gaussian_RMSprop_0.001.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Copy of scenes_nn_4 layers.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
    }
  },
  "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/gaussian_RMSprop_0.001.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": "b1250d10-df69-42c4-de52-70c068242089"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 32,
      "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": "67bb53a1-9174-404c-b103-b4431f4410ae"
      },
      "source": [
        "cd /content/drive/MyDrive/ "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z7VB1d4wVdov",
        "outputId": "64df2b7d-febc-488c-c245-29de35c980b2"
      },
      "source": [
        "ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'1101174267 m rifqi fadhila mixer.pdf'\n",
            "'1101174267_Muhammad Rifqi F_2_TT4107.pdf'\n",
            "'1101174267_MuhammadRifqiFadhila_1&3_TT4107.pdf'\n",
            " 1101174267_MuhammadRifqiFadhila_3.3_TT4107.pdf\n",
            "'1101174267_muhammad rifqi fadhila_Filter2.jpg'\n",
            "'1101174267 muhammad rifqi fadhila OSILATOR.pdf'\n",
            "'1101174267 muhammad rifqi fadhila penguat.pdf'\n",
            "'1101174267_Muhammad Rifqi Fadhila_Proposal Tugas Akhir dan lembar pemeriksaan.pdf'\n",
            " best_weight_scene_EfficientNetB0.h5\n",
            " \u001b[0m\u001b[01;34mCLAHE\u001b[0m/\n",
            " \u001b[01;34mClassroom\u001b[0m/\n",
            " \u001b[01;34mDataset\u001b[0m/\n",
            " DSC_7396.JPG\n",
            " \u001b[01;34mGaussian\u001b[0m/\n",
            "'Getting started.pdf'\n",
            " IMG-20210217-WA0001.jpg\n",
            "\u001b[01;34m'MPTI 2021'\u001b[0m/\n",
            " \u001b[01;34mNormalized\u001b[0m/\n",
            "'PMMB2021_BATCH1_1101174267_MUHAMMAD RIFQI FADHILA.rar'\n",
            " Screenshot_20210217-060246.jpg\n",
            " TT_41_07_101174267_MUHAMMADRIFQIFADHILA.pdf\n",
            " \u001b[01;34mValo\u001b[0m/\n",
            " \u001b[01;34mVisualizable\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5IGm26ZzVGBs"
      },
      "source": [
        "# import the necessary packages\n",
        "from keras.models import Sequential\n",
        "from keras.layers.convolutional import Conv2D\n",
        "from keras.layers.convolutional import MaxPooling2D\n",
        "from keras.layers.core import Activation\n",
        "from keras.layers.core import Flatten\n",
        "from keras.layers.core import Dense\n",
        "from keras.optimizers import Adam, SGD, RMSprop\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from PIL import Image\n",
        "from imutils import paths\n",
        "from skimage import io\n",
        "from skimage.filters import gaussian\n",
        "from skimage import exposure\n",
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 33,
      "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": "35984c12-5ab9-405a-876f-3c12163708e3"
      },
      "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(\"Gaussian\")\n",
        "data = []\n",
        "labels = []"
      ],
      "execution_count": 34,
      "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/Gaussian/Covid/IM00001.tif', -1)\n",
        "im_rgb = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "me1s-uFI7kJi",
        "outputId": "4cba3b48-0b7a-4f40-9db0-c20e168f3109"
      },
      "source": [
        "\n",
        "plt.imshow(im_rgb)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f85143456d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "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": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rzbolh88VGB2",
        "outputId": "04d1ff8e-cf86-4029-eb87-5ec74612c02b"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "aGbsj1UcytpV",
        "outputId": "a65fe80c-0121-47aa-e604-d4ea6ec5fd59"
      },
      "source": [
        "\n",
        "print(labels[500])\n",
        "plt.imshow(data[10])"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normal\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f8514635350>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "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": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FPCyIqcVGB7",
        "outputId": "82716f34-ce1c-41a4-85cd-a52da57edfe7"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LynLhwt_pvNc",
        "outputId": "91ee0480-71b6-4b61-8bd9-e14ad57b7d15"
      },
      "source": [
        "data = np.array(data)\n",
        "data.shape"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ktZ5hNYwVGB9",
        "outputId": "5f59d50a-9ad3-41e5-950c-c542cd7be0d6"
      },
      "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": 43,
      "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": "19cbec0c-23a1-4709-fa2a-32a59d6af3a6"
      },
      "source": [
        "print(trainX[0])"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]]\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": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTvu4IV0i7Fj",
        "outputId": "32fc1144-8873-478e-8fe3-8994d4e3078f"
      },
      "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": 46,
      "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": 47,
      "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": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0TdWxuFPkT3",
