CLAHEOnly_adam_1e-2ipynb

{
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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Copy of scenes_nn_4 layers.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rfadhila07/Test-result/blob/main/CLAHEOnly_adam_1e-2ipynb\" 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": "b4ec982d-6cfe-4aaa-b7d4-569deaebf679"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H215ikbNVVZS",
        "outputId": "97fcc0e3-b931-4a6d-d02b-14b0001eb162"
      },
      "source": [
        "cd /content/drive/MyDrive/ "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z7VB1d4wVdov",
        "outputId": "293108bc-4d0d-4e4b-a091-e23473cad9e6"
      },
      "source": [
        "ls"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'1101174267 m rifqi fadhila mixer.pdf'\n",
            "'1101174267_Muhammad Rifqi F_2_TT4107.pdf'\n",
            "'1101174267_MuhammadRifqiFadhila_1&3_TT4107.pdf'\n",
            " 1101174267_MuhammadRifqiFadhila_3.3_TT4107.pdf\n",
            "'1101174267_muhammad rifqi fadhila_Filter2.jpg'\n",
            "'1101174267 muhammad rifqi fadhila OSILATOR.pdf'\n",
            "'1101174267 muhammad rifqi fadhila penguat.pdf'\n",
            "'1101174267_Muhammad Rifqi Fadhila_Proposal Tugas Akhir dan lembar pemeriksaan.pdf'\n",
            " best_weight_scene_EfficientNetB0.h5\n",
            " \u001b[0m\u001b[01;34mCLAHE\u001b[0m/\n",
            " \u001b[01;34mCLAHEOnly\u001b[0m/\n",
            " \u001b[01;34mClassroom\u001b[0m/\n",
            " \u001b[01;34mDataset\u001b[0m/\n",
            " DSC_7396.JPG\n",
            " \u001b[01;34mGaussian\u001b[0m/\n",
            "'Getting started.pdf'\n",
            " IMG-20210217-WA0001.jpg\n",
            "\u001b[01;34m'MPTI 2021'\u001b[0m/\n",
            " \u001b[01;34mNormalized\u001b[0m/\n",
            "'PMMB2021_BATCH1_1101174267_MUHAMMAD RIFQI FADHILA.rar'\n",
            " Screenshot_20210217-060246.jpg\n",
            " \u001b[01;34mTesting\u001b[0m/\n",
            " TT_41_07_101174267_MUHAMMADRIFQIFADHILA.pdf\n",
            " \u001b[01;34mValo\u001b[0m/\n",
            " \u001b[01;34mVisualizable\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5IGm26ZzVGBs"
      },
      "source": [
        "# import the necessary packages\n",
        "from keras.models import Sequential\n",
        "from keras.layers.convolutional import Conv2D\n",
        "from keras.layers.convolutional import MaxPooling2D\n",
        "from keras.layers.core import Activation\n",
        "from keras.layers.core import Flatten\n",
        "from keras.layers.core import Dense\n",
        "from keras.optimizers import Adam, SGD, RMSprop\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from PIL import Image\n",
        "from imutils import paths\n",
        "from skimage import io\n",
        "from skimage.filters import gaussian\n",
        "from skimage import exposure\n",
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iF6yEboXxV9J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbbf98c9-9f0f-44c2-ff5a-fd5489e4addb"
      },
      "source": [
        "#Normalized dataset\n",
        "import PIL.Image as pil_image\n",
        "import io\n",
        "path ='/content/drive/MyDrive/Dataset/Normal/'\n",
        "path_no = 1\n",
        "for paths in os.listdir(path) :\n",
        "  if paths.endswith(\".tif\"):\n",
        "    filepath = os.path.join(path, paths)\n",
        "    img = Image.open(filepath)\n",
        "    array=np.array(img)\n",
        "    max_val=np.amax(array)\n",
        "    normalized=(array/max_val)\n",
        "    im = pil_image.fromarray(normalized)\n",
        "    if path_no < 10 :\n",
        "      save_path='/content/drive/MyDrive/Normalized/Normal/ ' + str('IM0000') +str(path_no) + '.tif'\n",
        "    elif path_no <100 :\n",
        "      save_path='/content/drive/MyDrive/Normalized/Normal/' + str('IM000') +str(path_no) + '.tif'\n",
        "    else :\n",
        "      save_path='/content/drive/MyDrive/Normalized/Normal/' + str('IM00') +str(path_no) + '.tif'\n",
        "    im.save(save_path,'TIFF') \n",
        "    path_no +=1\n",
        "print(\"Normalized Success\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normalized Success\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9Uvnrw4rqNh",
        "outputId": "6b80c02f-e9cb-4d0a-e464-0901dbf4c235"
      },
      "source": [
        "#Gaussian Filter\n",
        "import cv2\n",
