CLAHEOnly_adam_1e-2.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",
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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_adam_1e-2.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": "f543dd7c-70f5-471f-aa7b-935b55f78873"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H215ikbNVVZS",
        "outputId": "97fcc0e3-b931-4a6d-d02b-14b0001eb162"
      },
      "source": [
        "cd /content/drive/MyDrive/ "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z7VB1d4wVdov",
        "outputId": "293108bc-4d0d-4e4b-a091-e23473cad9e6"
      },
      "source": [
        "ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'1101174267 m rifqi fadhila mixer.pdf'\n",
            "'1101174267_Muhammad Rifqi F_2_TT4107.pdf'\n",
            "'1101174267_MuhammadRifqiFadhila_1&3_TT4107.pdf'\n",
            " 1101174267_MuhammadRifqiFadhila_3.3_TT4107.pdf\n",
            "'1101174267_muhammad rifqi fadhila_Filter2.jpg'\n",
            "'1101174267 muhammad rifqi fadhila OSILATOR.pdf'\n",
            "'1101174267 muhammad rifqi fadhila penguat.pdf'\n",
            "'1101174267_Muhammad Rifqi Fadhila_Proposal Tugas Akhir dan lembar pemeriksaan.pdf'\n",
            " best_weight_scene_EfficientNetB0.h5\n",
            " \u001b[0m\u001b[01;34mCLAHE\u001b[0m/\n",
            " \u001b[01;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\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": 22,
      "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": "997777bd-6cb2-4d2f-9e70-0522e524d543"
      },
      "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": 23,
      "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": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "me1s-uFI7kJi",
        "outputId": "ee65e6ec-f105-4484-ded0-aa7c9f110acc"
      },
      "source": [
        "\n",
        "plt.imshow(im_rgb)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f303d83bc10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1QQBNriY1JrE"
      },
      "source": [
        "\n",
        "width = 224\n",
        "height = 224\n",
        "dsize = (width, height)\n",
        "# loop over our input images\n",
        "for imagePath in imagePaths:\n",
        "  # load the input image from disk, resize it to 64x64 pixels, scale\n",
        "\t# the pixel intensities to the range [0, 1], and then update our\n",
        "\t# images list\n",
        "    image = cv2.imread(imagePath,-1)\n",
        "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "  #image = image.convert('L')\n",
        "    image = cv2.resize(image,dsize)\n",
        "    image = np.array(image)\n",
        "    data.append(image)\n",
        "\n",
        "  # extract the class label from the file path and update the\n",
        "\t# labels list\n",
        "    label = imagePath.split(os.path.sep)[-2]\n",
        "    labels.append(label)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rzbolh88VGB2",
        "outputId": "868cacd2-71db-47a7-b6e3-7c85f46afadc"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 26,
      "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', 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'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', '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": "b1634a49-0412-4bab-d388-c242ee5f586f"
      },
      "source": [
        "\n",
        "print(labels[500])\n",
        "plt.imshow(data[10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normal\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7bba4d2950>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "twd1IeuKVGB5"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# encode class values as integers\n",
