gaussian_nadam_0.001.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Copy of scenes_nn_4 layers.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
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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/gaussian_nadam_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": "0b21d599-051d-4691-d184-07fdfd4fb16f"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H215ikbNVVZS",
        "outputId": "98c2a272-69a7-4053-86fb-1a307ebfa728"
      },
      "source": [
        "cd /content/drive/MyDrive/ "
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z7VB1d4wVdov",
        "outputId": "64df2b7d-febc-488c-c245-29de35c980b2"
      },
      "source": [
        "ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'1101174267 m rifqi fadhila mixer.pdf'\n",
            "'1101174267_Muhammad Rifqi F_2_TT4107.pdf'\n",
            "'1101174267_MuhammadRifqiFadhila_1&3_TT4107.pdf'\n",
            " 1101174267_MuhammadRifqiFadhila_3.3_TT4107.pdf\n",
            "'1101174267_muhammad rifqi fadhila_Filter2.jpg'\n",
            "'1101174267 muhammad rifqi fadhila OSILATOR.pdf'\n",
            "'1101174267 muhammad rifqi fadhila penguat.pdf'\n",
            "'1101174267_Muhammad Rifqi Fadhila_Proposal Tugas Akhir dan lembar pemeriksaan.pdf'\n",
            " best_weight_scene_EfficientNetB0.h5\n",
            " \u001b[0m\u001b[01;34mCLAHE\u001b[0m/\n",
            " \u001b[01;34mClassroom\u001b[0m/\n",
            " \u001b[01;34mDataset\u001b[0m/\n",
            " DSC_7396.JPG\n",
            " \u001b[01;34mGaussian\u001b[0m/\n",
            "'Getting started.pdf'\n",
            " IMG-20210217-WA0001.jpg\n",
            "\u001b[01;34m'MPTI 2021'\u001b[0m/\n",
            " \u001b[01;34mNormalized\u001b[0m/\n",
            "'PMMB2021_BATCH1_1101174267_MUHAMMAD RIFQI FADHILA.rar'\n",
            " Screenshot_20210217-060246.jpg\n",
            " TT_41_07_101174267_MUHAMMADRIFQIFADHILA.pdf\n",
            " \u001b[01;34mValo\u001b[0m/\n",
            " \u001b[01;34mVisualizable\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5IGm26ZzVGBs"
      },
      "source": [
        "# import the necessary packages\n",
        "from keras.models import Sequential\n",
        "from keras.layers.convolutional import Conv2D\n",
        "from keras.layers.convolutional import MaxPooling2D\n",
        "from keras.layers.core import Activation\n",
        "from keras.layers.core import Flatten\n",
        "from keras.layers.core import Dense\n",
        "from keras.optimizers import Adam, SGD, RMSprop\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from PIL import Image\n",
        "from imutils import paths\n",
        "from skimage import io\n",
        "from skimage.filters import gaussian\n",
        "from skimage import exposure\n",
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "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": "ba65c872-ce3c-41f1-c455-0040d5849a1c"
      },
      "source": [
        "# grab all image paths in the input dataset directory, then initialize\n",
        "# our list of images and corresponding class labels\n",
        "print(\"[INFO] loading images...\")\n",
        "imagePaths = paths.list_images(\"Gaussian\")\n",
        "data = []\n",
        "labels = []"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] loading images...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KsbyXzXK0k-4"
      },
      "source": [
        "import cv2\n",
        "\n",
        "im = cv2.imread('/content/drive/MyDrive/Gaussian/Covid/IM00001.tif', -1)\n",
