resnet.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ZwqFf0oJXrsG"
      },
      "outputs": [],
      "source": [
        "# import necessary packages\n",
        "import itertools\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import  train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from imutils import paths\n",
        "import os\n",
        "import glob\n",
        "import cv2\n",
        "import numpy as np\n",
        "#from skimage import data, io, filters\n",
        "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\n",
        "from keras.datasets import mnist\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv2D, Dropout, MaxPool2D\n",
        "from PIL import Image, ImageEnhance\n",
        "#from keras.utils import np_utils"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HaehhMhsumzs",
        "outputId": "b93a14fc-4ef6-4055-ae51-8a78436ccff1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "07O3vItrQ0t1",
        "outputId": "c2e15888-25d9-4de5-ea7c-0f61ec80540d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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'/content/drive/MyDrive/data/Dysgraphia/dysgraphia_88_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_105_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_50_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_97_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_8_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_32_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_57_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_48_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_60_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_92_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_64_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_96_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_1_yes.png', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_45_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_34_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_43_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_78_yes.jpeg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_23_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_89_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_108_yes.png', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_111_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_24_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_68_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_99_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_101_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_9_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_109_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_49_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_87_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_112_yes.jpg', 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'/content/drive/MyDrive/data/Dysgraphia/dysgraphia_7_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_106_yes.png', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_91_yes.png', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_2474_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_38_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_2904_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_2922_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_2915_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_62_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_70_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_2461_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_5_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_12_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_26_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_31_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_79_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_81_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_84_yes.jpg', '/content/drive/MyDrive/data/Dysgraphia/dysgraphia_93_yes.jpg']\n"
          ]
        }
      ],
      "source": [
        "\n",
        "imagePaths = list(paths.list_images(\"/content/drive/MyDrive/data\"))\n",
        "print(imagePaths)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j667w6nzuTcI",
        "outputId": "b3a2593c-e52e-4f1f-bdb7-4648cbcf0d0d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "no\n"
          ]
        }
      ],
      "source": [
        "print(os.path.split(imagePaths[0])[1].split('_')[-1].split('.')[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "mc_kWu2-CZaE"
      },
      "outputs": [],
      "source": [
        "def prepro_angle(img):\n",
        "    img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
        "    img=cv2.resize(img,(512,512))\n",
        "    filtered_image= cv2.bilateralFilter(img, 5, 50, 50)\n",
        "    ret,threshold_img=cv2.threshold(filtered_image,210,255,cv2.THRESH_BINARY)\n",
        "    element=np.ones((2,2),np.uint8)\n",
        "    threshold_img=cv2.erode(threshold_img,element)\n",
        "    return threshold_img\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7V7rRBHYQ4pq",
        "outputId": "5b2b9a72-4065-4623-a79a-af314fbd254a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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          ]
        }
      ],
      "source": [
        "#To read and preprocess image data\n",
        "data = []\n",
        "lables = []\n",
        "c = 0 ## to see the progress\n",
        "for image in imagePaths:\n",
        "\n",
        "    lable = os.path.split(image)[1].split('_')[-1].split('.')[0]\n",
        "    #print(lable)\n",
        "    lables.append(lable)\n",
        "    #im_gray = cv2.imread(image, cv2.IMREAD_GRAYSCALE)\n",
        "    img=Image.open(image)\n",
        "    # img = cv2.imread(image)\n",
        "    img=img.resize((256,256))\n",
        "    enhancer = ImageEnhance.Sharpness(img.convert('RGB'))\n",
        "    img=enhancer.enhance(20)\n",
        "   \n",
        "    img=np.array(img)\n",
        "    height, width, channels = img.shape\n",
        "# Number of pieces Horizontally\n",
        "    print(img.shape)\n",
        "    data.append(img)\n",
        "    c=c+1\n",
        "    print(c)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yqIONJD6Q9w2",