        "outputId": "f525e7ad-9b37-4231-f92e-778ca3fc292a"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 49,
      "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": "ec6ac4fc-c7d0-4118-b29c-316062ae9176"
      },
      "source": [
        "# train the model using the Adam optimizer\n",
        "print(\"[INFO] training network...\")\n",
        "opt = RMSprop(learning_rate=0.001, 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": 50,
      "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",
            "22/24 [==========================>...] - ETA: 0s - loss: 14.5051 - accuracy: 0.4888WARNING: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 [==============================] - 2s 44ms/step - loss: 13.7263 - accuracy: 0.5028 - val_loss: 1.3321 - val_accuracy: 0.6280\n",
            "\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.62800, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 2/50\n",
            "24/24 [==============================] - 1s 30ms/step - loss: 1.4095 - accuracy: 0.6985 - val_loss: 0.3092 - val_accuracy: 0.8960\n",
            "\n",
            "Epoch 00002: val_accuracy improved from 0.62800 to 0.89600, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 3/50\n",
            "24/24 [==============================] - 1s 27ms/step - loss: 0.5446 - accuracy: 0.8609 - val_loss: 0.1457 - val_accuracy: 0.9560\n",
            "\n",
            "Epoch 00003: val_accuracy improved from 0.89600 to 0.95600, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 4/50\n",
            "24/24 [==============================] - 1s 28ms/step - loss: 0.2486 - accuracy: 0.9626 - val_loss: 0.0905 - val_accuracy: 0.9760\n",
            "\n",
            "Epoch 00004: val_accuracy improved from 0.95600 to 0.97600, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 5/50\n",
            "24/24 [==============================] - 1s 28ms/step - loss: 0.0620 - accuracy: 0.9875 - val_loss: 0.0429 - val_accuracy: 0.9760\n",
            "\n",
            "Epoch 00005: val_accuracy did not improve from 0.97600\n",
            "Epoch 6/50\n",
            "24/24 [==============================] - 1s 30ms/step - loss: 0.6396 - accuracy: 0.8926 - val_loss: 0.0652 - val_accuracy: 0.9600\n",
            "\n",
            "Epoch 00006: val_accuracy did not improve from 0.97600\n",
            "Epoch 7/50\n",
            "24/24 [==============================] - 1s 27ms/step - loss: 0.2271 - accuracy: 0.9503 - val_loss: 0.0716 - val_accuracy: 0.9680\n",
            "\n",
            "Epoch 00007: val_accuracy did not improve from 0.97600\n",
            "Epoch 8/50\n",
            "24/24 [==============================] - 1s 27ms/step - loss: 0.0361 - accuracy: 0.9930 - val_loss: 0.0183 - val_accuracy: 0.9960\n",
            "\n",
            "Epoch 00008: val_accuracy improved from 0.97600 to 0.99600, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 9/50\n",
            "24/24 [==============================] - 1s 28ms/step - loss: 0.6945 - accuracy: 0.9284 - val_loss: 0.0052 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00009: val_accuracy improved from 0.99600 to 1.00000, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 10/50\n",
            "24/24 [==============================] - 1s 28ms/step - loss: 0.1483 - accuracy: 0.9877 - val_loss: 0.0203 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00010: val_accuracy did not improve from 1.00000\n",
            "Epoch 11/50\n",
            "24/24 [==============================] - 1s 29ms/step - loss: 0.0123 - accuracy: 1.0000 - val_loss: 0.0043 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00011: val_accuracy did not improve from 1.00000\n",
            "Epoch 12/50\n",
            "24/24 [==============================] - 1s 27ms/step - loss: 0.1511 - accuracy: 0.9778 - val_loss: 0.0080 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00012: val_accuracy did not improve from 1.00000\n",
            "Epoch 13/50\n",
            "24/24 [==============================] - 1s 27ms/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00013: val_accuracy did not improve from 1.00000\n",
            "Epoch 14/50\n",
            "24/24 [==============================] - 1s 27ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0039 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00014: val_accuracy did not improve from 1.00000\n",
            "Epoch 15/50\n",
            "24/24 [==============================] - 1s 27ms/step - loss: 0.0732 - accuracy: 0.9922 - val_loss: 0.0142 - val_accuracy: 0.9960\n",
            "\n",
            "Epoch 00015: val_accuracy did not improve from 1.00000\n",
            "Epoch 16/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.0031 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00016: val_accuracy did not improve from 1.00000\n",
            "Epoch 17/50\n",
            "24/24 [==============================] - 1s 27ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00017: val_accuracy did not improve from 1.00000\n",
            "Epoch 18/50\n",
            "24/24 [==============================] - 1s 28ms/step - loss: 4.5619e-04 - accuracy: 1.0000 - val_loss: 7.3228e-05 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00018: val_accuracy did not improve from 1.00000\n",
            "Epoch 19/50\n",
