CLAHE_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/CLAHE_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": "b5462a3d-f517-49c5-a1cb-d694a2136433"
      },
      "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": "d168cf02-80ca-4c32-fc0b-a649d4d00a91"
      },
      "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": "c12f8d14-7bb6-4b91-c5bb-1e5424891aca"
      },
      "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",
            " 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": "a1accf98-d24b-46d8-fcef-5cc45acd15a4"
      },
      "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(\"CLAHE\")\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/CLAHE/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": "64bbe497-6343-4a94-df5d-85d923cb3848"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 'Covid', 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'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal', 'Normal']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "aGbsj1UcytpV",
        "outputId": "3253643f-f612-4e22-a721-4c7cff95ae46"
      },
      "source": [
        "\n",
        "print(labels[500])\n",
        "plt.imshow(data[10])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normal\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f9163d22a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "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": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FPCyIqcVGB7",
        "outputId": "f30122b3-4b84-4326-f8af-8356483ead49"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LynLhwt_pvNc",
        "outputId": "0170f6d3-20e9-4254-cfbf-13c9e3255fd7"
      },
      "source": [
        "data = np.array(data)\n",
        "data.shape"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ktZ5hNYwVGB9",
        "outputId": "d5b48215-7531-4012-8b62-1ddcfc40e834"
      },
      "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": 13,
      "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": "33382553-6176-456f-e27b-4a658c888d0c"
      },
      "source": [
        "print(trainX[0])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  ...\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]]\n",
            "\n",
            " [[0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  ...\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]]\n",
            "\n",
            " [[0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  ...\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  ...\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]]\n",
            "\n",
            " [[0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  ...\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]]\n",
            "\n",
            " [[0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  ...\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]\n",
            "  [0.00596813 0.00596813 0.00596813]]]\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": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTvu4IV0i7Fj",