        "from skimage import io\n",
        "from skimage.filters import gaussian\n",
        "path ='/content/drive/MyDrive/Normalized/Normal/'\n",
        "path_no = 1\n",
        "for paths in os.listdir(path) :\n",
        "  if paths.endswith(\".tif\"):\n",
        "    filepath = os.path.join(path, paths)\n",
        "    img = Image.open(filepath)\n",
        "    img = np.array(img)\n",
        "    gausfilter = gaussian(img, sigma=1, mode='constant', cval=0.0)\n",
        "    im = Image.fromarray(gausfilter)\n",
        "    if path_no < 10 :\n",
        "      save_path='/content/drive/MyDrive/Gaussian/Normal/' + str('IM0000') +str(path_no) + '.tif'\n",
        "    elif path_no <100 :\n",
        "      save_path='/content/drive/MyDrive/Gaussian/Normal/' + str('IM000') +str(path_no) + '.tif'\n",
        "    else :\n",
        "      save_path='/content/drive/MyDrive/Gaussian/Normal/' + str('IM00') +str(path_no) + '.tif'\n",
        "    im.save(save_path,'TIFF') \n",
        "    path_no +=1\n",
        "\n",
        "print(\"Denoissing Success\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Denoissing Success\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6b1PTs21ki0",
        "outputId": "028554f8-d7b6-450b-d05a-def065b74cda"
      },
      "source": [
        "#CLAHE\n",
        "from skimage import exposure\n",
        "path ='/content/drive/MyDrive/Gaussian/Normal/'\n",
        "path_no = 1\n",
        "for paths in os.listdir(path) :\n",
        "  if paths.endswith(\".tif\"):\n",
        "    filepath = os.path.join(path, paths)\n",
        "    img = Image.open(filepath)\n",
        "    img = np.array(img)\n",
        "    clahe = exposure.equalize_adapthist(img)\n",
        "    im = Image.fromarray(clahe)\n",
        "    if path_no < 10 :\n",
        "      save_path='/content/drive/MyDrive/CLAHE/Normal/' + str('IM0000') +str(path_no) + '.tif'\n",
        "    elif path_no <100 :\n",
        "      save_path='/content/drive/MyDrive/CLAHE/Normal/' + str('IM000') +str(path_no) + '.tif'\n",
        "    else :\n",
        "      save_path='/content/drive/MyDrive/CLAHE/Normal/' + str('IM00') +str(path_no) + '.tif'\n",
        "    im.save(save_path,'TIFF') \n",
        "    path_no +=1\n",
        "\n",
        "print(\"CLAHE Success\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CLAHE Success\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HCs86pnwc5WL",
        "outputId": "f64c3fdd-48f4-4b68-e221-4101289bb64f"
      },
      "source": [
        "# grab all image paths in the input dataset directory, then initialize\n",
        "# our list of images and corresponding class labels\n",
        "print(\"[INFO] loading images...\")\n",
        "imagePaths = paths.list_images(\"CLAHEOnly\")\n",
        "data = []\n",
        "labels = []"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] loading images...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KsbyXzXK0k-4"
      },
      "source": [
        "import cv2\n",
        "\n",
        "im = cv2.imread('/content/drive/MyDrive/CLAHEOnly/Covid/IM00001.tif', -1)\n",
        "im_rgb = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "me1s-uFI7kJi",
        "outputId": "ee65e6ec-f105-4484-ded0-aa7c9f110acc"
      },
      "source": [
        "\n",
        "plt.imshow(im_rgb)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f303d83bc10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1QQBNriY1JrE"
      },
      "source": [
        "\n",
        "width = 224\n",
        "height = 224\n",
        "dsize = (width, height)\n",
        "# loop over our input images\n",
        "for imagePath in imagePaths:\n",
        "  # load the input image from disk, resize it to 64x64 pixels, scale\n",
        "\t# the pixel intensities to the range [0, 1], and then update our\n",
        "\t# images list\n",
        "    image = cv2.imread(imagePath,-1)\n",
        "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "  #image = image.convert('L')\n",
        "    image = cv2.resize(image,dsize)\n",
        "    image = np.array(image)\n",
        "    data.append(image)\n",
        "\n",
        "  # extract the class label from the file path and update the\n",
        "\t# labels list\n",
        "    label = imagePath.split(os.path.sep)[-2]\n",
        "    labels.append(label)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rzbolh88VGB2",
