CLAHE_Adamax_0.001.ipynb

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    "accelerator": "GPU",
    "colab": {
      "name": "Copy of scenes_nn_4 layers.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "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"
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rfadhila07/Test-result/blob/main/CLAHE_Adamax_0.001.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_dJvuvIBVLS_",
        "outputId": "d3d70107-6631-40de-d5fe-2a95cbc8f84c"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 4,
      "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": "d72602a5-d24b-491f-eb41-3657a8a9924d"
      },
      "source": [
        "cd /content/drive/MyDrive/ "
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z7VB1d4wVdov",
        "outputId": "135e00fc-6468-47cf-fe1e-18696813ff2f"
      },
      "source": [
        "ls"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'1101174267 m rifqi fadhila mixer.pdf'\n",
            "'1101174267_Muhammad Rifqi F_2_TT4107.pdf'\n",
            "'1101174267_MuhammadRifqiFadhila_1&3_TT4107.pdf'\n",
            " 1101174267_MuhammadRifqiFadhila_3.3_TT4107.pdf\n",
            "'1101174267_muhammad rifqi fadhila_Filter2.jpg'\n",
            "'1101174267 muhammad rifqi fadhila OSILATOR.pdf'\n",
            "'1101174267 muhammad rifqi fadhila penguat.pdf'\n",
            "'1101174267_Muhammad Rifqi Fadhila_Proposal Tugas Akhir dan lembar pemeriksaan.pdf'\n",
            " best_weight_scene_EfficientNetB0.h5\n",
            " \u001b[0m\u001b[01;34mCLAHE\u001b[0m/\n",
            " \u001b[01;34mCLAHEOnly\u001b[0m/\n",
            " \u001b[01;34mClassroom\u001b[0m/\n",
            " \u001b[01;34mDataset\u001b[0m/\n",
            " DSC_7396.JPG\n",
            " \u001b[01;34mGaussian\u001b[0m/\n",
            "'Getting started.pdf'\n",
            " IMG-20210217-WA0001.jpg\n",
            "\u001b[01;34m'MPTI 2021'\u001b[0m/\n",
            " \u001b[01;34mNormalized\u001b[0m/\n",
            "'PMMB2021_BATCH1_1101174267_MUHAMMAD RIFQI FADHILA.rar'\n",
            " Screenshot_20210217-060246.jpg\n",
            " \u001b[01;34mTesting\u001b[0m/\n",
            " TT_41_07_101174267_MUHAMMADRIFQIFADHILA.pdf\n",
            " \u001b[01;34mValo\u001b[0m/\n",
            " \u001b[01;34mVisualizable\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5IGm26ZzVGBs"
      },
      "source": [
        "# import the necessary packages\n",
        "from keras.models import Sequential\n",
        "from keras.layers.convolutional import Conv2D\n",
        "from keras.layers.convolutional import MaxPooling2D\n",
        "from keras.layers.core import Activation\n",
        "from keras.layers.core import Flatten\n",
        "from keras.layers.core import Dense\n",
        "from keras.optimizers import Adam, SGD, RMSprop, Adamax, Nadam\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": 8,
      "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": "20007060-b1e0-47ec-f2d1-473643c42fa9"
      },
      "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": 9,
      "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": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "me1s-uFI7kJi",
        "outputId": "7b725f8d-b1a8-481a-b48f-ca2783617f1d"
      },
      "source": [
        "\n",
        "plt.imshow(im_rgb)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7b38d1a190>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "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": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rzbolh88VGB2",
        "outputId": "fb11aaf3-9e19-4975-d41a-22242ef57955"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 13,
      "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": "1e48031e-0194-40d5-c4a2-6138abce3536"
      },
      "source": [
        "\n",
        "print(labels[500])\n",
        "plt.imshow(data[10])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normal\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7b38848350>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "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": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FPCyIqcVGB7",
        "outputId": "4b0d9e2e-c821-4b27-99ed-47f73bc69879"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 16,
      "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": "194d8cc2-2114-4afe-e554-ffb3fb2f0322"
      },
      "source": [
        "data = np.array(data)\n",
        "data.shape"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ktZ5hNYwVGB9",
        "outputId": "875a5cbc-65b3-4679-887d-5f3345b12a25"
      },
      "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": 19,
      "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": "e4bc2556-0be4-4783-8bd6-5d848b284a39"
      },
      "source": [
        "print(trainX[0])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  ...\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]]\n",
            "\n",
            " [[0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  ...\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]]\n",
            "\n",
            " [[0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  ...\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  ...\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]]\n",
            "\n",
            " [[0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  ...\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]]\n",
            "\n",
            " [[0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  ...\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]\n",
            "  [0.00681483 0.00681483 0.00681483]]]\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": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTvu4IV0i7Fj",
        "outputId": "aa1490de-a1f4-4258-cc17-3da21871dbeb"
      },