        "outputId": "3cfa85a2-8e2a-408e-b365-7bdc2bf36fac"
      },
      "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": 16,
      "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": 17,
      "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": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0TdWxuFPkT3",
        "outputId": "708433d6-a333-4458-c20f-cca205dd84f9"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 19,
      "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": "edb0cd69-8340-40ff-9281-ef83ec79b585"
      },
      "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": 20,
      "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: 783208098.6976 - accuracy: 0.4890WARNING: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 [==============================] - 16s 36ms/step - loss: 865226856.9897 - accuracy: 0.4900 - val_loss: 0.9438 - val_accuracy: 0.5480\n",
            "\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.54800, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 2/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.9159 - accuracy: 0.4952 - val_loss: 0.8371 - val_accuracy: 0.5480\n",
            "\n",
            "Epoch 00002: val_accuracy did not improve from 0.54800\n",
            "Epoch 3/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.8244 - accuracy: 0.4928 - val_loss: 0.7925 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00003: val_accuracy did not improve from 0.54800\n",
            "Epoch 4/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.7843 - accuracy: 0.5280 - val_loss: 0.7710 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00004: val_accuracy did not improve from 0.54800\n",
            "Epoch 5/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7639 - accuracy: 0.5057 - val_loss: 0.7590 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00005: val_accuracy did not improve from 0.54800\n",
            "Epoch 6/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7495 - accuracy: 0.5166 - val_loss: 0.7482 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00006: val_accuracy did not improve from 0.54800\n",
            "Epoch 7/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7402 - accuracy: 0.5209 - val_loss: 0.7421 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00007: val_accuracy did not improve from 0.54800\n",
            "Epoch 8/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7316 - accuracy: 0.5396 - val_loss: 0.7334 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00008: val_accuracy did not improve from 0.54800\n",
            "Epoch 9/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.7291 - accuracy: 0.5066 - val_loss: 0.7305 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00009: val_accuracy did not improve from 0.54800\n",
            "Epoch 10/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7243 - accuracy: 0.5268 - val_loss: 0.7263 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00010: val_accuracy did not improve from 0.54800\n",