        "outputId": "00d8f11f-e2a6-42d1-98aa-aca8e49341dd"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 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'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "aGbsj1UcytpV",
        "outputId": "b1634a49-0412-4bab-d388-c242ee5f586f"
      },
      "source": [
        "\n",
        "print(labels[500])\n",
        "plt.imshow(data[10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normal\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7bba4d2950>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "twd1IeuKVGB5"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# encode class values as integers\n",
        "encoder = LabelEncoder()\n",
        "encoder.fit(labels)\n",
        "labels = encoder.transform(labels)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FPCyIqcVGB7",
        "outputId": "14f8f72c-c5e9-493e-b2bc-8e63fcc70ddf"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LynLhwt_pvNc",
        "outputId": "e7c2d2a2-8c0f-482a-ed6f-370ec32971a9"
      },
      "source": [
        "data = np.array(data)\n",
        "data.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ktZ5hNYwVGB9",
        "outputId": "70bef871-4687-481c-8166-f121d34a5b05"
      },
      "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": 12,
      "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": "8146d29d-4d83-4915-f39c-2f8255908948"
      },
      "source": [
        "print(trainX[0])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  ...\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]]\n",
            "\n",
            " [[0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  ...\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]]\n",
            "\n",
            " [[0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  [0.0324712  0.0324712  0.0324712 ]\n",
            "  ...\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  [0.04296447 0.04296447 0.04296447]\n",
            "  ...\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]]\n",
            "\n",
            " [[0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  ...\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]]\n",
            "\n",
            " [[0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  ...\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]\n",
            "  [0.00265727 0.00265727 0.00265727]]]\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": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTvu4IV0i7Fj",
        "outputId": "4f1782a2-1ea6-4478-f108-b0690e7d982b"
      },
      "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": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_4\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_5 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "rescaling_4 (Rescaling)         (None, 224, 224, 3)  0           input_5[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "normalization_4 (Normalization) (None, 224, 224, 3)  7           rescaling_4[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "stem_conv_pad (ZeroPadding2D)   (None, 225, 225, 3)  0           normalization_4[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_4 (Glo (None, 1280)         0           top_activation[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "dropout_4 (Dropout)             (None, 1280)         0           global_average_pooling2d_4[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 1280)         5120        dropout_4[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_8 (Dense)                 (None, 512)          655872      batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "dense_9 (Dense)                 (None, 2)            1026        dense_8[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": 78,
      "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": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0TdWxuFPkT3",
        "outputId": "e45df38d-254f-434e-cc49-e0858d0cd9c0"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_4 (Flatten)          (None, 150528)            0         \n",
            "_________________________________________________________________\n",
            "layer1 (Dense)               (None, 100)               15052900  \n",
            "_________________________________________________________________\n",
            "layer2 (Dense)               (None, 16)                1616      \n",
            "_________________________________________________________________\n",