        "encoder = LabelEncoder()\n",
        "encoder.fit(labels)\n",
        "labels = encoder.transform(labels)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FPCyIqcVGB7",
        "outputId": "e5edff50-27b4-4852-8db1-d8f8fad7ec65"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 28,
      "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": "fa3b4d35-598d-41c9-e1ce-0a5360398a20"
      },
      "source": [
        "data = np.array(data)\n",
        "data.shape"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ktZ5hNYwVGB9",
        "outputId": "3bf1318f-b50f-4890-afc1-f4b128492a85"
      },
      "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": 30,
      "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": "3397b7f5-21e5-4ded-b1d1-8ba670e4b89a"
      },
      "source": [
        "print(trainX[0])"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  ...\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]]\n",
            "\n",
            " [[0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  ...\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]]\n",
            "\n",
            " [[0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  ...\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  ...\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]]\n",
            "\n",
            " [[0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  ...\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]]\n",
            "\n",
            " [[0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  ...\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]\n",
            "  [0.00399435 0.00399435 0.00399435]]]\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": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTvu4IV0i7Fj",
        "outputId": "6a00f18a-d474-48e7-f94c-2b2772745614"
      },
      "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": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "rescaling_1 (Rescaling)         (None, 224, 224, 3)  0           input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "normalization_1 (Normalization) (None, 224, 224, 3)  7           rescaling_1[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "stem_conv_pad (ZeroPadding2D)   (None, 225, 225, 3)  0           normalization_1[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_1 (Glo (None, 1280)         0           top_activation[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 1280)         0           global_average_pooling2d_1[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 1280)         5120        dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 512)          655872      batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 2)            1026        dense_2[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": 34,
      "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": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0TdWxuFPkT3",
        "outputId": "6be5857e-9314-4e90-9f02-ce44b7c0ef8e"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_1 (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": "4ee5d2ca-f9fa-472b-c659-a89acb91970a"
      },
      "source": [
        "# train the model using the Adam optimizer\n",
        "print(\"[INFO] training network...\")\n",
        "opt = Adam(learning_rate=1e-2, 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": 37,