        "im_rgb = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "me1s-uFI7kJi",
        "outputId": "ecba136a-2f82-4f45-9284-75292aa28133"
      },
      "source": [
        "\n",
        "plt.imshow(im_rgb)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fbf70150510>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        },
        {
          "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": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rzbolh88VGB2",
        "outputId": "966152a1-93f2-4d55-8af9-299a3433cdeb"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 67,
      "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": "435a64f9-473a-4a57-a3b6-d0e29c87b294"
      },
      "source": [
        "\n",
        "print(labels[500])\n",
        "plt.imshow(data[10])"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normal\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fbf70041190>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        },
        {
          "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": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FPCyIqcVGB7",
        "outputId": "49ae7f1b-cfea-4655-91f0-c7529e927685"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 70,
      "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": "72bffff4-b4ac-40ba-86a4-4c04c0360e98"
      },
      "source": [
        "data = np.array(data)\n",
        "data.shape"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ktZ5hNYwVGB9",
        "outputId": "698880fd-0f00-4338-c625-dcfee1b2b7ae"
      },
      "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": 72,
      "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": "7955b0fa-6beb-469f-ea87-a6ce76cf974a"
      },
      "source": [
        "print(trainX[0])"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1LK97ZhDrRZ"
      },
      "source": [
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from keras.models import Model\n",
        "from keras.layers import GlobalAveragePooling2D, BatchNormalization, Dropout\n",
        "\n",
        "from keras.applications.efficientnet import EfficientNetB0\n",
        "from keras.layers import Dense, Conv2D, MaxPool2D , Flatten\n",
        "from keras.applications.resnet import ResNet50\n",
        "import tensorflow as tf\n"
      ],
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTvu4IV0i7Fj",
        "outputId": "134bf7f1-8804-4d27-b6e0-99fcc390b5db"
      },
      "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": 142,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_10\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_11 (InputLayer)           [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "rescaling_10 (Rescaling)        (None, 224, 224, 3)  0           input_11[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "normalization_10 (Normalization (None, 224, 224, 3)  7           rescaling_10[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "stem_conv_pad (ZeroPadding2D)   (None, 225, 225, 3)  0           normalization_10[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_10 (Gl (None, 1280)         0           top_activation[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "dropout_10 (Dropout)            (None, 1280)         0           global_average_pooling2d_10[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 1280)         5120        dropout_10[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "dense_20 (Dense)                (None, 512)          655872      batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "dense_21 (Dense)                (None, 2)            1026        dense_20[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": 143,
