CLAHEOnly_Nadam_0.01.ipynb

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    "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"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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      },
      "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/CLAHEOnly_Nadam_0.01.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": "3d87d225-2576-49e8-ff73-82f7196ec1f0"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "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": "d9646c84-3614-4b6e-9a50-d31245952382"
      },
      "source": [
        "cd /content/drive/MyDrive/ "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z7VB1d4wVdov",
        "outputId": "94933a91-d08a-4851-b90c-6c8e2c48f1cf"
      },
      "source": [
        "ls"
      ],
      "execution_count": 3,
      "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;34mCLAHEOnly\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",
            " \u001b[01;34mTesting\u001b[0m/\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, Nadam, Adamax\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": 4,
      "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": "5e8b65cc-063c-448c-b632-176d56e2dd91"
      },
      "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(\"CLAHEOnly\")\n",
        "data = []\n",
        "labels = []"
      ],
      "execution_count": 5,
      "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/CLAHEOnly/Covid/IM00001.tif', -1)\n",
        "im_rgb = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "me1s-uFI7kJi",
        "outputId": "917dd29f-4334-48d2-b4c1-61cce639fa90"
      },
      "source": [
        "\n",
        "plt.imshow(im_rgb)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f42d7861fd0>"
            ]
          },
          "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": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rzbolh88VGB2",
        "outputId": "80167154-f247-4daf-8bd6-5b18d6565567"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['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', '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', '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', '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', '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', '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', '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', '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', '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', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', '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', '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', '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']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "aGbsj1UcytpV",
        "outputId": "00700e80-a55f-4a4d-db82-9f62dc0a54e9"
      },
      "source": [
        "\n",
        "print(labels[500])\n",
        "plt.imshow(data[10])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Covid\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f42d5b52d10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "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": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FPCyIqcVGB7",
        "outputId": "5bd59212-71ce-48a0-838e-46b804e953a9"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 12,
      "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 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 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 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LynLhwt_pvNc",
        "outputId": "e77239c9-8695-48e9-e143-f51592b15217"
      },
      "source": [
        "data = np.array(data)\n",
        "data.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ktZ5hNYwVGB9",
        "outputId": "c0ddb6d7-12b6-4e22-c0fb-b01026091e10"
      },
      "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": 14,
      "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": "bab675d9-8407-468d-9424-7a935cfc93eb"
      },
      "source": [
        "print(trainX[0])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  ...\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]]\n",
            "\n",
            " [[0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  ...\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]]\n",
            "\n",
            " [[0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  ...\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  ...\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]]\n",
            "\n",
            " [[0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  ...\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]]\n",
            "\n",
            " [[0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  ...\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]\n",
            "  [0.00649311 0.00649311 0.00649311]]]\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": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTvu4IV0i7Fj",
        "outputId": "c94f67fa-eba1-4e53-c525-438537b3bb01"
      },
      "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": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_5\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_6 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "rescaling_5 (Rescaling)         (None, 224, 224, 3)  0           input_6[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "normalization_5 (Normalization) (None, 224, 224, 3)  7           rescaling_5[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "stem_conv_pad (ZeroPadding2D)   (None, 225, 225, 3)  0           normalization_5[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_5 (Glo (None, 1280)         0           top_activation[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "dropout_5 (Dropout)             (None, 1280)         0           global_average_pooling2d_5[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 1280)         5120        dropout_5[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_10 (Dense)                (None, 512)          655872      batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "dense_11 (Dense)                (None, 2)            1026        dense_10[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": 74,
      "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": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0TdWxuFPkT3",
        "outputId": "6cbec239-a1ef-432b-c205-c1421df521bc"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_4 (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": "b269c5a6-9f85-48ba-8a39-ab8b6bff8a7a"
      },
      "source": [
        "# train the model using the Adam optimizer\n",
        "print(\"[INFO] training network...\")\n",
        "opt = Nadam(learning_rate=0.01, 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": 77,
      "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_4_input'), name='flatten_4_input', description=\"created by layer 'flatten_4_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_4_input'), name='flatten_4_input', description=\"created by layer 'flatten_4_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "24/24 [==============================] - ETA: 0s - loss: 18.4616 - accuracy: 0.3023WARNING:tensorflow:Model was constructed with shape (None, 150528) for input KerasTensor(type_spec=TensorSpec(shape=(None, 150528), dtype=tf.float32, name='flatten_4_input'), name='flatten_4_input', description=\"created by layer 'flatten_4_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "24/24 [==============================] - 2s 36ms/step - loss: 18.0355 - accuracy: 0.3079 - val_loss: 0.9730 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.46000, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 2/50\n",
            "24/24 [==============================] - 1s 27ms/step - loss: 0.9411 - accuracy: 0.5280 - val_loss: 0.8789 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00002: val_accuracy did not improve from 0.46000\n",
            "Epoch 3/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.8595 - accuracy: 0.5190 - val_loss: 0.8230 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00003: val_accuracy did not improve from 0.46000\n",
            "Epoch 4/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.8111 - accuracy: 0.5026 - val_loss: 0.7910 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00004: val_accuracy did not improve from 0.46000\n",
            "Epoch 5/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.7827 - accuracy: 0.4889 - val_loss: 0.7710 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00005: val_accuracy did not improve from 0.46000\n",
            "Epoch 6/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.7623 - accuracy: 0.4979 - val_loss: 0.7540 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00006: val_accuracy did not improve from 0.46000\n",
            "Epoch 7/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.7486 - accuracy: 0.4679 - val_loss: 0.7437 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00007: val_accuracy did not improve from 0.46000\n",
            "Epoch 8/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.7356 - accuracy: 0.5344 - val_loss: 0.7325 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00008: val_accuracy did not improve from 0.46000\n",
            "Epoch 9/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.7305 - accuracy: 0.4979 - val_loss: 0.7292 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00009: val_accuracy did not improve from 0.46000\n",
            "Epoch 10/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.7255 - accuracy: 0.4928 - val_loss: 0.7265 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00010: val_accuracy did not improve from 0.46000\n",
            "Epoch 11/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.7217 - accuracy: 0.5011 - val_loss: 0.7213 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00011: val_accuracy did not improve from 0.46000\n",
            "Epoch 12/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.7174 - accuracy: 0.5084 - val_loss: 0.7179 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00012: val_accuracy did not improve from 0.46000\n",
            "Epoch 13/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.7152 - accuracy: 0.4955 - val_loss: 0.7175 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00013: val_accuracy did not improve from 0.46000\n",
            "Epoch 14/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.7129 - accuracy: 0.5110 - val_loss: 0.7116 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00014: val_accuracy did not improve from 0.46000\n",
            "Epoch 15/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.7109 - accuracy: 0.4763 - val_loss: 0.7131 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00015: val_accuracy did not improve from 0.46000\n",
            "Epoch 16/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.7101 - accuracy: 0.4861 - val_loss: 0.7172 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00016: val_accuracy did not improve from 0.46000\n",
            "Epoch 17/50\n",
            "24/24 [==============================] - 1s 29ms/step - loss: 0.7092 - accuracy: 0.5082 - val_loss: 0.7097 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00017: val_accuracy did not improve from 0.46000\n",
            "Epoch 18/50\n",
            "24/24 [==============================] - 1s 28ms/step - loss: 0.7053 - accuracy: 0.5268 - val_loss: 0.7058 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00018: val_accuracy did not improve from 0.46000\n",
            "Epoch 19/50\n",
            "24/24 [==============================] - 1s 28ms/step - loss: 0.7050 - accuracy: 0.4959 - val_loss: 0.7065 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00019: val_accuracy did not improve from 0.46000\n",
            "Epoch 20/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.7047 - accuracy: 0.4900 - val_loss: 0.7069 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00020: val_accuracy did not improve from 0.46000\n",
            "Epoch 21/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.7048 - accuracy: 0.4722 - val_loss: 0.7080 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00021: val_accuracy did not improve from 0.46000\n",
            "Epoch 22/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.7055 - accuracy: 0.4583 - val_loss: 0.7091 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00022: val_accuracy did not improve from 0.46000\n",
            "Epoch 23/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.7007 - accuracy: 0.5270 - val_loss: 0.7024 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00023: val_accuracy did not improve from 0.46000\n",
            "Epoch 24/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.7023 - accuracy: 0.4660 - val_loss: 0.7062 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00024: val_accuracy did not improve from 0.46000\n",
            "Epoch 25/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.7000 - accuracy: 0.5144 - val_loss: 0.7048 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00025: val_accuracy did not improve from 0.46000\n",
            "Epoch 26/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.7008 - accuracy: 0.5048 - val_loss: 0.7017 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00026: val_accuracy did not improve from 0.46000\n",
            "Epoch 27/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.7004 - accuracy: 0.4853 - val_loss: 0.7020 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00027: val_accuracy did not improve from 0.46000\n",
            "Epoch 28/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.7014 - accuracy: 0.4664 - val_loss: 0.7037 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00028: val_accuracy did not improve from 0.46000\n",
            "Epoch 29/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.6966 - accuracy: 0.5501 - val_loss: 0.6983 - val_accuracy: 0.5400\n",
            "\n",
            "Epoch 00029: val_accuracy improved from 0.46000 to 0.54000, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 30/50\n",
            "24/24 [==============================] - 1s 27ms/step - loss: 0.6989 - accuracy: 0.5063 - val_loss: 0.7065 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00030: val_accuracy did not improve from 0.54000\n",
            "Epoch 31/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.6977 - accuracy: 0.5298 - val_loss: 0.6996 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00031: val_accuracy did not improve from 0.54000\n",
            "Epoch 32/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.6985 - accuracy: 0.4939 - val_loss: 0.7091 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00032: val_accuracy did not improve from 0.54000\n",
            "Epoch 33/50\n",
            "24/24 [==============================] - 1s 28ms/step - loss: 0.6970 - accuracy: 0.5285 - val_loss: 0.6973 - val_accuracy: 0.5400\n",
            "\n",
            "Epoch 00033: val_accuracy did not improve from 0.54000\n",
            "Epoch 34/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.6984 - accuracy: 0.5043 - val_loss: 0.7022 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00034: val_accuracy did not improve from 0.54000\n",
            "Epoch 35/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.6979 - accuracy: 0.5148 - val_loss: 0.7020 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00035: val_accuracy did not improve from 0.54000\n",
            "Epoch 36/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.6972 - accuracy: 0.5228 - val_loss: 0.7011 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00036: val_accuracy did not improve from 0.54000\n",
            "Epoch 37/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.6948 - accuracy: 0.5457 - val_loss: 0.6942 - val_accuracy: 0.5400\n",
            "\n",
            "Epoch 00037: val_accuracy did not improve from 0.54000\n",
            "Epoch 38/50\n",
            "24/24 [==============================] - 1s 24ms/step - loss: 0.6998 - accuracy: 0.4789 - val_loss: 0.6962 - val_accuracy: 0.5400\n",
            "\n",
            "Epoch 00038: val_accuracy did not improve from 0.54000\n",
            "Epoch 39/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.6980 - accuracy: 0.5189 - val_loss: 0.7002 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00039: val_accuracy did not improve from 0.54000\n",
            "Epoch 40/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.6958 - accuracy: 0.5372 - val_loss: 0.6937 - val_accuracy: 0.5400\n",
            "\n",
            "Epoch 00040: val_accuracy did not improve from 0.54000\n",
            "Epoch 41/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.6990 - accuracy: 0.4927 - val_loss: 0.7014 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00041: val_accuracy did not improve from 0.54000\n",
            "Epoch 42/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.6946 - accuracy: 0.5398 - val_loss: 0.6964 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00042: val_accuracy did not improve from 0.54000\n",
            "Epoch 43/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.6975 - accuracy: 0.4776 - val_loss: 0.7040 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00043: val_accuracy did not improve from 0.54000\n",
            "Epoch 44/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.6975 - accuracy: 0.5108 - val_loss: 0.6966 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00044: val_accuracy did not improve from 0.54000\n",
            "Epoch 45/50\n",
            "24/24 [==============================] - 1s 29ms/step - loss: 0.6970 - accuracy: 0.4892 - val_loss: 0.7015 - val_accuracy: 0.4600\n",
            "\n",
            "Epoch 00045: val_accuracy did not improve from 0.54000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "98_oXi-lcgux",
        "outputId": "9d66f84d-afd6-4033-c9a0-09aa442f04a7"
      },
      "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": 78,
      "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": "0c5175fa-b25f-4f63-ba79-6bf6b3d5b076"
      },
      "source": [
        "predictions = model.predict(testX, batch_size=32)"
      ],
      "execution_count": 79,
      "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_4_input'), name='flatten_4_input', description=\"created by layer 'flatten_4_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": "2dea9d98-c363-4959-9891-a1481717ac46"
      },
      "source": [
        "print(predictions.argmax(axis=1))"
      ],
      "execution_count": 80,
      "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]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6puOgflYR_e"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPAZkmBJVGCD",
        "outputId": "70c0433f-55f9-42c3-fb0c-f43c0c087d51"
      },
      "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": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] evaluating network...\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Covid       0.54      1.00      0.70       135\n",
            "      Normal       0.00      0.00      0.00       115\n",
            "\n",
            "    accuracy                           0.54       250\n",
            "   macro avg       0.27      0.50      0.35       250\n",
            "weighted avg       0.29      0.54      0.38       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": "8a364c5a-7fc3-46ac-8e8d-52caacdea2a1"
      },
      "source": [
        "cf_matrix = confusion_matrix(testY,predictions.argmax(axis=1))\n",
        "print(cf_matrix)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[135   0]\n",
            " [115   0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLp1ooZeY90p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "35093b6b-c14e-4521-c313-b47519f49d16"
      },
      "source": [
        "\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cf_matrix, display_labels=encoder.classes_)\n",
        "disp.plot(cmap=plt.cm.Blues)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f424840db50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        },
        {
          "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"
          }
        }
      ]
    }
  ]
}