      "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_1_input'), name='flatten_1_input', description=\"created by layer 'flatten_1_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_1_input'), name='flatten_1_input', description=\"created by layer 'flatten_1_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 97.0222 - accuracy: 0.5563 WARNING:tensorflow:Model was constructed with shape (None, 150528) for input KerasTensor(type_spec=TensorSpec(shape=(None, 150528), dtype=tf.float32, name='flatten_1_input'), name='flatten_1_input', description=\"created by layer 'flatten_1_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "24/24 [==============================] - 1s 29ms/step - loss: 89.6175 - accuracy: 0.5618 - val_loss: 13.0296 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.50400, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 2/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 6.3539 - accuracy: 0.6512 - val_loss: 3.1567 - val_accuracy: 0.8400\n",
            "\n",
            "Epoch 00002: val_accuracy improved from 0.50400 to 0.84000, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 3/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 2.3982 - accuracy: 0.8524 - val_loss: 1.1337 - val_accuracy: 0.9520\n",
            "\n",
            "Epoch 00003: val_accuracy improved from 0.84000 to 0.95200, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 4/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.4108 - accuracy: 0.9540 - val_loss: 0.2460 - val_accuracy: 0.9400\n",
            "\n",
            "Epoch 00004: val_accuracy did not improve from 0.95200\n",
            "Epoch 5/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.1645 - accuracy: 0.9546 - val_loss: 0.0786 - val_accuracy: 0.9800\n",
            "\n",
            "Epoch 00005: val_accuracy improved from 0.95200 to 0.98000, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 6/50\n",
            "24/24 [==============================] - 0s 20ms/step - loss: 0.0323 - accuracy: 0.9871 - val_loss: 0.0140 - val_accuracy: 0.9960\n",
            "\n",
            "Epoch 00006: val_accuracy improved from 0.98000 to 0.99600, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 7/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.0070 - accuracy: 0.9954 - val_loss: 0.0276 - val_accuracy: 0.9840\n",
            "\n",
            "Epoch 00007: val_accuracy did not improve from 0.99600\n",
            "Epoch 8/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00008: val_accuracy improved from 0.99600 to 1.00000, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 9/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 4.7495e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00009: val_accuracy did not improve from 1.00000\n",
            "Epoch 10/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 3.3954e-04 - accuracy: 1.0000 - val_loss: 0.0051 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00010: val_accuracy did not improve from 1.00000\n",
            "Epoch 11/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 3.2850e-04 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00011: val_accuracy did not improve from 1.00000\n",
            "Epoch 12/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 3.0659e-04 - accuracy: 1.0000 - val_loss: 0.0039 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00012: val_accuracy did not improve from 1.00000\n",
            "Epoch 13/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 2.0500e-04 - accuracy: 1.0000 - val_loss: 0.0036 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00013: val_accuracy did not improve from 1.00000\n",
            "Epoch 14/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 2.7723e-04 - accuracy: 1.0000 - val_loss: 0.0041 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00014: val_accuracy did not improve from 1.00000\n",
            "Epoch 15/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 2.6718e-04 - accuracy: 1.0000 - val_loss: 0.0038 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00015: val_accuracy did not improve from 1.00000\n",