      "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": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0TdWxuFPkT3",
        "outputId": "75a22c23-da01-405b-cc95-136e6fe718cd"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 145,
      "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": "2cc9d174-3693-4b74-ab42-41e68a418b90"
      },
      "source": [
        "# train the model using the Adam optimizer\n",
        "print(\"[INFO] training network...\")\n",
        "opt = Nadam(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": 146,
      "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",
            "23/24 [===========================>..] - ETA: 0s - loss: 3.0193 - accuracy: 0.4444WARNING: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 [==============================] - 2s 38ms/step - loss: 2.9428 - accuracy: 0.4554 - val_loss: 0.1547 - val_accuracy: 0.9480\n",
            "\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.94800, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 2/50\n",
            "24/24 [==============================] - 1s 28ms/step - loss: 0.1864 - accuracy: 0.9285 - val_loss: 0.0763 - val_accuracy: 0.9640\n",
            "\n",
            "Epoch 00002: val_accuracy improved from 0.94800 to 0.96400, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 3/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.2053 - accuracy: 0.9226 - val_loss: 0.0830 - val_accuracy: 0.9800\n",
            "\n",
            "Epoch 00003: val_accuracy improved from 0.96400 to 0.98000, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 4/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.0596 - accuracy: 0.9897 - val_loss: 0.0245 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00004: val_accuracy improved from 0.98000 to 1.00000, saving model to best_weight_scene_EfficientNetB0.h5\n",
            "Epoch 5/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.0295 - accuracy: 1.0000 - val_loss: 0.0211 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00005: val_accuracy did not improve from 1.00000\n",
            "Epoch 6/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.0167 - accuracy: 1.0000 - val_loss: 0.0141 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00006: val_accuracy did not improve from 1.00000\n",
            "Epoch 7/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.0128 - accuracy: 1.0000 - val_loss: 0.0257 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00007: val_accuracy did not improve from 1.00000\n",
            "Epoch 8/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.0140 - accuracy: 1.0000 - val_loss: 0.0088 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00008: val_accuracy did not improve from 1.00000\n",
            "Epoch 9/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.0083 - accuracy: 1.0000 - val_loss: 0.0076 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00009: val_accuracy did not improve from 1.00000\n",
            "Epoch 10/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.0071 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00010: val_accuracy did not improve from 1.00000\n",
            "Epoch 11/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.0066 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00011: val_accuracy did not improve from 1.00000\n",
            "Epoch 12/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.0060 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00012: val_accuracy did not improve from 1.00000\n",
