CLAHE_Adam_1e-1.ipynb

{
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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Copy of scenes_nn_4 layers.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "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": "3.7.4"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rfadhila07/Test-result/blob/main/CLAHE_Adam_1e-1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_dJvuvIBVLS_",
        "outputId": "60dc0f8e-6ee7-4c22-9b29-25237699ce13"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H215ikbNVVZS",
        "outputId": "67bb53a1-9174-404c-b103-b4431f4410ae"
      },
      "source": [
        "cd /content/drive/MyDrive/ "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z7VB1d4wVdov",
        "outputId": "64df2b7d-febc-488c-c245-29de35c980b2"
      },
      "source": [
        "ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'1101174267 m rifqi fadhila mixer.pdf'\n",
            "'1101174267_Muhammad Rifqi F_2_TT4107.pdf'\n",
            "'1101174267_MuhammadRifqiFadhila_1&3_TT4107.pdf'\n",
            " 1101174267_MuhammadRifqiFadhila_3.3_TT4107.pdf\n",
            "'1101174267_muhammad rifqi fadhila_Filter2.jpg'\n",
            "'1101174267 muhammad rifqi fadhila OSILATOR.pdf'\n",
            "'1101174267 muhammad rifqi fadhila penguat.pdf'\n",
            "'1101174267_Muhammad Rifqi Fadhila_Proposal Tugas Akhir dan lembar pemeriksaan.pdf'\n",
            " best_weight_scene_EfficientNetB0.h5\n",
            " \u001b[0m\u001b[01;34mCLAHE\u001b[0m/\n",
            " \u001b[01;34mClassroom\u001b[0m/\n",
            " \u001b[01;34mDataset\u001b[0m/\n",
            " DSC_7396.JPG\n",
            " \u001b[01;34mGaussian\u001b[0m/\n",
            "'Getting started.pdf'\n",
            " IMG-20210217-WA0001.jpg\n",
            "\u001b[01;34m'MPTI 2021'\u001b[0m/\n",
            " \u001b[01;34mNormalized\u001b[0m/\n",
            "'PMMB2021_BATCH1_1101174267_MUHAMMAD RIFQI FADHILA.rar'\n",
            " Screenshot_20210217-060246.jpg\n",
            " TT_41_07_101174267_MUHAMMADRIFQIFADHILA.pdf\n",
            " \u001b[01;34mValo\u001b[0m/\n",
            " \u001b[01;34mVisualizable\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5IGm26ZzVGBs"
      },
      "source": [
        "# import the necessary packages\n",
        "from keras.models import Sequential\n",
        "from keras.layers.convolutional import Conv2D\n",
        "from keras.layers.convolutional import MaxPooling2D\n",
        "from keras.layers.core import Activation\n",
        "from keras.layers.core import Flatten\n",
        "from keras.layers.core import Dense\n",
        "from keras.optimizers import Adam, SGD, RMSprop\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from PIL import Image\n",
        "from imutils import paths\n",
        "from skimage import io\n",
        "from skimage.filters import gaussian\n",
        "from skimage import exposure\n",
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iF6yEboXxV9J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbbf98c9-9f0f-44c2-ff5a-fd5489e4addb"
      },
      "source": [
        "#Normalized dataset\n",
        "import PIL.Image as pil_image\n",
        "import io\n",
        "path ='/content/drive/MyDrive/Dataset/Normal/'\n",
        "path_no = 1\n",
        "for paths in os.listdir(path) :\n",
        "  if paths.endswith(\".tif\"):\n",
        "    filepath = os.path.join(path, paths)\n",
        "    img = Image.open(filepath)\n",
        "    array=np.array(img)\n",
        "    max_val=np.amax(array)\n",
        "    normalized=(array/max_val)\n",
        "    im = pil_image.fromarray(normalized)\n",
        "    if path_no < 10 :\n",
        "      save_path='/content/drive/MyDrive/Normalized/Normal/ ' + str('IM0000') +str(path_no) + '.tif'\n",
        "    elif path_no <100 :\n",
        "      save_path='/content/drive/MyDrive/Normalized/Normal/' + str('IM000') +str(path_no) + '.tif'\n",
        "    else :\n",
        "      save_path='/content/drive/MyDrive/Normalized/Normal/' + str('IM00') +str(path_no) + '.tif'\n",
        "    im.save(save_path,'TIFF') \n",
        "    path_no +=1\n",
        "print(\"Normalized Success\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normalized Success\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9Uvnrw4rqNh",
        "outputId": "6b80c02f-e9cb-4d0a-e464-0901dbf4c235"
      },
      "source": [
        "#Gaussian Filter\n",
        "import cv2\n",
        "from skimage import io\n",
        "from skimage.filters import gaussian\n",
        "path ='/content/drive/MyDrive/Normalized/Normal/'\n",
        "path_no = 1\n",
        "for paths in os.listdir(path) :\n",
        "  if paths.endswith(\".tif\"):\n",
        "    filepath = os.path.join(path, paths)\n",
        "    img = Image.open(filepath)\n",
        "    img = np.array(img)\n",
        "    gausfilter = gaussian(img, sigma=1, mode='constant', cval=0.0)\n",
        "    im = Image.fromarray(gausfilter)\n",
        "    if path_no < 10 :\n",
        "      save_path='/content/drive/MyDrive/Gaussian/Normal/' + str('IM0000') +str(path_no) + '.tif'\n",
        "    elif path_no <100 :\n",
        "      save_path='/content/drive/MyDrive/Gaussian/Normal/' + str('IM000') +str(path_no) + '.tif'\n",
        "    else :\n",
        "      save_path='/content/drive/MyDrive/Gaussian/Normal/' + str('IM00') +str(path_no) + '.tif'\n",
        "    im.save(save_path,'TIFF') \n",
        "    path_no +=1\n",
        "\n",
        "print(\"Denoissing Success\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Denoissing Success\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6b1PTs21ki0",
        "outputId": "028554f8-d7b6-450b-d05a-def065b74cda"
      },
      "source": [
        "#CLAHE\n",
        "from skimage import exposure\n",
        "path ='/content/drive/MyDrive/Gaussian/Normal/'\n",
        "path_no = 1\n",
        "for paths in os.listdir(path) :\n",
        "  if paths.endswith(\".tif\"):\n",
        "    filepath = os.path.join(path, paths)\n",
        "    img = Image.open(filepath)\n",
        "    img = np.array(img)\n",
        "    clahe = exposure.equalize_adapthist(img)\n",
        "    im = Image.fromarray(clahe)\n",
        "    if path_no < 10 :\n",
        "      save_path='/content/drive/MyDrive/CLAHE/Normal/' + str('IM0000') +str(path_no) + '.tif'\n",
        "    elif path_no <100 :\n",
        "      save_path='/content/drive/MyDrive/CLAHE/Normal/' + str('IM000') +str(path_no) + '.tif'\n",
        "    else :\n",
        "      save_path='/content/drive/MyDrive/CLAHE/Normal/' + str('IM00') +str(path_no) + '.tif'\n",
        "    im.save(save_path,'TIFF') \n",
        "    path_no +=1\n",
        "\n",
        "print(\"CLAHE Success\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CLAHE Success\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HCs86pnwc5WL",
        "outputId": "6fb67d94-0a6d-4f18-8a7f-84abb4a8c8f9"
      },
      "source": [