        "outputId": "c6de5eb1-8397-40da-9d35-146a5fb10f5a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(206, 256, 256, 3)\n"
          ]
        }
      ],
      "source": [
        "# encode the labels as integer\n",
        "data = np.array(data)\n",
        "print(data.shape)\n",
        "lables = np.array(lables)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yJ1n-rb7uTcI",
        "outputId": "19cd87b9-dc44-41d9-87be-eec066bcfb99"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'yes', 'no'}\n"
          ]
        }
      ],
      "source": [
        "print(set(lables))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJEeJAklRB8H",
        "outputId": "3032489e-de7c-42bd-fd2f-56920b15c760"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{0, 1}\n"
          ]
        }
      ],
      "source": [
        "le = LabelEncoder()\n",
        "lables = le.fit_transform(lables)\n",
        "\n",
        "myset = set(lables)\n",
        "print(myset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L72uEUGURL2c",
        "outputId": "87b67e23-125a-48f5-fbea-b71d73af76d6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "X_train shape (185, 256, 256, 3)\n",
            "y_train shape (185, 1)\n",
            "X_test shape (21, 256, 256, 3)\n",
            "y_test shape (21, 1)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "#Splitting of dataset into training and test dataset\n",
        "(trainX, testX, trainY, testY ) = train_test_split(data, lables, test_size= 0.10, shuffle = True, random_state=42,stratify=lables)\n",
        "\n",
        "trainY=trainY.reshape(len(trainY),1)\n",
        "testY=testY.reshape(len(testY),1)\n",
        "# valY=valY.reshape(len(valY),1)\n",
        "print(\"X_train shape\", trainX.shape)\n",
        "print(\"y_train shape\", trainY.shape)\n",
        "print(\"X_test shape\", testX.shape)\n",
        "print(\"y_test shape\", testY.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3foatd5ZApO",
        "outputId": "4f3e88a7-3b0b-4509-8e8c-0c3967dd7b77"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:116: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:116: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ],
      "source": [
        "from sklearn import preprocessing\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "label = preprocessing.LabelEncoder()\n",
        "trainY= label.fit_transform(trainY)\n",
        "testY= label.fit_transform(testY)\n",
        "# valY= label.fit_transform(valY)\n",
        "# print(valY)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "taiROsTjOSs1",
        "outputId": "a0f371de-7ddd-40bb-9009-1d0337817486"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7dd52aedddb0>"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(trainX[126,:])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fL_Rb82KhlQ6"
      },
      "source": [
        "**resnet50 Model**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NAROgvEniUz3"
      },
      "source": [
        "**Data Augmentation**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "2gVGhvVP1cfr"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import AveragePooling2D\n",
        "from tensorflow.keras.models import Model\n",
        "# data augmentation\n",
        "datagen = ImageDataGenerator(\n",
        "\n",
        "        zca_whitening=False,  # dimesion reduction\n",
        "        #rotation_range=5,  # randomly rotate images in the range 5 degrees\n",
        "        zoom_range = 0.1, # Randomly zoom image 10%\n",
        "        width_shift_range=0.1,  # randomly shift images horizontally 10%\n",
        "        height_shift_range=0.1,  # randomly shift images vertically 10%\n",
        "        #horizontal_flip=True,  # randomly flip images\n",
        "        vertical_flip=False)  # randomly flip images\n",
        "\n",
        "datagen.fit(trainX)\n",
        "\n",
        "\n",
        "\n",
        "# datagen.fit(valX)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1UJffayKkQcF"
      },
      "source": [
        "**Transfer Learning using resnet50 (Imagenet)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "HN2_hkEvkEtq"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.applications.resnet50 import ResNet50\n",
        "\n",
        "conv_base=ResNet50(weights='imagenet',\n",
        "                include_top=False,##we only want to use \"conv\" base\n",
        "                input_shape=(256,256,3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hnPaeQzYkPiS",
        "outputId": "22298482-7968-49b0-c9dc-48e5767a3275"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"resnet50\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)        [(None, 256, 256, 3)]        0         []                            \n",
            "                                                                                                  \n",
            " conv1_pad (ZeroPadding2D)   (None, 262, 262, 3)          0         ['input_1[0][0]']             \n",
            "                                                                                                  \n",
            " conv1_conv (Conv2D)         (None, 128, 128, 64)         9472      ['conv1_pad[0][0]']           \n",
            "                                                                                                  \n",
            " conv1_bn (BatchNormalizati  (None, 128, 128, 64)         256       ['conv1_conv[0][0]']          \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv1_relu (Activation)     (None, 128, 128, 64)         0         ['conv1_bn[0][0]']            \n",
            "                                                                                                  \n",
            " pool1_pad (ZeroPadding2D)   (None, 130, 130, 64)         0         ['conv1_relu[0][0]']          \n",
            "                                                                                                  \n",
            " pool1_pool (MaxPooling2D)   (None, 64, 64, 64)           0         ['pool1_pad[0][0]']           \n",
            "                                                                                                  \n",