      "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": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_8\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_9 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "rescaling_8 (Rescaling)         (None, 224, 224, 3)  0           input_9[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "normalization_8 (Normalization) (None, 224, 224, 3)  7           rescaling_8[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "stem_conv_pad (ZeroPadding2D)   (None, 225, 225, 3)  0           normalization_8[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_8 (Glo (None, 1280)         0           top_activation[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "dropout_8 (Dropout)             (None, 1280)         0           global_average_pooling2d_8[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 1280)         5120        dropout_8[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_16 (Dense)                (None, 512)          655872      batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "dense_17 (Dense)                (None, 2)            1026        dense_16[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": 110,
      "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": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0TdWxuFPkT3",
        "outputId": "69933181-f8e5-4efa-9401-c86fc8621b36"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_8\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_8 (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": "c5c0f8e7-0898-43b1-f347-6c66c2ead5d4"
      },
      "source": [
        "# train the model using the Adam optimizer\n",
        "print(\"[INFO] training network...\")\n",
        "opt = Adamax(learning_rate=0.001, decay=1e-6)\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=opt,\n",
        "\tmetrics=[\"accuracy\"])\n",
        "H = model.fit(trainX, trainY, validation_data=(testX, testY), epochs=50, batch_size=32, callbacks=[es, checkpoint])"
      ],
      "execution_count": 113,
      "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_8_input'), name='flatten_8_input', description=\"created by layer 'flatten_8_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_8_input'), name='flatten_8_input', description=\"created by layer 'flatten_8_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 4.3302 - accuracy: 0.6107WARNING:tensorflow:Model was constructed with shape (None, 150528) for input KerasTensor(type_spec=TensorSpec(shape=(None, 150528), dtype=tf.float32, name='flatten_8_input'), name='flatten_8_input', description=\"created by layer 'flatten_8_input'\"), but it was called on an input with incompatible shape (None, 224, 224, 3).\n",
            "24/24 [==============================] - 1s 29ms/step - loss: 3.9601 - accuracy: 0.6198 - val_loss: 0.4832 - val_accuracy: 0.7880\n",
            "\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.78800, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 2/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.2041 - accuracy: 0.9248 - val_loss: 0.1186 - val_accuracy: 0.9760\n",
            "\n",
            "Epoch 00002: val_accuracy improved from 0.78800 to 0.97600, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 3/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.1223 - accuracy: 0.9755 - val_loss: 0.1296 - val_accuracy: 0.9440\n",
            "\n",
            "Epoch 00003: val_accuracy did not improve from 0.97600\n",
            "Epoch 4/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0921 - accuracy: 0.9761 - val_loss: 0.0650 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00004: val_accuracy improved from 0.97600 to 1.00000, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 5/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.0598 - accuracy: 0.9988 - val_loss: 0.0513 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00005: val_accuracy did not improve from 1.00000\n",
            "Epoch 6/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.0466 - accuracy: 1.0000 - val_loss: 0.0436 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00006: val_accuracy did not improve from 1.00000\n",
            "Epoch 7/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.0393 - accuracy: 1.0000 - val_loss: 0.0389 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00007: val_accuracy did not improve from 1.00000\n",
            "Epoch 8/50\n",
            "24/24 [==============================] - 0s 20ms/step - loss: 0.0334 - accuracy: 1.0000 - val_loss: 0.0290 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00008: val_accuracy did not improve from 1.00000\n",
            "Epoch 9/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0272 - accuracy: 1.0000 - val_loss: 0.0262 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00009: val_accuracy did not improve from 1.00000\n",
            "Epoch 10/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0226 - accuracy: 1.0000 - val_loss: 0.0228 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00010: val_accuracy did not improve from 1.00000\n",
            "Epoch 11/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.0199 - accuracy: 1.0000 - val_loss: 0.0200 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00011: val_accuracy did not improve from 1.00000\n",
            "Epoch 12/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0170 - accuracy: 1.0000 - val_loss: 0.0179 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00012: val_accuracy did not improve from 1.00000\n",
            "Epoch 13/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0157 - accuracy: 1.0000 - val_loss: 0.0165 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00013: val_accuracy did not improve from 1.00000\n",
            "Epoch 14/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0126 - accuracy: 1.0000 - val_loss: 0.0144 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00014: val_accuracy did not improve from 1.00000\n",