        "# grab all image paths in the input dataset directory, then initialize\n",
        "# our list of images and corresponding class labels\n",
        "print(\"[INFO] loading images...\")\n",
        "imagePaths = paths.list_images(\"CLAHE\")\n",
        "data = []\n",
        "labels = []"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] loading images...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KsbyXzXK0k-4"
      },
      "source": [
        "import cv2\n",
        "\n",
        "im = cv2.imread('/content/drive/MyDrive/CLAHE/Covid/IM00001.tif', -1)\n",
        "im_rgb = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "me1s-uFI7kJi",
        "outputId": "ee65e6ec-f105-4484-ded0-aa7c9f110acc"
      },
      "source": [
        "\n",
        "plt.imshow(im_rgb)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f303d83bc10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1QQBNriY1JrE"
      },
      "source": [
        "\n",
        "width = 224\n",
        "height = 224\n",
        "dsize = (width, height)\n",
        "# loop over our input images\n",
        "for imagePath in imagePaths:\n",
        "  # load the input image from disk, resize it to 64x64 pixels, scale\n",
        "\t# the pixel intensities to the range [0, 1], and then update our\n",
        "\t# images list\n",
        "    image = cv2.imread(imagePath,-1)\n",
        "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "  #image = image.convert('L')\n",
        "    image = cv2.resize(image,dsize)\n",
        "    image = np.array(image)\n",
        "    data.append(image)\n",
        "\n",
        "  # extract the class label from the file path and update the\n",
        "\t# labels list\n",
        "    label = imagePath.split(os.path.sep)[-2]\n",
        "    labels.append(label)"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rzbolh88VGB2",
        "outputId": "9fa3228c-665a-41c2-cae7-54a0b505eddb"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 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'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "aGbsj1UcytpV",
        "outputId": "b1634a49-0412-4bab-d388-c242ee5f586f"
      },
      "source": [
        "\n",
        "print(labels[500])\n",
        "plt.imshow(data[10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normal\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7bba4d2950>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "twd1IeuKVGB5"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# encode class values as integers\n",
        "encoder = LabelEncoder()\n",
        "encoder.fit(labels)\n",
        "labels = encoder.transform(labels)"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FPCyIqcVGB7",
        "outputId": "a99b79f3-ad61-4dbe-f93c-244f95aa52a8"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LynLhwt_pvNc",
        "outputId": "ab143b27-08b8-4e05-fd27-562059943009"
      },
      "source": [
        "data = np.array(data)\n",
        "data.shape"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ktZ5hNYwVGB9",
        "outputId": "6aed9989-e733-487d-e2b0-8c523f757a8e"
      },
      "source": [
        "# perform a training and testing split, using 75% of the data for\n",
        "# training and 25% for evaluation\n",
        "(trainX, testX, trainY, testY) = train_test_split(np.array(data),\tnp.array(labels), test_size=0.25)\n",
        "print(trainX.shape)\n",
        "print(testX.shape)\n",
        "print(trainY.shape)\n",
        "print(testY.shape)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(750, 224, 224, 3)\n",
            "(250, 224, 224, 3)\n",
            "(750,)\n",
            "(250,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "akW74c5YfOGq",
        "outputId": "5918e5f2-0fc0-4920-b14b-b2b603242770"
      },
      "source": [
        "print(trainX[0])"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[0.00807058 0.00807058 0.00807058]\n",
            "  [0.01741401 0.01741401 0.01741401]\n",
            "  [0.02690735 0.02690735 0.02690735]\n",
            "  ...\n",
            "  [0.03169393 0.03169393 0.03169393]\n",
            "  [0.02255028 0.02255028 0.02255028]\n",
            "  [0.00716122 0.00716122 0.00716122]]\n",
            "\n",
            " [[0.02066173 0.02066173 0.02066173]\n",
            "  [0.02131128 0.02131128 0.02131128]\n",
            "  [0.02400939 0.02400939 0.02400939]\n",
            "  ...\n",
            "  [0.03444197 0.03444197 0.03444197]\n",
            "  [0.02739712 0.02739712 0.02739712]\n",
            "  [0.02909573 0.02909573 0.02909573]]\n",
            "\n",
            " [[0.0178637  0.0178637  0.0178637 ]\n",
            "  [0.03115436 0.03115436 0.03115436]\n",
            "  [0.01926272 0.01926272 0.01926272]\n",
            "  ...\n",
            "  [0.02839637 0.02839637 0.02839637]\n",
            "  [0.0293957  0.0293957  0.0293957 ]\n",
            "  [0.03304299 0.03304299 0.03304299]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0.02674746 0.02674746 0.02674746]\n",
            "  [0.02674746 0.02674746 0.02674746]\n",
            "  [0.03654058 0.03654058 0.03654058]\n",
            "  ...\n",
            "  [0.02234056 0.02234056 0.02234056]\n",
            "  [0.02443917 0.02443917 0.02443917]\n",
            "  [0.02788658 0.02788658 0.02788658]]\n",
            "\n",
            " [[0.02604782 0.02604782 0.02604782]\n",
            "  [0.02604782 0.02604782 0.02604782]\n",
            "  [0.01765398 0.01765398 0.01765398]\n",
            "  ...\n",
            "  [0.02234056 0.02234056 0.02234056]\n",
            "  [0.03143404 0.03143404 0.03143404]\n",
            "  [0.01814333 0.01814333 0.01814333]]\n",
            "\n",
            " [[0.0157052  0.0157052  0.0157052 ]\n",
            "  [0.02604795 0.02604795 0.02604795]\n",
            "  [0.02324988 0.02324988 0.02324988]\n",
            "  ...\n",
            "  [0.02159104 0.02159104 0.02159104]\n",
            "  [0.01884297 0.01884297 0.01884297]\n",
            "  [0.00680142 0.00680142 0.00680142]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1LK97ZhDrRZ"
      },
      "source": [
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from keras.models import Model\n",
        "from keras.layers import GlobalAveragePooling2D, BatchNormalization, Dropout\n",
        "\n",
        "from keras.applications.efficientnet import EfficientNetB0\n",
        "from keras.layers import Dense, Conv2D, MaxPool2D , Flatten\n",
        "from keras.applications.resnet import ResNet50\n",
        "import tensorflow as tf\n"
      ],
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTvu4IV0i7Fj",
        "outputId": "bdf1ef38-4379-43ca-8623-23d2896f1a5c"
      },
      "source": [
        "base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224,224,3))\n",
        "x = base_model.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Dropout(0.5)(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = Dense(512, activation='relu')(x)\n",
        "out = Dense(2, activation='softmax')(x)\n",
        "\n",