            " conv2_block1_1_conv (Conv2  (None, 64, 64, 64)           4160      ['pool1_pool[0][0]']          \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_1_bn (BatchNo  (None, 64, 64, 64)           256       ['conv2_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_1_relu (Activ  (None, 64, 64, 64)           0         ['conv2_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block1_2_conv (Conv2  (None, 64, 64, 64)           36928     ['conv2_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_2_bn (BatchNo  (None, 64, 64, 64)           256       ['conv2_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_2_relu (Activ  (None, 64, 64, 64)           0         ['conv2_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block1_0_conv (Conv2  (None, 64, 64, 256)          16640     ['pool1_pool[0][0]']          \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_3_conv (Conv2  (None, 64, 64, 256)          16640     ['conv2_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_0_bn (BatchNo  (None, 64, 64, 256)          1024      ['conv2_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_3_bn (BatchNo  (None, 64, 64, 256)          1024      ['conv2_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_add (Add)      (None, 64, 64, 256)          0         ['conv2_block1_0_bn[0][0]',   \n",
            "                                                                     'conv2_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv2_block1_out (Activati  (None, 64, 64, 256)          0         ['conv2_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv2_block2_1_conv (Conv2  (None, 64, 64, 64)           16448     ['conv2_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_1_bn (BatchNo  (None, 64, 64, 64)           256       ['conv2_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block2_1_relu (Activ  (None, 64, 64, 64)           0         ['conv2_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block2_2_conv (Conv2  (None, 64, 64, 64)           36928     ['conv2_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_2_bn (BatchNo  (None, 64, 64, 64)           256       ['conv2_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block2_2_relu (Activ  (None, 64, 64, 64)           0         ['conv2_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block2_3_conv (Conv2  (None, 64, 64, 256)          16640     ['conv2_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_3_bn (BatchNo  (None, 64, 64, 256)          1024      ['conv2_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block2_add (Add)      (None, 64, 64, 256)          0         ['conv2_block1_out[0][0]',    \n",
            "                                                                     'conv2_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv2_block2_out (Activati  (None, 64, 64, 256)          0         ['conv2_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv2_block3_1_conv (Conv2  (None, 64, 64, 64)           16448     ['conv2_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_1_bn (BatchNo  (None, 64, 64, 64)           256       ['conv2_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block3_1_relu (Activ  (None, 64, 64, 64)           0         ['conv2_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block3_2_conv (Conv2  (None, 64, 64, 64)           36928     ['conv2_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_2_bn (BatchNo  (None, 64, 64, 64)           256       ['conv2_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block3_2_relu (Activ  (None, 64, 64, 64)           0         ['conv2_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block3_3_conv (Conv2  (None, 64, 64, 256)          16640     ['conv2_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_3_bn (BatchNo  (None, 64, 64, 256)          1024      ['conv2_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block3_add (Add)      (None, 64, 64, 256)          0         ['conv2_block2_out[0][0]',    \n",
            "                                                                     'conv2_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv2_block3_out (Activati  (None, 64, 64, 256)          0         ['conv2_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block1_1_conv (Conv2  (None, 32, 32, 128)          32896     ['conv2_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_1_bn (BatchNo  (None, 32, 32, 128)          512       ['conv3_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_1_relu (Activ  (None, 32, 32, 128)          0         ['conv3_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block1_2_conv (Conv2  (None, 32, 32, 128)          147584    ['conv3_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_2_bn (BatchNo  (None, 32, 32, 128)          512       ['conv3_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_2_relu (Activ  (None, 32, 32, 128)          0         ['conv3_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block1_0_conv (Conv2  (None, 32, 32, 512)          131584    ['conv2_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_3_conv (Conv2  (None, 32, 32, 512)          66048     ['conv3_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_0_bn (BatchNo  (None, 32, 32, 512)          2048      ['conv3_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_3_bn (BatchNo  (None, 32, 32, 512)          2048      ['conv3_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_add (Add)      (None, 32, 32, 512)          0         ['conv3_block1_0_bn[0][0]',   \n",
            "                                                                     'conv3_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block1_out (Activati  (None, 32, 32, 512)          0         ['conv3_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block2_1_conv (Conv2  (None, 32, 32, 128)          65664     ['conv3_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_1_bn (BatchNo  (None, 32, 32, 128)          512       ['conv3_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block2_1_relu (Activ  (None, 32, 32, 128)          0         ['conv3_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block2_2_conv (Conv2  (None, 32, 32, 128)          147584    ['conv3_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_2_bn (BatchNo  (None, 32, 32, 128)          512       ['conv3_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block2_2_relu (Activ  (None, 32, 32, 128)          0         ['conv3_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block2_3_conv (Conv2  (None, 32, 32, 512)          66048     ['conv3_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_3_bn (BatchNo  (None, 32, 32, 512)          2048      ['conv3_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block2_add (Add)      (None, 32, 32, 512)          0         ['conv3_block1_out[0][0]',    \n",