            "layer3 (Dense)               (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "layer4 (Dense)               (None, 3)                 51        \n",
            "=================================================================\n",
            "Total params: 15,054,839\n",
            "Trainable params: 15,054,839\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ggcSdj_aVGCB",
        "scrolled": true,
        "outputId": "e84dcb8e-1ca2-41da-8cab-b50eddb8f427"
      },
      "source": [
        "# train the model using the Adam optimizer\n",
        "print(\"[INFO] training network...\")\n",
        "opt = Adam(learning_rate=1e-2, decay=1e-6)\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=opt,\n",
        "\tmetrics=[\"accuracy\"])\n",
        "H = model.fit(trainX, trainY, validation_data=(testX, testY), epochs=50, batch_size=32, callbacks=[es, checkpoint])"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] training network...\n",
            "Epoch 1/50\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 150528) for input KerasTensor(type_spec=TensorSpec(shape=(None, 150528), dtype=tf.float32, name='flatten_4_input'), name='flatten_4_input', description=\"created by layer 'flatten_4_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 150528) for input KerasTensor(type_spec=TensorSpec(shape=(None, 150528), dtype=tf.float32, name='flatten_4_input'), name='flatten_4_input', description=\"created by layer 'flatten_4_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 55.8471 - accuracy: 0.4665WARNING:tensorflow:Model was constructed with shape (None, 150528) for input KerasTensor(type_spec=TensorSpec(shape=(None, 150528), dtype=tf.float32, name='flatten_4_input'), name='flatten_4_input', description=\"created by layer 'flatten_4_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "24/24 [==============================] - 1s 28ms/step - loss: 54.5336 - accuracy: 0.4766 - val_loss: 8.8359 - val_accuracy: 0.8080\n",
            "\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.80800, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 2/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 14.3693 - accuracy: 0.7325 - val_loss: 0.6705 - val_accuracy: 0.9600\n",
            "\n",
            "Epoch 00002: val_accuracy improved from 0.80800 to 0.96000, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 3/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.3780 - accuracy: 0.9605 - val_loss: 0.1008 - val_accuracy: 0.9880\n",
            "\n",
            "Epoch 00003: val_accuracy improved from 0.96000 to 0.98800, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 4/50\n",
            "24/24 [==============================] - 0s 21ms/step - loss: 0.0582 - accuracy: 0.9872 - val_loss: 0.0227 - val_accuracy: 0.9960\n",
            "\n",
            "Epoch 00004: val_accuracy improved from 0.98800 to 0.99600, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 5/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 4.6146e-04 - accuracy: 1.0000 - val_loss: 1.9945e-05 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00005: val_accuracy improved from 0.99600 to 1.00000, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 6/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 2.2926e-05 - accuracy: 1.0000 - val_loss: 1.1639e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00006: val_accuracy did not improve from 1.00000\n",
            "Epoch 7/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 2.1647e-05 - accuracy: 1.0000 - val_loss: 5.5688e-05 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00007: val_accuracy did not improve from 1.00000\n",
            "Epoch 8/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 2.7389e-05 - accuracy: 1.0000 - val_loss: 3.6277e-05 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00008: val_accuracy did not improve from 1.00000\n",
            "Epoch 9/50\n",
            "24/24 [==============================] - 0s 21ms/step - loss: 1.6073e-05 - accuracy: 1.0000 - val_loss: 3.0356e-05 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00009: val_accuracy did not improve from 1.00000\n",
            "Epoch 10/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 7.8898e-06 - accuracy: 1.0000 - val_loss: 2.6502e-05 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00010: 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": "62d0f235-6bfc-4f0d-f62e-54dade9dbb7f"
      },