        "model = Model(inputs=base_model.input, outputs=out)\n",
        "model.summary()"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_6\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_7 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "rescaling_6 (Rescaling)         (None, 224, 224, 3)  0           input_7[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "normalization_6 (Normalization) (None, 224, 224, 3)  7           rescaling_6[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "stem_conv_pad (ZeroPadding2D)   (None, 225, 225, 3)  0           normalization_6[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "stem_conv (Conv2D)              (None, 112, 112, 32) 864         stem_conv_pad[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "stem_bn (BatchNormalization)    (None, 112, 112, 32) 128         stem_conv[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "stem_activation (Activation)    (None, 112, 112, 32) 0           stem_bn[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "block1a_dwconv (DepthwiseConv2D (None, 112, 112, 32) 288         stem_activation[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block1a_bn (BatchNormalization) (None, 112, 112, 32) 128         block1a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block1a_activation (Activation) (None, 112, 112, 32) 0           block1a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_squeeze (GlobalAvera (None, 32)           0           block1a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_reshape (Reshape)    (None, 1, 1, 32)     0           block1a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_reduce (Conv2D)      (None, 1, 1, 8)      264         block1a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_expand (Conv2D)      (None, 1, 1, 32)     288         block1a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_excite (Multiply)    (None, 112, 112, 32) 0           block1a_activation[0][0]         \n",
            "                                                                 block1a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block1a_project_conv (Conv2D)   (None, 112, 112, 16) 512         block1a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block1a_project_bn (BatchNormal (None, 112, 112, 16) 64          block1a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block2a_expand_conv (Conv2D)    (None, 112, 112, 96) 1536        block1a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_expand_bn (BatchNormali (None, 112, 112, 96) 384         block2a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block2a_expand_activation (Acti (None, 112, 112, 96) 0           block2a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_dwconv_pad (ZeroPadding (None, 113, 113, 96) 0           block2a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block2a_dwconv (DepthwiseConv2D (None, 56, 56, 96)   864         block2a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_bn (BatchNormalization) (None, 56, 56, 96)   384         block2a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block2a_activation (Activation) (None, 56, 56, 96)   0           block2a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_squeeze (GlobalAvera (None, 96)           0           block2a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_reshape (Reshape)    (None, 1, 1, 96)     0           block2a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_reduce (Conv2D)      (None, 1, 1, 4)      388         block2a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_expand (Conv2D)      (None, 1, 1, 96)     480         block2a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_excite (Multiply)    (None, 56, 56, 96)   0           block2a_activation[0][0]         \n",
            "                                                                 block2a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_project_conv (Conv2D)   (None, 56, 56, 24)   2304        block2a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_project_bn (BatchNormal (None, 56, 56, 24)   96          block2a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block2b_expand_conv (Conv2D)    (None, 56, 56, 144)  3456        block2a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_expand_bn (BatchNormali (None, 56, 56, 144)  576         block2b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block2b_expand_activation (Acti (None, 56, 56, 144)  0           block2b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_dwconv (DepthwiseConv2D (None, 56, 56, 144)  1296        block2b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block2b_bn (BatchNormalization) (None, 56, 56, 144)  576         block2b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block2b_activation (Activation) (None, 56, 56, 144)  0           block2b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_squeeze (GlobalAvera (None, 144)          0           block2b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_reshape (Reshape)    (None, 1, 1, 144)    0           block2b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block2b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block2b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_excite (Multiply)    (None, 56, 56, 144)  0           block2b_activation[0][0]         \n",
            "                                                                 block2b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_project_conv (Conv2D)   (None, 56, 56, 24)   3456        block2b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_project_bn (BatchNormal (None, 56, 56, 24)   96          block2b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block2b_drop (Dropout)          (None, 56, 56, 24)   0           block2b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_add (Add)               (None, 56, 56, 24)   0           block2b_drop[0][0]               \n",
            "                                                                 block2a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_expand_conv (Conv2D)    (None, 56, 56, 144)  3456        block2b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block3a_expand_bn (BatchNormali (None, 56, 56, 144)  576         block3a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block3a_expand_activation (Acti (None, 56, 56, 144)  0           block3a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_dwconv_pad (ZeroPadding (None, 59, 59, 144)  0           block3a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block3a_dwconv (DepthwiseConv2D (None, 28, 28, 144)  3600        block3a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_bn (BatchNormalization) (None, 28, 28, 144)  576         block3a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block3a_activation (Activation) (None, 28, 28, 144)  0           block3a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_squeeze (GlobalAvera (None, 144)          0           block3a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_reshape (Reshape)    (None, 1, 1, 144)    0           block3a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block3a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block3a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_excite (Multiply)    (None, 28, 28, 144)  0           block3a_activation[0][0]         \n",