            "                                                                     'conv3_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block2_out (Activati  (None, 32, 32, 512)          0         ['conv3_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block3_1_conv (Conv2  (None, 32, 32, 128)          65664     ['conv3_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_1_bn (BatchNo  (None, 32, 32, 128)          512       ['conv3_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block3_1_relu (Activ  (None, 32, 32, 128)          0         ['conv3_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block3_2_conv (Conv2  (None, 32, 32, 128)          147584    ['conv3_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_2_bn (BatchNo  (None, 32, 32, 128)          512       ['conv3_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block3_2_relu (Activ  (None, 32, 32, 128)          0         ['conv3_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block3_3_conv (Conv2  (None, 32, 32, 512)          66048     ['conv3_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_3_bn (BatchNo  (None, 32, 32, 512)          2048      ['conv3_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block3_add (Add)      (None, 32, 32, 512)          0         ['conv3_block2_out[0][0]',    \n",
            "                                                                     'conv3_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block3_out (Activati  (None, 32, 32, 512)          0         ['conv3_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block4_1_conv (Conv2  (None, 32, 32, 128)          65664     ['conv3_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_1_bn (BatchNo  (None, 32, 32, 128)          512       ['conv3_block4_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block4_1_relu (Activ  (None, 32, 32, 128)          0         ['conv3_block4_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block4_2_conv (Conv2  (None, 32, 32, 128)          147584    ['conv3_block4_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_2_bn (BatchNo  (None, 32, 32, 128)          512       ['conv3_block4_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block4_2_relu (Activ  (None, 32, 32, 128)          0         ['conv3_block4_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block4_3_conv (Conv2  (None, 32, 32, 512)          66048     ['conv3_block4_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_3_bn (BatchNo  (None, 32, 32, 512)          2048      ['conv3_block4_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block4_add (Add)      (None, 32, 32, 512)          0         ['conv3_block3_out[0][0]',    \n",
            "                                                                     'conv3_block4_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block4_out (Activati  (None, 32, 32, 512)          0         ['conv3_block4_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block1_1_conv (Conv2  (None, 16, 16, 256)          131328    ['conv3_block4_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_1_bn (BatchNo  (None, 16, 16, 256)          1024      ['conv4_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_1_relu (Activ  (None, 16, 16, 256)          0         ['conv4_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block1_2_conv (Conv2  (None, 16, 16, 256)          590080    ['conv4_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_2_bn (BatchNo  (None, 16, 16, 256)          1024      ['conv4_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_2_relu (Activ  (None, 16, 16, 256)          0         ['conv4_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block1_0_conv (Conv2  (None, 16, 16, 1024)         525312    ['conv3_block4_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_3_conv (Conv2  (None, 16, 16, 1024)         263168    ['conv4_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_0_bn (BatchNo  (None, 16, 16, 1024)         4096      ['conv4_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_3_bn (BatchNo  (None, 16, 16, 1024)         4096      ['conv4_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_add (Add)      (None, 16, 16, 1024)         0         ['conv4_block1_0_bn[0][0]',   \n",
            "                                                                     'conv4_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block1_out (Activati  (None, 16, 16, 1024)         0         ['conv4_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block2_1_conv (Conv2  (None, 16, 16, 256)          262400    ['conv4_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_1_bn (BatchNo  (None, 16, 16, 256)          1024      ['conv4_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block2_1_relu (Activ  (None, 16, 16, 256)          0         ['conv4_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block2_2_conv (Conv2  (None, 16, 16, 256)          590080    ['conv4_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_2_bn (BatchNo  (None, 16, 16, 256)          1024      ['conv4_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block2_2_relu (Activ  (None, 16, 16, 256)          0         ['conv4_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block2_3_conv (Conv2  (None, 16, 16, 1024)         263168    ['conv4_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_3_bn (BatchNo  (None, 16, 16, 1024)         4096      ['conv4_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block2_add (Add)      (None, 16, 16, 1024)         0         ['conv4_block1_out[0][0]',    \n",