            "24/24 [==============================] - 1s 27ms/step - loss: 2.7164e-05 - accuracy: 1.0000 - val_loss: 3.4106e-05 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00019: val_accuracy did not improve from 1.00000\n",
            "Epoch 20/50\n",
            "24/24 [==============================] - 1s 31ms/step - loss: 1.1522e-05 - accuracy: 1.0000 - val_loss: 6.3252e-06 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00020: val_accuracy did not improve from 1.00000\n",
            "Epoch 21/50\n",
            "24/24 [==============================] - 1s 30ms/step - loss: 0.3986 - accuracy: 0.9566 - val_loss: 0.0015 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00021: val_accuracy did not improve from 1.00000\n",
            "Epoch 22/50\n",
            "24/24 [==============================] - 1s 30ms/step - loss: 5.7224e-04 - accuracy: 1.0000 - val_loss: 6.4843e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00022: val_accuracy did not improve from 1.00000\n",
            "Epoch 23/50\n",
            "24/24 [==============================] - 1s 29ms/step - loss: 3.7725e-04 - accuracy: 1.0000 - val_loss: 3.6502e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00023: val_accuracy did not improve from 1.00000\n",
            "Epoch 24/50\n",
            "24/24 [==============================] - 1s 27ms/step - loss: 2.4201e-04 - accuracy: 1.0000 - val_loss: 1.7796e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00024: val_accuracy did not improve from 1.00000\n",
            "Epoch 25/50\n",
            "24/24 [==============================] - 1s 27ms/step - loss: 0.3267 - accuracy: 0.9825 - val_loss: 4.9831e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00025: 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": "0d564aa9-77a6-498b-d4fa-6f308af6c735"
      },
      "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": 51,
      "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": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEWCAYAAABliCz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXxU9b3/8dcnG1nYEggooIIbhYKyBFzbolbrVveqdWmrvaWLtvq7rVe7aFdvvfd6rdZrVay41O1aXFu1F61atSoYEAUFBZQlbAlLIAlZZz6/P84EAgSYLCeTzHk/H495zOTMOef7PYy+z3e+c77fY+6OiIikv4xUV0BERLqGAl9EJCIU+CIiEaHAFxGJCAW+iEhEKPBFRCJCgS8CmNn9ZvabJNddZmZf7Oh+RLqaAl9EJCIU+CIiEaHAlx4j0ZVyjZm9b2Y1ZnavmQ02sxfMrMrMXjKzwhbrn2FmH5hZpZm9amajWrw33szmJrb7XyB3p7JON7N5iW3fNLPD2lnnb5nZEjPbaGbPmtmQxHIzs9+ZWbmZbTGz+WY2JvHeqWb2YaJuq8zsR+36BxPZiQJfeppzgROBQ4EvAy8APwGKCf57/gGAmR0KPApcnXjveeAvZpZjZjnA08CfgCLgz4n9kth2PDAd+DYwALgbeNbMerWlomZ2PPBb4HxgX2A58Fji7ZOAzyeOo19inQ2J9+4Fvu3ufYAxwMttKVdkdxT40tPc7u7r3H0V8Dowy93fdfc64ClgfGK9C4Dn3P1Fd28EbgbygKOBI4Fs4FZ3b3T3GcA7LcqYCtzt7rPcPebuDwD1ie3a4mJgurvPdfd64MfAUWY2HGgE+gCfAczdF7r7msR2jcBoM+vr7pvcfW4byxVplQJfepp1LV7XtvJ378TrIQQtagDcPQ6sBIYm3lvlO84cuLzF6wOAHya6cyrNrBLYL7FdW+xch2qCVvxQd38Z+B/gDqDczKaZWd/EqucCpwLLzewfZnZUG8sVaZUCX9LVaoLgBoI+c4LQXgWsAYYmljXbv8XrlcCN7t6/xSPf3R/tYB0KCLqIVgG4++/dfSIwmqBr55rE8nfc/UxgEEHX0+NtLFekVQp8SVePA6eZ2Qlmlg38kKBb5k3gLaAJ+IGZZZvZOcDkFtveA3zHzI5I/LhaYGanmVmfNtbhUeAyMxuX6P//d4IuqGVmNimx/2ygBqgD4onfGC42s36JrqgtQLwD/w4i2yjwJS25+0fAJcDtwHqCH3i/7O4N7t4AnAN8A9hI0N//ZIttS4FvEXS5bAKWJNZtax1eAq4HniD4VnEQcGHi7b4EJ5ZNBN0+G4D/Srx3KbDMzLYA3yH4LUCkw0w3QBERiQa18EVEIkKBLyISEQp8EZGIUOCLiEREVqor0NLAgQN9+PDhqa6GiEiPMWfOnPXuXpzMut0q8IcPH05paWmqqyEi0mOY2fK9rxVQl46ISEQo8EVEIkKBLyISEd2qD781jY2NlJWVUVdXl+qqhCo3N5dhw4aRnZ2d6qqISJoKNfDN7P8B/wI4MB+4LDFvedLKysro06cPw4cPZ8fJDdOHu7NhwwbKysoYMWJEqqsjImkqtC4dMxtKcPehEncfA2SyfeKopNXV1TFgwIC0DXsAM2PAgAFp/y1GRFIr7D78LCDPzLKAfIL5wdssncO+WRSOUURSK7TAT9yC7mZgBcHUsJvdfebO65nZVDMrNbPSioqK9pTDui11VNU1drjOIiLpLMwunULgTGAEwa3eCszskp3Xc/dp7l7i7iXFxUkNFtu5HNZX1VNV19ThOremsrKSP/zhD23e7tRTT6WysjKEGomItE+YXTpfBD5194rEnXueJLiBdKfLzDSa4uHM67+7wG9q2vMJ5vnnn6d///6h1ElEpD3CvEpnBXCkmeUT3Fz6BCCUeROyMjKIhRT41113HUuXLmXcuHFkZ2eTm5tLYWEhixYt4uOPP+ass85i5cqV1NXVcdVVVzF16lRg+zQR1dXVnHLKKRx77LG8+eabDB06lGeeeYa8vLxQ6isisjuhBb67zzKzGcBcgvuHvgtM68g+f/mXD/hw9ZZdltc1xnAgLzuzzfscPaQvP//yZ3f7/k033cSCBQuYN28er776KqeddhoLFizYdvnk9OnTKSoqora2lkmTJnHuuecyYMCAHfaxePFiHn30Ue655x7OP/98nnjiCS65ZJfeLRGRUIV6Hb67/xz4eZhlQNCPHw+phb+zyZMn73Ct/O9//3ueeuopAFauXMnixYt3CfwRI0Ywbtw4ACZOnMiyZcu6pK4iIi11+5G2Le2uJb6mspYNNQ2MGdov9DoUFBRse/3qq6/y0ksv8dZbb5Gfn8+UKVNavZa+V69e215nZmZSW1sbej1FRHaWFnPpZGYacfdQWvl9+vShqqqq1fc2b95MYWEh+fn5LFq0iLfffrvTyxcR6Sw9qoW/O1kZwaClpriTk9G5A5gGDBjAMcccw5gxY8jLy2Pw4MHb3jv55JO56667GDVqFCNHjuTII4/s1LJFRDqTuXdN33cySkpKfOcboCxcuJBRo0btcbvNtY0s31DDIYN6k5fTc89hyRyriEhLZjbH3UuSWTctunRatvBFRKR1aRH4mYnAD+tafBGRdJAWga8WvojI3qVF4GdmGAY0xRT4IiK7kxaBb2ZkZmQQi8dTXRURkW4rLQIfgla+unRERHYvbQI/K8NC+dG2vdMjA9x6661s3bq1k2skItI+6RP4IU2RrMAXkXTRc0cp7SQzpBZ+y+mRTzzxRAYNGsTjjz9OfX09Z599Nr/85S+pqanh/PPPp6ysjFgsxvXXX8+6detYvXo1xx13HAMHDuSVV17p9LqJiLRFzwr8F66DtfNbfWtQLEb/mOM5mQTX7CRpn7Fwyk27fbvl9MgzZ85kxowZzJ49G3fnjDPO4LXXXqOiooIhQ4bw3HPPAcEcO/369eOWW27hlVdeYeDAgW06TBGRMKRNlw4YhPyb7cyZM5k5cybjx49nwoQJLFq0iMWLFzN27FhefPFFrr32Wl5//XX69Qt/1k4RkbbqWS38PbTEa2oaWLlpKyP36UOvrLbfCCUZ7s6Pf/xjvv3tb+/y3ty5c3n++ef52c9+xgknnMANN9wQSh1ERNorzJuYjzSzeS0eW8zs6rDK2za9QicPvmo5PfKXvvQlpk+fTnV1NQCrVq2ivLyc1atXk5+fzyWXXMI111zD3Llzd9lWRCTVwrzF4UfAOAAzywRWAU+FVV5WZjjTK7ScHvmUU07hoosu4qijjgKgd+/ePPTQQyxZsoRrrrmGjIwMsrOzufPOOwGYOnUqJ598MkOGDNGPtiKScl0yPbKZnQT83N2P2dN67Z0eGaChKcaitVUMK8ynqCCnQ/VNFU2PLCJt1R2nR74QeLS1N8xsqpmVmllpRUVFuwvIzAgORTNmioi0LvTAN7Mc4Azgz6297+7T3L3E3UuKi4vbXU6GBXPqNGk+HRGRVnVFC/8UYK67r2vvDpLpdjKzYHqFHjpjZne