            "                                                                 block3a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_project_conv (Conv2D)   (None, 28, 28, 40)   5760        block3a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_project_bn (BatchNormal (None, 28, 28, 40)   160         block3a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block3b_expand_conv (Conv2D)    (None, 28, 28, 240)  9600        block3a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_expand_bn (BatchNormali (None, 28, 28, 240)  960         block3b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block3b_expand_activation (Acti (None, 28, 28, 240)  0           block3b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_dwconv (DepthwiseConv2D (None, 28, 28, 240)  6000        block3b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block3b_bn (BatchNormalization) (None, 28, 28, 240)  960         block3b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block3b_activation (Activation) (None, 28, 28, 240)  0           block3b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_squeeze (GlobalAvera (None, 240)          0           block3b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_reshape (Reshape)    (None, 1, 1, 240)    0           block3b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_reduce (Conv2D)      (None, 1, 1, 10)     2410        block3b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_expand (Conv2D)      (None, 1, 1, 240)    2640        block3b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_excite (Multiply)    (None, 28, 28, 240)  0           block3b_activation[0][0]         \n",
            "                                                                 block3b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_project_conv (Conv2D)   (None, 28, 28, 40)   9600        block3b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_project_bn (BatchNormal (None, 28, 28, 40)   160         block3b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block3b_drop (Dropout)          (None, 28, 28, 40)   0           block3b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_add (Add)               (None, 28, 28, 40)   0           block3b_drop[0][0]               \n",
            "                                                                 block3a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_expand_conv (Conv2D)    (None, 28, 28, 240)  9600        block3b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block4a_expand_bn (BatchNormali (None, 28, 28, 240)  960         block4a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4a_expand_activation (Acti (None, 28, 28, 240)  0           block4a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_dwconv_pad (ZeroPadding (None, 29, 29, 240)  0           block4a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block4a_dwconv (DepthwiseConv2D (None, 14, 14, 240)  2160        block4a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_bn (BatchNormalization) (None, 14, 14, 240)  960         block4a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block4a_activation (Activation) (None, 14, 14, 240)  0           block4a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_squeeze (GlobalAvera (None, 240)          0           block4a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_reshape (Reshape)    (None, 1, 1, 240)    0           block4a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_reduce (Conv2D)      (None, 1, 1, 10)     2410        block4a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_expand (Conv2D)      (None, 1, 1, 240)    2640        block4a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_excite (Multiply)    (None, 14, 14, 240)  0           block4a_activation[0][0]         \n",
            "                                                                 block4a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_project_conv (Conv2D)   (None, 14, 14, 80)   19200       block4a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_project_bn (BatchNormal (None, 14, 14, 80)   320         block4a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block4b_expand_conv (Conv2D)    (None, 14, 14, 480)  38400       block4a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_expand_bn (BatchNormali (None, 14, 14, 480)  1920        block4b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4b_expand_activation (Acti (None, 14, 14, 480)  0           block4b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_dwconv (DepthwiseConv2D (None, 14, 14, 480)  4320        block4b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block4b_bn (BatchNormalization) (None, 14, 14, 480)  1920        block4b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block4b_activation (Activation) (None, 14, 14, 480)  0           block4b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_squeeze (GlobalAvera (None, 480)          0           block4b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_reshape (Reshape)    (None, 1, 1, 480)    0           block4b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block4b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block4b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_excite (Multiply)    (None, 14, 14, 480)  0           block4b_activation[0][0]         \n",
            "                                                                 block4b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_project_conv (Conv2D)   (None, 14, 14, 80)   38400       block4b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_project_bn (BatchNormal (None, 14, 14, 80)   320         block4b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block4b_drop (Dropout)          (None, 14, 14, 80)   0           block4b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_add (Add)               (None, 14, 14, 80)   0           block4b_drop[0][0]               \n",
            "                                                                 block4a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_expand_conv (Conv2D)    (None, 14, 14, 480)  38400       block4b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block4c_expand_bn (BatchNormali (None, 14, 14, 480)  1920        block4c_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4c_expand_activation (Acti (None, 14, 14, 480)  0           block4c_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_dwconv (DepthwiseConv2D (None, 14, 14, 480)  4320        block4c_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block4c_bn (BatchNormalization) (None, 14, 14, 480)  1920        block4c_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block4c_activation (Activation) (None, 14, 14, 480)  0           block4c_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_squeeze (GlobalAvera (None, 480)          0           block4c_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_reshape (Reshape)    (None, 1, 1, 480)    0           block4c_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block4c_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block4c_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_excite (Multiply)    (None, 14, 14, 480)  0           block4c_activation[0][0]         \n",