            "Epoch 11/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7223 - accuracy: 0.5063 - val_loss: 0.7242 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00011: val_accuracy did not improve from 0.54800\n",
            "Epoch 12/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7175 - accuracy: 0.5341 - val_loss: 0.7215 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00012: val_accuracy did not improve from 0.54800\n",
            "Epoch 13/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7159 - accuracy: 0.5255 - val_loss: 0.7176 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00013: val_accuracy did not improve from 0.54800\n",
            "Epoch 14/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.7158 - accuracy: 0.4811 - val_loss: 0.7188 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00014: val_accuracy did not improve from 0.54800\n",
            "Epoch 15/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.7144 - accuracy: 0.4945 - val_loss: 0.7177 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00015: val_accuracy did not improve from 0.54800\n",
            "Epoch 16/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7119 - accuracy: 0.5215 - val_loss: 0.7180 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00016: val_accuracy did not improve from 0.54800\n",
            "Epoch 17/50\n",
            "24/24 [==============================] - 0s 15ms/step - loss: 0.7107 - accuracy: 0.5193 - val_loss: 0.7151 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00017: val_accuracy did not improve from 0.54800\n",
            "Epoch 18/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7082 - accuracy: 0.5333 - val_loss: 0.7138 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00018: val_accuracy did not improve from 0.54800\n",
            "Epoch 19/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7092 - accuracy: 0.5101 - val_loss: 0.7123 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00019: val_accuracy did not improve from 0.54800\n",
            "Epoch 20/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7093 - accuracy: 0.4949 - val_loss: 0.7124 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00020: val_accuracy did not improve from 0.54800\n",
            "Epoch 21/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7073 - accuracy: 0.5172 - val_loss: 0.7122 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00021: val_accuracy did not improve from 0.54800\n",
            "Epoch 22/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7062 - accuracy: 0.5189 - val_loss: 0.7084 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00022: val_accuracy did not improve from 0.54800\n",
            "Epoch 23/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7074 - accuracy: 0.4726 - val_loss: 0.7104 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00023: val_accuracy did not improve from 0.54800\n",
            "Epoch 24/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7041 - accuracy: 0.5291 - val_loss: 0.7093 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00024: val_accuracy did not improve from 0.54800\n",