            "Epoch 13/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.0050 - accuracy: 1.0000 - val_loss: 0.0044 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00013: val_accuracy did not improve from 1.00000\n",
            "Epoch 14/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.0039 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00014: val_accuracy did not improve from 1.00000\n",
            "Epoch 15/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.0038 - accuracy: 1.0000 - val_loss: 0.0036 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00015: val_accuracy did not improve from 1.00000\n",
            "Epoch 16/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.0033 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00016: val_accuracy did not improve from 1.00000\n",
            "Epoch 17/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0027 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00017: val_accuracy did not improve from 1.00000\n",
            "Epoch 18/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0026 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00018: val_accuracy did not improve from 1.00000\n",
            "Epoch 19/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00019: val_accuracy did not improve from 1.00000\n",
            "Epoch 20/50\n",
            "24/24 [==============================] - 1s 27ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00020: val_accuracy did not improve from 1.00000\n",
            "Epoch 21/50\n",
            "24/24 [==============================] - 1s 28ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00021: val_accuracy did not improve from 1.00000\n",
            "Epoch 22/50\n",
            "24/24 [==============================] - 1s 30ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00022: val_accuracy did not improve from 1.00000\n",
            "Epoch 23/50\n",
            "24/24 [==============================] - 1s 28ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00023: val_accuracy did not improve from 1.00000\n",
            "Epoch 24/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00024: val_accuracy did not improve from 1.00000\n",
            "Epoch 25/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00025: val_accuracy did not improve from 1.00000\n",
            "Epoch 26/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00026: val_accuracy did not improve from 1.00000\n",
            "Epoch 27/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 9.4756e-04 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00027: val_accuracy did not improve from 1.00000\n",
            "Epoch 28/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 8.8279e-04 - accuracy: 1.0000 - val_loss: 9.0858e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00028: val_accuracy did not improve from 1.00000\n",
            "Epoch 29/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 7.9700e-04 - accuracy: 1.0000 - val_loss: 8.4421e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00029: val_accuracy did not improve from 1.00000\n",
            "Epoch 30/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 8.4012e-04 - accuracy: 1.0000 - val_loss: 7.9478e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00030: val_accuracy did not improve from 1.00000\n",
            "Epoch 31/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 6.8138e-04 - accuracy: 1.0000 - val_loss: 7.4894e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00031: val_accuracy did not improve from 1.00000\n",