            "                                                                 block4c_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_project_conv (Conv2D)   (None, 14, 14, 80)   38400       block4c_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_project_bn (BatchNormal (None, 14, 14, 80)   320         block4c_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block4c_drop (Dropout)          (None, 14, 14, 80)   0           block4c_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_add (Add)               (None, 14, 14, 80)   0           block4c_drop[0][0]               \n",
            "                                                                 block4b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block5a_expand_conv (Conv2D)    (None, 14, 14, 480)  38400       block4c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block5a_expand_bn (BatchNormali (None, 14, 14, 480)  1920        block5a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5a_expand_activation (Acti (None, 14, 14, 480)  0           block5a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_dwconv (DepthwiseConv2D (None, 14, 14, 480)  12000       block5a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block5a_bn (BatchNormalization) (None, 14, 14, 480)  1920        block5a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block5a_activation (Activation) (None, 14, 14, 480)  0           block5a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_squeeze (GlobalAvera (None, 480)          0           block5a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_reshape (Reshape)    (None, 1, 1, 480)    0           block5a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block5a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block5a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_excite (Multiply)    (None, 14, 14, 480)  0           block5a_activation[0][0]         \n",
            "                                                                 block5a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_project_conv (Conv2D)   (None, 14, 14, 112)  53760       block5a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_project_bn (BatchNormal (None, 14, 14, 112)  448         block5a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block5b_expand_conv (Conv2D)    (None, 14, 14, 672)  75264       block5a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_expand_bn (BatchNormali (None, 14, 14, 672)  2688        block5b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5b_expand_activation (Acti (None, 14, 14, 672)  0           block5b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_dwconv (DepthwiseConv2D (None, 14, 14, 672)  16800       block5b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block5b_bn (BatchNormalization) (None, 14, 14, 672)  2688        block5b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block5b_activation (Activation) (None, 14, 14, 672)  0           block5b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_squeeze (GlobalAvera (None, 672)          0           block5b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_reshape (Reshape)    (None, 1, 1, 672)    0           block5b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block5b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block5b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_excite (Multiply)    (None, 14, 14, 672)  0           block5b_activation[0][0]         \n",
            "                                                                 block5b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_project_conv (Conv2D)   (None, 14, 14, 112)  75264       block5b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_project_bn (BatchNormal (None, 14, 14, 112)  448         block5b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block5b_drop (Dropout)          (None, 14, 14, 112)  0           block5b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_add (Add)               (None, 14, 14, 112)  0           block5b_drop[0][0]               \n",
            "                                                                 block5a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_expand_conv (Conv2D)    (None, 14, 14, 672)  75264       block5b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block5c_expand_bn (BatchNormali (None, 14, 14, 672)  2688        block5c_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5c_expand_activation (Acti (None, 14, 14, 672)  0           block5c_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_dwconv (DepthwiseConv2D (None, 14, 14, 672)  16800       block5c_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block5c_bn (BatchNormalization) (None, 14, 14, 672)  2688        block5c_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block5c_activation (Activation) (None, 14, 14, 672)  0           block5c_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_squeeze (GlobalAvera (None, 672)          0           block5c_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_reshape (Reshape)    (None, 1, 1, 672)    0           block5c_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block5c_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block5c_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_excite (Multiply)    (None, 14, 14, 672)  0           block5c_activation[0][0]         \n",
            "                                                                 block5c_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_project_conv (Conv2D)   (None, 14, 14, 112)  75264       block5c_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_project_bn (BatchNormal (None, 14, 14, 112)  448         block5c_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block5c_drop (Dropout)          (None, 14, 14, 112)  0           block5c_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_add (Add)               (None, 14, 14, 112)  0           block5c_drop[0][0]               \n",
