CLAHEOnly_sgd_0.1.ipynb

{
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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Copy of scenes_nn_4 layers.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
    }
  },
  "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_sgd_0.1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_dJvuvIBVLS_",
        "outputId": "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": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTvu4IV0i7Fj",
        "outputId": "ca476ffc-386d-45f5-d4d2-14209b52670a"
      },
      "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": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0_notop.h5\n",
            "16711680/16705208 [==============================] - 0s 0us/step\n",
            "16719872/16705208 [==============================] - 0s 0us/step\n",
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "rescaling (Rescaling)           (None, 224, 224, 3)  0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "normalization (Normalization)   (None, 224, 224, 3)  7           rescaling[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "stem_conv_pad (ZeroPadding2D)   (None, 225, 225, 3)  0           normalization[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 (Globa (None, 1280)         0           top_activation[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 1280)         0           global_average_pooling2d[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 1280)         5120        dropout[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 512)          655872      batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 2)            1026        dense[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": 16,
      "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": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0TdWxuFPkT3",
        "outputId": "bd9d10c5-a652-4a23-87aa-d54b328a6016"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (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": "479180a7-6b68-4066-8559-277ac621c7cc"
      },
      "source": [
        "# train the model using the Adam optimizer\n",
        "print(\"[INFO] training network...\")\n",
        "opt = SGD(learning_rate=0.1, decay=1e-6)\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=opt,\n",
        "\tmetrics=[\"accuracy\"])\n",
        "H = model.fit(trainX, trainY, validation_data=(testX, testY), epochs=50, batch_size=32, callbacks=[es, checkpoint])"
      ],
      "execution_count": 19,
      "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_input'), name='flatten_input', description=\"created by layer 'flatten_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_input'), name='flatten_input', description=\"created by layer 'flatten_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "24/24 [==============================] - ETA: 0s - loss: 475855203.1649 - accuracy: 0.4598WARNING:tensorflow:Model was constructed with shape (None, 150528) for input KerasTensor(type_spec=TensorSpec(shape=(None, 150528), dtype=tf.float32, name='flatten_input'), name='flatten_input', description=\"created by layer 'flatten_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "24/24 [==============================] - 15s 35ms/step - loss: 468396171.9983 - accuracy: 0.4599 - val_loss: 2443437.5000 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.49600, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 2/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 66950874568.3819 - accuracy: 0.5059 - val_loss: 0.8321 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00002: val_accuracy improved from 0.49600 to 0.50400, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 3/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.8194 - accuracy: 0.5025 - val_loss: 0.7889 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00003: val_accuracy did not improve from 0.50400\n",
            "Epoch 4/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.7825 - accuracy: 0.5146 - val_loss: 0.7659 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00004: val_accuracy did not improve from 0.50400\n",
            "Epoch 5/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7620 - accuracy: 0.5186 - val_loss: 0.7514 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00005: val_accuracy did not improve from 0.50400\n",
            "Epoch 6/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7493 - accuracy: 0.4897 - val_loss: 0.7417 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00006: val_accuracy did not improve from 0.50400\n",
            "Epoch 7/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7399 - accuracy: 0.5069 - val_loss: 0.7348 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00007: val_accuracy did not improve from 0.50400\n",
            "Epoch 8/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7337 - accuracy: 0.4762 - val_loss: 0.7295 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00008: val_accuracy did not improve from 0.50400\n",
            "Epoch 9/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7277 - accuracy: 0.5359 - val_loss: 0.7254 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00009: val_accuracy did not improve from 0.50400\n",
            "Epoch 10/50\n",
            "24/24 [==============================] - 0s 15ms/step - loss: 0.7251 - accuracy: 0.4897 - val_loss: 0.7221 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00010: val_accuracy did not improve from 0.50400\n",
            "Epoch 11/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7220 - accuracy: 0.4896 - val_loss: 0.7194 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00011: val_accuracy did not improve from 0.50400\n",