      "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": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bV60re0SkpCj496GpMc3SNW30qlOniJQIBf8+TG9Ms7Wrhz9u2hZ1KSIiWaHg3wdN8IpIqVHw78MhB4xiZHlS4/wiUjIU/PuQTBhHN6R1ZY+IlAwF/yA0NaZZ/voWurrVqVNEip+CfxCaG9N0Z5wXXt8SdSkiIvtNwT8I0ydpgldESoeCfxDG1VRwYE2FJnhFpCQo+AepuVETvCJSGhT8g9Q8Kc2rm7bz5radUZciIrJfFPyD1NQQjPOrP7+IFDsF/yAd3VBLwrQUo4gUv0EFv5ldamY1FrjBzJaY2QdzXVwhGTkixaHq1CkiJWCwZ/yfdvctwAeB0cD5wHU5q6pANYWf4HVXp04RKV6DDX4Lv58K3Ozuz/fbFhvNk9K0be/m1U3boy5FRGTYBhv8i81sIUHw/9rMqoHe3JVVmPomeDXcIyLFbLDBfzFwJXCsu28HyoCLclZVgTp03Cgqy9SpU0SK22CDfxbworu3mdkc4GqgPXdlFaZUMsFRDbUKfhEpaoMN/uuB7WbWBFwGvAT8JGdVFbDmxjQvrNvCjh516hSR4jTY4O/x4FKWM4HvuPt3gerclVW4mhvT7Mz0suL1rVGXIiIyLIMN/q1mdhXBZZy/NLMEwTh/7DQ3aoJXRIrbYIP/48AOguv51wMNwH/mrKoCNr62gvrqEQp+ESlagwr+MOxvBWrN7HSgy91jOcZvZkGnTgW/iBSpwbZsOBd4EvgYcC7whJmdk8vCCllzY5qX39hG+/buqEsRERmy1CCf9/cE1/C3AphZPfAgsCBXhRWyt8b5W9p436H1EVcjIjI0gx3jT/SFfmjTvvY1s0Yze9jMXjCz583s0nD7GDN7wMxWhd9HD7P2yBzdUIuZWjSLSHEabPD/j5n92swuNLMLgV8C9+9jnx7gMnefBhwHfNbMphF8Avghd38n8FB4v6hUV5RxSP0oTfCKSFEa1FCPu19uZmcDx4eb5rn73fvY53Xg9fD2VjNbDkwk+CzA+8On3QQ8Alwx5Moj1tSY5jcrWnF3zGLXr05Eithgx/hx9zuBO4dzEDObDEwHngDGhb8UANYD44bzmlFrbkyzYHELLZs7aRxTFXU5IiKDNmDwm9lWYE/N5w1wd6/Z1wHMbBTBL4wvuPuW/mfH7u5mtsfm9mY2F5gLMGnSpH0dJu/6JnifXtOm4BeRojLgGL+7V7t7zR6+qgcZ+mUEoX+ru98Vbt5gZuPDx8cDrXva193nufsMd59RX194V84cdmA1I1IJTfCKSNHJ2Zq7Fpza3wAsd/ev93voPuCC8PYFwL25qiGXypIJjpqoTp0iUnxyudj68QS9fU4ws6Xh16kESzaebGargJMo4iUcmxrTPLe2ne5M7NakEZEiNujJ3aFy99+x9+UZT8zVcfOpuTHNDb/7Iy+u38qRE2ujLkdEZFByecZf8vpP8IqIFAsF/35oGF1J3chylr6m4BeR4qHg3w9vdepsUfCLSPFQ8O+n5sY0L23sYEuXOnWKSHFQ8O+npsY07vDMmtitPS8iRUrBv5+awgleDfeISLFQ8O+n2soyDqofydOa4BWRIqHgz4LmhjRL17Thvse2QyIiBUXBnwXNk9K80bGDde1dUZciIrJPCv4saGoIl2LUcI+IFAEFfxZMHV9DeSqhCV4RKQoK/iwoTyU4YkKNzvhFpCgo+LOkqSHNs2vb6VGnThEpcAr+LJk+KU1nd4aVGzqiLkVEZEAK/izp69SphVlEpNAp+LNk0pgqRleVsXTN5qhLEREZkII/S8yMpsY0y9SzR0QKnII/i5ob06xs3UrHjp6oSxER2SsFfxa91alT1/OLSAFT8GdRc/gJXg33iEghU/Bn0eiR5Uyuq9IEr4gUNAV/lmmCV0QKnYI/y5ob06zf0sV6deoUkQKl4M+ytz/IpeEeESlMCv4smzq+hrKksVTDPSJSoBT8WVZRlmTa+Bqd8YtIwVLw50BTY5pnW9rJ9GopRhEpPAr+HGhuTLNtZ4bVrerUKSKFR8GfA5rgFZFCpuDPgcl1I6mpSKlFs4gUJAV/DiQSQadOXdkjIoVIwZ8j0xvTvLh+C9t3qlOniBQWBX+ONDWm6XV4tkVn/SJSWBT8OdI3wbtMLZpFpMAo+HOkbtQIGsdUaoJXRAqOgj+HmhrUqVNECk/Ogt/M5ptZq5k912/bGDN7wMxWhd9H5+r4haC5Mc3atk5at6pTp4gUjlye8f8YmL3btiuBh9z9ncBD4f2SNX1S+EGu1zTcIyKFI2fB7+6PAm/utvlM4Kbw9k3AWbk6fiE4YkItqYRpgldECkq+x/jHufvr4e31wLi9PdHM5prZIjNbtHHjxvxUl2UVZUkOH1+tCV4RKSiRTe66uwN7bV/p7vPcfYa7z6ivr89jZdnV1JDmmTXt9KpTp4gUiHwH/wYzGw8Qfm/N8/HzrrkxzdYdPbz8hjp1ikhhyHfw3wdcEN6+ALg3p0dzh0x3Tg+xL30TvE9rgldECkQuL+e8DXgcOMzMWszsYuA64GQzWwWcFN7PDXf4xaWw4NOQia5fzkFjR1FdkeK+Zeu0MIuIFIRcXtXzSXcf7+5l7t7g7je4+yZ3P9Hd3+nuJ7n77lf9ZI8ZHDAVlt8Hv/g89Pbm7FADSSSMyz90GI+teoNrf/E8wdSGiEh0UlEXkFPH/TV0tcMjX4ERNTD7K8EvhDz7i1mTWbu5kx88+jIT05Vc8r6D816DiEif0g5+gPddEYT/H74HFbXwgasiKeOK2Yezrr2Lr/xqBQfWVnBm88RI6hARKf3gN4MP/it0bYHfXgcVNTDrs3kvI5Ewvvqxo2nd0sUXf76M+uoRvOfgsXmvQ0QkHk3aEgn48Ldg6hnw6y/BkpsjKWNEKsm882cwuW4kl9y8mBfXb42kDhGJt3gEP0AyBWf/CA4+IZjsff6eSMqorSrjx5+eSWVZkgtvfJL17WrgJiL5FZ/gB0iNgI/fAg0z4c6/hNUPRlLGxHQlN150LFu7erjwxifZ0hXtZw1EJF7iFfwA5SPhvJ/BAYfD7XPg1ccjKeOICbVcP+ddrG7t4K9vWczOnmguNxWR+Ilf8ANUpmHO3VA7EX56LqxbGkkZf/7Oev797KP539WbuPLOZ3SNv4jkRTyDH2BUPfzFvcElnrd8FDaujKSMs49p4LKTD+Wup9fytYXR1CAi8RLf4AeobYDz7wFLwM1nQdtrkZTxuRMO4ZMzG/nOw6u59YlXI6lBROIj3sEPMPaQIPx3dsBPzoSO/DcMNTP++cwj+cBh9Vxzz3M8tHxD3msQkfhQ8AMceCR8agFsXQ83fwQ6N+e9hFQywXfOexdHTqzlcz99Wou3iEjOKPj7NM6ET9wKb6yEWz8GO/LfP3/kiBQ3XHAsY6vLufjHT/Hqpm15r0FESp+Cv7+DT4Bz5sPaxXD7edCd/w9X1VeP4KaLZtLrzoU3PsWb23bmvQYRKW0K/t1N/TCc+V3442/hzosj6eV/UP0ofnTBDNa1dXLxTU/RuTOT9xpEpHQp+Pek+Tw45T9gxX/DfZ+LpJf/Me8Yw7c+MZ2la9q49PantYiLiGSNgn9v3n0JfOBqWHYb/M+VwYpeeTb7yAP58unTWPjCBi3iIiJZU/ptmffHe78IXW3w+HeCD3qd8Pd5L+HC46ewrr2LeVrERUSyRME/EDP44L8EC7k8+h9BL//3/E3ey7hy9uGsa+vUIi4ikhUK/n0xC3r579gKC6+GEdVwzIV5LSGRML52bhMbt+7g8p8/wwHVFcw6uC6vNYhI6dAY/2AkkvDRH8IhJ8EvvgDP3Zn3EvoWcXlHXRVzb17Eyg1axEVEhkfBP1ipcjj3Zpg0C+6aCysX5r2EXRZxma9FXERkeBT8Q1FeBefdDuOOgDvOh1f+N+8l9C3i0t7ZzYU3PslWLeIiIkOk4B+qilqYcxekJ8FPPw7rns57CcEiLseEi7gs0SIuIjIkCv7hGDk26OhZORpuORs2vpj3Et57aD3XnX00v1v9hhZxEZEhUfAPV+1E+It7wJJBO+fNr+S9hHO0iIuIDIOCf3/UHRyEf3cn/OSsoK1znvVfxOWnT0SzkIyIFBcF//4adwTMuTNYwOXmj8D2N/N6+P6LuFx9z7NaxEVE9knBnw0NM+CTt8Gml+DWc4IPe+VR3yIuR0wIFnFZpkVcRGQACv5sOeh98LEbYd3SSHr5jxyRYv6FwSIun9YiLiIyAAV/Nh1+Gpx1PfzxUVhwEWTye419ffUIfnzRTDJaxEVEBqDgz7amj8OpX4UX74d7/l/ee/kfXD+KG8JFXP7ypqfo6tYiLiKyKwV/Lsz8DJxwDTx7B/zq8rz38g8WcWnmaS3iIiJ7oODPlT+/DI6/FJ76Efzmn/N++NlHjufLp0/j189v4J