            "                                                                 block5b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6a_expand_conv (Conv2D)    (None, 14, 14, 672)  75264       block5c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6a_expand_bn (BatchNormali (None, 14, 14, 672)  2688        block6a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6a_expand_activation (Acti (None, 14, 14, 672)  0           block6a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_dwconv_pad (ZeroPadding (None, 17, 17, 672)  0           block6a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6a_dwconv (DepthwiseConv2D (None, 7, 7, 672)    16800       block6a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_bn (BatchNormalization) (None, 7, 7, 672)    2688        block6a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6a_activation (Activation) (None, 7, 7, 672)    0           block6a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_squeeze (GlobalAvera (None, 672)          0           block6a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_reshape (Reshape)    (None, 1, 1, 672)    0           block6a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block6a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block6a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_excite (Multiply)    (None, 7, 7, 672)    0           block6a_activation[0][0]         \n",
            "                                                                 block6a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_project_conv (Conv2D)   (None, 7, 7, 192)    129024      block6a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_project_bn (BatchNormal (None, 7, 7, 192)    768         block6a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6b_expand_conv (Conv2D)    (None, 7, 7, 1152)   221184      block6a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_expand_bn (BatchNormali (None, 7, 7, 1152)   4608        block6b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6b_expand_activation (Acti (None, 7, 7, 1152)   0           block6b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_dwconv (DepthwiseConv2D (None, 7, 7, 1152)   28800       block6b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6b_bn (BatchNormalization) (None, 7, 7, 1152)   4608        block6b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6b_activation (Activation) (None, 7, 7, 1152)   0           block6b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_squeeze (GlobalAvera (None, 1152)         0           block6b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_excite (Multiply)    (None, 7, 7, 1152)   0           block6b_activation[0][0]         \n",
            "                                                                 block6b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_project_conv (Conv2D)   (None, 7, 7, 192)    221184      block6b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_project_bn (BatchNormal (None, 7, 7, 192)    768         block6b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6b_drop (Dropout)          (None, 7, 7, 192)    0           block6b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_add (Add)               (None, 7, 7, 192)    0           block6b_drop[0][0]               \n",
            "                                                                 block6a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_expand_conv (Conv2D)    (None, 7, 7, 1152)   221184      block6b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6c_expand_bn (BatchNormali (None, 7, 7, 1152)   4608        block6c_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6c_expand_activation (Acti (None, 7, 7, 1152)   0           block6c_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_dwconv (DepthwiseConv2D (None, 7, 7, 1152)   28800       block6c_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6c_bn (BatchNormalization) (None, 7, 7, 1152)   4608        block6c_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6c_activation (Activation) (None, 7, 7, 1152)   0           block6c_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_squeeze (GlobalAvera (None, 1152)         0           block6c_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6c_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6c_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6c_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_excite (Multiply)    (None, 7, 7, 1152)   0           block6c_activation[0][0]         \n",
            "                                                                 block6c_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_project_conv (Conv2D)   (None, 7, 7, 192)    221184      block6c_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_project_bn (BatchNormal (None, 7, 7, 192)    768         block6c_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6c_drop (Dropout)          (None, 7, 7, 192)    0           block6c_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_add (Add)               (None, 7, 7, 192)    0           block6c_drop[0][0]               \n",
            "                                                                 block6b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6d_expand_conv (Conv2D)    (None, 7, 7, 1152)   221184      block6c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6d_expand_bn (BatchNormali (None, 7, 7, 1152)   4608        block6d_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6d_expand_activation (Acti (None, 7, 7, 1152)   0           block6d_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_dwconv (DepthwiseConv2D (None, 7, 7, 1152)   28800       block6d_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6d_bn (BatchNormalization) (None, 7, 7, 1152)   4608        block6d_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6d_activation (Activation) (None, 7, 7, 1152)   0           block6d_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_squeeze (GlobalAvera (None, 1152)         0           block6d_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6d_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6d_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6d_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_excite (Multiply)    (None, 7, 7, 1152)   0           block6d_activation[0][0]         \n",
            "                                                                 block6d_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_project_conv (Conv2D)   (None, 7, 7, 192)    221184      block6d_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_project_bn (BatchNormal (None, 7, 7, 192)    768         block6d_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6d_drop (Dropout)          (None, 7, 7, 192)    0           block6d_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_add (Add)               (None, 7, 7, 192)    0           block6d_drop[0][0]               \n",
            "                                                                 block6c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block7a_expand_conv (Conv2D)    (None, 7, 7, 1152)   221184      block6d_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block7a_expand_bn (BatchNormali (None, 7, 7, 1152)   4608        block7a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block7a_expand_activation (Acti (None, 7, 7, 1152)   0           block7a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_dwconv (DepthwiseConv2D (None, 7, 7, 1152)   10368       block7a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block7a_bn (BatchNormalization) (None, 7, 7, 1152)   4608        block7a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block7a_activation (Activation) (None, 7, 7, 1152)   0           block7a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_squeeze (GlobalAvera (None, 1152)         0           block7a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block7a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block7a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block7a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_excite (Multiply)    (None, 7, 7, 1152)   0           block7a_activation[0][0]         \n",