            "                                                                     'conv4_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block2_out (Activati  (None, 16, 16, 1024)         0         ['conv4_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block3_1_conv (Conv2  (None, 16, 16, 256)          262400    ['conv4_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_1_bn (BatchNo  (None, 16, 16, 256)          1024      ['conv4_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block3_1_relu (Activ  (None, 16, 16, 256)          0         ['conv4_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block3_2_conv (Conv2  (None, 16, 16, 256)          590080    ['conv4_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_2_bn (BatchNo  (None, 16, 16, 256)          1024      ['conv4_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block3_2_relu (Activ  (None, 16, 16, 256)          0         ['conv4_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block3_3_conv (Conv2  (None, 16, 16, 1024)         263168    ['conv4_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_3_bn (BatchNo  (None, 16, 16, 1024)         4096      ['conv4_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block3_add (Add)      (None, 16, 16, 1024)         0         ['conv4_block2_out[0][0]',    \n",
            "                                                                     'conv4_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block3_out (Activati  (None, 16, 16, 1024)         0         ['conv4_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block4_1_conv (Conv2  (None, 16, 16, 256)          262400    ['conv4_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_1_bn (BatchNo  (None, 16, 16, 256)          1024      ['conv4_block4_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block4_1_relu (Activ  (None, 16, 16, 256)          0         ['conv4_block4_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block4_2_conv (Conv2  (None, 16, 16, 256)          590080    ['conv4_block4_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_2_bn (BatchNo  (None, 16, 16, 256)          1024      ['conv4_block4_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block4_2_relu (Activ  (None, 16, 16, 256)          0         ['conv4_block4_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block4_3_conv (Conv2  (None, 16, 16, 1024)         263168    ['conv4_block4_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_3_bn (BatchNo  (None, 16, 16, 1024)         4096      ['conv4_block4_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block4_add (Add)      (None, 16, 16, 1024)         0         ['conv4_block3_out[0][0]',    \n",
            "                                                                     'conv4_block4_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block4_out (Activati  (None, 16, 16, 1024)         0         ['conv4_block4_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block5_1_conv (Conv2  (None, 16, 16, 256)          262400    ['conv4_block4_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_1_bn (BatchNo  (None, 16, 16, 256)          1024      ['conv4_block5_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block5_1_relu (Activ  (None, 16, 16, 256)          0         ['conv4_block5_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block5_2_conv (Conv2  (None, 16, 16, 256)          590080    ['conv4_block5_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_2_bn (BatchNo  (None, 16, 16, 256)          1024      ['conv4_block5_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block5_2_relu (Activ  (None, 16, 16, 256)          0         ['conv4_block5_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block5_3_conv (Conv2  (None, 16, 16, 1024)         263168    ['conv4_block5_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_3_bn (BatchNo  (None, 16, 16, 1024)         4096      ['conv4_block5_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block5_add (Add)      (None, 16, 16, 1024)         0         ['conv4_block4_out[0][0]',    \n",
            "                                                                     'conv4_block5_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block5_out (Activati  (None, 16, 16, 1024)         0         ['conv4_block5_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block6_1_conv (Conv2  (None, 16, 16, 256)          262400    ['conv4_block5_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_1_bn (BatchNo  (None, 16, 16, 256)          1024      ['conv4_block6_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block6_1_relu (Activ  (None, 16, 16, 256)          0         ['conv4_block6_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block6_2_conv (Conv2  (None, 16, 16, 256)          590080    ['conv4_block6_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_2_bn (BatchNo  (None, 16, 16, 256)          1024      ['conv4_block6_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block6_2_relu (Activ  (None, 16, 16, 256)          0         ['conv4_block6_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block6_3_conv (Conv2  (None, 16, 16, 1024)         263168    ['conv4_block6_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_3_bn (BatchNo  (None, 16, 16, 1024)         4096      ['conv4_block6_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block6_add (Add)      (None, 16, 16, 1024)         0         ['conv4_block5_out[0][0]',    \n",
            "                                                                     'conv4_block6_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block6_out (Activati  (None, 16, 16, 1024)         0         ['conv4_block6_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv5_block1_1_conv (Conv2  (None, 8, 8, 512)            524800    ['conv4_block6_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_1_bn (BatchNo  (None, 8, 8, 512)            2048      ['conv5_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block1_1_relu (Activ  (None, 8, 8, 512)            0         ['conv5_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block1_2_conv (Conv2  (None, 8, 8, 512)            2359808   ['conv5_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_2_bn (BatchNo  (None, 8, 8, 512)            2048      ['conv5_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block1_2_relu (Activ  (None, 8, 8, 512)            0         ['conv5_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block1_0_conv (Conv2  (None, 8, 8, 2048)           2099200   ['conv4_block6_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_3_conv (Conv2  (None, 8, 8, 2048)           1050624   ['conv5_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_0_bn (BatchNo  (None, 8, 8, 2048)           8192      ['conv5_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block1_3_bn (BatchNo  (None, 8, 8, 2048)           8192      ['conv5_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block1_add (Add)      (None, 8, 8, 2048)           0         ['conv5_block1_0_bn[0][0]',   \n",