            "Epoch 16/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 2.3443e-04 - accuracy: 1.0000 - val_loss: 0.0033 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00016: val_accuracy did not improve from 1.00000\n",
            "Epoch 17/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 1.7669e-04 - accuracy: 1.0000 - val_loss: 0.0030 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00017: val_accuracy did not improve from 1.00000\n",
            "Epoch 18/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 1.9143e-04 - accuracy: 1.0000 - val_loss: 0.0029 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00018: val_accuracy did not improve from 1.00000\n",
            "Epoch 19/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 1.7812e-04 - accuracy: 1.0000 - val_loss: 0.0029 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00019: val_accuracy did not improve from 1.00000\n",
            "Epoch 20/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 1.5725e-04 - accuracy: 1.0000 - val_loss: 0.0027 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00020: val_accuracy did not improve from 1.00000\n",
            "Epoch 21/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 1.9018e-04 - accuracy: 1.0000 - val_loss: 0.0028 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00021: val_accuracy did not improve from 1.00000\n",
            "Epoch 22/50\n",
            "24/24 [==============================] - 0s 20ms/step - loss: 1.8535e-04 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00022: val_accuracy did not improve from 1.00000\n",
            "Epoch 23/50\n",
            "24/24 [==============================] - 1s 22ms/step - loss: 1.4431e-04 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00023: val_accuracy did not improve from 1.00000\n",
            "Epoch 24/50\n",
            "24/24 [==============================] - 0s 21ms/step - loss: 1.4280e-04 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00024: val_accuracy did not improve from 1.00000\n",
            "Epoch 25/50\n",
            "24/24 [==============================] - 1s 21ms/step - loss: 2.1514e-04 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00025: val_accuracy did not improve from 1.00000\n",
            "Epoch 26/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 1.2993e-04 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00026: val_accuracy did not improve from 1.00000\n",
            "Epoch 27/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 1.8064e-04 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00027: val_accuracy did not improve from 1.00000\n",
            "Epoch 28/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 1.3134e-04 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00028: val_accuracy did not improve from 1.00000\n",
            "Epoch 29/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 1.0280e-04 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00029: val_accuracy did not improve from 1.00000\n",
            "Epoch 30/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 1.2151e-04 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00030: val_accuracy did not improve from 1.00000\n",
            "Epoch 31/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 1.3960e-04 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00031: val_accuracy did not improve from 1.00000\n",
            "Epoch 32/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 1.0286e-04 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00032: val_accuracy did not improve from 1.00000\n",
            "Epoch 33/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 1.1026e-04 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00033: val_accuracy did not improve from 1.00000\n",