            "                                                                 block7a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_project_conv (Conv2D)   (None, 7, 7, 320)    368640      block7a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_project_bn (BatchNormal (None, 7, 7, 320)    1280        block7a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "top_conv (Conv2D)               (None, 7, 7, 1280)   409600      block7a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "top_bn (BatchNormalization)     (None, 7, 7, 1280)   5120        top_conv[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "top_activation (Activation)     (None, 7, 7, 1280)   0           top_bn[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_6 (Glo (None, 1280)         0           top_activation[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "dropout_6 (Dropout)             (None, 1280)         0           global_average_pooling2d_6[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 1280)         5120        dropout_6[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_12 (Dense)                (None, 512)          655872      batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "dense_13 (Dense)                (None, 2)            1026        dense_12[0][0]                   \n",
            "==================================================================================================\n",
            "Total params: 4,711,589\n",
            "Trainable params: 4,667,006\n",
            "Non-trainable params: 44,583\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6gVx0PzEF5p2"
      },
      "source": [
        "es = EarlyStopping(monitor='val_loss',\n",
        "                   patience=5,\n",
        "                   mode='auto',\n",
        "                   restore_best_weights=True)\n",
        "checkpoint = ModelCheckpoint('best_weight_scene_EfficientNetB0.h5',\n",
        "                             monitor='val_accuracy',\n",
        "                             verbose=1,\n",
        "                             save_best_only=True,\n",
        "                             mode='auto',)"
      ],
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sny_9-wU8PGB"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers.core import Flatten\n",
        "from keras.layers.core import Dense\n",
        "\n",
        "model = Sequential(\n",
        "    [\n",
        "        Flatten(input_shape=(224*224*3,)),\n",
        "        Dense(100, activation=\"relu\", name=\"layer1\"),\n",
        "        Dense(16, activation=\"relu\", name=\"layer2\"),\n",
        "        Dense(16, activation=\"relu\", name=\"layer3\"),\n",
        "        Dense(3, activation = \"softmax\", name=\"layer4\"),\n",
        "    ]\n",
        ")"
      ],
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0TdWxuFPkT3",
        "outputId": "42b7ce65-30e1-4afc-8c9d-13098cabf6a7"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_6 (Flatten)          (None, 150528)            0         \n",
            "_________________________________________________________________\n",
            "layer1 (Dense)               (None, 100)               15052900  \n",
            "_________________________________________________________________\n",
            "layer2 (Dense)               (None, 16)                1616      \n",
            "_________________________________________________________________\n",
            "layer3 (Dense)               (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "layer4 (Dense)               (None, 3)                 51        \n",
            "=================================================================\n",
            "Total params: 15,054,839\n",
            "Trainable params: 15,054,839\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ggcSdj_aVGCB",
        "scrolled": true,
        "outputId": "ab8daefb-0b4a-4e86-9398-e2a23a9a5459"
      },
      "source": [
        "# train the model using the Adam optimizer\n",
        "print(\"[INFO] training network...\")\n",
        "opt = Adam(learning_rate=1e-1, decay=1e-6)\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=opt,\n",
        "\tmetrics=[\"accuracy\"])\n",
        "H = model.fit(trainX, trainY, validation_data=(testX, testY), epochs=50, batch_size=32, callbacks=[es, checkpoint])"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] training network...\n",
            "Epoch 1/50\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 150528) for input KerasTensor(type_spec=TensorSpec(shape=(None, 150528), dtype=tf.float32, name='flatten_6_input'), name='flatten_6_input', description=\"created by layer 'flatten_6_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 150528) for input KerasTensor(type_spec=TensorSpec(shape=(None, 150528), dtype=tf.float32, name='flatten_6_input'), name='flatten_6_input', description=\"created by layer 'flatten_6_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 4456.9888 - accuracy: 0.4516WARNING:tensorflow:Model was constructed with shape (None, 150528) for input KerasTensor(type_spec=TensorSpec(shape=(None, 150528), dtype=tf.float32, name='flatten_6_input'), name='flatten_6_input', description=\"created by layer 'flatten_6_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "24/24 [==============================] - 1s 31ms/step - loss: 4050.3614 - accuracy: 0.4576 - val_loss: 0.9894 - val_accuracy: 0.4560\n",
            "\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.45600, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 2/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.7484 - accuracy: 0.5296 - val_loss: 0.6947 - val_accuracy: 0.4800\n",
            "\n",
            "Epoch 00002: val_accuracy improved from 0.45600 to 0.48000, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 3/50\n",
            "24/24 [==============================] - 0s 21ms/step - loss: 0.6943 - accuracy: 0.4986 - val_loss: 0.6943 - val_accuracy: 0.4800\n",
            "\n",
            "Epoch 00003: val_accuracy did not improve from 0.48000\n",
            "Epoch 4/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.6939 - accuracy: 0.4880 - val_loss: 0.6946 - val_accuracy: 0.4800\n",
            "\n",
            "Epoch 00004: val_accuracy did not improve from 0.48000\n",
            "Epoch 5/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.6939 - accuracy: 0.5120 - val_loss: 0.6931 - val_accuracy: 0.5200\n",
            "\n",