            "Epoch 25/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7052 - accuracy: 0.5155 - val_loss: 0.7109 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00025: val_accuracy did not improve from 0.54800\n",
            "Epoch 26/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7050 - accuracy: 0.5122 - val_loss: 0.7093 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00026: val_accuracy did not improve from 0.54800\n",
            "Epoch 27/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7031 - accuracy: 0.5305 - val_loss: 0.7053 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00027: val_accuracy did not improve from 0.54800\n",
            "Epoch 28/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7038 - accuracy: 0.5150 - val_loss: 0.7076 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00028: val_accuracy did not improve from 0.54800\n",
            "Epoch 29/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7023 - accuracy: 0.5281 - val_loss: 0.7065 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00029: val_accuracy did not improve from 0.54800\n",
            "Epoch 30/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7042 - accuracy: 0.5029 - val_loss: 0.7075 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00030: val_accuracy did not improve from 0.54800\n",
            "Epoch 31/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.7034 - accuracy: 0.5064 - val_loss: 0.7073 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00031: val_accuracy did not improve from 0.54800\n",
            "Epoch 32/50\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.7027 - accuracy: 0.5108 - val_loss: 0.7054 - val_accuracy: 0.4520\n",
            "\n",
            "Epoch 00032: val_accuracy did not improve from 0.54800\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "98_oXi-lcgux",
        "outputId": "02ee9f6f-aa2e-46d9-9490-30022e533e7f"
      },
      "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": 21,
      "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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xzuS1LhqkBCEi4ZXOBLEImGBm480sAlwGzI09ICghtJgJrA62R4CngF+7+xNpjLFzlCBEpA9IWy8md280s+uAF4BsYLa7rzSzO4HF7j4X+KaZzQQagV3AVcHplwKfAErNrGXbVe6+LF3xJkVVTCLSB6QtQQC4+3xgfpttt8Y8vhm4Oc55DwMPpzO2LlEJQkT6gEw3UvdOWnZURPqAtJYgQisF3Vxff6+KJ5eUJ3TsrMmjOH3C4E6/VosD9U386I/rqNxb1+GxRfk5XPXxcZSVFnbpNesbm3ls0Qe8U17dpet0xqDCCNeddQT983O75fUO1Dfx0MKNrNved744XHryGE4eV9Ll61QfaODuF9ZS29CUgqj6nrElBXzj7Akpv64SRGfk5EFWbpcSxPfnrWJD5T4GFbT/4bW3rpHnV2zjD9/5O4YWd23U9n2vrOcnC95jxIB8rINjq/bV8/DCTXzh1HF846wJDOggzrbcnRdXbeeu59bw/s59DC3OIyero1dNra17aqlraOKOWZPS+jrNzc6cZRX89wtr2Vpdy/D++XTzrWbErv31vFdZw5PXntbla/1u8WYeWrgpof+b8lEfjhyQlusqQXRWF5YdXbd9Lyu37OG2CyfyxdPGt3vshsoazrv3Ve56bg33XDq5U68H8P7OfTzw6gYunjKK//lsx9fZsaeWH7z4Lv/31/d5Ykk53zxrAlecUkYkp+NayXfKd/P9eat58/1dHDG0iF9edTLTjxqCWff+6d/69AoeWriJS08ew7Fp+gNauKGKf5u3muUV1Rw/egD3fnYyHzusNC2v1dP89E/vcddza9hUta/LJc05yyo4fvQA5l53eoqik1RQG0RnRYqgfl+nTp2zrILsLOPTx4/s8NjDhhTxlTMO48klFSzeuKtTr+fu3PHMSiI5Wdx8/tEJnTO0fz7/+ZnjmfeNM5g0cgB3PruK8+59lRdWbsMPMUnhlt0H+PZjy5j547/y3o4avn/RJJ6//gzOPHpotycHgBvOPYpBBRFue3rlIWPurA2VNVz968Vc9sBCqmrquPezk5lz7Wl9JjkAzDxhJGbw9LItXbrO+h17WVGxh1mT4020IJmkBNFZkSKo35v0ac3NzpylWzj9iMEMKc5L6JzrzjqCEQPyufXplTQ1J/9B9/LqHSxYW8m3zpnA0P7JVVNNHNmfh/7fNH551clkZxnXPPQWn31gIe+U/23exJq6Ru5+YS1n3r2Aecu38rXph7PgxulccUoZOdmZ+y82oCCX7844msWbPuSppRUdn5CAD/fVc/vclXzyf17lr+t3cuN5R/HH70znoimjyOoL9UoxRg7sx8fGlzBnaUWXEvCcpVvIMrjwhBEdHyzdqs9XMTU3O9v21FIYyUmunr2TVUyLN31Ixe4DfOe8IxM+pyCSwy0XTOTrv13Cb9/YxJWnjkv43NqGJu54ZiVHDiviCx9P/LxYZsaZRw/ljAmDeWTRZu596V1m/vivXDxlFJPHDORHf1zPzpo6Zk0eyY3nHcXoQQWdep10+MxJo/ntmx/w7/PXcM7EYZ1usK5rbOKh1zfxwz+so6aukcumjeXb5xyZcJIPq4smj+KmJ5cHVWwDkz7fPdp+c9oRg7vcxiap1+dLENv31vLxu/7IM+8kWUzu5LKjc5ZV0C83m09OHJ7UeZ86bjgfP7yU/35hLVU1HfdCavHTP71H+YcHuH3mseR28dt8TnYWV55Sxis3Tudr0w9n3vKt3DZ3JeNKC5jz9dP438um9KjkAJCVZdw561iq9tXxvy+v69Q1ahua+MLsN/n+vNWcWDaI57/1Cf794uP6fHIAOP+4EUSyszpdQntr04eUf3iAi6eoeqkn6vMJYlhxPpGcLD7YtT+5EyOFSbdB1Dc2M++drXzy2GEU5iVXeDMz7ph5LPvrm/iv59cmdM7mXfu5f8F7fPr4EXz88K53k23RPz9adfPKd6bzu6+eyu++eiqTxyT/7bG7HD96IJedPJZfvbaRtduSqxZsbnZuePxtFm7YxX9/5nh+9cVpHDmsOE2R9j4D+uVy9jFDeebtLTQ2fWQ+zQ49tTT6hem8Y5P7wiTdo88niKwso6ykgI07k2xw7sS61AvW7qD6QAMXdfLb0oRhxXzxtHE8tngzyzZ3vHbSHc+sIjvL+JcLjunU63Vk1MB+nDyuJCMN0Mn6p/OOojg/h9vmrki4vtzdufPZVcxbvpV//tTR/MPUj8xGL0TH6eysqeev71UldV59YzPzlm/l3InJf2GS7tHnEwRAWWlBJ0oQyTdSz1lWQWlhhDOO6Py3+evPOZKhxXnc+vSKdhusX1mzg5dXb+cbZ01gxIB+nX69sBhUGOE7nzyKhRt28cw7WxM654FXN/Cr1zbypdPG85UzDktzhL3XmUcPoX9+DnOSrGb607uV7N7foOqlHkwJAhhbUsgHu/Yn1xMjySqmPbUNvLx6BxeeMLJLPXuK8nL4lwuO4Z3yah5fvDnuMXWN0Ybpw4YU8v9Ob3+cRV9y+bSxTBrVn3+bt4qausZ2j31qaTn/8dwaPn38CG654JheUUrKlLycbC44fgQvrNzG/vr2/11jzVlWQUlhJCWzBEh6KEEQLUHsr2+iMonGX/KKoKkeGusTOvz55duob2zudPVSrJknjGTa+BL+6/k17N7/0df/+asb2Fi1n9svPDahgW19RXaWceesSWzfU8eP/njoBus/r6vkxt+9w6mHlfKDS0/oc91XO+OiyaPYX9/ES6u2J3T83toGXl61nQuPH9HlzhOSPnpngLGl0Z43H1QlUc2U5LrUTy2tYFxpASeM7vqI3pYG6z21jdz94sEN1hW7D/DjV9Yz49jhfOLInrOMd09x4thB/MNJo/m/P7/P+h0ffe9WVFTz1Yfe4oihRfzs8yeRl5OdgSh7n5PHlTByQH7C1UzPr9hGXWMzs1S91KMpQQDjgmkCNiaTIFrXhOi4HWJr9QEWvl/FRVNGpayq4pgR/bnylDJ+88YHrKj420R43392FQC3fDo9DdNh8N3zj6ZfJJvb5x48wnrzrv1c9ctFDCyI8OCXpnXbJH9hkJVlzJoyilfX7WRnAiXxOcsqKCstYEoP7v0mCSYIM3vSzC4ws1AmlFED+5Fl8EFVEj2ZWmd07ficucu24B4thqfSt889ktLCCLc+vYLmZufP6yp5bsU2vj79iB43HqEnGVyUxw3nHslf1u/k+RXbANi1r57Pz36ThqZmHvzSyQxLcsS5RP9/NzU7z77d/piibdW1vPZeFbMmp+4Lk6RHoh/4PwE+B6wzs7vM7Kg0xtTtIjlZjBzYj03J9GRKooppzrItTB4zkHGDuzahWVsD+kXHIyz5YDePLtrMbXNXUlZawFc+oR43HbnilDKOHl7M955dRVVNHV/61SK27D7A7KumcsRQjXPojKOGF3PMiP7M6WBupmfebvnC1PFcZJJZCSUId3/Z3f8ROBHYCLxsZq+Z2RfNLBTl8LLSAjaloYpp7ba9rN66J21/DJecOJoTxw7kljnL2VC5j9svPJb8XNWbdyQnO4vvXTSJLdW1nHfvq7xTvpsfXj6Fk8q6vrZBX3bxlJEs27yb99sZV/TU0gpOGDOQw4YUdWNk0hkJVxmZWSnRNaO/DCwF/pdowngpLZF1s7ElhWxKQxVT68ytJ6QnQWQFPXMAzjlmKGcePTQtrxNGJ48r4eIp0UFed86apNG8KTDzhFHBDK/xG6vf3b6XVWn8wiSpldDwRTN7CjgKeAi40N1bRho9ZmaL0xVcdxpXWsCH+xvYU9uQWONkAutSNzc7Ty+t4IwJgxlclL55eyaNGsCz3ziDcYPV7pCs//j747jy1DJOHDso06GEwvAB+Zx6WClzllZw/dkTPtLGMGdp4lPdS+YlWoL4obtPdPf/iEkOALj71DTE1e