            "Epoch 34/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 8.9308e-05 - accuracy: 1.0000 - val_loss: 0.0017 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00034: val_accuracy did not improve from 1.00000\n",
            "Epoch 35/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 8.8596e-05 - accuracy: 1.0000 - val_loss: 0.0017 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00035: val_accuracy did not improve from 1.00000\n",
            "Epoch 36/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 8.8233e-05 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00036: val_accuracy did not improve from 1.00000\n",
            "Epoch 37/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 8.1623e-05 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00037: val_accuracy did not improve from 1.00000\n",
            "Epoch 38/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 8.9958e-05 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00038: val_accuracy did not improve from 1.00000\n",
            "Epoch 39/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 8.7333e-05 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00039: val_accuracy did not improve from 1.00000\n",
            "Epoch 40/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 9.5125e-05 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00040: val_accuracy did not improve from 1.00000\n",
            "Epoch 41/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 8.7388e-05 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00041: val_accuracy did not improve from 1.00000\n",
            "Epoch 42/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 9.7526e-05 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00042: val_accuracy did not improve from 1.00000\n",
            "Epoch 43/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 7.2748e-05 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00043: val_accuracy did not improve from 1.00000\n",
            "Epoch 44/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 7.9121e-05 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00044: val_accuracy did not improve from 1.00000\n",
            "Epoch 45/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 6.5825e-05 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00045: val_accuracy did not improve from 1.00000\n",
            "Epoch 46/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 6.7237e-05 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00046: val_accuracy did not improve from 1.00000\n",
            "Epoch 47/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 7.0728e-05 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00047: val_accuracy did not improve from 1.00000\n",
            "Epoch 48/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 6.4433e-05 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00048: val_accuracy did not improve from 1.00000\n",
            "Epoch 49/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 6.5851e-05 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00049: val_accuracy did not improve from 1.00000\n",
            "Epoch 50/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 6.1477e-05 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00050: val_accuracy did not improve from 1.00000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "98_oXi-lcgux",
        "outputId": "c4ca8825-ecd4-457c-8896-d5beb810822b"
      },
      "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": 38,
      "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": "5d5dcacd-c44a-4bb0-bd86-0004f1380fb2"
      },