            "Epoch 00005: val_accuracy improved from 0.48000 to 0.52000, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 6/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.6935 - accuracy: 0.4997 - val_loss: 0.6975 - val_accuracy: 0.4800\n",
            "\n",
            "Epoch 00006: val_accuracy did not improve from 0.52000\n",
            "Epoch 7/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.6961 - accuracy: 0.4744 - val_loss: 0.6971 - val_accuracy: 0.4800\n",
            "\n",
            "Epoch 00007: val_accuracy did not improve from 0.52000\n",
            "Epoch 8/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.6920 - accuracy: 0.5297 - val_loss: 0.6924 - val_accuracy: 0.5200\n",
            "\n",
            "Epoch 00008: val_accuracy did not improve from 0.52000\n",
            "Epoch 9/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.6953 - accuracy: 0.5087 - val_loss: 0.7003 - val_accuracy: 0.4800\n",
            "\n",
            "Epoch 00009: val_accuracy did not improve from 0.52000\n",
            "Epoch 10/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.6917 - accuracy: 0.5261 - val_loss: 0.6926 - val_accuracy: 0.5200\n",
            "\n",
            "Epoch 00010: val_accuracy did not improve from 0.52000\n",
            "Epoch 11/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.6965 - accuracy: 0.4887 - val_loss: 0.6926 - val_accuracy: 0.5200\n",
            "\n",
            "Epoch 00011: val_accuracy did not improve from 0.52000\n",
            "Epoch 12/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.6972 - accuracy: 0.5260 - val_loss: 0.7067 - val_accuracy: 0.4800\n",
            "\n",
            "Epoch 00012: val_accuracy did not improve from 0.52000\n",
            "Epoch 13/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.6997 - accuracy: 0.4853 - val_loss: 0.6950 - val_accuracy: 0.4800\n",
            "\n",
            "Epoch 00013: val_accuracy did not improve from 0.52000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "98_oXi-lcgux",
        "outputId": "c0df583f-93e5-4ca6-8ec8-099d823e11d1"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(H.history.keys())\n",
        "# summarize history for accuracy\n",
        "plt.plot(H.history['accuracy'])\n",
        "plt.plot(H.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(H.history['loss'])\n",
        "plt.plot(H.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cu5SSxXyiOs3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2da5cd84-c646-4d7e-d31d-e0d58414ab04"
      },
      "source": [
        "predictions = model.predict(testX, batch_size=32)"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Model was constructed with shape (None, 150528) for input KerasTensor(type_spec=TensorSpec(shape=(None, 150528), dtype=tf.float32, name='flatten_6_input'), name='flatten_6_input', description=\"created by layer 'flatten_6_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FG6VqJuUYOgi",
        "outputId": "adc09c2f-5941-4822-fe30-4dc19e7574ab"
      },
      "source": [
        "print(predictions.argmax(axis=1))"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6puOgflYR_e"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
      ],
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPAZkmBJVGCD",
        "outputId": "538f477d-ffa5-4d43-adcc-d1c419adaaa0"
      },
      "source": [
        "# evaluate the network\n",
        "print(\"[INFO] evaluating network...\")\n",
        "predictions = model.predict(testX, batch_size=32)\n",
        "print(classification_report(testY,predictions.argmax(axis=1),target_names = encoder.classes_))"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] evaluating network...\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Covid       0.00      0.00      0.00       120\n",
            "      Normal       0.52      1.00      0.68       130\n",
            "\n",
            "    accuracy                           0.52       250\n",
            "   macro avg       0.26      0.50      0.34       250\n",
            "weighted avg       0.27      0.52      0.36       250\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJ-r6keDY5jt",
        "outputId": "e220fc22-182c-489b-dd19-e54f0efc854b"
      },
      "source": [
        "cf_matrix = confusion_matrix(testY,predictions.argmax(axis=1))\n",
        "print(cf_matrix)"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  0 120]\n",
            " [  0 130]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLp1ooZeY90p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "01f66b95-d74f-437a-af61-2bf5dae5ef9d"
      },
      "source": [
        "\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cf_matrix, display_labels=encoder.classes_)\n",
        "disp.plot(cmap=plt.cm.Blues)"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f90d5316290>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtE5PB_Vcqiu"
      },
      "source": [
        "\n",
        "import io\n",
        "\n",
        "path='/content/drive/MyDrive/Normalized/Normal/ IM00001.tif'\n",
        "save_path='/content/drive/MyDrive/Testing/Normal/covid1.tif'\n",
        "with open(path, 'rb') as f:\n",
        "    tif = Image.open(io.BytesIO(f.read()))\n",
        "array=np.array(tif)\n",
        "max_val=np.amax(array)\n",
        "normalized=(array/max_val)\n",
        "im = Image.fromarray(normalized)\n",
        "im.save(save_path,'TIFF')"
      ],
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gwPAgH02dIS6",
        "outputId": "18496750-9698-4c2b-cdd0-d918f4fac0b6"
      },
      "source": [
        "\n",
        "width = 224\n",
        "height = 224\n",
        "dsize = (width, height)\n",
        "image_testing = cv2.imread('/content/drive/MyDrive/Testing/Normal/covid1.tif', -1)\n",
        "image_testing = cv2.cvtColor(image_testing, cv2.COLOR_BGR2RGB)\n",
        "image_testing = cv2.resize(image_testing,dsize)\n",
        "image_testing = np.array(image_testing)\n",
        "image_testing.shape"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "wQijrpZ_dPjn",
        "outputId": "bd3267a8-0b8d-40f3-b650-376429972a1c"
      },
      "source": [
        "plt.imshow(image_testing)"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f90d4ef7550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IRTDbxzsdST7",
        "outputId": "00e180c0-ff45-4607-b6fd-2e67ddcc6e40"
      },
      "source": [
        "image_testing = np.expand_dims(image_testing, axis=0)\n",
        "print(image_testing.shape)"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 224, 224, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_VQoQn24dUtc",
        "outputId": "4a24f9d4-479b-453e-f187-8c089cee717f"
      },
      "source": [
        "\n",
        "output = model.predict(image_testing, 1)\n",
        "print(output)\n",
        "print(encoder.classes_[output.argmax(axis=1)])"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.02255073 0.973738   0.00371126]]\n",
            "['Normal']\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}