685iIpKeuCPyvspvunGTk5uayYcOGpAKxp06g5u5s2LCB3NzcVFdFRNJYqNfhm1kBcCKw64XrSRo2bBhlZWUk07+/vqoeB2orerW3uJTJzc1l2LBhqa6GiKSxUAPf3WuAAR3ZR3Z2NiNGjEhq3SsemcvCNVt4+YdTOlKkiEhaSqOpFWBAQQ6bahpSXQ0RkW4prQK/MD+HytpGXZopItKKtAr8ooIc3GFzbWOqqyIi0u2kVeAXJkbYbqypT3FNRES6n7QK/KL85sBXC19EZGdpFfiFBdkAbNQPtyIiu0irwG+eNG3TVgW+iMjO0irwC7d16SjwRUR2llaBn5udSUFOpq7FFxFpRVoFPgRX6mxUl46IyC7SLvCLCnLUpSMi0oq0C/zCfE2vICLSmrQL/CJ16YiItCrtAj9o4WvglYjIztIu8IsKsqmub6K+KZbqqoiIdCtpGPjBzU8qt6qVLyLSUqiBb2b9zWyGmS0ys4VmdlSY5UHQwgcNvhIR2Vmod7wCbgP+5u7nmVkOkB9yedtG2+pKHRGRHYUW+GbWD/g88A0Ad28AQk/h5vl0NijwRUR2EGaXzgigArjPzN41sz8mbmq+AzObamalZlaazI3K96ZQE6iJiLQqzMDPAiYAd7r7eKAGuG7nldx9mruXuHtJcXFxhwvtn6c+fBGR1oQZ+GVAmbvPSvw9g+AEEKqszAz65WWrD19EZCehBb67rwVWmtnIxKITgA/DKq+lYLStLssUEWkp7Kt0vg88nLhC5xPgspDLA4LAVwtfRGRHoQa+u88DSsIsozWF+Tmsrqzt6mJFRLq1tBtpC8HgK/1oKyKyo7QM/OaboLh7qqsiItJtpGXgF+Xn0NAUZ2uDJlATEWmWloHfPPhK3ToiItulZeAX5Wu0rYjIztIz8HurhS8isrP0DHy18EVEdpGWgd/ch7+hWoEvItIsLQO/b24WmRmmFr6ISAtpGfhmRmF+Dht1M3MRkW3SMvAhGG2r+XRERLZL28AvzA9G24qISCBtA39Ab82YKSLSUtoGfmF+jn60FRFpIW0Dv6ggh01bG4nHNYGaiAikceAX5ucQiztb6nSljogIhHwDFDNbBlQBMaDJ3bvsZihFLSZQ658YeSsiEmVh3+IQ4Dh3X98F5eygebSt+vFFRAJp26XTPJ+OBl+JiATCDnwHZprZHDOb2toKZjbVzErNrLSioqLTCi4syAbQpZkiIglhB/6x7j4BOAW4wsw+v/MK7j7N3UvcvaS4uLjTCh5Q0AtAg69ERBJCDXx3X5V4LgeeAiaHWV5LeTmZ5GZnqIUvIpIQWuCbWYGZ9Wl+DZwELAirvNYU5eewQYEvIgKEe5XOYOApM2su5xF3/1uI5e2isEDTK4iINAst8N39E+DwsPafjKICTaAmItIsbS/LhMR8Omrhi4gAaR74RQU5upG5iEhC2gf+lromGmPxVFdFRCTl0jrwm6dXqNyq0bYiImkd+NunV1C3johIWgd+8/QKCnwRkTQP/CLNmCkisk16B766dEREtknrwG++8YmuxRcRSfPAz8nKoE+vLI22FREhzQMfoKi3RtuKiEAEAr9QM2aKiAARCPyighxdpSMiQpKBb2ZXmVlfC9xrZnPN7KSwK9cZggnUNNJWRCTZFv7l7r6F4CYmhcClwE2h1aoTFRVk67JMERGSD3xLPJ8K/MndP2ixbM8bmmWa2btm9tf2VLCjCgtyqG2MUdsQS0XxIiLdRrKBP8fMZhIE/v8lbl2Y7BSUVwEL21O5ztA8+Er9+CISdckG/jeB64BJ7r4VyAYu29tGZjYMOA34Y7tr2EHN0yuoW0dEoi7ZwD8K+MjdK83sEuBnwOYktrsV+DeS/zbQ6RT4IiKBZAP/TmCrmR0O/BBYCjy4pw3M7HSg3N3n7GW9qWZWamalFRUVSVYneYWaQE1EBEg+8Jvc3YEzgf9x9zuAPnvZ5hjgDDNbBjwGHG9mD+28krtPc/cSdy8pLi5uQ9WTownUREQCyQZ+lZn9mOByzOfMLIOgH3+33P3H7j7M3YcDFwIvu/slHaptO/TNyybDNIGaiEiygX8BUE9wPf5aYBjwX6HVqhNlZhj983M0gZqIRF5SgZ8I+YeBfom++Tp332Mf/k7bv+rup7ezjh1WmJ+t0bYiEnnJTq1wPjAb+ApwPjDLzM4Ls2KdaUBBL/Xhi0jkZSW53k8JrsEvBzCzYuAlYEZYFetMhQXZLFu/NdXVEBFJqWT78DOawz5hQxu2TbmiAvXhi4gk28L/m5n9H/Bo4u8LgOfDqVLnC2bMbMDdMUtqCiARkbSTVOC7+zVmdi7BtfUA09z9qfCq1bmKCnJoijtV9U30zd3j1aQiImkr2RY+7v4E8ESIdQlNYYubmSvwRSSq9hj4ZlYFeGtvAe7ufUOpVSdrOZ/OAQMKUlwbEZHU2GPgu/vepk/oETSfjohID7rSpiMGbGvha/CViERXJAK/cFvg16e4JiIiqROJwC/IySQnM0MtfBGJtEgEvplRWJCtGTNFJNIiEfgQXJqp0bYiEmWRCfyighy18EUk0iIT+IWaT0dEIi4ygT9ALXwRibjQAt/Mcs1stpm9Z2YfmNkvwyorGYX5OVTWNhKLtzZwWEQk/YXZwq8Hjnf3w4FxwMlmdmSI5e1RUUEO7lCpbh0RiajQAt8D1Yk/sxOPlDWvNb2CiERdqH34ZpZpZvOAcuBFd5/VyjpTzazUzEorKipCq0tRvqZXEJFoCzXw3T3m7uOAYcBkMxvTyjrT3L3E3UuKi4tDq0thQTAtsu5tKyJR1SVX6bh7JfAKcHJXlNeaInXpiEjEhXmVTrGZ9U+8zgNOBBaFVd7eFOZvnxNfRCSKkr7jVTvsCzxgZpkEJ5bH3f2vIZa3R7nZmRTkZCrwRSSyQgt8d38fGB/W/tujUIOvRCTCIjPSFoJ+fE2vICJRFanAL8xXC19EoitSga8WvohEWaQCP2jha+CViERTpAK/qCCb6vom6ptiqa6KiEiXi1jg9wJQK19EIiliga/pFUQkuiIV+M2jbTW9gohEUaQCv3k+HbXwRSSKIhX4mhNfRKIsUoHfP099+CISXZEK/KzMDPrlZWu0rYhEUqQCH5pH2+qyTBGJnmgGfk19qqshItLlIhf4hfk5uq+tiERS5AK/qEB9+CISTWHe4nA/M3vFzD40sw/M7KqwymqLwsSMme6e6qqIiHSpMFv4TcAP3X00cCRwhZmNDrG8pBTl59DQFGdrgyZQE5FoCS3w3X2Nu89NvK4CFgJDwyovWYUabSsiEdUlffhmNpzg/razWnlvqpmVmllpRUVF6HUp0nw6IhJRoQe+mfUGngCudvctO7/v7tPcvcTdS4qLi8OuDkW9g8DfoBa+iERMqIFvZtkEYf+wuz8ZZlnJ2tbCV+CLSMSEeZWOAfcCC939lrDKaSv14YtIVIXZwj8GuBQ43szmJR6nhlheUvrmZpGZYerDF5HIyQprx+7+BmBh7b+9zEyjbUUkkiI30hY02lZEoimSgV+YH4y2FRGJkkgG/oDeOfrRVkQiJ5KBX5ifoy4dEYmcSAZ+UUEOm7Y2EI9rAjURiY5IBn5hfg5xhy11ulJHRKIjkoFfpMFXIhJBkQz8If3zAHhhwdoU10REpOtEMvAnDS/k9MP25b9nfsQbi9enujoiIl0iPQI/Hoem5LtnzIz/OPcwDh7Um+8/OpeyTVtDrJyISPfQ8wO/bjPceTTMvrtNmxX0yuLuS0toijnffWgudY26A5aIpLeeH/i5/aDPYPjnbdBQ06ZNRwws4HcXjGP+qs1c//QC3edWRNJazw98gC9cBzUVUDq9zZt+cfRgfnD8wfx5ThmPzF4RQuVERLqH9Aj8A46CA6e0q5UPcNUXD2XKyGJ+8ewHzF2xqdOrJyLSHaRH4EOHWvmZGcatF4xjn365fO+huVRU1YdQQRGR1EqfwO9gK79/fg53X1JCZW0DVz4yl6ZYvNOrKCKSSmHe4nC6mZWb2YKwythFB1r5AKOH9OW354xl1qcbuemFRZ1cORGR1AqzhX8/cHKI+99VB1v5AGePH8Y3jh7OH9/4lGffW92p1RMRSaXQAt/dXwM2hrX/3epgKx/gJ6eOYtLwQq6d8T6L1m7pxMqJiKROyvvwzWyqmZWaWWlFRUXHd7hDK799I2hzsjK446IJ9M7N4jt/msPmWs2qKSI9X8oD392nuXuJu5cUFxd3zk47oZU/qG8ud148gbJNtfzr/87T3Pki0uOlPPBDsa2Vf2u7W/kAJcOLuP700fx9UTm3v7yk06onIpIK6Rn40CmtfICvHXUA54wfyq1//5jn56+hUZdrikgPlRXWjs3sUWAKMNDMyoCfu/u9YZW3iwOOghFfCFr5JZdDTn67dmNm3Hj2WBatreJ7D88lK8PYf0A+BxX35sDiAg4q7p14FNA/P6eTD0JEpPNYd5owrKSkxEtLSztvh8vfhPtOgZNuhKOv7NCuNm9t5KWF6/hkfTVLy2tYWlHNsg01NMa2//sNKMjZ8UQwqICjDxpIbnZmR49ERKRVZjbH3UuSWTe0Fn63cMDRndLKB+iXn825E4ftsKwpFqdsUy1LK6pZWlHNJxXBiWDmh+vYWLMSgCMPLOLhfzmSzAzr0KGIiHRUegc+wJTrglZ+6fQOt/J3lpWZwfCBBQwfWMAJowbv8N6mmgaenreKX/7lQ25/eTFXf/HQTi1bRKSt0vdH22YtW/kduGKnrQoLcrjsmBGcM2Eot/19MW8u1a0URWRXC1ZtZsacsi4pK/0DH4JWfk0FzLmvy4v+9ZljGDGwgKsfm8f6as3CKSLbra+u59t/msMtMz+ipr4p9PKiEfjNrfw3uraVD8GtFO+4aAKVtY386+PvaQCXiADQ0BTnew/NZX11PdO+VkJBr/B72KMR+JBo5ZenpJU/at++3HD6aF77uIK7X/ukU/dd2xBj4ZotNDR1r/EBjbE4C1ZtZkl5NeVb6qhtiOkWknuhf59o+eVfPmD2so3853mHMWZovy4pM/1/tG3WspU/8bIOXbHTHhcfsT9vLd3AzTM/YvKIQiYeUNThfZZt2spl973D4vJqemVlcPh+/Zk0vJCSA4qYsH8h/fKzO6Hmbbd5ayOXP/AOc5bvePewrAyjT24WfXKz6ZObRe9eweu+uVnblp8wahDj9y9MSb1Txd25eeZHPDJrBXdcPIGjDxqY6ipJyB56ezkPz1rBd75wEGeOG9pl5ab3dfg7a74u/0v/DkddEV45u7GlrpHTfv86sZjz/FWf69BArQWrNnPZ/e9Q1xjjRyeNZNmGGuYs38QHq7cQS3QbjRzch4nDCyk5IDgJ7FeUh1m4l4eWV9XxtXtns7Simp+cOoqighy21DVRXddEVV0jVTs8N7GlxbLq+iYyM4xbzh/Hlw8fEmo9u4t43PnVXz/k/jeX0Sc3i4amOHddOpHjRg5KddUkJLM/3chF97zNsYcM5N6vT+rwJdttuQ4/WoEP8MAZUL4Qrnqvy1v5AO+trOS8u97kC4cO4p6vTWxXAL/yUTlXPDyX/nnZ3H/5ZA4d3Gfbe1sbmpi3opLS5ZsoXb6Jd5dvoirxY9CgPr0oGR58uzht7L7s0y+3044LYOXGrVxy7ywqquq5+9KJfO6Qtk2Gt3lrI996sJR3lm/khtNHc9kxIzq1fgBLyqt565MNXFCyHzlZqe3RjMWdnz41n8feWck3jx3B96YcxNemz+bjdVXc/tXxnDxm35TWTzrfqspazrj9DfrlZfPUFcfQL6/j38IV+HuS4lY+wL1vfMqv//ohN5w+msuPbVuoPTZ7BT99egEjB/fhvssmMbjvnkM7Fnc+WlvFnOUbg5PAsk2sqqylT68sfnXWZzlr3NBOafV/vK6KS++dRV1jnPsum8SEdnbL1DXGuOqxd/m/D9bxnS8cxLUnj+y0byV/Ll3JDc98QG1jjHH79eeOiycwtH9ep+y7rZpicX705/d4et5qrjzuYH540qGYGZtrG7nsvtm8V7aZm79yGGePH7b3nUVQUyzOx+uqGbVvn9C/tXaW2oYY5931Jis2bOWpK47h4EG9O2W/Cvy9aW7lX/gIDJ0IGV3b0nN3vvXgHP7xcTlPfPdoDhvWP6ltbnnxY25/eQmfP7SYP1w8gd7t/FV/aUU11854n9LlmzjtsH258awxHepemreykm/cN5uczAz+9M0jGLlPn71vtAexuHPDMwt4eNYKzpkwlP849zCyM9v/GdXUN3H9Mwt4cu4qjjywiLPGDeU3zy0kK9O45fzDOf4zg/e+k07U0BTnqsfe5YUFa7nmSyO54riDd6nvvzxQytufbuDGs8Zy0RH7d2n9urvF66r40Yz3eW9lJcd/ZhD/fvbYTv+22tncne8/+i7PzV/D9K9P4rjPdF6XnQJ/b8pK4f7ToakW+uwLnzkdRn0ZDjgGMrvmd+zKrQ2cetvrZGVm8NcfHEvf3N1/tWtoinPdE+/z5LuruKBkP35z9pgOBSAEoXrXP5byuxc/ZmDvXtz8lcM59pC2/1j4xuL1TP1TKQN79+Khbx7B/gM6p5vM3bn95SXc8uLHfCFxgmvPZWuL1m7hiofn8sn6Gn5w/CH84IRDyMwwPl1fwxUPz+XDNVv4zhcO4kcnHUpWB/9Nk1HXGOO7D83hlY8quP700XxzN9/wkl0vSppicaa9/gm3vriY3rlZnDVuKI/MXk52ZgY3nD6a8yYO67at/T+8uoT//NtHXHvyZ/julIM6dd8K/GTUVsLimbDwWVj8UhD+eYUw8rQg/A+cAtnhthpKl23kgmlvc/KYffifr45v9T/WLXWNfPehOfxzyQb+9cRD+f7xB3fqf9TzyzZz9f++y9KKGi4/ZgT/dvLIpCd7+9uCNfzg0XkcWFzAg5dPZtBeupfa49HZK/jpU/MZO7Qf078xiQG9eyW1nbvz2Dsr+cWzH9A3L5vbLhjH0QfveEKra4zxq79+yCOzVjBpeCG3f3VCqC3FrQ1NfOvBUt5cmlzLveU3gR+ddChXHn9IaHXr7j5eV8WP/vwe75dt5tSx+/CrM8cwsHcvlq2v4d9mvM/sZRuZMrKY354zln37paabbndeXrSObz5QyumHDeH3F47r9JOSAr+tGrbC0r/Dwr/AR3+D+s2Q0xsOOTEI/0NOgl4d66bYneYz/41nj+HiIw7Y4b3VlbVcdt87LK2o5qZzD+O8ieH059Y2xLjphYU88NZyDh3cm1svGM/oIX33uM3j76zkuiffZ9x+/bnvG5NDvQT0xQ/XceUjcxnSP48HL5/MfkV7/hZRXd/ET56cz7PvrebYgwfyuwvGUdxn9yeKZ+at4sdPzic3O5NbLxjH5w/tpDuvtbClrpHL73uHuSs2cfNXDuecCcl9lk2xONfMeJ+n3l3F96YcxDVf6rzfNHqCplicu1/7hNteClr1vz5zDKcdtuOP2fG48+Bby/iPv31EVoZx/emj+UpJ92jtLymv5uw7/sn+A/KZ8Z2jycvp/JlzFfgd0dQAy14Lwn/Rc8GUDJm94KDj4MDjoN8w6DskeBQUQ0bHPsB43Pn6fbOZ/elGnmkhRmYAAAwPSURBVL7iGEbtGwTth6u3cPn971Bd38Rdl0xsV3dLW736UTnXzHifyq0N/OikkfzL5w5s9ZKxe177hBufX8jnDhnI3ZdOJD8n/G6w0mUb+eYDpeRkZXD/ZZP47JDWB6osWLWZKx+Zy4qNW/nXEw/le1MOJiOJy96WlFdzxcNz+bi8iiuPO5irv3hop81wWrm1ga9Nn82Hq7dw24XjdwmsvYnHnZ8+vYBHZ6/gG0cP5+dfHt0twixsH60NWvXzV23mtMP25VdnfHaP3/CWbwha+7M+3cjnDy3mpnPGMiRFP8oDbK5t5Ow7/snm2kae/f6xoV0goMDvLPEYrJwVhP/Cv8DmlTu+n5EV/AbQZ9/tJ4HmR5/m530ga8/dEOur6znlttfpm5vFs1cey9wVm/juQ3Pp3SuL+y6btO0k0BU21jTwkyfn87cP1nLEiCL++/zDGVYYtKibBwjd8cpSThu7L7dccDi9srpurv/F66r4+vTZbKlrYtqlE3foonF3Hnp7Ob9+biGF+dn8/sLxHHHggDbtv7Yhxg3PLODPc8o46sAB3PbVcQzq07EunvXV9Vzyx1l8UlHDHy6ewBdHt+8HYnfn139dyPR/fsqFk/bjxrPHpu2U242xOHf/Yym3/X0xfXOz+fVZYzh1bHInyXjceXjWcn77wiIyzPjpaaO4cNJ+XX6CjMWdy+9/h38uWc8j3zqSySM6PtByd7pN4JvZycBtQCbwR3e/aU/rd7vAb8kdqsuhajVs2enRclljK3P15PaD3oMTj0HBc0HxDsve2ZDNhQ8v4bD9iphftpmDiwu472tj2TfXobEm6Hba9rwVGmoSz1uD3x/iMfBY8LzD6ybw+K7LcvsFJ6Pmk1WffYKTVHYu7s6MOWX84tkPyDDjV2d9ljMOH7rtypmvTt6P35yVmsBZs7mWr0+fzafra7YN0NpS18h1T7zP8/PXMmVkMf/9lcOT7utvzYw5Zfzs6fn07pXN7y/cte8/WWs313HxH99mVWUt93ytpM3jEnbW8kqtM8cN4eavHN7hH++7m4VrtnDNjPdYsGoLXz58CL/48uh2fZYrN27l32a8z1ufbOBzhwzkt+eM3dZw6Qq/fWEhd//jk1a7ajtbtwh8M8sEPgZOBMqAd4CvuvuHu9umWwd+MtyhbnOLk8Gq4CRRvS7xKA/m86kuh4bqXTaPk0Gl55OXESOXesw7MD9ORhZYZtDlZJnBpafNyywD6re0fnLKKwyCv88+VPcaxAvLYV5lHt57H5ZXOaccfgAXH30wltULMnMgKyd4zuy14+vmS13doakeYvXB87ZH3a7LYvWAQVZu8K1oN8+bGzOZ+sj7zF4RXGHz3PtrWFVZyzVfGsnUzx24YxeOe+IRBxLP7jv+u7Tio7VVfO/hOXy6voarv3goVx6XXNdQs5Ubt3LxH2exobqe+y6bzOQD+kGsEeKNwXPL1/EmiDUEdcvIhszmR04rf2du+93npNGDuf2i8V36LSssjbE4d766lNtfXky/vGx+c9aYDg88i8edR2av4LfPLwTgJ6eN4qLJ+4fe2n9m3iquemwelxy5P785a2yoZUH3CfyjgF+4+5cSf/8YwN1/u7ttenzgt0V99fbwT5wU4lXrKF+3muL+fcnsVRCMBM4ugJyWr1tZltUrCPO9hNgOmk9OVWuCx5Y1wTeVqrU7vPbqde078TSfbGINbd82SU1kUedZmEFulpHZMtA9vj3kk63rTidIt0yq6uPUNAEYGeYYjgGW2G/w9/Zlza/dnSyLkZcRJyPemFw9kmEZkJFNA1nUNBlxy9hWMtCiBq28tu1/Ny9L7LTF+uy4rEPhmPy28bjTFHf65GZS3DuHzOAfcVtNg9e+47LWyjLb5e/GmLO+poHahhhZmYa1oV7Q9n+DppjTKyuDIf3zWilpN/8d5A+Ab85sUznNusstDocCLTu9y4Ajdl7JzKYCUwH23z9CA0x69Q4eRQduW5QB7NNV5ZtBXv/gMWjU7leLNUFNObEta8mMJVrhscbtLfJtrxuCR1NiWaw+6D7Kyg1a/lm5iW8EubtZlvhmANu/ATTVtXjdclnwd0ZjHeXrNjKkfx6Z2VlBGO7wsNb/BojHt3dx7fC8fbl5jD7xGJvWV7GhJjhxOeAeBKNvi3iIY+DBswNmGYwaWkhB395B6zwjOxjjkZFoqW973dxyT9Q/3tSi9d+w/dtArGH7N4FYAzmxJtZUVLK2sobmINxWK/dErmyv5fagDE7elljF8G3Pu4Spbz+pdYUh/fPYp29uIrRt+zPsYRm7rXfz39nu7AOs2LiViqq6Ntaq7ceenWEM3acvtrupO1o7gfTqmt/pUj5bprtPA6ZB0MJPcXVkZ5lZ0HcImX2732RmGcCBe12rYww4IPHobrprvbqj7vw5dqUwf/FZBezX4u9hiWUiIpICYQb+O8AhZjbCzHKAC4FnQyxPRET2ILQuHXdvMrMrgf8juCxzurt/EFZ5IiKyZ6H24bv788DzYZYhIiLJSa9RGyIislsKfBGRiFDgi4hEhAJfRCQiutVsmWZWASxv5+YDgfWdWJ2eJMrHDtE+fh17dDUf/wHuntTMfN0q8DvCzEqTnU8i3UT52CHax69jj+axQ/uOX106IiIRocAXEYmIdAr8aamuQApF+dgh2sevY4+uNh9/2vThi4jInqVTC19ERPZAgS8iEhE9PvDN7GQz+8jMlpjZdamuT1czs2VmNt/M5plZWt8f0symm1m5mS1osazIzF40s8WJ58JU1jFMuzn+X5jZqsTnP8/MTk1lHcNiZvuZ2Stm9qGZfWBmVyWWp/3nv4djb/Nn36P78Ntzo/R0Y2bLgBJ3T/sBKGb2eaAaeNDdxySW/Sew0d1vSpzwC9392lTWMyy7Of5fANXufnMq6xY2M9sX2Nfd55pZH2AOcBbwDdL889/DsZ9PGz/7nt7CnwwscfdP3L0BeAw4M8V1kpC4+2vAxp0Wnwk8kHj9AMH/CGlpN8cfCe6+xt3nJl5XAQsJ7pud9p//Ho69zXp64Ld2o/R2/UP0YA7MNLM5iRvCR81gd1+TeL0WGJzKyqTIlWb2fqLLJ+26NHZmZsOB8cAsIvb573Ts0MbPvqcHvsCx7j4BOAW4IvG1P5I86J/suX2U7XMncBAwDlgD/HdqqxMuM+sNPAFc7e5bWr6X7p9/K8fe5s++pwd+5G+U7u6rEs/lwFME3VxRsi7Rx9nc11me4vp0