            "                                                                     'conv5_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv5_block1_out (Activati  (None, 8, 8, 2048)           0         ['conv5_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv5_block2_1_conv (Conv2  (None, 8, 8, 512)            1049088   ['conv5_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_1_bn (BatchNo  (None, 8, 8, 512)            2048      ['conv5_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block2_1_relu (Activ  (None, 8, 8, 512)            0         ['conv5_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block2_2_conv (Conv2  (None, 8, 8, 512)            2359808   ['conv5_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_2_bn (BatchNo  (None, 8, 8, 512)            2048      ['conv5_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block2_2_relu (Activ  (None, 8, 8, 512)            0         ['conv5_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block2_3_conv (Conv2  (None, 8, 8, 2048)           1050624   ['conv5_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_3_bn (BatchNo  (None, 8, 8, 2048)           8192      ['conv5_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block2_add (Add)      (None, 8, 8, 2048)           0         ['conv5_block1_out[0][0]',    \n",
            "                                                                     'conv5_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv5_block2_out (Activati  (None, 8, 8, 2048)           0         ['conv5_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv5_block3_1_conv (Conv2  (None, 8, 8, 512)            1049088   ['conv5_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_1_bn (BatchNo  (None, 8, 8, 512)            2048      ['conv5_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block3_1_relu (Activ  (None, 8, 8, 512)            0         ['conv5_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block3_2_conv (Conv2  (None, 8, 8, 512)            2359808   ['conv5_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_2_bn (BatchNo  (None, 8, 8, 512)            2048      ['conv5_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block3_2_relu (Activ  (None, 8, 8, 512)            0         ['conv5_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block3_3_conv (Conv2  (None, 8, 8, 2048)           1050624   ['conv5_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_3_bn (BatchNo  (None, 8, 8, 2048)           8192      ['conv5_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block3_add (Add)      (None, 8, 8, 2048)           0         ['conv5_block2_out[0][0]',    \n",
            "                                                                     'conv5_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv5_block3_out (Activati  (None, 8, 8, 2048)           0         ['conv5_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 23587712 (89.98 MB)\n",
            "Trainable params: 23534592 (89.78 MB)\n",
            "Non-trainable params: 53120 (207.50 KB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "conv_base.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "XDctgiVAkg3N"
      },
      "outputs": [],
      "source": [
        "model_3=Sequential()\n",
        "\n",
        "model_3.add(conv_base)\n",
        "model_3.add(Flatten())\n",
        "model_3.add(Dense(512,activation='relu'))\n",
        "model_3.add(Dense(128,activation='relu'))\n",
        "model_3.add(Dense(64,activation='relu'))\n",
        "model_3.add(Dense(1,activation='sigmoid'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q6y1WomKlJ6R",
        "outputId": "e48a62c6-a917-45c3-e077-4c682d4da223"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " resnet50 (Functional)       (None, 8, 8, 2048)        23587712  \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 131072)            0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               67109376  \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               65664     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 90771073 (346.26 MB)\n",
            "Trainable params: 90717953 (346.06 MB)\n",
            "Non-trainable params: 53120 (207.50 KB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model_3.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A1G-PhbGlOB8",
        "outputId": "dfd327cb-d127-4e04-9119-6d30105bea0e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.optimizers import RMSprop, Adam\n",
        "import tensorflow as tf\n",
        "#from tensorflow_addons.optimizers import AdamW\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "opt = Adam(lr=0.0001)\n",
        "model_3.compile(loss='binary_crossentropy',optimizer=opt,metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aSLLZCqYlyym",
        "outputId": "163529ec-7595-466d-f186-01f23acc1fe0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "6/6 [==============================] - 48s 1s/step - loss: 5.3070 - accuracy: 0.6054\n",
            "Epoch 2/20\n",
            "6/6 [==============================] - 5s 759ms/step - loss: 1.3178 - accuracy: 0.7784\n",
            "Epoch 3/20\n",
            "6/6 [==============================] - 4s 568ms/step - loss: 0.4087 - accuracy: 0.8432\n",
            "Epoch 4/20\n",
            "6/6 [==============================] - 4s 560ms/step - loss: 0.7020 - accuracy: 0.8649\n",
            "Epoch 5/20\n",
            "6/6 [==============================] - 4s 616ms/step - loss: 1.1328 - accuracy: 0.9243\n",
            "Epoch 6/20\n",
            "6/6 [==============================] - 4s 628ms/step - loss: 0.4311 - accuracy: 0.8865\n",
            "Epoch 7/20\n",
            "6/6 [==============================] - 4s 555ms/step - loss: 0.3068 - accuracy: 0.9459\n",
            "Epoch 8/20\n",
            "6/6 [==============================] - 6s 1s/step - loss: 0.1847 - accuracy: 0.9243\n",
            "Epoch 9/20\n",