            "Epoch 15/50\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.0125 - accuracy: 1.0000 - val_loss: 0.0134 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00015: val_accuracy did not improve from 1.00000\n",
            "Epoch 16/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0114 - accuracy: 1.0000 - val_loss: 0.0119 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00016: val_accuracy did not improve from 1.00000\n",
            "Epoch 17/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0104 - accuracy: 1.0000 - val_loss: 0.0109 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00017: val_accuracy did not improve from 1.00000\n",
            "Epoch 18/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0089 - accuracy: 1.0000 - val_loss: 0.0106 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00018: val_accuracy did not improve from 1.00000\n",
            "Epoch 19/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.0084 - accuracy: 1.0000 - val_loss: 0.0092 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00019: val_accuracy did not improve from 1.00000\n",
            "Epoch 20/50\n",
            "24/24 [==============================] - 0s 21ms/step - loss: 0.0083 - accuracy: 1.0000 - val_loss: 0.0087 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00020: val_accuracy did not improve from 1.00000\n",
            "Epoch 21/50\n",
            "24/24 [==============================] - 0s 20ms/step - loss: 0.0068 - accuracy: 1.0000 - val_loss: 0.0079 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00021: val_accuracy did not improve from 1.00000\n",
            "Epoch 22/50\n",
            "24/24 [==============================] - 0s 21ms/step - loss: 0.0067 - accuracy: 1.0000 - val_loss: 0.0074 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00022: val_accuracy did not improve from 1.00000\n",
            "Epoch 23/50\n",
            "24/24 [==============================] - 1s 21ms/step - loss: 0.0071 - accuracy: 1.0000 - val_loss: 0.0068 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00023: val_accuracy did not improve from 1.00000\n",
            "Epoch 24/50\n",
            "24/24 [==============================] - 1s 21ms/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00024: val_accuracy did not improve from 1.00000\n",
            "Epoch 25/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00025: val_accuracy did not improve from 1.00000\n",
            "Epoch 26/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00026: val_accuracy did not improve from 1.00000\n",
            "Epoch 27/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0046 - accuracy: 1.0000 - val_loss: 0.0051 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00027: val_accuracy did not improve from 1.00000\n",
            "Epoch 28/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0046 - accuracy: 1.0000 - val_loss: 0.0048 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00028: val_accuracy did not improve from 1.00000\n",
            "Epoch 29/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.0038 - accuracy: 1.0000 - val_loss: 0.0046 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00029: val_accuracy did not improve from 1.00000\n",
            "Epoch 30/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.0042 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00030: val_accuracy did not improve from 1.00000\n",
            "Epoch 31/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0035 - accuracy: 1.0000 - val_loss: 0.0039 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00031: val_accuracy did not improve from 1.00000\n",
            "Epoch 32/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.0039 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00032: val_accuracy did not improve from 1.00000\n",
            "Epoch 33/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.0035 - accuracy: 1.0000 - val_loss: 0.0037 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00033: val_accuracy did not improve from 1.00000\n",
            "Epoch 34/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.0033 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00034: val_accuracy did not improve from 1.00000\n",
            "Epoch 35/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0031 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00035: val_accuracy did not improve from 1.00000\n",
            "Epoch 36/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0029 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00036: val_accuracy did not improve from 1.00000\n",
            "Epoch 37/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0028 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00037: val_accuracy did not improve from 1.00000\n",
            "Epoch 38/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0026 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00038: val_accuracy did not improve from 1.00000\n",
            "Epoch 39/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0026 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00039: val_accuracy did not improve from 1.00000\n",
            "Epoch 40/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00040: val_accuracy did not improve from 1.00000\n",
            "Epoch 41/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00041: val_accuracy did not improve from 1.00000\n",
            "Epoch 42/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00042: val_accuracy did not improve from 1.00000\n",
            "Epoch 43/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00043: val_accuracy did not improve from 1.00000\n",
            "Epoch 44/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00044: val_accuracy did not improve from 1.00000\n",
            "Epoch 45/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00045: val_accuracy did not improve from 1.00000\n",