KXdf5+4xd48D95DGn7+ZZRME3sPu/mRicSQ+/9aOvT2ffU8P/EjfKN3MChI/4mBmBcBJwII9b5V2ngW+nnj9deCZFNalyzWHXcLZpOnnb2YG3AssdPdbWryV9p//7o69PZ99j75KByBxKdKtbL9R+o0prlKXMbMDCVr1ENyf+JF0Pn4zexSYQjAt7Drg58DTwOPA/gRTa5/v7mn5w+Zujn8KwVd6B5YB327Rp502zOxY4HVgPhBPLP4JQV92Wn/+ezj2r9LGz77HB76IiCSnp3fpiIhIkhT4IiIRocAXEYkIBb6ISEQo8EVEIkKBL9IJzGyKmf011fUQ2RMFvohIRCjwJVLM7BIzm52YP/xuM8s0s2oz+11irvG/m1lxYt1xZvZ2YnKqp5onpzKzg83sJTN7z8zmmtlBid33NrMZZrbIzB5OjJAU6TYU+BIZZjYKuAA4xt3HATHgYqAAKHX3zwL/IBjBCvAgcK27H0YwyrF5+cPAHe5+OHA0wcRVEMxieDUwGjgQOCb0gxJpg6xUV0CkC50ATATeSTS+8wgm24oD/5tY5yHgSTPrB/R3938klj8A/Dkxd9FQd38KwN3rABL7m+3uZYm/5wHDgTfCPyyR5CjwJUoMeMDdf7zDQrPrd1qvvfON1Ld4HUP/f0k3oy4diZK/A+eZ2SDYdj/UAwj+Pzgvsc5FwBvuvhnYZGafSyy/FPhH4o5DZWZ2VmIfvcwsv0uPQqSd1AKRyHD3D83sZwR3CMsAGoErgBpgcuK9coJ+fgim270rEeifAJclll8K3G1mv0rs4ytdeBgi7abZMiXyzKza3Xunuh4iYVOXjohIRKiFLyISEWrhi4hEhAJfRCQiFPgiIhGhwBcRiQgFvohIRPx/8U+yeC5Ied4AAAAASUVORK5CYII=\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": "e7137f66-d878-412f-bb94-9b17a4d607e2"
      },
      "source": [
        "predictions = model.predict(testX, batch_size=32)"
      ],
      "execution_count": 52,
      "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": "d53f495c-544f-4dd1-87b6-d49c6c5d7585"
      },
      "source": [
        "print(predictions.argmax(axis=1))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 1 0 1 1 0 1 0 1 0 1 0 1 1 0 1 0 1 1 1 0 1 1\n",
            " 0 0 1 0 0 0 1 0 1 1 1 0 0 0 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 0 1\n",
            " 0 0 1 0 1 1 0 1 0 0 1 0 0 0 1 0 1 1 1 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1\n",
            " 0 1 1 1 0 0 1 1 1 1 1 0 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 0 0 0 0 1 1 0 0 0\n",
            " 1 1 0 0 1 0 1 0 1 1 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 0 0 1 0 0 1 0 0 0 1 1 1\n",
            " 0 1 0 1 0 0 0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 0 0 1 0\n",
            " 0 1 0 0 1 0 1 1 0 0 1 0 1 0 1 0 1 0 1 1 1 0 1 1 1 0 1 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6puOgflYR_e"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPAZkmBJVGCD",
        "outputId": "976aa3e7-f2a3-4936-afbb-bb174eace8c4"
      },
      "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": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] evaluating network...\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Covid       1.00      1.00      1.00       116\n",
            "      Normal       1.00      1.00      1.00       134\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": "72cc897a-b608-427b-9cf2-f7ada633f5a9"
      },
      "source": [
        "cf_matrix = confusion_matrix(testY,predictions.argmax(axis=1))\n",
        "print(cf_matrix)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[116   0]\n",
            " [  0 134]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLp1ooZeY90p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "cab35c42-21ce-4fbb-eb20-785d4d346e05"
      },
      "source": [
        "\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cf_matrix, display_labels=encoder.classes_)\n",
        "disp.plot(cmap=plt.cm.Blues)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f85138d3a50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVYAAAEGCAYAAAA+Ib10AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deZxWZf3/8dd7GFkVBAYJQQVNMzLcKASNUCol/UKpuZdbqWVqlppLPzEr86uZW6RfUgNTwT3NFUMRVxJwA9TcV0QQRFFQBz6/P84ZuB1nuefm3DNz4P3kcR5zn+W+znVmhvdc93XOuY4iAjMzy05FS1fAzGxN42A1M8uYg9XMLGMOVjOzjDlYzcwyVtnSFWhpardeqGP3lq6GNcG2m1a1dBWsiWbOnLEgInqsThltOm8SUb200e1i6fy7I2K31dnX6nKwduxO++Gnt3Q1rAkemnhYS1fBmqjDOnp1dcuI6qW0+9I+jW637IkxLf6Xd60PVjPLC4Hy0XvpYDWzfBBQ0aala1EUB6uZ5YfU0jUoioPVzHLCXQFmZtlzi9XMLEPCLVYzs2zJLVYzs8z5qgAzsyz55JWZWbaEuwLMzDLnFquZWZbcFWBmli0BbXzyyswsW+5jNTPLkrsCzMyy5xarmVnG3GI1M8uQ8nNLaz7i38wMkltaG5uKIOkKSe9ImlWw7FxJz0p6StLNktYvWHeKpBckPSdp10arWdLBmZk1u/TkVWNTccYBtR84eA+wVUQMAP4LnAIgqT+wH/CV9D1/ldRggjtYzSw/aroDGpqKEBFTgYW1lk2KiOp09lGgT/p6FDAxIj6OiJeBF4CvN1S+g9XM8qFmPNbGW6xVkqYXTEeUsLfDgDvT172B1wvWvZEuq5dPXplZThR9HeuCiBhY8l6k04Bq4OpSy3Cwmll+lHk8VkmHAHsAwyMi0sVvAhsVbNYnXVYvdwWYWX5k1Mdad9HaDTgJGBkRHxWsuhXYT1I7Sf2AzYH/NFSWW6xmlg/K7pZWSROAYST9sW8Ao0muAmgH3KMkoB+NiKMiYrak64A5JF0ER0fE8obKd7CaWX5kdINAROxfx+LLG9j+D8Afii3fwWpmuaGc3HnlYDWzXEiezOJgNTPLjoQqHKxmZplyi9XMLGMOVjOzjDlYzcyypHTKAQermeWCkFusZmZZq6jIx134DlYzyw23WM3MsuQ+VjOz7LnFamaWIZ+8MjMrA9/SamaWJbkrwMwscw5WM7OMOVjNzDLkk1dmZuWQj1x1sJpZTsi3tJqZZc5dAWZmWctHrjpYW4uLj9yJ72y3EQveX8aOJ978ufV777gpx40cgARLln3Kry57hNmvLVytfbatrOCSo4eydb8qFi35mMMuvI/X5y9h2Fc35PT9B9K2soJPqlcw+urHeGD23NXalzXs3w/P4ZTzbmD5ihX8cNQQjj/kOy1dpVYpLy3WFu2wkPQFSRMlvShphqQ7JG3RxDIGSrqonnWvSKrKprbldc39z/ODP06qd/1r85ewx5l3sNNJ/+RPNz3JBUfsWHTZG/VYl1tPH/G55QftvAXvLfmEgb+4gUtun8UZBwwE4N0PlnHAufew00n/5Oi/TuWSo4c2/YCsaMuXr+DEc67j+gt/xqPX/YYbJ83g2Zf8h6w2SUVNRZZ1haR3JM0qWNZN0j2Snk+/dk2XS9JFkl6Q9JSk7Rorv8WCVcl34GZgSkRsFhHbA6cAPZtSTkRMj4hjy1HH5vTIs/NY9OHH9a7/z3/fYfGHnwDw2PPv0Ktbx5XrfrDTZtzz+//h/rNH8ecfD6GiyF+u7w7cmIlTnwfglmmvMPQrGwLw9CsLeXvRUgCeeeM9OrStpG1lPk4a5NGM2a+w6UZV9O1TRdt1Ktnz29txx/1PtXS1WqWsghUYB+xWa9nJwOSI2ByYnM4DjAA2T6cjgEsaK7wl/7fsDHwaEZfWLIiIJ4EHJZ0raZakpyXtC5C2bHev2VbSOEl7Sxom6bZ0WXdJkyTNlnQZuemRaZof7rwFk594A4AtNuzC9wf3Y8To2/jmybewfEXwg502K6qcXt068ea7HwKwfEXw/tJP6LZeu89sM3JQX558+V0+qV6R7UHYSnPnL6Z3z64r5zfs2ZW58xe3YI1aL1Wo0akYETEVqN2XNgoYn74eD3yvYPmVkXgUWF9Sr4bKb8k+1q2AGXUs3xPYBtgaqAIekzQVuBbYB7hdUltgOPBTYFDBe0