            "6/6 [==============================] - 4s 545ms/step - loss: 0.1315 - accuracy: 0.9568\n",
            "Epoch 10/20\n",
            "6/6 [==============================] - 4s 602ms/step - loss: 0.1526 - accuracy: 0.9243\n",
            "Epoch 11/20\n",
            "6/6 [==============================] - 4s 624ms/step - loss: 0.0729 - accuracy: 0.9838\n",
            "Epoch 12/20\n",
            "6/6 [==============================] - 4s 559ms/step - loss: 0.0834 - accuracy: 0.9676\n",
            "Epoch 13/20\n",
            "6/6 [==============================] - 4s 557ms/step - loss: 0.0437 - accuracy: 0.9838\n",
            "Epoch 14/20\n",
            "6/6 [==============================] - 5s 788ms/step - loss: 0.0655 - accuracy: 0.9730\n",
            "Epoch 15/20\n",
            "6/6 [==============================] - 4s 588ms/step - loss: 0.0522 - accuracy: 0.9784\n",
            "Epoch 16/20\n",
            "6/6 [==============================] - 4s 543ms/step - loss: 0.0285 - accuracy: 0.9892\n",
            "Epoch 17/20\n",
            "6/6 [==============================] - 5s 835ms/step - loss: 0.0262 - accuracy: 0.9892\n",
            "Epoch 18/20\n",
            "6/6 [==============================] - 4s 587ms/step - loss: 0.0300 - accuracy: 0.9892\n",
            "Epoch 19/20\n",
            "6/6 [==============================] - 4s 565ms/step - loss: 0.0647 - accuracy: 0.9784\n",
            "Epoch 20/20\n",
            "6/6 [==============================] - 5s 752ms/step - loss: 0.0359 - accuracy: 0.9838\n"
          ]
        }
      ],
      "source": [
        "history_VGG=model_3.fit(\n",
        "   datagen.flow(trainX,trainY),\n",
        "                              epochs = 20\n",
        "\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wnnKo0ZEtkzP",
        "outputId": "7d3e2de2-0fc9-48ee-9611-364220298744"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainY[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "qSA6sKLFPKnZ"
      },
      "outputs": [],
      "source": [
        "import sklearn.metrics as metrics\n",
        "from sklearn.metrics import accuracy_score\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "C_FaCYxTin2i"
      },
      "outputs": [],
      "source": [
        "\n",
        "acc = history_VGG.history['accuracy']\n",
        "# val_acc = history_VGG.history['val_accuracy']\n",
        "\n",
        "loss = history_VGG.history['loss']\n",
        "# val_loss = history_VGG.history['val_loss']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vikvW9ergoGw",
        "outputId": "d8e1578b-552a-478f-9ae8-035522d7c5f2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0.6054053902626038, 0.7783783674240112, 0.8432432413101196, 0.8648648858070374, 0.9243243336677551, 0.8864864706993103, 0.9459459185600281, 0.9243243336677551, 0.9567567706108093, 0.9243243336677551, 0.9837837815284729, 0.9675675630569458, 0.9837837815284729, 0.9729729890823364, 0.9783783555030823, 0.9891892075538635, 0.9891892075538635, 0.9891892075538635, 0.9783783555030823, 0.9837837815284729] [5.307031154632568, 1.3177520036697388, 0.4086630046367645, 0.7019987106323242, 1.1327590942382812, 0.43111899495124817, 0.306845098733902, 0.18471097946166992, 0.13149182498455048, 0.15261389315128326, 0.07294762134552002, 0.08335434645414352, 0.04374559596180916, 0.0655369684100151, 0.05220293998718262, 0.028475692495703697, 0.026159103959798813, 0.02996511198580265, 0.06470438838005066, 0.03586960583925247]\n"
          ]
        }
      ],
      "source": [
        "print(acc,loss)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "Dd3wc8wbiu-P",
        "outputId": "d9a26536-daf2-4263-e8e9-9db542e4b2ea"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "EPOCHS=20\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(range(EPOCHS), acc, label='Training Accuracy')\n",
        "# plt.plot(range(EPOCHS), val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(range(EPOCHS), loss, label='Training Loss')\n",
        "# plt.plot(range(EPOCHS), val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vp-j-lWR7eBA",
        "outputId": "809d7378-5af7-486a-d2f3-f81248d59861"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 131ms/step - loss: 0.0154 - accuracy: 1.0000\n",
            "\n",
            "Loss: 100.00%\n"
          ]
        }
      ],
      "source": [
        "scores = model_3.evaluate(testX, testY)\n",
        "print(\"\\nLoss: %.2f%%\" % (scores[1]*100))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 933
        },
        "id": "JiJ8Dp0Ci6YA",
        "outputId": "9f47a6b4-cc1a-4b83-96a0-0e2bfea9f6c9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 24ms/step\n",
            "Accuracy: 1.0\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        11\n",
            "           1       1.00      1.00      1.00        10\n",
            "\n",
            "    accuracy                           1.00        21\n",
            "   macro avg       1.00      1.00      1.00        21\n",
            "weighted avg       1.00      1.00      1.00        21\n",
            "\n",
            "1.0\n",
            "1.0\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 250x250 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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rc/7a/gCQMyp/AEjI+oI/yR8AEuzwBwBkisofABLs8AcAOWO1PwCQKSp/AEjI+oI/yR8AEjzqBwA5Y84fAMgUlT8AJJjzB4Ccyfqcv7Y/AOSMyh8AErJe+Uv+AJBQzvicv7Y/AOSM5A8ACe0pHvvrpptuirq6upg9e/Y7uMq+afsDQEK15/zXr18fX/7yl+P444+vyPVV/gBQQ3bu3BlTp06NxYsXxyGHHFKRe0j+AJBQTvEolUrR1tbW6SiVSm957xkzZsSZZ54ZEydOrNTXk/wBIKm9Lr2jWCxGfX19p6NYLO7zvt/85jdj48aNb3k+Leb8ASAhzTn/pqamaGxs7DRWKBT2+txLL70UV1xxRTz22GPRq1evFCPYm+QPABVUKBT2meyTNmzYENu3b48TTzyxY2zPnj2xcuXKuOuuu6JUKkWPHj1SiUnyB4CEaqz2P/300+MnP/lJp7Hp06fHiBEj4uqrr04t8UdI/gCwl3IV7tm3b9849thjO4316dMn+vfvv9f4O2XBHwDkjMofABLaa2Rv/xUrVlTkupI/ACRUe4e/StP2B4CcUfkDQEI1Fvx1J8kfABLaM57+tf0BIGdU/gCQkPUFf5I/ACRku+kv+QPAXrJe+ZvzB4CcUfkDQEKt7PBXKZI/ACR41A8AyBSVPwAkZLvul/wBYC9W+wMAmaLyB4CErC/4k/wBICHbqV/bHwByR+UPAAlZX/An+QNAgjl/AMiZbKd+c/4AkDsqfwBIMOcPADlTznjjX9sfAHJG5Q8ACdr+AJAzWX/UT9sfAHJG5Q8ACdmu+1X+/IXLLp0WW55bGzvbfhmrV/1bjB0zqtohQbfa8MzWmPm//iUmzrw5Rv7D3Hj8qZ93Ol8ul2PRd/5vnP7Zm+KkixbEJTfdGy82t1YnWCqqPcqpHbVI8iciIqZMOSduu3V+fP6G22PsuE/F5qd/Hv/+/Qdi4MD+1Q4Nus1rpd3xoWEN0TTt7H2ev+/7P4pv/J+1MXf6ufH1BZdG78JBcdktS