      "source": [
        "predictions = model.predict(testX, batch_size=32)"
      ],
      "execution_count": 39,
      "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_1_input'), name='flatten_1_input', description=\"created by layer 'flatten_1_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": "409d62a7-fb87-4e81-9d6e-0316fdab4b5e"
      },
      "source": [
        "print(predictions.argmax(axis=1))"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 1 1 1 0 0 0 1 1 1 0 0 1 1 1 1 1 0 0 0 0 0 1 0 0 1 0 1 1 0 1 1 0 0 0 0 1\n",
            " 0 0 0 1 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 1 1 1 0 1 0 1 1 1 1 0 0 1 1 0 0 0 0\n",
            " 0 1 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 1 1 1 0 1 0 1 0 1 0 0 0 1 1 1\n",
            " 0 0 1 1 0 0 0 0 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 0 1 0 1 0 1 1 1 0 0 1 1 0 1\n",
            " 0 1 0 0 1 0 0 1 1 1 0 0 1 1 0 1 1 1 0 1 1 0 0 0 0 0 1 1 1 0 1 1 1 0 1 0 1\n",
            " 1 1 0 0 1 0 0 1 1 1 0 1 0 0 1 0 1 1 0 1 1 1 1 1 1 0 0 1 0 1 1 1 0 0 1 1 0\n",
            " 0 1 0 1 0 1 1 0 0 0 1 1 1 1 0 0 0 1 1 0 0 1 0 0 1 0 1 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6puOgflYR_e"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPAZkmBJVGCD",
        "outputId": "90a70757-d745-4dc7-f3c8-0deb23d58c82"
      },
      "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": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] evaluating network...\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Covid       1.00      1.00      1.00       124\n",
            "      Normal       1.00      1.00      1.00       126\n",
            "\n",
            "    accuracy                           1.00       250\n",
            "   macro avg       1.00      1.00      1.00       250\n",
            "weighted avg       1.00      1.00      1.00       250\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJ-r6keDY5jt",
        "outputId": "69b732af-8715-4f23-8b93-e6a9b8ddb3be"
      },
      "source": [
        "cf_matrix = confusion_matrix(testY,predictions.argmax(axis=1))\n",
        "print(cf_matrix)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[124   0]\n",
            " [  0 126]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLp1ooZeY90p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "ac8d157c-1127-485c-8e10-7a54ab89b82f"
      },
      "source": [
        "\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cf_matrix, display_labels=encoder.classes_)\n",
        "disp.plot(cmap=plt.cm.Blues)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f3624ed7e50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PaGZWqYw91t6SppYsh9VpZiAwB7hU0uOSLpLUFegbEW+nx7wD9K00ziw3CJwPLCaZBXAK8CFwA7B5pSc1M6tExmtXcyNiaCP7OwCbAT+LiMmSzqbO1/6ICElRaZxZhgKGRcRRwKfpCecDnSo9oZlZJQR0kMouGbwJvBkRk9P160kS7WxJ/QDSn+9WGmuWxPq5pPYkc1eR1IekB2tm1qLyePx1RLwDvCHpK+mmHYBngVuA0em20cDNlcaZZSjgHOAmYHVJfyKpdvW7Sk9oZlYJKddbWn8GXCmpE/AycDBJR/NaSYcCrwH7VNp4lloBV0qaRpLVBXw3ImZWekIzs0rllVcj4gmgvnHYHfJoP0uh67WBT4BbS7dFxOt5BGBmllUbKbdaVpahgAksfajgSiRTFZ4HvlbFuMzMvkDQZgpZl5NlKODrpetp1asjGzjczKw6mnZnVavK9DDBUhExXdKwagRjZtYYFeSpV1nGWH9ZstqOZL7XW1WLyMysHsvb469XLXldQzLmekN1wjEza9hykVjTGwNWjYhjWygeM7MGFb7QtaQOEVEjaeuWDMjMrD7J469bO4psGuuxPkYynvqEpFuA64CPa3dGxI1Vjs3M7AsK/zDBEisB75FUt6qdzxqAE6uZtZjl5eLV6umMgGdYmlBrVVxOy8ysUgXpsDaaWNsDq0C9E8ecWM2shYl2y8E81rcj4pQWi8TMrBFi+eixFuQjmNkKQdChIIOsjSXWXMpnmZnlYbnosUbEvJYMxMysnOVpupWZWZtQkLzqxGpmxSCyPaSvLXBiNbNikIcCzMxyldx55cRqZparYqRVJ1YzK5CCdFidWM2sKFT8eqxmZm2JZwWYmVWBL16ZmeVJxXk0S1F61ma2gqsdCii3ZG5Pai/pcUm3pesDJU2W9KKkayR1qjRWJ1YzKwxJZZcm+Dkws2T9/4AzI+LLwHzg0ErjdGI1s8JQhiVTO1J/YFfgonRdJI+fuj49ZBzw3Urj9BirmRWCgPbZeqS9JU0tWR8bEWPrHHMWcDywarq+GvB+RNSk628Ca1UaqxOrmRVGxm/6cyNiaMNtaDfg3YiYJmlETqF9gROrmRWEUD43tW4N7C5pF5KnUHcDzgZ6SOqQ9lr7A7MqPYHHWM2sMKTySzkRcWJE9I+IdYEfAPdHxCjgX8Be6WGjgZsrjdOJ1cwKIZlupbJLM/wa+KWkF0nGXC+utCEPBZhZMWTskTZFREwEJqavXwa2yKNdJ1YzKwzf0mpmlqOk0HVrR5GNE6uZFUZOswKqzonVzAqjICMBTqzLo3sffpYTz7ieRYsXc+AewznmoB1bO6Tl0rn/M4qdttmIufM/ZPgP/rzM/pHbfp3fHrEbiyOoqVnMb/56PY8++XKzztmj28pc8udDWLtfL15/ex4Hn3gxCz5cyN47D+XnP/w2kvjok0/51anX8MwLFU/DbLOK0mOt2nQrSSHpjJL1YyWdXK3zNRDDREkN3oGxPFq0aDHHnXYt1519JI9e+ztuuHsaz738dmuHtVwaf9uj7HX0eQ3unzTlebbZ/3/ZdtSp/OwP/+Ts3+2fue2tN1uf88YcsMz2Y0Z/m0lTnmfo909h0pTnOWZ08o/ma2+9x66Hn8XW+/2Z0y++kzN/s1/TP1AbVzvGWm5pC6o5j/W/wJ6SelfyZknuTVdg2oxX+dKA3qzbvzedOnZgz29vxu3/fqq1w1ouPfz4S8z/4JMG93+88LMlr1fu0pmIpft+dsAO3DfuOB686kROOGyXzOcc+c3BjL9tMgDjb5vMLiMGA/DYU6+w4MOFAEx5+hXWXL1HUz5KMUi0y7C0BdVMXjXAWOAY4LelOyStC1wC9AbmAAdHxOuSLgM+BTYFHpLUC1iYrq8OHAL8ENgKmBwRB6XtXQBsDnQBro+IMVX8XG3a23MWsFbfnkvW1+zbk2nPvNp6Aa3gdh0xmJOO2p0+PVdl32MuBGC7YRvypbVXZ4fRpyOJ8WcczvBN1+Phx18q297qvVZl9nsfADD7vQ9Yvdeqyxxz4B7DuffhZ/P9IG1E20ib5VW7V3ge8JSk0+psPxcYFxHjJB0CnMPSEl39geERsShNtD1JEunuwC0k9/n+CJgiaZOIeAL4bUTMk9QeuE/S4IhosJsm6TDgMIABa6+d12c1W8aEiU8xYeJTDN90PX5zxK5876i/sd2WX2X7YRsy6coTAOjapTNfGrA6Dz/+EvdceiydO3Wga5fO9Oy28pJjTj73Zu5/dOYy7Zf2ggG2GbI+B+y+FSN/fGbVP1tLS4YCipFaq5pYI+IDSZcDR5P0PGttBeyZvr4CKE2810XEopL1WyMiJD0NzI6IpwEkzQDWBZ4A9kmTZQegHzAIaDCxpiXExgIMGTI0GjquiPr16c6s2fOXrL81ez79+nRvxYgMkmGDddfqTa/uXZHgzMvu5rKbHlrmuG8f/BcgGWPd/zvDOOr3//zC/nfnfUjf1box+70P6LtaN+bM/3DJvq99eU3O+d3+7P3zC5i/4OPqfqBWUoy02jK1As4iqcTdNePxdf+L+G/6c3HJ69r1DpIGAscCO0TEYGACScWaFdJmg9bhpdfn8NqsuXz2eQ033jOdkdsObu2wVkgD+y+9vDD4K/3p1LED8xZ8zP2PzGTU7lvRtUvy5I9+fbrTu+cqmdq8c9LT7LfbMAD2220Yd6Tj5/379uTy037MEWMu56XX3835k7QheVW6rrKqXyBKv6JfS5JcL0k3P0xSVeYKYBTwQDNO0Y0kGS+Q1BcYSXrv74qoQ4f2nHb8Pnz/6PNYtCgYtfuWfHW9fq0d1nLpoj8exNZD1me1HqvwzG1/4NSxt9OxQ3sALr3xQXbffhP23XUYNTWLWPjp5xz6m+Q//39Nfo4NBq7B3ZccC8BHn/yXw08ax9z5H5U955nj7uHS/z2EA3bfijfemcfBJyZtHvejkfTq3pW//HpfAGpqFrP96LojcMVXlKEARd1Bmrwalj6KiFXS132BV4DTIuJkSesAl1L/xavbIuL69H1L1tMLXrdFxEb17LsMGA68ASwAbomIyyRNBI6NiNJq4l8wZMjQeGhyg7utDeq5+U9bOwRrok+fOG9aY8Wns/jq1zeNy2+eWPa4Ldbr0exzNVfVeqy1STV9PRtYuWT9NZLny9R9z0ENrUfEq8BGDez7wvtKto9ocuBm1nYVo8PqO6/MrBiSIdRiZFYnVjMrhirUY60WJ1YzK4yC5FUnVjMrCqGCdFmdWM2sMAqSV51YzawY2tD8/7KcWM2sOAqSWZ1YzawwPN3KzCxnHmM1M8uT57GameWvKEMBLVE20Mys2UTSYy23lG1HGiDpX5KelTRD0s/T7b0k3SPphfRnz3JtNcSJ1cwKI6dyrDXAryJiELAlcJSkQcAJwH0RsT5wX7peESdWMyuOHDJrRLwdEdPT1x8CM4G1gD2Acelh41j6uKgm8xirmRVGxkLXvSWVFlkemz6OaRlpnedNgclA34iofVb8O0DfSuN0YjWzwsj4VX9ulkLXklYBbgB+kT6fb8m+9Dl7FT8FwEMBZlYcOQ2ySupIklSvjIgb082zJfVL9/cDKn54mBOrmRVCbaHrcv8r207SNb0YmBkRfy3ZdQswOn09Gri50lg9FGBmxZDfDQJbAwcCT0t6It32G+BU4FpJhwKvAftUegInVjMrjDzyakQ82EhTO+RwCidWMysKF7o2M8tdQfKqE6uZFYMLXZuZVUNBMqsTq5kVRlGqWzmxmllheIzVzCxPgnZOrGZmeStGZnViNbNCqC10XQROrGZWGAXJq06sZlYc7rGameXMt7SameWsGGnVidXMCiLrU1jbAidWMysM33llZpa3YuRVJ1YzK46C5FUnVjMrCmV9/HWrc2I1s0Io0p1XfkqrmVnO3GM1s8IoSo/VidXMCsPTrczM8uQbBMzM8lWki1dOrGZWGB4KMDPLWVF6rJ5uZWaFoQxLpnaknSU9L+lFSSfkHacTq5kVRw6ZVVJ74DxgJDAI2E/SoDzDdGI1s0IQ0E4qu2SwBfBiRLwcEZ8BVwN75BnrCj/GOn36tLldOuq11o6jSnoDc1s7CMtsef57rdPcBqZPn3ZXl47qneHQlSRNLVkfGxFjS9bXAt4oWX8TGNbc+Eqt8Ik1Ivq0dgzVImlqRAxt7TgsG/+9GhcRO7d2DFl5KMDMVjSzgAEl6/3TbblxYjWzFc0UYH1JAyV1An4A3JLnCVb4oYDl3Njyh1gb4r9XC4iIGkk/Be4C2gOXRMSMPM+hiMizPTOzFZ6HAszMcubEamaWMyfWgpC0hqSrJb0kaZqk2yVt0MQ2hko6p4F9r0qZ5ghaPSSFpDNK1o+VdHILxzBRkqdrtQFOrAUgScBNwMSIWC8ihgAnAn2b0k5ETI2Io6sRo/FfYM9K/3GS5AvJyxEn1mLYDvg8Ii6s3RARTwIPSjpd0jOSnpa0L0Das9219lhJl0naS9IISbel21aTdLekGZIuojhPFm6rakiu6h9Td4ekdSXdL+kpSfdJWjvdfpmkCyVNBk5L1y+Q9Kikl9O/1yWSZkq6rKS9CyRNTf92v2+pD2jZObEWw0bAtHq27wlsAmwMfAs4XVI/4BpgH4B0nt4OwIQ67x0DPBgRXyPpDa9dndBXKOcBoyR1r7P9XGBcRAwGrgRKh2P6A8Mj4pfpek9gK5IEfQtwJvA14OuSNkmP+W16h9Zg4JuSBlfl01jFnFiLbRtgfEQsiojZwL+BzYE7gO0kdSap4DMpIhbWee+2wD8BImICML/lwl4+RcQHwOVA3eGWrYCr0tdXkPzdal0XEYtK1m+NZA7k08DsiHg6IhYDM4B102P2kTQdeJwk6eZamcmaz4m1GGYAQ7IeHBGfAhOBnYB9SXqw1jLOAg4FumY8/uM66/9Nfy4ueV273kHSQOBYYIe0BzwBWKnycK0anFiL4X6gs6TDajekX//eB/aV1F5SH5Je6GPpIdcABwPfAO6sp81JwP5pWyNJvoJaM0XEPOBakuRa62GS2yYBRgEPNOMU3UiS8QJJfUm+kVgb4yuRBRARIel7wFmSfg18CrwK/AJYBXgSCOD4iHgnfdvdJF87b05rTtb1e2C8pBkk/8d/vbqfYoVyBvDTkvWfAZdKOg6YQ/IPXkUi4klJjwPPkZS+e6g5gVp1+JZWM7OceSjAzCxnTqxmZjlzYjUzy5kTq5lZzpxYzcxy5sRqZUlaJOmJtCbBdZJWbkZbl0naK319UWPPc0/vlR9ewTnqrdSVpYKXpI+aeK6TJR3b1Bht+ebEalksjIhNImIj4DPgiNKdlVZmiogfRcSzjRwyAmhyYjVrbU6s1lQPAF9Oe5MPSLoFeDa9++t0SVPSKk6HQ1LyUNLfJD0v6V5g9dqGSuuHStpZ0nRJT6YVoNYlSeDHpL3lb0jqI+mG9BxTJG2dvrfJlbok/b+0ru2M0jva0n1nptvvS+9oQ9J6ku5M3/OApA3z+GXa8sl3Xllmac90JEtvkd0M2CgiXkmT04KI2Dwt/vKQpLuBTYGvkBQK6Qs8C1xSp90+wD+AbdO2ekXEPEkXAh9FxF/S464CzoyIB9PSe3cBX2Vppa5T0nKJpbeTNuSQ9BxdgCmSboiI90ju8Z8aEcdIOilt+6ckJQGPiIgXJA0Dzge2r+DXaCsAJ1bLooukJ9LXDwAXk3xFfywiXkm37wgMrh0/BboD65PULxifVnB6S9L99bS/JUkFrldgyf329fkWMEha0iHtJmmV9Bx7pu+dIClLpa6j09uEIXnG/PrAeyTFTmqL1vwTuDE9x3DgupJzd85wDltBObFaFgsjYpPSDWmCKa3MJOBnEXFXneN2yTGOdsCWafWuurFkJmkESZLeKiI+kTSRhitERXre9+v+Dswa4jFWy8tdwE8kdQSQtIGkriRVtGorcPUjeRpCXY8C26Yl8ZDUK93+IbBqyXF3kxQ0IT2uNtE1tVJXd2B+mlQ3JOkx12oH1Pa69ycZYvgAeEXS3uk5JGnjMuewFZgTq+XlIpLx0+mSngH+TvKN6CbghXTf5cAjdd8YEXOAw0i+dj/J0q/itwLfq714RVJAemh6cexZls5O+D1JYp5BMiRQrlLXnSS1TWcCp18BVDYAAABOSURBVJIk9lofA1ukn2F74JR0+yjg0DS+GcAeGX4ntoJydSszs5y5x2pmljMnVjOznDmxmpnlzInVzCxnTqxmZjlzYjUzy5kTq5lZzv4/ybptWJ9AeC0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F1A1qh_sOObb"
      },
      "source": [
        "import io\n",
        "\n",
        "path='/content/drive/MyDrive/Normalized/Covid/ IM00007.tif'\n",
        "save_path='/content/drive/MyDrive/Testing/Covid/covid7.tif'\n",
        "with open(path, 'rb') as f:\n",
        "    tif = Image.open(io.BytesIO(f.read()))\n",
        "array=np.array(tif)\n",
        "max_val=np.amax(array)\n",
        "normalized=(array/max_val)\n",
        "im = Image.fromarray(normalized)\n",
        "im.save(save_path,'TIFF')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RZt7FOFIOVj9",
        "outputId": "3fa6d597-fe40-4193-e61f-e23c752b07a5"
      },
      "source": [
        "width = 224\n",
        "height = 224\n",
        "dsize = (width, height)\n",
        "image_testing = cv2.imread('/content/drive/MyDrive/Testing/Covid/covid7.tif', -1)\n",
        "image_testing = cv2.cvtColor(image_testing, cv2.COLOR_BGR2RGB)\n",
        "image_testing = cv2.resize(image_testing,dsize)\n",
        "image_testing = np.array(image_testing)\n",
        "image_testing.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "iGHhwsyQOZS1",
        "outputId": "c29a1443-3a17-493b-8a15-5a2ac2077925"
      },
      "source": [
        "plt.imshow(image_testing)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fcfdeda04d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CE8OIku1Obvw",
        "outputId": "beb3b3f6-613c-4ef6-b867-2478eaaff4eb"
      },
      "source": [
        "image_testing = np.expand_dims(image_testing, axis=0)\n",
        "print(image_testing.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 224, 224, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kiBXgNbUOd7-",
        "outputId": "cc2991ac-86a4-467f-aca3-5e84d61d2606"
      },
      "source": [
        "output = model.predict(image_testing, 1)\n",
        "print(output)\n",
        "print(encoder.classes_[output.argmax(axis=1)])"
      ],
      "execution_count": null,
      "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_5_input'), name='flatten_5_input', description=\"created by layer 'flatten_5_input'\"), but it was called on an input with incompatible shape (1, 224, 224, 3).\n",
            "[[9.9854207e-01 1.4579202e-03 1.1352947e-26]]\n",
            "['Covid']\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}