cDD0bEmWkIH17XjiUdQfKXB3Xons3RNJOd+n+Bg3beghGjbwdg6Fc3ZOt+VUz+w0gA2retZMH7ywC48pfD2WSDdWlbWUHvqnW5/+xRAPzfnXO45v7nG93Xln3WZ/QBA9nrrLvLdDRmTVPmPtaeEVHTB/M2qz499wZeL9jujXRZvf01rfHk1U7AhIhYDsyTdD/wNeBO4EJJ7Uia8FMjYmmtb/RQkmAmIm6XtKiuHUTEWGAsQEXXvlG2I8lY/427cuGRO7HP2ZNYtCTpNhAwcerz/G7i5/9G/ejPk4Gkj3XMT7/ByDPv/Mz6uQs/pHf3Try18CPaVIjOHdqy8IOk3A27deTKXw3nZ2Om8sq8D8p7YGu5Xj268Oa8Vb+qb81bRK8eXVqwRq1U8YOwVEmaXjA/Nv0/X7SICEklZ0NLdgXMBrYvduOIWAZMAXYF9iVpwa41enfvxJW/HM5Px0zlxbnvr1w+ddZcRg7qS1Xn9gCs36ktfao6FVXmnTNeZ7+hmwMwalDflWf+O3dsy8Rff4czr5nOtP++k/GRWG3b9d+EF1+bz6tvLuCTT6u56Z6ZjBg6oKWr1eoIkBqfgAURMbBgKjZU59V8xE+/1vzyvwlsVLBdn3RZvVqyxXovcJakI2oOXNIA4D1gX0njgW4krdAT0/dcC/wYGAgcUkeZU4EDgN9LGgF0rWObVulvxwxjx/5foPt67Zk1Zl/OvmEmlW2Sv3vj/v0cJ+21Dd3Wbce5hw0GoHp5MPy0W3nuzfc467qZ3HjqrlRIfLp8BSdd8QhvLPiw0X1edd9/ufTooUy/YG8WLfmYH180BYCf7Ppl+vVcjxP32oYT99oGgL3OuntlF4Nlq7KyDeectA97HTuG5cuDA0fuwJc3a7ALby1V9rECbgUOBs5Ov95SsPznkiaSdD0uLugyqLumES33SVjShsAFJC3XZcArwC9I+j9HAAH8PiKuTbdfB5gH3BIRh6bLhgEnRMQekroDE0j6Px4GvgNsHxEL6qtDRde+0X746WU5PiuPhRMPa+kqWBN1WEczImLg6pTR/gtbxCYHX9zodv89Z7dG9yVpAjCM5DzOPJLzM/8ErgM2Bl4F9omIhekVTH8h6YL8CDg0IqbXVW6NFu1jjYi3SE5I1XYiq1qphdt/StKKLVw2haSLgIh4lyRMzWxNs+qj/mqLiP3rWTW8jm0DOLop5bfGk1dmZp8joMKPZjEzy1ZO7mh1sJpZfuRlrAAHq5nlQ4Z9rOXmYDWzXBDyQNdmZllzi9XMLGPuYzUzy5L7WM3MspWMFZCPZHWwmllu5CRXHaxmlh++88rMLEvFj8fa4hysZpYLNeOx5oGD1cxyouzjsWbGwWpmuZGTXHWwmllOyCevzMwy5etYzczKwMFqZpaxnOSqg9XM8sMtVjOzLHkQFjOzbCUDXecjWR2sZpYbFTlpsjpYzSw3cpKr5OMBMma21lM6CEtjU3Fl6XhJsyXNkjRBUntJ/SRNk/SCpGsltS21rg5WM8uNCjU+NUZSb+BYYGBEbAW0AfYD/hc4PyK+CCwCDi+1nvV2BUi6GIj61kfEsaXu1MysFBmevKoEOkj6FOgIzAV2AQ5I148HzgAuKbXw+kwvpUAzs3IQyZUBRaiSVJhfYyNibM1MRLwp6U/Aa8BSYBIwA3gvIqrTzd4Aepda13qDNSLGF85L6hgRH5W6IzOz1VVkg3VBRAysb6WkrsAooB/wHnA9sFsW9avRaB+rpMGS5gDPpvNbS/prlpUwM2tUESeuijx59S3g5YiYHxGfAjcBOwLrS6ppbPYB3iy1qsWcvLoA2BV4FyAingSGlrpDM7NSSY1PRXgN2EFSRyVJPByYA9wH7J1uczBwS6n1LOqqgIh4vdai5aXu0MysFCK5QaCxqTERMQ24AZgJPE2Sg2OBXwO/lPQC0B24vNS6FnODwOuShgAhaR3gOOCZUndoZlaqrK4KiIjRwOhai18Cvp5F+cW0WI8CjiY5Q/YWsE06b2bWbIrpBmgtd2Y12mKNiAXAgc1QFzOzBuVlrIBirgrYVNK/JM2X9I6kWyRt2hyVMzMrpCKm1qCYroBrgOuAXsCGJNd8TShnpczM6pLVWAHlVkywdoyIf0REdTpdBbQvd8XMzAolVwWs/lgBzaGhsQK6pS/vlHQyMJFk7IB9gTuaoW5mZqtozRjoegZJkNYcyZEF6wI4pVyVMjOrS2v5qN+YhsYK6NecFTEza0hNV0AeFPUEAUlbAf0p6FuNiCvLVSkzs7rkvsVaQ9JoYBhJsN4BjAAeBBysZtas8hGrxV0VsDfJIAVvR8ShwNZAl7LWysysFgnaVKjRqTUopitgaUSskFQtqTPwDrBRmetlZvY5a0xXADBd0vrA30iuFFgCPFLWWpmZ1SEnuVrUWAE/S19eKukuoHNEPFXeapmZfZYobljA1qChGwS2a2hdRMwsT5XMzOrQikavakxDLdbzGlgXJE80zL1tN63ioYmHtXQ1rAm6fu3nLV0FayG572ONiJ2bsyJmZg0R0CbvwWpm1tq0kqupGuVgNbPccLCamWUoefRKPpK1mCcISNJBkk5P5zeWlMkDt8zMmiIv47EWc0vrX4HBwP7p/AfAmLLVyMysHmvMwwSBQRGxnaTHASJikaS2Za6XmdlnCKhsLcnZiGJarJ9KakNy7SqSegArylorM7M6ZNVilbS+pBskPSvpGUmDJXWTdI+k59OvXUutZzHBehFwM7CBpD+QDBl4Vqk7NDMrhZTc0trYVKQLgbsiYkuSEfueAU4GJkfE5sDkdL4kxYwVcLWkGSRDBwr4XkQ8U+oOzcxKlUVPgKQuwFDgEICI+AT4RNIokrGnAcYDU4Bfl7KPYga63hj4CPhX4bKIeK2UHZqZlarIs/5VkqYXzI+NiLEF8/2A+cDfJW1NMmrfcUDPiJibbvM20LPUehZz8up2Vj1UsH1aqeeAr5S6UzOzphIUO5D1gogY2MD6SmA74JiImCbpQmp97I+IkBSl1rWYroCvFs6no179rJ7NzczKI7vrVN8A3oiIaen8DSTBOk9Sr4iYK6kXyaD+JSnm5NVnpMMFDip1h2ZmpVIR/xoTEW8Dr0v6UrpoODAHuBU4OF12MHBLqfUspo/1lwWzFSRN6LdK3aGZWSkyfvz1McDV6TX5LwGHkuTbdZIOB14F9im18GL6WNcreF1N0ud6Y6k7NDMrVVbBGhFPAHX1ww7PovwGgzW9MWC9iDghi52Zma2OvAzC0tCjWSojolrSjs1ZITOzuiSPv27pWhSnoRbrf0j6U5+QdCtwPfBhzcqIuKnMdTMz+4zcP0ywQHvgXZJnXNVczxqAg9XMmk3GJ6/KqqFg3SC9ImAWqwK1RskXzpqZlSonDdYGg7UNsC7UeWGYg9XMmpmoKOI61dagoWCdGxFnNltNzMwaINaMFmtODsHM1gqCypx0sjYUrJlcKGtmloU1osUaEQubsyJmZo1Zky63MjNrFXKSqw5WM8sHUcJwfC3EwWpm+SB3BZiZZSq588rBamaWqXzEqoPVzHIkJw1WB6uZ5YXyPx6rmVlr4qsCzMzKwCevzMyypDXg0SxmZq2JuwLMzMrALVYzs4zlI1YdrGaWEwLa5KTFmpcuCzMzpMan4stSG0mPS7otne8naZqkFyRdK6ltqfV0sJpZTqiof01wHPBMwfz/AudHxBeBRcDhpdbUwWpmuZFVi1VSH2B34LJ0XsAuwA3pJuOB75VaT/exmlkuJJdbFZWcVZKmF8yPjYixtba5ADgJWC+d7w68FxHV6fwbQO9S6+pgNbN8KL5FuiAiBtZbjLQH8E5EzJA0LKPafYaD1cxyI6NbWncERkr6LtAe6AxcCKwvqTJttfYB3iy5nlnU0sys3JKBrhufGhMRp0REn4joC+wH3BsRBwL3AXunmx0M3FJqXR2sZpYbGV8VUNuvgV9KeoGkz/XyUgtyV4CZ5UbW9wdExBRgSvr6JeDrWZTrYF0D/fvhOZxy3g0sX7GCH44awvGHfKelq7RGuvj/HciuO23FgkUfMGS/sz63fsTQr3LaUXuwIoLq6hWc+ucbePTJl1Zrn+t37sgVZx3Gxr268drchRx6yuUs/mApP9htIMf96NtIYslHy/jV2dcy6/mSuwhbrdVskTabsnUFSApJ5xXMnyDpjHLtr546TJFU79nBNdHy5Ss48ZzruP7Cn/Hodb/hxkkzePaluS1drTXShNseZe9jx9S7fupjz7HTAX9k6IFnc8zvruLC3xxQdNk7brc5Y0Yf9Lnlxx/8baY+9hwD9zqTqY89x/EHJ380X33rXXY/8gJ23P8szr38Ls4/df+mH1Arl1Ufa3MoZx/rx8CekqpKebMkt6ZLMGP2K2y6URV9+1TRdp1K9vz2dtxx/1MtXa010sOPv8ii9z+qd/2HSz9Z+bpjh3ZErFp3zEHDmTz+RB685hROPuK7Re9zxDcHMOG2aQBMuG0a3x02AID/PPUyiz9YCsBjT7/Mhhus35RDyQeJiiKm1qCc4VUNjAWOB04rXCGpL3AFUAXMBw6NiNckjQOWAdsCD0nqBixN5zcADgN+BAwGpkXEIWl5lwBfAzoAN0TE6DIeV6s2d/5ievfsunJ+w55dmTHrlZar0Fpu92EDOP3okfTouh77Hn8pADsP2pJNN96A4QefiyQmnHckQ7bdjIcff7HR8jboth7z3n0fgHnvvs8G3db73DY/HDWEfz88J9sDaSVaR2w2rtytwjHAU5LOqbX8YmB8RIyXdBhwEatuH+sDDImI5WnQdiUJ0pHArSTXoP0YeEzSNhHxBHBaRCyU1AaYLGlARNTbTJN0BHAEwEYbb5zVsZp9zu1TnuL2KU8xZNvNOPWo3fn+0X9h5x2+zC6DtmTq1ScD0KlDOzbdaAMefvxF7vn7CbRrW0mnDu3o2rnjym3OuPgW7n30mc+VX9gKBthp+805aORgRvzk/LIfW3NLugLyEa1lDdaIeF/SlcCxJC3PGoOBPdPX/wAKg/f6iFheMP+viAhJTwPzIuJpAEmzgb7AE8A+aVhWAr2A/kC9wZre3jYWYPvtB0Z92+VRrx5deHPeopXzb81bRK8eXVqwRgZJt0Hf3lV069IJCc4fN4lxNz/0ue2+feifgKSP9YD/GcTRv73qM+vfWfgBPbt3Zt6779Oze2fmL/pg5bqvfHFDLvrNAfzguEtYtPjD8h5QC8lHrDbPdawXkIwS06nI7Wv/Rnycfl1R8LpmvlJSP+AEYHhEDABuJ7mbYq20Xf9NePG1+bz65gI++bSam+6ZyYihA1q6Wmulfn1WnV4Y8KU+tF2nkoWLP+TeR57hwJGD6dQhGZWuV48uVHVdt6gy75r6NPvvMQiA/fcYxJ1p/3mfnl258pyfcNToK3nxtXcyPpJWREVMrUDZTxClH9GvIwnXK9LFD5Pc8fAP4EDggdXYRWeSMF4sqScwgvS6tLVRZWUbzjlpH/Y6dgzLlwcHjtyBL2/Wq6WrtUa67PeHsOP2m9N9/XWZddvvOHvsHaxT2QaAv9/0ICN32YZ9dx9EdfVyli77lMNPTX7975v2LFv0+wKTrjgBgCUffcyRp49nwaIlje7z/PH38Pc/HsZBIwfz+tsLOfSUpMwTfzyCbl068adf7wtAdfUKdjm4dg9c/uWlK0BRu5Mmq4KlJRGxbvq6J/AycE5EnCFpE+Dv1H3y6raIuCF938r59ITXbRGxVR3rxgFDgNeBxcCtETFO0hTghIgoHOnmM7bffmA8NK3e1dYKdf3az1u6CtZEy54YM6OhgVGK8eWvbhtX3jKl0e2+vtn6q72v1VW2FmtNqKav5wEdC+ZfJRn7sPZ7DqlvPiJeAbaqZ91n3lewfFiTK25mrVc+Gqy+88rM8iHpQs1HsjpYzSwfmvhMq5bkYDWz3MhJrjpYzSwvhHLSZHWwmllu5CRXHaxmlg+t6Pr/RjlYzSw/cpKsDlYzyw1fbmVmljH3sZqZZcnXsZqZZc9dAWZmGRJusZqZZS4nudosA12bmWUjg4GuJW0k6T5JcyTNlnRcurybpHskPZ9+7dpYWfVxsJpZbmT0lNZq4FcR0R/YAThaUn/gZGByRGwOTE7nS6tnqW80M2tuWTyZJSLmRsTM9PUHwDNAb2AUMD7dbDyrHnDaZO5jNbP8KK6TtUpS4WNBxqYPEP18ccmTSbYFpgE9I2JuuuptoGep1XSwmlkuNGGg6wXFPJpF0rrAjcAv0idKr1yXPhm65OdWuSvAzPIhvUGgsamooqR1SEL16oi4KV08T1KvdH0voOTH3TpYzSw3suhjVdI0vRx4JiL+XLDqVuDg9PXBwC2l1tNdAWaWE5kNdL0j8EPgaUlPpMtOBc4GrpN0OPAqsE+pO3CwmlluZJGrEfEg9Tduh6/+HhysZpYTHujazKwccpKsDlYzyw2PbmVmljGPbmVmliVBhYPVzCxr+UhWB6uZ5YIHujYzK4Oc5KqD1czywy1WM7OMZXRLa9k5WM0sN/IRqw5WM8uJpgwL2NIcrGaWG77zyswsa/nIVQermeVHTnLVwWpmeVH0461bnIPVzHIhT3de+ZlXZmYZc4vVzHIjLy1WB6uZ5YYvtzIzy5JvEDAzy1aeTl45WM0sN9wVYGaWsby0WH25lZnlhoqYiipH2k3Sc5JekHRy1vV0sJpZfmSQrJLaAGOAEUB/YH9J/bOspoPVzHJBQIXU6FSErwMvRMRLEfEJMBEYlWVd1/o+1pkzZyzosI5ebel6lEkVsKClK2FFW5N/XpusbgEzZ864u8M6qipi0/aSphfMj42IsQXzvYHXC+bfAAatbv0KrfXBGhE9WroO5SJpekQMbOl6WHH882pYROzW0nUolrsCzGxt8yawUcF8n3RZZhysZra2eQzYXFI/SW2B/YBbs9zBWt8VsIYb2/gm1or459UMIqJa0s+Bu4E2wBURMTvLfSgisizPzGyt564AM7OMOVjNzDLmYM0JSV+QNFHSi5JmSLpD0hZNLGOgpIvqWfeKVNQ1glYHSSHpvIL5EySd0cx1mCLJl2u1Ag7WHJAk4GZgSkRsFhHbA6cAPZtSTkRMj4hjy1FH42Ngz1L/OEnyieQ1iIM1H3YGPo2IS2sWRMSTwIOSzpU0S9LTkvYFSFu2u9dsK2mcpL0lDZN0W7qsu6RJkmZLuoz8PFm4taomOat/fO0VkvpKulfSU5ImS9o4XT5O0qWSpgHnpPOXSHpU0kvpz+sKSc9IGldQ3iWSpqc/u9821wFa8Rys+bAVMKOO5XsC2wBbA98CzpXUC7gW2AcgvU5vOHB7rfeOBh6MiK+QtIY3Lk/V1ypjgAMldam1/GJgfEQMAK4GCrtj+gBDIuKX6XxXYDBJQN8KnA98BfiqpG3SbU5L79AaAHxT0oCyHI2VzMGabzsBEyJieUTMA+4HvgbcCewsqR3JCD5TI2JprfcOBa4CiIjbgUXNV+01U0S8D1wJ1O5uGQxck77+B8nPrcb1EbG8YP5fkVwD+TQwLyKejogVwGygb7rNPpJmAo+ThG6mIzPZ6nOw5sNsYPtiN46IZcAUYFdgX5IWrDWPC4DDgU5Fbv9hrfmP068rCl7XzFdK6gecAAxPW8C3A+1Lr66Vg4M1H+4F2kk6omZB+vHvPWBfSW0k9SBphf4n3eRa4FDgG8BddZQ5FTggLWsEyUdQW00RsRC4jiRcazxMctskwIHAA6uxi84kYbxYUk+STyTWyvhMZA5EREj6PnCBpF8Dy4BXgF8A6wJPAgGcFBFvp2+bRPKx85Z0zMnafgtMkDSb5D/+a+U9irXKecDPC+aPAf4u6URgPskfvJJExJOSHgeeJRn67qHVqaiVh29pNTPLmLsCzMwy5mA1M8uYg9XMLGMOVjOzjDlYzcwy5mC1RklaLumJdEyC6yV1XI2yxknaO319WUPPc0/vlR9Swj7qHKmrmBG8JC1p4r7OkHRCU+toazYHqxVjaURsExFbAZ8ARxWuLHVkpoj4cUTMaWCTYUCTg9WspTlYrakeAL6YtiYfkHQrMCe9++tcSY+lozgdCcmQh5L+Iuk5Sf8GNqgpqHD8UEm7SZop6cl0BKi+JAF+fNpa/oakHpJuTPfxmKQd0/c2eaQuSf9Mx7WdXXhHW7ru/HT55PSONiRtJumu9D0PSNoyi2+mrZl855UVLW2ZjmDVLbLbAVtFxMtpOC2OiK+lg788JGkSsC3wJZKBQnoCc4ArapXbA/gbMDQtq1tELJR0KbAkIv6UbncNcH5EPJgOvXc38GVWjdR1ZjpcYuHtpPU5LN1HB+AxSTdGxLsk9/hPj4jjJZ2elv1zkiEBj4qI5yUNAv4K7FLCt9HWAg5WK0YHSU+krx8ALif5iP6fiHg5Xf4dYEBN/ynQBdicZPyCCekITm9JureO8ncgGYHrZVh5v31dvgX0l1Y2SDtLWjfdx57pe2+XVMxIXcemtwlD8oz5zYF3SQY7qRm05irgpnQfQ4DrC/bdroh92FrKwWrFWBoR2xQuSAOmcGQmAcdExN21tvtuhvWoAHZIR++qXZeiSRpGEtKDI+IjSVOof4SoSPf7Xu3vgVl93MdqWbkb+KmkdQAkbSGpE8koWjUjcPUieRpCbY8CQ9Mh8ZDULV3+AbBewXaTSAY0Id2uJuiaOlJXF2BRGqpbkrSYa1QANa3uA0i6GN4HXpb0g3QfkrR1I/uwtZiD1bJyGUn/6UxJs4D/I/lEdDPwfLruSuCR2m+MiPnAESQfu59k1UfxfwHfrzl5RTKA9MD05NgcVl2d8FuSYJ5N0iXQ2Ehdd5GMbfoMcDZJsNf4EPh6egy7AGemyw8EDk/rNxsYVcT3xNZSHt3KzCxjbrGamWXMwWpmljEHq5lZxhysZiNrhHUAAAAYSURBVGYZc7CamWXMwWpmljEHq5lZxv4/9yeDve/cIsMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HeUuRugbHOIO"
      },
      "source": [
        "import io\n",
        "\n",
        "path='/content/drive/MyDrive/Dataset/Normal/IM00009.tif'\n",
        "save_path='/content/drive/MyDrive/Testing/Normal/normal9.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": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UAtrBsc8HgE1",
        "outputId": "0a5d5f3c-d7df-4bc2-e90f-d7d790197dfe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "width = 224\n",
        "height = 224\n",
        "dsize = (width, height)\n",
        "image_testing = cv2.imread('/content/drive/MyDrive/Testing/Normal/normal9.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": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7aOiNoF-Hpts",
        "outputId": "cd7dff7c-9d8a-457b-9e4d-85cec49893ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "plt.imshow(image_testing)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f8510715450>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        },
        {
          "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": "UUxECajwHsGc",
        "outputId": "0185c21c-5625-4858-b0c5-6c03dd2809d1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "image_testing = np.expand_dims(image_testing, axis=0)\n",
        "print(image_testing.shape)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 224, 224, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXHZXKxuHtJQ",
        "outputId": "e804077e-3dc2-4d66-a697-540c90e0dad2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "output = model.predict(image_testing, 1)\n",
        "print(output)\n",
        "print(encoder.classes_[output.argmax(axis=1)])"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.0152094e-07 9.9999988e-01 3.6290699e-19]]\n",
            "['Normal']\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}