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block2_2_conv (Conv2  (None, 56, 56, 64)           36928     ['conv2_block2_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_2_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block2_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block2_2_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block2_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block2_3_conv (Conv2  (None, 56, 56, 256)          16640     ['conv2_block2_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_3_bn (BatchNo  (None, 56, 56, 256)          1024      ['conv2_block2_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block2_add (Add)      (None, 56, 56, 256)          0         ['conv2_block1_out[0][0]',    \n",
      "                                                                     'conv2_block2_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv2_block2_out (Activati  (None, 56, 56, 256)          0         ['conv2_block2_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv2_block3_1_conv (Conv2  (None, 56, 56, 64)           16448     ['conv2_block2_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_1_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block3_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block3_1_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block3_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block3_2_conv (Conv2  (None, 56, 56, 64)           36928     ['conv2_block3_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_2_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block3_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block3_2_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block3_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block3_3_conv (Conv2  (None, 56, 56, 256)          16640     ['conv2_block3_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_3_bn (BatchNo  (None, 56, 56, 256)          1024      ['conv2_block3_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block3_add (Add)      (None, 56, 56, 256)          0         ['conv2_block2_out[0][0]',    \n",
      "                                                                     'conv2_block3_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv2_block3_out (Activati  (None, 56, 56, 256)          0         ['conv2_block3_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv3_block1_1_conv (Conv2  (None, 28, 28, 128)          32896     ['conv2_block3_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_1_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block1_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block1_1_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block1_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block1_2_conv (Conv2  (None, 28, 28, 128)          147584    ['conv3_block1_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_2_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block1_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block1_2_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block1_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block1_0_conv (Conv2  (None, 28, 28, 512)          131584    ['conv2_block3_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_3_conv (Conv2  (None, 28, 28, 512)          66048     ['conv3_block1_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_0_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block1_0_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block1_3_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block1_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block1_add (Add)      (None, 28, 28, 512)          0         ['conv3_block1_0_bn[0][0]',   \n",
      "                                                                     'conv3_block1_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv3_block1_out (Activati  (None, 28, 28, 512)          0         ['conv3_block1_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv3_block2_1_conv (Conv2  (None, 28, 28, 128)          65664     ['conv3_block1_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_1_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block2_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block2_1_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block2_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block2_2_conv (Conv2  (None, 28, 28, 128)          147584    ['conv3_block2_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_2_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block2_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block2_2_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block2_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block2_3_conv (Conv2  (None, 28, 28, 512)          66048     ['conv3_block2_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_3_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block2_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block2_add (Add)      (None, 28, 28, 512)          0         ['conv3_block1_out[0][0]',    \n",
      "                                                                     'conv3_block2_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv3_block2_out (Activati  (None, 28, 28, 512)          0         ['conv3_block2_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv3_block3_1_conv (Conv2  (None, 28, 28, 128)          65664     ['conv3_block2_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_1_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block3_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block3_1_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block3_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block3_2_conv (Conv2  (None, 28, 28, 128)          147584    ['conv3_block3_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_2_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block3_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block3_2_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block3_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block3_3_conv (Conv2  (None, 28, 28, 512)          66048     ['conv3_block3_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_3_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block3_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block3_add (Add)      (None, 28, 28, 512)          0         ['conv3_block2_out[0][0]',    \n",
      "                                                                     'conv3_block3_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv3_block3_out (Activati  (None, 28, 28, 512)          0         ['conv3_block3_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv3_block4_1_conv (Conv2  (None, 28, 28, 128)          65664     ['conv3_block3_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_1_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block4_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block4_1_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block4_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block4_2_conv (Conv2  (None, 28, 28, 128)          147584    ['conv3_block4_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_2_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block4_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block4_2_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block4_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block4_3_conv (Conv2  (None, 28, 28, 512)          66048     ['conv3_block4_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_3_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block4_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block4_add (Add)      (None, 28, 28, 512)          0         ['conv3_block3_out[0][0]',    \n",
      "                                                                     'conv3_block4_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv3_block4_out (Activati  (None, 28, 28, 512)          0         ['conv3_block4_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block1_1_conv (Conv2  (None, 14, 14, 256)          131328    ['conv3_block4_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block1_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block1_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block1_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block1_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block1_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block1_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block1_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block1_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block1_0_conv (Conv2  (None, 14, 14, 1024)         525312    ['conv3_block4_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block1_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_0_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block1_0_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block1_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block1_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block1_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block1_0_bn[0][0]',   \n",
      "                                                                     'conv4_block1_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block1_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block1_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block2_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block1_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block2_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block2_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block2_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block2_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block2_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block2_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block2_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block2_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block2_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block2_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block2_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block2_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block1_out[0][0]',    \n",