3Kku3qmsC61G9u3MWW6tpuGSk6cWR/CiKariBZ+bnZSg4pdtHkUWys2s/bbZaZbW52nl6W3FT3klmJJoiJZtbaH83MBpnZtWmKKSPGlkSrjBJuh2gtQRy6DeLpZRUURLI5d+KwroYn0mvMOG44kZysj4yJaJnqXlNr9B6JJoivuHvr7HDu/iHwlfSElBktg+U27UqwHSInDyz7kG0QdY1NzHtnK+cdO1zf7KVP6Z+fyznBDK8NMTO8tkx1ry9MvUeiCSLbYioTzSwbiKQnpMwoysthcFGETTsTLEGYtbto0CtrKtlT25iSqTVEepuLJo+ial89f1m/E4j9wqSZW3uTRBPE80QbpM82s7OBR4JtoVJWWph4CQKiYyEO0Ug9Z2kFg4sinHZ431mjWKTF9KOGMqBfbms104K1lVQfaNDUGr1Mogniu8ArwNeCnz8A/5SuoDKlrKQgyfmYCuOOg6g+0MAf13R95laR3iqSk8UFx4/gxZXb2VfXyNMpmOpeul+iA+Wa3f1+d/9M8PMzd29Kd3DdbWxpAVv31FLXmOCt5RXFbYN4bvlW6puaUz61hkhvcvGUURxoaOL3S8pTMtW9dL9E52KaYGZPmNkqM9vQ8pPu4LpbWWkB7rB514HETjjEutRPLa3gsMGFHJ+CmVtFequTxg5i1MB+3PXcmpRNdS/dK9F0/kvgfqAROBP4NfBwuoLKlLLSlq6uCbZDRD7aSF2x+wBvvL9LE5FJn5eVZVw0ZST765sYP7gwJVPdS/dKNEH0c/c/AObum9z9duCC9IWVGWUlQVfXhAfLfbQE8UIwO+gsTSUg0jrm4eIUTnUv3SfR/mZ1wVTf68zsOqACCN1MWyWFEYrychJfnzpOFdPb5bsZ3j8/5TO3ivRGRwwtZs7XT+OYEZohtzdKtARxPVAAfBM4CbgC+EK6gsoUM2NsSUHiVUxxxkEsr6hm0igVpUVaTB4zUCvz9VIdJohgUNxn3b3G3cvd/Yvufom7L0zg3BlmttbM1pvZTXH2X2VmlWa2LPj5csy+L5jZuuCn25JRUtN+R4qgqQ6aGgCoqWvk/Z37OE4JQkRCoMMqJndvMrPTk71wkFjuA84FyoFFZjbX3Ve1OfQxd7+uzbklwG3AVMCBt4JzP0w2jmSVlRby8urtNDU72R0tVh87o2u/Qazasgd3OG50/3SHKSKSdolWMS01s7lmdqWZ/X3LTwfnTAPWu/sGd68HHgVmJfh65wEvufuuICm8BMxI8NwuKSstoKHJ2VqdQFfX1kWDotVMy4O1oVXFJCJhkGiCyAeqgLOAC4OfT3dwzihgc8zz8mBbW5eY2TvBOIsxyZxrZleb2WIzW1xZWZnYnXSgpSdTQiOqWxcNiiaIFRXVDOufx9BirWcsIr1fQr2Y3P2LaXr9Z4BH3L3OzK4BHiSahBLi7g8ADwBMnTrVUxHQ32Z13c/HOzq4dV3qaKP28opqJo1U6UFEwiHRFeV+SbQt4CDu/qV2TqsAxsQ8Hx1siz2/KubpL4D/ijl3eptzFyQSa1eNGNCP3GxjYyI9mWLWpd5X18h7lTVccNyI9AYoItJNEq1iehaYF/z8AegPHHoptahFwAQzG29mEeAyYG7sAWYW+2k6E1gdPH4B+GSwMNEg4JPBtrTLzjLGJDppX0wj9aqtQQO12h9EJCQSrWL6fexzM3sE+EsH5zQGg+peALKB2e6+0szuBBa7+1zgm2Y2k+gUHruAq4Jzd5nZ94gmGYA73X1X4rfVNWUlCXZ1bWmDqKtheVW0gfo4TScgIiHR2ZU7JgBDOzrI3ecD89tsuzXm8c3AzYc4dzYwu5PxdUlZaSGLNn6Iu7c/PUDLutT1NazYUs2Q4jyG9VcDtYiEQ6JtEHs5uA1iG9E1IkJpbEkBNXWN7NpXT2lRO4urx1QxraioVvWSiIRKolVMfWoilbKgJ9PGqv3tJ4jcfmBZNOzfw/odNcyYpAZqEQmPRNeDuNjMBsQ8H2hmF6UvrMxqmfb7g46WHzWDSBG7dn9IsxqoRSRkEu3FdJu7V7c8cffdRKfCCKUxJf0wS3Da70gR1bujM4BMGqUpNkQkPBJNEPGO62wDd4+Xl5PNiP75iXV1zStif001g4siDFcDtYiESKIJYrGZ3WNmhwc/9wBvpTOwTBtbWpDYYLlIIQ37o1N8a0EUEQmTRBPEN4B64DGikxL2QSMAAA70SURBVO7VAl9PV1A9QVlJYUILBzXlFkG9pvgWkfBJtBfTPuAj6zmEWdngAnbW1FNT10hR3qH/mfZ6HoXUagZXEQmdRHsxvWRmA2OeDzKzbpn6IlPKSoKeTB20Q+xqiFDIAZUgRCR0Eq1iGhz0XAIgWKOhw5HUvVnLWIiOurpur82hKKuOEQPUQC0i4ZJogmg2s7EtT8xsHHFmdw2TsTGD5dpTsT+bIqtVA7WIhE6iXVX/BfiLmf0JMOAM4Oq0RdUD9M/PZVBBbrtjIWobmti8L5u8nDpoaoTs0Pb8FZE+KKEShLs/T3R96LXAI8ANQAJrcvZuZaWF7VYxrd66hxoPpuKo72j2cxGR3iXRyfq+DFxPdOGeZcApwOsksfpbb1RWWsBbmz485P4VFdXso1/0Sf0+6DfwkMeKiPQ2ibZBXA+cDGxy9zOBKcDu9k/p/cpKCtiy+wD1jc1x96+o2HPQjK4iImGSaIKodfdaADPLc/c1wFHpC6tnGFtaSLND+Yfx2yGWV1QzpLQk+qROCUJEwiXRBFEejIOYA7xkZk8Dm9IXVs/Q0tV1U5wR1bUNTby7fS8jhw6JblAJQkRCJtGR1BcHD283s1eAAcDzaYuqh2gdCxGnJ9PabXtpbHbGDh8Kq1CCEJHQSbpfprv/KR2B9ERDivIoiGTH7eq6vCI6+/lho4dHN6iKSURCJtEqpj7JzBhbUsCmOLO6rqioZkC/XIYPLo1uqN/bzdGJiKSXEkQHxpYUxG2DWB6sQW15wWqs9QlMDS4i0osoQXSgrLSAD3btp7n5bzOL1DVGG6gnjRoAuQWAqYpJREJHCaIDZaWF1Dc2s31vbeu2d7fV0NDk0Rlcs7IgUqhGahEJHSWIDrR2dY1pqG5poG6d4jtSBHVqgxCRcFGC6EDLuhCxDdXLK6rpn5/DmJJgmo28IrVBiEjoKEF0YOTAfHKy7KASxIqKNmtQR4pUxSQioaME0YGc7CxGDerX2pOpvrGZtdv2HryCXKRIjdQiEjpKEAkoKy1sHU397va91Dc1H7wGdZ5KECISPkoQCSgrKWBj1T7c/aMN1KAqJhEJJSWIBJSVFrC3tpHd+xtYUVFNcX5Oa+8mINrNVVVMIhIyShAJGFvyt1ldV1RUM2nkgIPXoM4rVglCREJHCSIBZaXRrq7v7ahh9ba9HDd6wMEHRIqgYT80N2UgOhGR9EhrgjCzGWa21szWm9lN7Rx3iZm5mU0Nnuea2YNmttzMVpvZzemMsyMtJYiXV2+nvrFNAzVEG6lBYyFEJFTSliDMLBu4DzgfmAhcbmYT4xxXTHRJ0zdiNv8DkOfuxwEnAdeY2bh0xdqRfpFshvXP449rdgAwaWT/gw+IREsYqmYSkTBJZwliGrDe3Te4ez3wKDArznHfA/4TqI3Z5kChmeUA/YB6YE8aY+1QWUkhdY3NFOXlMC6ocmoV0YyuIhI+6UwQo4DNMc/Lg22tzOxEYIy7z2tz7hPAPmAr8AFwt7vvavsCZna1mS02s8WVlZUpDb6tsUGvpWNH9icryw7e2VLFpPmYRCREMtZIbWZZwD3ADXF2TwOagJHAeOAGMzus7UHu/oC7T3X3qUOGDElrvGVBO8RxbdsfQFVMIhJKSS85moQKYEzM89HBthbFwCRgQdBldDgw18xmAp8Dnnf3BmCHmf0VmApsSGO87SobHE0CH+nBBNFeTKCxECISKuksQSwCJpjZeDOLAJcBc1t2unu1uw9293HuPg5YCMx098VEq5XOAjCzQuAUYE0aY+3Q6UcMZtbkkfzdkXFKKlpVTkRCKG0Jwt0bgeuAF4DVwOPuvtLM7gxKCe25Dygys5VEE80v3f2ddMWaiJLCCP972RQGFkQ+urOlBKF1qUUkRNJZxYS7zwfmt9l26yGOnR7zuIZoV9feoaUNQlVMIhIiGkmdChENlBOR8FGCSIWsLMjVutQiEi5KEKkSKdQ4CBEJFSWIVNGiQSISMkoQqRIpUhuEiISKEkSq5BWrF5OIhIoSRKpECjUOQkRCRQkiVVTFJCIhowSRKnlFqmISkVBRgkiViHoxiUi4KEGkSkuCaG7OdCQiIimhBJEqLYsGNezPbBwiIimiBJEqrfMxqZpJRMJBCSJVtGiQiISMEkSq5GlNCBEJFyWIVNGU3yISMkoQqaIqJhEJGSWIVMlTI7WIhIsSRKqoF5OIhIwSRKrkqYpJRMJFCSJVcgujv1WCEJGQUIJIlewcyOmnZUdFJDSUIFIpT1N+i0h4KEGkUqRQVUwiEhpKEKkU0bKjIhIeShCplKc1IUQkPJQgUkmLBolIiChBpFKkUFVMIhIaShCppComEQkRJYhUihSrm6uIhIYSRCq1dHN1z3QkIiJdpgSRSnlF4M1al1pEQiGtCcLMZpjZWjNbb2Y3tXPcJWbmZjY1ZtvxZva6ma00s+Vmlp/OWFNCiwaJSIikLUGYWTZwH3A+MBG43MwmxjmuGLgeeCNmWw7wMPBVdz8WmA40pCvWlMkrjv7WfEwiEgLpLEFMA9a7+wZ3rwceBWbFOe57wH8CtTHbPgm84+5vA7h7lbs3pTHW1IhoRlcRCY90JohRwOaY5+XBtlZmdiIwxt3ntTn3SMDN7AUzW2Jm/xTvBczsajNbbGaLKysrUxl752jZUREJkYw1UptZFnAPcEOc3TnA6cA/Br8vNrOz2x7k7g+4+1R3nzpkyJC0xpuQliomtUGISAikM0FUAGNino8OtrUoBiYBC8xsI3AKMDdoqC4HXnX3ne6+H5gPnJjGWFOjtYpJbRAi0vulM0EsAiaY2XgziwCXAXNbdrp7tbsPdvdx7j4OWAjMdPfFwAvAcWZWEDRY/x2wKo2xpoaqmEQkRNKWINy9EbiO6If9auBxd19pZnea2cwOzv2QaPXTImAZsCROO0XPk6duriISHjnpvLi7zydaPRS77dZDHDu9zfOHiXZ17T1ax0GoBCEivZ9GUqdSdi5k52kchIiEghJEqmlGVxEJCSWIVIsUqQ1CREJBCSLVIkXqxSQioaAEkWp5RRoHISKhoASRaqpiEpGQUIJItTxVMYlIOChBpFpEvZhEJByUIFJNjdQiEhJKEKnWMg5C61KLSC+X1qk2+qRIIXgT3PcxMMt0NCLSFww7Fj4zO+WXVYJItaMvhO0robkx05GISF8xsCwtl1WCSLUhR6Ylk4uIdDe1QYiISFxKECIiEpcShIiIxKUEISIicSlBiIhIXEoQIiISlxKEiIjEpQQhIiJxmYdkziAzqwQ2deESg4GdKQonU3QPPYPuoWfQPSSmzN2HxNsRmgTRVWa22N2nZjqOrtA99Ay6h55B99B1qmISEZG4lCBERCQuJYi/eSDTAaSA7qFn0D30DLqHLlIbhIiIxKUShIiIxKUEISIicfX5BGFmM8xsrZmtN7ObMh1PZ5jZRjNbbmbLzGxxpuNJlJnNNrMdZrYiZluJmb1kZuuC34MyGWNHDnEPt5tZRfB+LDOzT2UyxvaY2Rgze8XMVpnZSjO7Ptjea96Hdu6h17wPAGaWb2ZvmtnbwX3cEWwfb2ZvBJ9Rj5lZpNti6sttEGaWDbwLnAuUA4uAy919VUYDS5KZbQSmunuvGhRkZp8AaoBfu/ukYNt/Abvc/a4gYQ9y9+9mMs72HOIebgdq3P3uTMaWCDMbAYxw9yVmVgy8BVwEXEUveR/auYdL6SXvA4CZGVDo7jVmlgv8Bbge+P+AJ939UTP7KfC2u9/fHTH19RLENGC9u29w93rgUWBWhmPqM9z9VWBXm82zgAeDxw8S/UPvsQ5xD72Gu2919yXB473AamAUveh9aOceehWPqgme5gY/DpwFPBFs79b3oq8niFHA5pjn5fTC/1hE/xO9aGZvmdnVmQ6mi4a5+9bg8TZgWCaD6YLrzOydoAqqx1bPxDKzccAU4A166fvQ5h6gl70PZpZtZsuAHcBLwHvAbndvDA7p1s+ovp4gwuJ0dz8ROB/4elDt0et5tP6zN9aB3g8cDkwGtgI/yGw4HTOzIuD3wLfcfU/svt7yPsS5h173Prh7k7tPBkYTreE4OpPx9PUEUQGMiXk+OtjWq7h7RfB7B/AU0f9YvdX2oE65pW55R4bjSZq7bw/+0JuBn9PD34+gvvv3wG/c/clgc696H+LdQ297H2K5+27gFeBUYKCZ5QS7uvUzqq8niEXAhKCXQAS4DJib4ZiSYmaFQcMcZlYIfBJY0f5ZPdpc4AvB4y8AT2cwlk5p+WANXEwPfj+ChtH/A1a7+z0xu3rN+3Coe+hN7wOAmQ0xs4HB435EO8+sJpooPhMc1q3vRZ/uxQQQdH27F8gGZrv7v2U4pKSY2WFESw0AOcBve8s9mNkjwHSiUxpvB24D5gCPA2OJTt9+qbv32EbgQ9zDdKLVGg5sBK6Jqc/vUczsdODPwHKgOdj8z0Tr8HvF+9DOPVxOL3kfAMzseKKN0NlEv7w/7u53Bn/jjwIlwFLgCnev65aY+nqCEBGR+Pp6FZOIiByCEoSIiMSlBCEiInEpQYiISFxKECIiEpcShEgPYGbTzezZTMchEksJQkRE4lKCEEmCmV0RzNm/zMx+FkyuVmNm/xPM4f8HMxsSHDvZzBYGk8U91TJZnJkdYWYvB/P+LzGzw4PLF5nZE2a2xsx+E4wQFskYJQiRBJnZMcBngdOCCdWagH8ECoHF7n4s8Ceio6kBfg18192PJzrKt2X7b4D73P0E4ONEJ5KD6Cyk3wImAocBp6X9pkTakdPxISISOBs4CVgUfLnvR3QSu2bgseCYh4EnzWwAMNDd/xRsfxD4XTBv1ih3fwrA3WsBguu96e7lwfNlwDiii8aIZIQShEjiDHjQ3W8+aKPZv7Y5rrPz18TOr9OE/j4lw1TFJJK4PwCfMbOh0LpucxnRv6OW2TY/B/zF3auBD83sjGD7lcCfghXPys3souAaeWZW0K13IZIgfUMRSZC7rzKzW4iu3pcFNABfB/YB04J9O4i2U0B0auafBglgA/DFYPuVwM/M7M7gGv/QjbchkjDN5irSRWZW4+5FmY5DJNVUxSQiInGpBCEiInGpBCEiInEpQYiISFxKECIiEpcShIiIxKUEISIicf3/mq0MppVlb6sAAAAASUVORK5CYII=\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": "72cfdd35-cd83-48bb-eb03-db6f8fe36ea9"
      },
      "source": [
        "predictions = model.predict(testX, batch_size=32)"
      ],
      "execution_count": 22,
      "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": "bc8c13a1-c71b-4cbb-b951-3eef42913656"
      },
      "source": [
        "print(predictions.argmax(axis=1))"
      ],
      "execution_count": 23,
      "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]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6puOgflYR_e"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPAZkmBJVGCD",
        "outputId": "3a2da5cf-c155-457d-f2b6-2a1466ad7ae0"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] evaluating network...\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Covid       0.56      0.99      0.71       117\n",
            "      Normal       0.98      0.31      0.47       133\n",
            "\n",
            "    accuracy                           0.63       250\n",
            "   macro avg       0.77      0.65      0.59       250\n",
            "weighted avg       0.78      0.63      0.58       250\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJ-r6keDY5jt",
        "outputId": "fe16d7e1-03e7-474c-e343-1fb747270235"
      },
      "source": [
        "cf_matrix = confusion_matrix(testY,predictions.argmax(axis=1))\n",
        "print(cf_matrix)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  0 137]\n",
            " [  0 113]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLp1ooZeY90p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "7869d9ee-5ff3-476e-b1ff-5f60892ba561"
      },
      "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 0x7f90f34fa250>"
            ]
          },
          "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"
          }
        }
      ]
    }
  ]
}