+0iIuI9KO2zLliBiddG/Tyf+xr8MrvYNQBUDkGqsbs/XtFGpLZ+c9y4fFTWNvWyQ8f+yMTR1cy971axEVEIgp+M5sNfAtIAj9y9+uiqCPnzOC0rwehvuYJeGM1dL4ZXOvfO8DEb0XtAL8gRgffq+p2fay8ao8vddUpU1nX3sW/3b+CA2srOaNpQo5+WBEpFpbvIQAzSwIrgZOBFuAp4JPu/sLe9pkxY4YvWrQoTxXmgXtwrX/fL4HON2H75t3u7/59M+wc4PMBqYo//eUQ3u8ekWbeU20s25TgkPFjSCSSWDJBIpEKbieSJJLhVyJJMpnCEkmSySSJZIpkMrydSJJMpUgkUiRTCVJvPZYKvlJJUskUqVSwLZVKkUoYZckEqaSRShipZIKyhGFmJAwSZiTMsETf7eC79XssYcEH1URkaMxssbvP2H17FGf8M4HV7v4ygJndDpwJ7DX4S45ZsIxjRQ2Mnjz4/Xp2QufuvyA27fmXR+vycPtmyjzDZyH4r70xNz/S3mTcyJDASZAJvxzDAScI897d7vc9DtZvW9/je7lvb+8Lu972t35n2NuP99smAc0CFaayOT9n4kFTs/qaUQT/RGBNv/stwLt3f5KZzQXmAkyaNCk/lRW6VDlUjwu+Bqu3F3ZsCX4hdG4OPlvQmwHvBQ+/9/a/3e+x3gy9vb30ZHrwTIZMJkOmt4feTC+9mR4yvRk800NvbybY1ttDb2/vW9u8NxN8z/TinsEzmeB7bwYcHAf3YOLZgygP/gDt29YbhFH4GO5v3e/bt//rWN9+gO92/09fo3BaWRdC4FpBVCF70jCiIuuvWbCTu+4+D5gHwVBPxOUUr0QCKtPB13B2B8qzW5GIRCyKyznXAo397jeE20REJA+iCP6ngHea2RQzKwc+AdwXQR0iIrGU96Eed+8xs88Bvya4nHO+uz+f7zpEROIqkjF+d78fuD+KY4uIxJ1aNoiIxIyCX0QkZhT8IiIxo+AXEYmZvPfqGQ4z2wi8OszdxwJvZLGcYqf34216L3al92NXpfB+vMPd63ffWBTBvz/MbNGemhTFld6Pt+m92JXej12V8vuhoR4RkZhR8IuIxEwcgn9e1AUUGL0fb9N7sSu9H7sq2fej5Mf4RURkV3E44xcRkX4U/CIiMVPSwW9ms83sRTNbbWZXRl1PVMys0cweNrMXzOx5M7s06poKgZklzexpM/vvqGuJmpmlzWyBma0ws+VmNivqmqJiZv8//HfynJndZmbZXwIrYiUb/OGi7t8FTgGmAZ80s2nRVhWZHuAyd58GHAd8NsbvRX+XAsujLqJAfAv4H3c/HGgipu+LmU0EPg/McPcjCVrHfyLaqrKvZIOffou6u/tOoG9R99hx99fdfUl4eyvBP+qJ0VYVLTNrAE4DfhR1LVEzs1rgvcANAO6+093boq0qUimg0sxSQBWwLuJ6sq6Ug39Pi7rHOuwAzGwyMB14ItpKIvdN4O+Awll1PTpTgI3AjeHQ14/MbGTURUXB3dcCXwVeA14H2t19YbRVZV8pB7/sxsxGAXcCX3D3LVHXExUzOx1odffFUddSIFLAu4Dr3X06sA2I5ZyYmY0mGBmYAkwARprZnGiryr5SDn4t6t6PmZURhP6t7n5X1PVE7HjgDDN7hWAI8AQzuyXakiLVArS4e99fgQsIfhHE0UnAH919o7t3A3cB74m4pqwr5eDXou4hMzOC8dvl7v71qOuJmuB6dRwAAAIvSURBVLtf5e4N7j6Z4P+L37h7yZ3VDZa7rwfWmNlh4aYTgRciLClKrwHHmVlV+O/mREpwojuSNXfzQYu67+J44HzgWTNbGm77Urj2sQjA3wC3hidJLwMXRVxPJNz9CTNbACwhuBruaUqwdYNaNoiIxEwpD/WIiMgeKPhFRGJGwS8iEjMKfhGRmFHwi4jEjIJfJMfM7P3qACqFRMEvIhIzCn6RkJnNMbMnzWypmf0g7NffYWbfCPuzP2Rm9eFzm83sD2b2jJndHfZ4wcwOMbMHzWyZmS0xs4PDlx/Vr9/9reGnQkUioeAXAcxsKvBx4Hh3bwYywKeAkcAidz8C+C3w5XCXnwBXuPvRwLP9tt8KfNfdmwh6vLwebp8OfIFgbYiDCD5NLRKJkm3ZIDJEJwLHAE+FJ+OVQCtB2+afhc+5Bbgr7F+fdvffhttvAn5uZtXARHe/G8DduwDC13vS3VvC+0uBycDvcv9jifwpBb9IwICb3P2qXTaaXbPb84bb42RHv9sZ9G9PIqShHpHAQ8A5ZnYAgJmNMbN3EPwbOSd8znnA79y9HdhsZn8ebj8f+G24ulmLmZ0VvsYIM6vK608hMgg66xAB3P0FM7saWGhmCaAb+CzBoiQzw8daCeYBAC4Avh8Ge/9ulucDPzCzfwpf42N5/DFEBkXdOUUGYGYd7j4q6jpEsklDPSIiMaMzfhGRmNEZv4hIzCj4RURiRsEvIhIzCn4RkZhR8IuIxMz/ARMXSUBHLK9EAAAAAElFTkSuQmCC\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": "dbec204a-e478-4a3d-981f-a4df643c3bb4"
      },
      "source": [
        "predictions = model.predict(testX, batch_size=32)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Model was constructed with shape (None, 150528) for input KerasTensor(type_spec=TensorSpec(shape=(None, 150528), dtype=tf.float32, name='flatten_4_input'), name='flatten_4_input', description=\"created by layer 'flatten_4_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FG6VqJuUYOgi",
        "outputId": "f5af6e49-109d-4bf7-966d-1d1ad6f6a590"
      },
      "source": [
        "print(predictions.argmax(axis=1))"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 1 1 0 1 0 1 1 1 1 1 0 1 0 0 0 1 1 0 0 1 0 1 1 1 0 1 0 0 0 0 0 0 1 1 0\n",
            " 0 1 1 0 0 0 1 1 1 1 1 0 1 1 1 1 0 1 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 1 0 1 0\n",
            " 1 0 1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1\n",
            " 1 0 1 0 1 1 0 1 0 0 1 0 1 1 0 1 1 0 1 0 0 1 0 1 0 1 0 1 1 0 0 1 0 0 0 0 0\n",
            " 0 1 1 0 1 1 1 1 1 0 1 1 0 0 0 1 0 1 1 0 0 0 0 1 1 1 0 1 0 0 1 1 0 0 1 0 1\n",
            " 0 1 1 1 0 0 0 0 0 1 0 1 0 1 1 0 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 0 0 0 1 0 1\n",
            " 0 0 0 0 1 0 0 1 1 0 0 0 1 1 0 1 0 1 1 0 0 1 1 0 1 0 0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6puOgflYR_e"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPAZkmBJVGCD",
        "outputId": "6d9704ba-7453-492a-9cc6-f8c566fd3d4b"
      },
      "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": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] evaluating network...\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Covid       1.00      1.00      1.00       126\n",
            "      Normal       1.00      1.00      1.00       124\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": "246334f5-2b25-4596-c299-e474dd47c058"
      },
      "source": [