      "                                                                     'conv4_block2_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block2_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block2_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block3_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block2_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block3_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block3_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block3_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block3_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block3_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block3_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block3_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block3_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block3_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block3_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block3_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block3_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block2_out[0][0]',    \n",
      "                                                                     'conv4_block3_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block3_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block3_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block4_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block3_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block4_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block4_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block4_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block4_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block4_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block4_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block4_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block4_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block4_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block4_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block4_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block4_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block3_out[0][0]',    \n",
      "                                                                     'conv4_block4_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block4_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block4_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block5_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block4_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block5_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block5_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block5_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block5_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block5_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block5_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block5_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block5_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block5_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block5_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block5_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block5_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block4_out[0][0]',    \n",
      "                                                                     'conv4_block5_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block5_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block5_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block6_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block5_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block6_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block6_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block6_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block6_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block6_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block6_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block6_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block6_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block6_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block6_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block6_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block6_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block5_out[0][0]',    \n",
      "                                                                     'conv4_block6_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block6_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block6_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv5_block1_1_conv (Conv2  (None, 7, 7, 512)            524800    ['conv4_block6_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_1_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block1_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block1_1_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block1_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block1_2_conv (Conv2  (None, 7, 7, 512)            2359808   ['conv5_block1_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_2_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block1_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block1_2_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block1_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block1_0_conv (Conv2  (None, 7, 7, 2048)           2099200   ['conv4_block6_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_3_conv (Conv2  (None, 7, 7, 2048)           1050624   ['conv5_block1_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_0_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block1_0_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block1_3_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block1_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block1_add (Add)      (None, 7, 7, 2048)           0         ['conv5_block1_0_bn[0][0]',   \n",
      "                                                                     'conv5_block1_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv5_block1_out (Activati  (None, 7, 7, 2048)           0         ['conv5_block1_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv5_block2_1_conv (Conv2  (None, 7, 7, 512)            1049088   ['conv5_block1_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_1_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block2_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block2_1_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block2_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block2_2_conv (Conv2  (None, 7, 7, 512)            2359808   ['conv5_block2_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_2_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block2_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block2_2_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block2_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block2_3_conv (Conv2  (None, 7, 7, 2048)           1050624   ['conv5_block2_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_3_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block2_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block2_add (Add)      (None, 7, 7, 2048)           0         ['conv5_block1_out[0][0]',    \n",
      "                                                                     'conv5_block2_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv5_block2_out (Activati  (None, 7, 7, 2048)           0         ['conv5_block2_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv5_block3_1_conv (Conv2  (None, 7, 7, 512)            1049088   ['conv5_block2_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_1_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block3_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block3_1_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block3_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block3_2_conv (Conv2  (None, 7, 7, 512)            2359808   ['conv5_block3_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_2_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block3_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block3_2_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block3_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block3_3_conv (Conv2  (None, 7, 7, 2048)           1050624   ['conv5_block3_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_3_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block3_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block3_add (Add)      (None, 7, 7, 2048)           0         ['conv5_block2_out[0][0]',    \n",
      "                                                                     'conv5_block3_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv5_block3_out (Activati  (None, 7, 7, 2048)           0         ['conv5_block3_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " global_average_pooling2d (  (None, 2048)                 0         ['conv5_block3_out[0][0]']    \n",
      " GlobalAveragePooling2D)                                                                          \n",
      "                                                                                                  \n",
      " flatten (Flatten)           (None, 2048)                 0         ['global_average_pooling2d[0][\n",
      "                                                                    0]']                          \n",
      "                                                                                                  \n",
      " dense (Dense)               (None, 128)                  262272    ['flatten[0][0]']             \n",
      "                                                                                                  \n",
      " dropout (Dropout)           (None, 128)                  0         ['dense[0][0]']               \n",
      "                                                                                                  \n",
      " dense_1 (Dense)             (None, 2)                    258       ['dropout[0][0]']             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 23850242 (90.98 MB)\n",
      "Trainable params: 262530 (1.00 MB)\n",
      "Non-trainable params: 23587712 (89.98 MB)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# load model\n",
    "savedModel=load_model('RESNET.h5')\n",
    "savedModel.summary()"
   ]
  }
 ],
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