            "Epoch 32/50\n",
            "24/24 [==============================] - 1s 28ms/step - loss: 6.1506e-04 - accuracy: 1.0000 - val_loss: 7.1462e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00032: val_accuracy did not improve from 1.00000\n",
            "Epoch 33/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 6.3426e-04 - accuracy: 1.0000 - val_loss: 6.7699e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00033: val_accuracy did not improve from 1.00000\n",
            "Epoch 34/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 6.9491e-04 - accuracy: 1.0000 - val_loss: 6.3983e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00034: val_accuracy did not improve from 1.00000\n",
            "Epoch 35/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 5.9611e-04 - accuracy: 1.0000 - val_loss: 6.0967e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00035: val_accuracy did not improve from 1.00000\n",
            "Epoch 36/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 5.9601e-04 - accuracy: 1.0000 - val_loss: 5.7932e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00036: val_accuracy did not improve from 1.00000\n",
            "Epoch 37/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 5.4410e-04 - accuracy: 1.0000 - val_loss: 5.5273e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00037: val_accuracy did not improve from 1.00000\n",
            "Epoch 38/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 4.8982e-04 - accuracy: 1.0000 - val_loss: 5.4128e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00038: val_accuracy did not improve from 1.00000\n",
            "Epoch 39/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 4.5732e-04 - accuracy: 1.0000 - val_loss: 5.0304e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00039: val_accuracy did not improve from 1.00000\n",
            "Epoch 40/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 4.1336e-04 - accuracy: 1.0000 - val_loss: 4.7992e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00040: val_accuracy did not improve from 1.00000\n",
            "Epoch 41/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 3.9701e-04 - accuracy: 1.0000 - val_loss: 4.6043e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00041: val_accuracy did not improve from 1.00000\n",
            "Epoch 42/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 3.8525e-04 - accuracy: 1.0000 - val_loss: 4.5046e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00042: val_accuracy did not improve from 1.00000\n",
            "Epoch 43/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 4.0912e-04 - accuracy: 1.0000 - val_loss: 4.2176e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00043: val_accuracy did not improve from 1.00000\n",
            "Epoch 44/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 3.2435e-04 - accuracy: 1.0000 - val_loss: 4.0615e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00044: val_accuracy did not improve from 1.00000\n",
            "Epoch 45/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 3.2495e-04 - accuracy: 1.0000 - val_loss: 3.8888e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00045: val_accuracy did not improve from 1.00000\n",
            "Epoch 46/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 3.3394e-04 - accuracy: 1.0000 - val_loss: 3.7410e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00046: val_accuracy did not improve from 1.00000\n",