            "Epoch 46/50\n",
            "24/24 [==============================] - 0s 19ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00046: val_accuracy did not improve from 1.00000\n",
            "Epoch 47/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0017 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00047: val_accuracy did not improve from 1.00000\n",
            "Epoch 48/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00048: val_accuracy did not improve from 1.00000\n",
            "Epoch 49/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00049: val_accuracy did not improve from 1.00000\n",
            "Epoch 50/50\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00050: val_accuracy did not improve from 1.00000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "98_oXi-lcgux",
        "outputId": "c159e732-4206-4986-df96-21c9cd482116"
      },
      "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": 114,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cu5SSxXyiOs3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d2eea59-96c6-4644-c164-d509de69063c"
      },
      "source": [
        "predictions = model.predict(testX, batch_size=32)"
      ],
      "execution_count": 115,
      "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_8_input'), name='flatten_8_input', description=\"created by layer 'flatten_8_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": "7af2532a-e24f-4d91-a576-3545e8d6b120"
      },
      "source": [
        "print(predictions.argmax(axis=1))"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 1 1 0 1 1 0 0 1 1 0 1 0 0 0 0 0 1 1\n",
            " 0 0 1 0 1 0 1 0 1 0 0 1 1 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0\n",
            " 0 0 0 0 1 1 1 0 0 1 0 1 0 1 0 1 1 0 0 1 1 1 1 0 1 1 1 0 1 0 1 1 0 0 0 1 0\n",
            " 1 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1\n",
            " 0 1 1 1 0 1 0 1 1 0 1 0 1 1 0 1 1 0 0 1 0 0 0 1 1 0 1 1 1 1 1 0 0 0 1 1 1\n",
            " 0 1 0 1 1 0 0 1 0 0 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 0 0 1 0 0 0 0 1 1 1 1 1\n",
            " 0 0 0 1 0 0 1 1 0 1 1 1 0 1 0 0 1 1 0 1 0 1 1 1 0 1 1 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6puOgflYR_e"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
      ],
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPAZkmBJVGCD",
        "outputId": "3c9c51a0-2016-4642-d920-86be9efee476"
      },
      "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": 118,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] evaluating network...\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Covid       1.00      1.00      1.00       130\n",
            "      Normal       1.00      1.00      1.00       120\n",
            "\n",
            "    accuracy                           1.00       250\n",
            "   macro avg       1.00      1.00      1.00       250\n",
            "weighted avg       1.00      1.00      1.00       250\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJ-r6keDY5jt",
        "outputId": "ce450386-2683-45cb-db81-923cb7b59348"
      },
      "source": [
        "cf_matrix = confusion_matrix(testY,predictions.argmax(axis=1))\n",
        "print(cf_matrix)"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[130   0]\n",
            " [  0 120]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLp1ooZeY90p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "ea68d13a-a1a1-47d0-8b96-b3072aeac02a"
      },
      "source": [
        "\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cf_matrix, display_labels=encoder.classes_)\n",
        "disp.plot(cmap=plt.cm.Blues)"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f7ada78ee50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtE5PB_Vcqiu"
      },
      "source": [
        "\n",
        "import io\n",
        "\n",
        "path='/content/drive/MyDrive/Normalized/Normal/ IM00001.tif'\n",
        "save_path='/content/drive/MyDrive/Testing/Normal/covid1.tif'\n",
        "with open(path, 'rb') as f:\n",
        "    tif = Image.open(io.BytesIO(f.read()))\n",
        "array=np.array(tif)\n",
        "max_val=np.amax(array)\n",
        "normalized=(array/max_val)\n",
        "im = Image.fromarray(normalized)\n",
        "im.save(save_path,'TIFF')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gwPAgH02dIS6",
        "outputId": "18496750-9698-4c2b-cdd0-d918f4fac0b6"
      },
      "source": [
        "\n",
        "width = 224\n",
        "height = 224\n",
        "dsize = (width, height)\n",
        "image_testing = cv2.imread('/content/drive/MyDrive/Testing/Normal/covid1.tif', -1)\n",
        "image_testing = cv2.cvtColor(image_testing, cv2.COLOR_BGR2RGB)\n",
        "image_testing = cv2.resize(image_testing,dsize)\n",
        "image_testing = np.array(image_testing)\n",
        "image_testing.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "wQijrpZ_dPjn",
        "outputId": "bd3267a8-0b8d-40f3-b650-376429972a1c"
      },
      "source": [
        "plt.imshow(image_testing)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f90d4ef7550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IRTDbxzsdST7",
        "outputId": "00e180c0-ff45-4607-b6fd-2e67ddcc6e40"
      },
      "source": [
        "image_testing = np.expand_dims(image_testing, axis=0)\n",
        "print(image_testing.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 224, 224, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_VQoQn24dUtc",
        "outputId": "4a24f9d4-479b-453e-f187-8c089cee717f"
      },
      "source": [
        "\n",
        "output = model.predict(image_testing, 1)\n",
        "print(output)\n",
        "print(encoder.classes_[output.argmax(axis=1)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.02255073 0.973738   0.00371126]]\n",
            "['Normal']\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}