            "Epoch 12/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7191 - accuracy: 0.5303 - val_loss: 0.7172 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00012: val_accuracy did not improve from 0.50400\n",
            "Epoch 13/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.7170 - accuracy: 0.4848 - val_loss: 0.7153 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00013: val_accuracy did not improve from 0.50400\n",
            "Epoch 14/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7149 - accuracy: 0.5095 - val_loss: 0.7138 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00014: val_accuracy did not improve from 0.50400\n",
            "Epoch 15/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7137 - accuracy: 0.5018 - val_loss: 0.7125 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00015: val_accuracy did not improve from 0.50400\n",
            "Epoch 16/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.7127 - accuracy: 0.4881 - val_loss: 0.7111 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00016: val_accuracy did not improve from 0.50400\n",
            "Epoch 17/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7117 - accuracy: 0.4687 - val_loss: 0.7101 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00017: val_accuracy did not improve from 0.50400\n",
            "Epoch 18/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7099 - accuracy: 0.5022 - val_loss: 0.7091 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00018: val_accuracy did not improve from 0.50400\n",
            "Epoch 19/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7099 - accuracy: 0.4770 - val_loss: 0.7083 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00019: val_accuracy did not improve from 0.50400\n",
            "Epoch 20/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7086 - accuracy: 0.5014 - val_loss: 0.7075 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00020: val_accuracy did not improve from 0.50400\n",
            "Epoch 21/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7078 - accuracy: 0.4564 - val_loss: 0.7069 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00021: val_accuracy did not improve from 0.50400\n",
            "Epoch 22/50\n",
            "24/24 [==============================] - 0s 15ms/step - loss: 0.7066 - accuracy: 0.5116 - val_loss: 0.7061 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00022: val_accuracy did not improve from 0.50400\n",
            "Epoch 23/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.7060 - accuracy: 0.5296 - val_loss: 0.7060 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00023: val_accuracy did not improve from 0.50400\n",
            "Epoch 24/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.7052 - accuracy: 0.5171 - val_loss: 0.7050 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00024: val_accuracy did not improve from 0.50400\n",
            "Epoch 25/50\n",
            "24/24 [==============================] - 0s 20ms/step - loss: 0.7052 - accuracy: 0.4787 - val_loss: 0.7045 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00025: val_accuracy did not improve from 0.50400\n",
            "Epoch 26/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7052 - accuracy: 0.4745 - val_loss: 0.7041 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00026: val_accuracy did not improve from 0.50400\n",
            "Epoch 27/50\n",
            "24/24 [==============================] - 0s 15ms/step - loss: 0.7044 - accuracy: 0.5083 - val_loss: 0.7038 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00027: val_accuracy did not improve from 0.50400\n",
            "Epoch 28/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7041 - accuracy: 0.4954 - val_loss: 0.7038 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00028: val_accuracy did not improve from 0.50400\n",
            "Epoch 29/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7023 - accuracy: 0.5305 - val_loss: 0.7030 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00029: val_accuracy did not improve from 0.50400\n",
            "Epoch 30/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7042 - accuracy: 0.4382 - val_loss: 0.7027 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00030: val_accuracy did not improve from 0.50400\n",
            "Epoch 31/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.7033 - accuracy: 0.4801 - val_loss: 0.7024 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00031: val_accuracy did not improve from 0.50400\n",
            "Epoch 32/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7027 - accuracy: 0.5041 - val_loss: 0.7024 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00032: val_accuracy did not improve from 0.50400\n",
            "Epoch 33/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7028 - accuracy: 0.4731 - val_loss: 0.7022 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00033: val_accuracy did not improve from 0.50400\n",
            "Epoch 34/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.7022 - accuracy: 0.5154 - val_loss: 0.7015 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00034: val_accuracy did not improve from 0.50400\n",
            "Epoch 35/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7019 - accuracy: 0.4869 - val_loss: 0.7012 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00035: val_accuracy did not improve from 0.50400\n",
            "Epoch 36/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7016 - accuracy: 0.5258 - val_loss: 0.7011 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00036: val_accuracy did not improve from 0.50400\n",