6L0xu5ujhTeGcmfiIiYc8XF8c9fXRpLvvat+MUvno/LZ1wTr776Wky/8Pxqhwbd5tSRR8Vnp/ynOH3M0XudK5fL8cAjq+Picz4eE0Z/OI4a1hA3fOa/xst/+GM8vuEXVYiWSmpP8ahFkj/Rs2fPOPHE42P54z/qGCuXy7H88VXxkY+MrmJkUDu2vfz7aN2xM8Yd+3cdY30P7hXHfeD98fSWl6oYGZVQTvG/WiT5EwMG9IsDDzwwtrd0nrvcvv3laBg8sEpRQW1p/cPOiIjoX/+eTuP9698TrTv+WI2QqCCVfxe99NJLcdFFF73tZ0qlUrS1tXU6yuXa/OsIALIm9eT/yiuvxJIlS972M8ViMerr6zsd5XZ/OVdLa+sr8eabb8agwQM6jQ8aNDCaW16uUlRQWwa8708V/+927Ow0/rsdO2NAfd9qhEQFZb3t3+Xn/B9++OG3Pf/CCy/81Ws0NTVFY2Njp7FD+o/oaiikZPfu3bFx49PxiQmnxsMPPxoREXV1dfGJCafGl+6+r8rRQW04dOAhMaD+PfHkz34ZIw4fEhERO197PX7ywq9jyuknVTk60lar7fq0dDn5T548Oerq6t62TV9XV/e21ygUClEoFLr0O1TWHf+0OO776h2xYePTsX79j2PWzIujT5/ecf+SZdUODbrNq6+X4lctr3T8vO3l38czL/426vv0jiED3hdTP3VKLP7uiji8oX8cOvCQWPS/l8fA9/WNT4z+cBWjhq7rcvIfMmRIfOlLX4pzzz13n+c3bdoUo0dbIf5u8+1vPxwDB/SLBfM+Fw0NA2Pz5p/FmWddENu328CE/PjZ1m3x32+8t+Pn25b+ICIizjn1hPj8Z/5LTD/ztHit9EZcf+9344+vvh4nHDUsvnTltCgc1LNaIVMh7Rlfh1ZX7uJKu3POOSdGjRoV119//T7Pb968OU444YRob+9a0+TAgw7t0uchD3auWljtEKAm9TppSkWvf8Hhf5/atb7+4r+mdq20dLnyv/LKK2PXrl1vef6DH/xg/PCHP3xHQQEAldPl5H/aaae97fk+ffrExz72sf0OCACqrVb35E+Lt/oBQEKtPqKXFjv8AUDOqPwBIMFz/gCQM+b8ASBnzPkDAJmi8geABHP+AJAzWX/NvLY/AOSMyh8AEqz2B4Ccyfqcv7Y/AOSMyh8AErL+nL/kDwAJWZ/z1/YHgBpRLBZj7Nix0bdv3xg0aFBMnjw5nn322dTvI/kDQEK5XE7t6IonnngiZsyYEWvXro3HHnssdu/eHZ/85Cdj165dqX4/bX8ASKjWav9HHnmk08/3339/DBo0KDZs2BAf/ehHU7uP5A8ACWku+CuVSlEqlTqNFQqFKBQKf/V3d+zYERER/fr1Sy2eCG1/AKioYrEY9fX1nY5isfhXf6+9vT1mz54d48ePj2OPPTbVmFT+AJCQ5mr/pqamaGxs7DT2t1T9M2bMiJ/+9KexatWq1GL5M8kfABLSfLHP39ri/0uf/exn43vf+16sXLky3v/+96cWy59J/gBQI8rlcsycOTMefPDBWLFiRQwfPrwi95H8ASChWpv8zJgxI5YuXRrf/e53o2/fvtHc3BwREfX19dG7d+/U7mPBHwAklFP8ryvuvvvu2LFjR3z84x+PIUOGdBzLli1L9fup/AGgRqS51uDtSP4AkNDeTUm4WiR/AEjIduo35w8AuaPyB4CErL/SV/IHgATJHwByprtW3VeLOX8AyBmVPwAkaPsDQM50dWe+dxttfwDIGZU/ACRkfcGf5A8ACVmf89f2B4CcUfkDQIK2PwDkjLY/AJApKn8ASMj6c/6SPwAktJvzB4B8yXrlb84fAHJG5Q8ACdr+AJAz2v4AQKao/AEgQdsfAHJG2x8AyBSVPwAkaPsDQM5o+wMAmaLyB4CEcrm92iFUlOQPAAntGW/7S/4AkFDO+II/c/4AkDMqfwBI0PYHgJzR9gcAMkXlDwAJdvgDgJyxwx8AkCkqfwBIyPqCP8kfABKy/qiftj8A5IzKHwAStP0BIGc86gcAOZP1yt+cPwDkjMofABKyvtpf8geABG1/ACBTVP4AkGC1PwDkjBf7AACZovIHgARtfwDIGav9AYBMUfkDQIIFfwCQM+VyObWjqxYtWhRHHHFE9OrVK8aNGxfr1q1L/ftJ/gCQUK3kv2zZsmhsbIz58+fHxo0bY+TIkXHGGWfE9u3bU/1+kj8A1Ijbb789Lr744pg+fXocffTRcc8998TBBx8c9957b6r3kfwBIKGc4lEqlaKtra3TUSqV9rrnG2+8ERs2bIiJEyd2jB1wwAExceLEWLNmTarfr2YW/L35xrZqh0D86X/SYrEYTU1NUSgUqh0O1AT/LvInzZy0YMGCuO666zqNzZ8/PxYsWNBprLW1Nfbs2RODBw/uND548OB45plnUosnIqKunPWHGemStra2qK+vjx07dsR73/veaocDNcG/C96JUqm0V6VfKBT2+kPyN7/5TRx66KGxevXqOPnkkzvGr7rqqnjiiSfiySefTC2mmqn8ASCL9pXo92XAgAHRo0ePaGlp6TTe0tISDQ0NqcZkzh8AasBBBx0Uo0ePjuXLl3eMtbe3x/Llyzt1AtKg8geAGtHY2BjTpk2LMWPGxEknnRQLFy6MXbt2xfTp01O9j+RPJ4VCIebPn29RE/wF/y7oLuedd168/PLLMW/evGhubo5Ro0bFI488stciwHfKgj8AyBlz/gCQM5I/AOSM5A8AOSP5A0DOSP506I7XSMK7ycqVK+Pss8+OoUOHRl1dXTz00EPVDglSIfkTEd33Gkl4N9m1a1eMHDkyFi1aVO1QIFUe9SMiIsaNGxdjx46Nu+66KyL+tKvUYYcdFjNnzoxrrrmmytFB9dXV1cWDDz4YkydPrnYo8I6p/OnW10gCUH2SP2/7Gsnm5uYqRQVApUj+AJAzkj/d+hpJAKpP8qdbXyMJQPV5qx8R0X2vkYR3k507d8aWLVs6ft66dWts2rQp+vXrF8OGDatiZPDOeNSPDnfddVfceuutHa+RvPPOO2PcuHHVDguqZsWKFTFhwoS9xqdNmxb3339/9wcEKZH8ASBnzPkDQM5I/gCQM5I/AOSM5A8AOSP5A0DOSP4AkDOSPwDkjOQPADkj+QNAzkj+AJAzkj8A5IzkDwA58/8BKCtOZORor2oAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "# Model Accuracy, how often is the classifier correct?\n",
        "predictions = model_3.predict(testX)\n",
        "\n",
        "predictions = predictions>0.5\n",
        "#print(predictions)\n",
        "from sklearn.metrics import classification_report\n",
        "print(\"Accuracy:\",metrics.accuracy_score(testY, predictions))\n",
        "print(classification_report(testY, predictions))\n",
        "from sklearn.metrics import confusion_matrix\n",
        "confmat = confusion_matrix(y_true=testY, y_pred=predictions)\n",
        "sensitivity = recall_score(testY, predictions)\n",
        "print(sensitivity)\n",
        "specificity=confmat[0][0]/ (confmat[0][0] + confmat[0][1])\n",
        "print(specificity)\n",
        "fig, ax = plt.subplots(figsize=(2.5, 2.5))\n",
        "ax.matshow(confmat, cmap=plt.cm.Blues, alpha=0.3)\n",
        "for i in range(confmat.shape[0]):\n",
        "  for j in range(confmat.shape[1]):\n",
        "     ax.text(x=j, y=i,s=confmat[i, j],va='center', ha='center')\n",
        "plt.xlabel('predicted label')\n",
        "plt.ylabel('true label')\n",
        "plt.show()\n",
        "import seaborn as sns\n",
        "sns.heatmap(confmat, annot=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "YvCTl16Imc89",
        "outputId": "849b8532-da80-48f9-a704-bab63386241e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "pd.DataFrame(history_VGG.history).plot(figsize=(8,5))\n",
        "plt.grid(True)\n",
        "plt.gca().set_ylim(0,1)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PP7O9wO-nWBG",
        "outputId": "066d34bb-ae6d-44c2-9e8d-a18936b0f27f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 142ms/step - loss: 0.0154 - accuracy: 1.0000\n",
            "\n",
            "Accuracy: 100.00%\n"
          ]
        }
      ],
      "source": [
        "scores = model_3.evaluate(testX, testY)\n",
        "print(\"\\nAccuracy: %.2f%%\" % (scores[1]*100))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R0NxVn7E-YwL",
        "outputId": "953a48ea-4574-4e67-e4b9-629b29cbfb1e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 103ms/step\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "y_pred_keras = model_3.predict(testX).ravel()\n",
        "fpr_keras, tpr_keras, thresholds_keras = roc_curve(testY, y_pred_keras)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "d3atzyfG-rNh"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import auc\n",
        "auc_keras = auc(fpr_keras, tpr_keras)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "NE2JH0rW-7sI",
        "outputId": "1da1bbb1-efac-47bc-ea9c-0780a56d303a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(1)\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.title('ROC curve')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "iTfjU_tRboKs"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "\n",
        "pickle.dump(model_3,open('resnet.pkl','wb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oY3fXwv9_ytv"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.11"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}