        "cf_matrix = confusion_matrix(testY,predictions.argmax(axis=1))\n",
        "print(cf_matrix)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[126   0]\n",
            " [  0 124]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLp1ooZeY90p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "20f695ee-5485-45ad-d674-5d610b355b57"
      },
      "source": [
        "\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cf_matrix, display_labels=encoder.classes_)\n",
        "disp.plot(cmap=plt.cm.Blues)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fcfde2e21d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kkuWoOs30B+YA10h6StKVklYE+kTErPSYd4E+lcaZZYLAZcBikrsAzgY+Am4Dtqr0pGZmlch47WpuRAxqZH8HYEvguIiYKOki6nztj4iQFJXGmWUoYJuIOBb4LD3hfKBTpSc0M6uEgA5S2SWDt4G3I2Jiun4rSaKdLakvQPrzvUpjzZJYv5DUnuTeVSStStKDNTNrUXk8/joi3gXekrRhumkX4HngLmB4um04cGelcWYZCrgYuANYTdIfSKpdnVHpCc3MKiHlOqX1OOB6SZ2AV4HDSTqaN0s6EngDOKDSxrPUCrhe0hSSrC7g+xExo9ITmplVKq+8GhFPA/WNw+6SR/tZCl2vBXwK3F26LSLezCMAM7Os2ki51bKyDAWMY8lDBVcguVXhRWDTKsZlZvYVgjZTyLqcLEMB3yhdT6teHdPA4WZm1dG0mVWtKtPDBEtFxFRJ21QjGDOzxqggT73KMsZaWqOtHcn9Xu9ULSIzs3osa4+/XrnkdQ3JmOtt1QnHzKxhy0RiTScGrBwRJ7VQPGZmDSp8oWtJHSKiRtL2LRmQmVl9ksdft3YU2TTWY/0XyXjq05LuAm4BPqndGRG3Vzk2M7OvKPzDBEusALxPUt2q9n7WAJxYzazFLCsXr1ZL7wh4jiUJtVbF5bTMzCpVkA5ro4m1PbAS1HvjmBOrmbUw0W4ZuI91VkSc3WKRmJk1QiwbPdaCfAQzWy4IOhRkkLWxxJpL+SwzszwsEz3WiJjXkoGYmZWzLN1uZWbWJhQkrzqxmlkxiGwP6WsLnFjNrBjkoQAzs1wlM6+cWM3MclWMtOrEamYFUpAOqxOrmRWFil+P1cysLfFdAWZmVeCLV2ZmeVJxHs1SlJ61mS3naocCyi2Z25PaS3pK0j3pen9JEyW9LOkmSZ0qjdWJ1cwKQ1LZpQl+AcwoWf9fYGRErAfMB46sNE4nVjMrDGVYMrUj9QP2BK5M10Xy+Klb00PGAN+vNE6PsZpZIQhon61H2lvS5JL1URExqs4xFwKnACun66sAH0RETbr+NrBGpbE6sZpZYWT8pj83IgY13Ib2At6LiCmShuQU2lc4sZpZQQjlM6l1e2BvSXuQPIW6G3AR0ENSh7TX2g+YWekJPMZqZoUhlV/KiYjTIqJfRKwDHAQ8GhHDgL8B+6WHDQfurDROJ1YzK4TkdiuVXZrh18CJkl4mGXO9qtKGPBRgZsWQsUfaFBExHhifvn4V2DqPdp1YzawwPKXVzCxHSaHr1o4iGydWMyuMnO4KqDonVjMrjIKMBDixLosefuJ5TrvgVhYtXsxh+wzmhB/t2tohLZMu+c2B7DZ4E+bO/5jBh5631P79d92SXxy2M5L4+NPP+NW5t/Hcy+8065ydOrbn8t8ewuYbrcm8BZ9wxBnX8ta78xmy1QaMOGZPOnXswOdf1PDbP93NY1Nebta52qKi9FirdruVpJB0Qcn6SZLOrNb5GohhvKQGZ2AsixYtWszJ597MLRcdw5M3n8FtD07hhVdntXZYy6Sx4yax3wl1Z0ou8caseex5zKVsf+h5nHf1Q4w8df/Mba/5tZ7cfekxS20/7HvbsOCjhQzc/49cfuPfOfPYvQB4f8EnHHzyVWx/6Hkc87uxXDFiWNM/UBtXO8ZabmkLqnkf63+AfSX1ruTNktybrsCU6a/z9TV7s06/3nTq2IF9v7sl9/59WmuHtUx64ulXmf/hpw3u/9ezr7Pgo4UATJr+Bquv1uPLfQfsNpCHr/olE8b8ipG/3p92GTPC0G9txth7JwFw59+m8e1B6wPw7L9n8u7cDwGY8eq7dOnckU4d21f0udosiXYZlragmom1BhgFnFB3h6R1JD0qaZqkRyStlW4fLekKSROBc9P1yyU9KelVSUMkXS1phqTRJe1dLmmypOmSzqriZ2rzZs1ZwBp9en65vnqfnsyas6AVIzJIepoP/zOpULfB2qvxg+9szu5HXcyOwy9g0aLF7L/bwEztrL5qd2bO/gBIvp18+PFn9Oq+4leO2XunATzz4tt8/sWifD9EG5BXdatqq3av8FJgmqRz62y/BBgTEWMkHQFczJISXf2AwRGxKE2ePYHtgL2Bu0jm+f4YmCRp84h4GvhNRMyT1B54RNKAiGiwmybpKOAogDXXWiuvz2pWrx22XI9Dv7cNQ396CQDf3moDvrlhPx69OulzrNC5I3PmfwzAdeccztp9e9GxY3v69enJhDG/AuCKmydww7hJZc+1Uf8+nHnMXuz7yz9X6dO0nmQooK2kzsZVNbFGxIeSrgWOBxaW7NoO2Dd9fR1QmnhviYjSf2rvjoiQ9CwwOyKeBZA0HVgHeBo4IE2WHYC+wCZAg4k1LSE2CmDgwEFR+Sdse/qu2p2Zs+d/uf7O7Pn0XbV7K0a0fNt03b5cfNoB7H/iX74ybHDjfZM5+/JxSx1/2KnXAMkY62X/fTDfO/ayr+x/Z84C1ujTg3fmLKB9+3Z0W2kF5i34BEh6s9edczg/+90NvD7z/Sp+qtZTjLTaMrUCLiSpxL1iuQNTn9RZ/0/6c3HJ69r1DpL6AycBu0TEAGAcScWa5dKWm6zNK2/O4Y2Zc/n8ixpuf2gqQ3cc0NphLZf69enBteccztFn38Arb835cvuEyS+x904D6N1zJQB6dOvKml/r2VAzX3H/49M5eI+tANhnpwFMSK/8d1tpBW664Cecddk4Jk57Pd8P0pYUZCyg6heI0q/oN5Mk16vTzU+QVJW5DhgGPNaMU3QjScYLJPUBhpLO/V0edejQnnNPOYAfHn8pixYFw/belo3X7dvaYS2TrjzrULbfcj1W6bEiz935W8658gE6dkj6Ktfc8U9OPmJXenXryvkn/RCAmkWL2fmIkbz4+mz+8Of7uP3Cn9KunfiiZhEnn387b707v7HTAXDd3RO5YsQhTLnldOZ/+ClH/ve1APxkvx3o328VTjliV045Irm9bt9f/pm56RDDsqIoQwGKqM43YUkfR8RK6es+wGvAuRFxpqS1gWuA3sAc4PCIeDMdU70nIm5N3/fluqR10teb1bNvNDAYeAtYANwVEaMljQdOiojSauJfMXDgoPjHxAZ3WxvUc7sTWzsEa6LPJo+c0ljx6Sw2/sYWce2d48set/W6PZp9ruaqWo+1Nqmmr2cDXUvW3yB5vkzd9/yoofWIeB3YrIF9X3lfyfYhTQ7czNquYnRYPfPKzIohGUItRmZ1YjWzYqhCPdZqcWI1s8IoSF51YjWzohAqSJfVidXMCqMgedWJ1cyKoQ3d/1+WE6uZFUdBMqsTq5kVhm+3MjPLmcdYzczy5PtYzczyV5ShgJYoG2hm1mwi6bGWW8q2I60p6W+Snk+fOvKLdHsvSQ9Jein9ma2WYz2cWM2sMHIqx1oD/CoiNgG2BY6VtAlwKvBIRKwPPJKuV8SJ1cyKI4fMGhGzImJq+vojYAawBrAPMCY9bAxLHhfVZB5jNbPCyFjourek0iLLo9LHMS0lrfO8BTAR6BMRtc+KfxfoU2mcTqxmVhgZv+rPzVLoWtJKwG3AL9Pn8325L33OXsVPAfBQgJkVR06DrJI6kiTV6yPi9nTzbEl90/19gfcqDdOJ1cwKobbQdbn/yraTdE2vAmZExP+V7LoLGJ6+Hg7cWWmsHgows2LIb4LA9sBhwLOSnk63nQ6cA9ws6UjgDeCASk/gxGpmhZFHXo2IxxtpapccTuHEamZF4ULXZma5K0hedWI1s2JwoWszs2ooSGZ1YjWzwihKdSsnVjMrDI+xmpnlSdDOidXMLG/FyKxOrGZWCLWFrovAidXMCqMgedWJ1cyKwz1WM7OceUqrmVnOipFWnVjNrCCyPoW1LXBiNbPC8MwrM7O8FSOvOrGaWXEUJK86sZpZUSjr469bnROrmRVCkWZe+SmtZmY5c4/VzAqjKD1WJ1YzKwzfbmVmlidPEDAzy1eRLl45sZpZYXgowMwsZ0Xpsfp2KzMrDGVYMrUj7S7pRUkvSzo17zidWM2sOHLIrJLaA5cCQ4FNgIMlbZJnmE6sZlYIAtpJZZcMtgZejohXI+Jz4EZgnzxjXe7HWKdOnTK3S0e90dpxVElvYG5rB2GZLcu/r7Wb28DUqVMe6NJRvTMcuoKkySXroyJiVMn6GsBbJetvA9s0N75Sy31ijYhVWzuGapE0OSIGtXYclo1/X42LiN1bO4asPBRgZsubmcCaJev90m25cWI1s+XNJGB9Sf0ldQIOAu7K8wTL/VDAMm5U+UOsDfHvqwVERI2knwMPAO2BqyNiep7nUETk2Z6Z2XLPQwFmZjlzYjUzy5kTa0FI+pqkGyW9ImmKpHslbdDENgZJuriBfa9Lme4RtHpICkkXlKyfJOnMFo5hvCTfrtUGOLEWgCQBdwDjI2LdiBgInAb0aUo7ETE5Io6vRozGf4B9K/3HSZIvJC9DnFiLYSfgi4i4onZDRDwDPC7pPEnPSXpW0oEAac92z9pjJY2WtJ+kIZLuSbetIulBSdMlXUlxnizcVtWQXNU/oe4OSetIelTSNEmPSFor3T5a0hWSJgLnpuuXS3pS0qvp7+tqSTMkjS5p73JJk9Pf3Vkt9QEtOyfWYtgMmFLP9n2BzYFvAt8BzpPUF7gJOAAgvU9vF2BcnfeOAB6PiE1JesNrVSf05cqlwDBJ3etsvwQYExEDgOuB0uGYfsDgiDgxXe8JbEeSoO8CRgKbAt+QtHl6zG/SGVoDgG9LGlCVT2MVc2Itth2AsRGxKCJmA38HtgLuA3aS1Jmkgs+EiFhY5707An8FiIhxwPyWC3vZFBEfAtcCdYdbtgNuSF9fR/J7q3VLRCwqWb87knsgnwVmR8SzEbEYmA6skx5zgKSpwFMkSTfXykzWfE6sxTAdGJj14Ij4DBgP7AYcSNKDtZZxIXAksGLG4z+ps/6f9Ofikte16x0k9QdOAnZJe8DjgBUqD9eqwYm1GB4FOks6qnZD+vXvA+BASe0lrUrSC/1XeshNwOHAt4D762lzAnBI2tZQkq+g1kwRMQ+4mSS51nqCZNokwDDgsWacohtJMl4gqQ/JNxJrY3wlsgAiIiT9ALhQ0q+Bz4DXgV8CKwHPAAGcEhHvpm97kORr551pzcm6zgLGSppO8hf/zep+iuXKBcDPS9aPA66RdDIwh+QfvIpExDOSngJeICl994/mBGrV4SmtZmY581CAmVnOnFjNzHLmxGpmljMnVjOznDmxmpnlzInVypK0SNLTaU2CWyR1bUZboyXtl76+srHnuadz5QdXcI56K3VlqeAl6eMmnutMSSc1NUZbtjmxWhYLI2LziNgM+Bw4unRnpZWZIuLHEfF8I4cMAZqcWM1amxOrNdVjwHppb/IxSXcBz6ezv86TNCmt4vRTSEoeSvqTpBclPQysVttQaf1QSbtLmirpmbQC1DokCfyEtLf8LUmrSrotPcckSdun721ypS5J/y+tazu9dEZbum9kuv2RdEYbktaVdH/6nsckbZTHH6YtmzzzyjJLe6ZDWTJFdktgs4h4LU1OCyJiq7T4yz8kPQhsAWxIUiikD/A8cHWddlcF/gLsmLbVKyLmSboC+Dgizk+PuwEYGRGPp6X3HgA2ZkmlrrPTcoml00kbckR6ji7AJEm3RcT7JHP8J0fECZJ+m7b9c5KSgEdHxEuStgEuA3au4I/RlgNOrJZFF0lPp68fA64i+Yr+r4h4Ld2+KzCgdvwU6A6sT1K/YGxawekdSY/W0/62JBW4XoMv59vX5zvAJtKXHdJuklZKz7Fv+t5xkrJU6jo+nSYMyTPm1wfeJyl2Ulu05q/A7ek5BgO3lJy7c4Zz2HLKidWyWBgRm5duSBNMaWUmAcdFxAN1jtsjxzjaAdum1bvqxpKZpCEkSXq7iPhU0ngarhAV6Xk/qPtnYNYQj7FaXh4AfiapI4CkDSStSFJFq7YCV1+SpyHU9SSwY1oSD0m90u0fASuXHPcgSUET0uNqE11TK3V1B+anSXUjkh5zrXZAba/7EJIhhg+B1yTtn55Dkr5Z5hy2HHNitbxcSTJ+OlXSc8CfSb4R3QG8lO67Fvhn3TdGxBzgKJKv3c+w5Kv43cAPai9ekRSQHpReHHueJXcnnEWSmKeTDAmUq9R1P0lt07utblgAAABRSURBVBnAOSSJvdYnwNbpZ9gZODvdPgw4Mo1vOrBPhj8TW065upWZWc7cYzUzy5kTq5lZzpxYzcxy5sRqZpYzJ1Yzs5w5sZqZ5cyJ1cwsZ/8f71hmTp/waDcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F1A1qh_sOObb"
      },
      "source": [
        "import io\n",
        "\n",
        "path='/content/drive/MyDrive/Normalized/Covid/ IM00006.tif'\n",
        "save_path='/content/drive/MyDrive/Testing/Covid/covid6.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": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RZt7FOFIOVj9",
        "outputId": "0665a657-69d3-42a6-c965-e40fc4b49b55"
      },
      "source": [
        "width = 224\n",
        "height = 224\n",
        "dsize = (width, height)\n",
        "image_testing = cv2.imread('/content/drive/MyDrive/Testing/Covid/covid6.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": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "iGHhwsyQOZS1",
        "outputId": "977bcbd3-5b92-42ce-e18b-9798d448ca45"
      },
      "source": [
        "plt.imshow(image_testing)"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fcfde31fcd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CE8OIku1Obvw",
        "outputId": "942ebd0b-2890-4570-fec4-1dd1681e426b"
      },
      "source": [
        "image_testing = np.expand_dims(image_testing, axis=0)\n",
        "print(image_testing.shape)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 224, 224, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kiBXgNbUOd7-",
        "outputId": "578a1a36-4617-4397-a6d4-5cf1c81c65c9"
      },
      "source": [
        "output = model.predict(image_testing, 1)\n",
        "print(output)\n",
        "print(encoder.classes_[output.argmax(axis=1)])"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Model was constructed with shape (None, 150528) for input KerasTensor(type_spec=TensorSpec(shape=(None, 150528), dtype=tf.float32, name='flatten_4_input'), name='flatten_4_input', description=\"created by layer 'flatten_4_input'\"), but it was called on an input with incompatible shape (1, 224, 224, 3).\n",
            "[[9.9979156e-01 2.0843762e-04 0.0000000e+00]]\n",
            "['Covid']\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}