            "Epoch 37/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7011 - accuracy: 0.5159 - val_loss: 0.7013 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00037: val_accuracy did not improve from 0.50400\n",
            "Epoch 38/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7001 - accuracy: 0.5160 - val_loss: 0.7006 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00038: val_accuracy did not improve from 0.50400\n",
            "Epoch 39/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7010 - accuracy: 0.5024 - val_loss: 0.7007 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00039: val_accuracy did not improve from 0.50400\n",
            "Epoch 40/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7012 - accuracy: 0.4770 - val_loss: 0.7003 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00040: val_accuracy did not improve from 0.50400\n",
            "Epoch 41/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7002 - accuracy: 0.5176 - val_loss: 0.7000 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00041: val_accuracy did not improve from 0.50400\n",
            "Epoch 42/50\n",
            "24/24 [==============================] - 0s 15ms/step - loss: 0.7008 - accuracy: 0.4734 - val_loss: 0.6999 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00042: val_accuracy did not improve from 0.50400\n",
            "Epoch 43/50\n",
            "24/24 [==============================] - 0s 15ms/step - loss: 0.6998 - accuracy: 0.5285 - val_loss: 0.6998 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00043: val_accuracy did not improve from 0.50400\n",
            "Epoch 44/50\n",
            "24/24 [==============================] - 0s 15ms/step - loss: 0.6998 - accuracy: 0.5007 - val_loss: 0.6996 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00044: val_accuracy did not improve from 0.50400\n",
            "Epoch 45/50\n",
            "24/24 [==============================] - 0s 15ms/step - loss: 0.6999 - accuracy: 0.4713 - val_loss: 0.6995 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00045: val_accuracy did not improve from 0.50400\n",
            "Epoch 46/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7000 - accuracy: 0.4796 - val_loss: 0.6993 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00046: val_accuracy did not improve from 0.50400\n",
            "Epoch 47/50\n",
            "24/24 [==============================] - 0s 15ms/step - loss: 0.6998 - accuracy: 0.4861 - val_loss: 0.6994 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00047: val_accuracy did not improve from 0.50400\n",
            "Epoch 48/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.6997 - accuracy: 0.5035 - val_loss: 0.6992 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00048: val_accuracy did not improve from 0.50400\n",
            "Epoch 49/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.6997 - accuracy: 0.4705 - val_loss: 0.6990 - val_accuracy: 0.4960\n",
            "\n",
            "Epoch 00049: val_accuracy did not improve from 0.50400\n",
            "Epoch 50/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.6985 - accuracy: 0.5287 - val_loss: 0.6988 - val_accuracy: 0.5040\n",
            "\n",
            "Epoch 00050: val_accuracy did not improve from 0.50400\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "98_oXi-lcgux",
        "outputId": "98384698-4ac9-4dd6-89c8-d47fb020ae93"
      },
      "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": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cu5SSxXyiOs3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6b08ca2-b84b-42f7-d341-3c35a6fb751e"
      },
      "source": [
        "predictions = model.predict(testX, batch_size=32)"
      ],
      "execution_count": 21,
      "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_input'), name='flatten_input', description=\"created by layer 'flatten_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": "b04e530e-54f1-43ea-c6b0-d8ef498656b3"
      },
      "source": [
        "print(predictions.argmax(axis=1))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6puOgflYR_e"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPAZkmBJVGCD",
        "outputId": "a96deef4-36d4-4517-9aeb-dc67bbea269d"
      },
      "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": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] evaluating network...\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Covid       0.50      1.00      0.67       126\n",
            "      Normal       0.00      0.00      0.00       124\n",
            "\n",
            "    accuracy                           0.50       250\n",
            "   macro avg       0.25      0.50      0.34       250\n",
            "weighted avg       0.25      0.50      0.34       250\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJ-r6keDY5jt",
        "outputId": "039d3fac-4237-4e57-cb7d-3e23672a9c8d"
      },
      "source": [
        "cf_matrix = confusion_matrix(testY,predictions.argmax(axis=1))\n",
        "print(cf_matrix)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[126   0]\n",
            " [124   0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLp1ooZeY90p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "87174a71-3e4b-45af-a8d5-3ba09cc27507"
      },
      "source": [
        "\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cf_matrix, display_labels=encoder.classes_)\n",
        "disp.plot(cmap=plt.cm.Blues)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fcfe17afcd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}