            "Epoch 47/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 3.4216e-04 - accuracy: 1.0000 - val_loss: 3.6486e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00047: val_accuracy did not improve from 1.00000\n",
            "Epoch 48/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 3.1382e-04 - accuracy: 1.0000 - val_loss: 3.5028e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00048: val_accuracy did not improve from 1.00000\n",
            "Epoch 49/50\n",
            "24/24 [==============================] - 1s 26ms/step - loss: 3.0744e-04 - accuracy: 1.0000 - val_loss: 3.3618e-04 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00049: val_accuracy did not improve from 1.00000\n",
            "Epoch 50/50\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 2.8051e-04 - accuracy: 1.0000 - val_loss: 3.2710e-04 - 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": "704be7db-f591-4b12-dd94-c972e57f8706"
      },
      "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": 147,
      "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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kGNmW/I/CzBKV/N2vXKnS0daKvOiMmSUq+SAoVaqMOgDWGjAzaxQHQVcPHW4WMrOEFXoHlDRf0sOSVkm6dJDjR0q6VdJvJN0macgn2y93Vw+I1cfMzBqlsCCQ1ApcDZwOzAYulDR7wGn/Anw9Il4FXAn8c1Hl2ZVyV7Xhq4+ZmTVSkTWCecCqiHgsIrqA64GzB5wzG/hZ/nrpIMcLV6pUPXTUzJJWZBBMA1bXbHfm+2r9D/An+etzgbGSJg38IEkLJC2TtGx/L0dZrrhGYGZpa3Qv6YeA10m6D3gdsAbYaXGAiLgmIuZGxNwpU6bs1wKUKj2uEZhZ0oqcYmINcHjN9vR8X5+IeJK8RiBpDHBeRGwqsEw7KXv4qJklrsgawd3ALEkzJY0ALgAW1Z4gabKk3jJcBiwssDyDyh4oa3TFyMyscQq7A0ZEN/A+4KfAQ8ANEfGApCslnZWfdjLwsKRHgEOBTxRVnl3xA2VmlrpCZx+NiMXA4gH7Lq95fSNwY5Fl2JOSh4+aWeKSbhPp6Ql2dPcw0kFgZglLOgh2dGeL0rhGYGYpSzoISn2L0iT9YzCzxCV9Byx7URozs7SDoK9G4FFDZpawtIOgyzUCM7Okg2BHt4PAzCzpICh1edSQmVnaQdA3ashBYGbpSjoInh81lPSPwcwSl/QdsOTho2ZmaQdB2cNHzcwcBOAagZmlLekg6B015PUIzCxlSd8BS5UqI1pbaGtN+sdgZolL+g5YrlQZ6RFDZpa4pO+C5YoXpTEzSzoIvEylmVnqQdBVpaPNQWBmaUs6CMrdPXS4RmBmiUs7CLqqXp3MzJKX9F2wVKn6YTIzS17SQeBRQ2ZmiQdByUFgZpZ2EGQPlDkIzCxtiQdBj2sEZpa8uoJA0vcknSGpaYIjIvIHyprmkszM9kq9d8EvAG8BVkr6pKSjCizTkKhUg2pPuEZgZsmrKwgi4paI+HPgOOBx4BZJt0t6h6T2IgtYlHK31yIwM4MX0EcgaRLwduCvgfuAfyMLhiWFlKxg5S4HgZkZQFs9J0m6CTgK+AZwZkQ8lR/6jqRlRRWuSL3rFbtpyMxSV1cQAJ+NiKWDHYiIufuxPEOmXMlXJ3MQmFni6m0ami1pfO+GpAmS3lNQmYZEX43Ao4bMLHH13gXfGRGbejciYiPwzmKKNDRK7iMwMwPqD4JWSerdkNQKjCimSEPDo4bMzDL19hH8hKxj+Mv59rvyfcNW76ghdxabWerqDYIPk938/ybfXgJ8tZASDRGPGjIzy9QVBBHRA3wx/9cUPGrIzCxT73MEs4B/BmYDHb37I+LFBZWrcK4RmJll6u0s/g+y2kA3cArwdeCbe3qTpPmSHpa0StKlgxw/QtJSSfdJ+o2kN72Qwu+Lch4EHR4+amaJq/cuOCoibgUUEU9ExMeAM3b3hnxk0dXA6WQ1iQslzR5w2keAGyLiWOACssnthkS5UkWCEa0OAjNLW72dxTvyKahXSnofsAYYs4f3zANWRcRjAJKuB84GHqw5J4CD89fjgCfrLfi+KnVlq5PVjIo1M0tSvX8OXwyMBv4WeDVwEfC2PbxnGrC6Zrsz31frY8BFkjqBxcD7B/sgSQskLZO0bN26dXUWefe8TKWZWWaPQZA38fxZRGyLiM6IeEdEnBcRd+6H778Q+FpETAfeBHxjsMVvIuKaiJgbEXOnTJmyH742GzXkEUNmZnUEQURUgZP24rPXAIfXbE/P99X6K+CG/HvuIBuRNHkvvusFK1eqdLS7f8DMrN4+gvskLQL+E9jeuzMivreb99wNzJI0kywALiBb5azW74FTga9JOposCPZP288eZMtUukZgZlZvEHQA64HX1+wLYJdBEBHdecfyT4FWYGFEPCDpSmBZRCwCPgh8RdLf5Z/39oiIvbiOF6xcqdLR5iAwM6v3yeJ37M2HR8Risk7g2n2X17x+EDhxbz57X5UqVcaMrDcHzcyaV71PFv8H2V/s/UTEX+73Eg2RUleVyWNGNroYZmYNV++fxD+sed0BnMsQjvkvwo5ujxoyM4P6m4a+W7st6TrgV4WUaIhkD5R51JCZ2d7eCWcBh+zPggw1P1BmZpapt49gK/37CJ4mW6Ng2CpVqm4aMjOj/qahsUUXZCj19ARd7iMwMwPqbBqSdK6kcTXb4yWdU1yxitW7XrEfKDMzq7+P4IqI2Ny7ERGbgCuKKVLxSvl6xR1t7iw2M6v3TjjYecP2aaxyd7ZMpWsEZmb1B8EySVdJekn+7yrgniILVqS+GoH7CMzM6g6C9wNdwHeA64Ey8N6iClW0stcrNjPrU++ooe3ATmsOD1d96xU7CMzM6h41tETS+JrtCZJ+WlyxilWqeNSQmVmvepuGJucjhQCIiI0M4yeLe/sI3DRkZlZ/EPRIOqJ3Q9IMBpmNdLjoHTXkFcrMzOofAvp/gF9J+jkg4I+ABYWVqmBljxoyM+tTb2fxTyTNJbv53wd8HygVWbAilTxqyMysT72Tzv01cDHZAvTLgeOBO+i/dOWw4VFDZmbPq7eR/GLgNcATEXEKcCywafdvOXCVHARmZn3qDYJyRJQBJI2MiBXAUcUVq1ilSpURbS20tqjRRTEza7h6O4s78+cIvg8skbQReKK4YhVrR6XHE86ZmeXq7Sw+N3/5MUlLgXHATworVcFKXVU/TGZmlnvBM4hGxM+LKMhQ8jKVZmbPS7J9pOxlKs3M+iQZBF6v2MzseUkGQdlNQ2ZmfRINgh7PM2RmlkvybliqeNSQmVmvNIOgy30EZma9kgyCHd0OAjOzXkkGQanLncVmZr2SC4KI8ANlZmY1kguCSjXoCa9OZmbWK7m7oaegNjPrL7kg6F2UxsNHzcwyyQZBR5uDwMwMEgyCkmsEZmb9pBcEXV643sysVqFBIGm+pIclrZJ06SDHPy1pef7vEUmFr4NcrvQAMNKjhszMgL1YmKZeklqBq4HTgE7gbkmLIuLB3nMi4u9qzn8/cGxR5enV11nsGoGZGVBsjWAesCoiHouILuB64OzdnH8hcF2B5QHcR2BmNlCRQTANWF2z3Znv24mkI4GZwM92cXyBpGWSlq1bt26fCuU+AjOz/g6UhvILgBsjojrYwYi4JiLmRsTcKVOm7NMXlbv9QJmZWa0ig2ANcHjN9vR832AuYAiaheD5GoGDwMwsU2QQ3A3MkjRT0giym/2igSdJejkwAbijwLL0cWexmVl/hQVBRHQD7wN+CjwE3BARD0i6UtJZNadeAFwfEVFUWWqVKz20CNpbNRRfZ2Z2wCts+ChARCwGFg/Yd/mA7Y8VWYaBeqeglhwEZmZw4HQWDxmvV2xm1l9yQVCuVBnpCefMzPokGQSuEZiZPS+5IPB6xWZm/SUXBOVKj5epNDOrkdwdsVSp+mEyM7MayQVBueKmITOzWkkGgWsEZmbPSy4ISq4RmJn1k14QdHn4qJlZreSCoNzd42UqzcxqJHVHrPYEXd09bhoyM6uRVBB4Cmozs50lGQQeNWRm9rykgqDkGoGZ2U6SCoK+GoFHDZmZ9UksCHoA6GhL6rLNzHYrqTtiX9OQawRmZn3SCoIu9xGYmQ2UVBB41JCZ2c6SCoKSg8DMbCdJBUHZfQRmZjtJLAg8asjMbKCk7ogeNWRmtrO0giAfNdTR5iAwM+uVVBCUu6uMbGuhpUWNLoqZ2QEjrSDo8jKVZmYDJRUEXqbSzGxnSQVBudLjjmIzswGSCoJSJesjMDOz5yV1VyxXvHC9mdlASQVBqct9BGZmAyUVBOVujxoyMxsoqSBwjcDMbGdJBUG50uMagZnZAIkFQZWO9qQu2cxsj5K6K/qBMjOznSUTBBGRBYGHj5qZ9VNoEEiaL+lhSaskXbqLc86X9KCkByR9u6iydFV7iPDqZGZmA7UV9cGSWoGrgdOATuBuSYsi4sGac2YBlwEnRsRGSYcUVZ5yV74ojYPAzKyfImsE84BVEfFYRHQB1wNnDzjnncDVEbERICLWFlWYvkVpHARmZv0UGQTTgNU12535vlovA14m6b8l3Slp/mAfJGmBpGWSlq1bt26vClPuW7g+mW4RM7O6NPqu2AbMAk4GLgS+Imn8wJMi4pqImBsRc6dMmbJXX+QagZnZ4IoMgjXA4TXb0/N9tTqBRRFRiYjfAY+QBcN+1xsEHR41ZGbWT5FBcDcwS9JMSSOAC4BFA875PlltAEmTyZqKHiuiMH1NQ16v2Mysn8KCICK6gfcBPwUeAm6IiAckXSnprPy0nwLrJT0ILAUuiYj1RZSnNwj8HIGZWX+FDR8FiIjFwOIB+y6veR3A3+f/ClXKh4+6j8DMrL9GdxYPGY8aMjMbXDJ3RY8aMjMbXDJBUPaoITOzQSUTBEdMHM3przjMNQIzswEK7Sw+kLzhmMN4wzGHNboYZmYHnGRqBGZmNjgHgZlZ4hwEZmaJcxCYmSXOQWBmljgHgZlZ4hwEZmaJcxCYmSVO2QSgw4ekdcATe/n2ycCz+7E4w0Wq1w3pXruvOy31XPeRETHoEo/DLgj2haRlETG30eUYaqleN6R77b7utOzrdbtpyMwscQ4CM7PEpRYE1zS6AA2S6nVDutfu607LPl13Un0EZma2s9RqBGZmNoCDwMwscckEgaT5kh6WtErSpY0uT1EkLZS0VtL9NfsmSloiaWX+vxMaWcYiSDpc0lJJD0p6QNLF+f6mvnZJHZLukvQ/+XV/PN8/U9Kv89/370ga0eiyFkFSq6T7JP0w327665b0uKTfSlouaVm+b59+z5MIAkmtwNXA6cBs4EJJsxtbqsJ8DZg/YN+lwK0RMQu4Nd9uNt3AByNiNnA88N78/+Nmv/YdwOsj4g+AOcB8SccDnwI+HREvBTYCf9XAMhbpYuChmu1UrvuUiJhT8+zAPv2eJxEEwDxgVUQ8FhFdwPXA2Q0uUyEi4hfAhgG7zwauzV9fC5wzpIUaAhHxVETcm7/eSnZzmEaTX3tktuWb7fm/AF4P3Jjvb7rrBpA0HTgD+Gq+LRK47l3Yp9/zVIJgGrC6Zrsz35eKQyPiqfz108ChjSxM0STNAI4Ffk0C1543jywH1gJLgEeBTRHRnZ/SrL/vnwH+AejJtyeRxnUHcLOkeyQtyPft0+95MovXWyYiQlLTjhmWNAb4LvCBiNiS/ZGYadZrj4gqMEfSeOAm4OUNLlLhJL0ZWBsR90g6udHlGWInRcQaSYcASyStqD24N7/nqdQI1gCH12xPz/el4hlJLwLI/3dtg8tTCEntZCHwrYj4Xr47iWsHiIhNwFLgBGC8pN4/9Jrx9/1E4CxJj5M19b4e+Dea/7qJiDX5/64lC/557OPveSpBcDcwKx9RMAK4AFjU4DINpUXA2/LXbwN+0MCyFCJvH/534KGIuKrmUFNfu6QpeU0ASaOA08j6R5YCf5qf1nTXHRGXRcT0iJhB9t/zzyLiz2ny65Z0kKSxva+BNwD3s4+/58k8WSzpTWRtiq3Awoj4RIOLVAhJ1wEnk01L+wxwBfB94AbgCLIpvM+PiIEdysOapJOAXwK/5fk2438k6ydo2muX9CqyzsFWsj/sboiIKyW9mOwv5YnAfcBFEbGjcSUtTt409KGIeHOzX3d+fTflm23AtyPiE5ImsQ+/58kEgZmZDS6VpiEzM9sFB4GZWeIcBGZmiXMQmJklzkFgZpY4B4HZEJJ0cu9MmWYHCgeBmVniHARmg5B0UT7P/3JJX84ndtsm6dP5vP+3SpqSnztH0p2SfiPppt654CW9VNIt+VoB90p6Sf7xYyTdKGmFpG+pdkIkswZwEJgNIOlo4M+AEyNiDlAF/hw4CFgWEccAPyd7ahvg68CHI+JVZE829+7/FnB1vlbAHwK9s0MeC3yAbG2MF5PNm2PWMJ591GxnpwKvBu7O/1gfRTaJVw/wnfycbwLfkzQOGB8RP8/3Xwv8Zz4fzLSIuAkgIsoA+efdFRGd+fZyYAbwq+Ivy2xwDgKznQm4NiIu67dT+uiA8/Z2fpbauW+q+L9DazA3DZnt7FbgT/P53nvXgz2S7L+X3pkt3wL8KiI2Axsl/VG+/63Az/NV0jolnZN/xkhJo4f0Kszq5L9EzAaIiAclfYRsFagWoAK8F9gOzMuPrSXrR4Bs2t8v5RE06BsAAABSSURBVDf6x4B35PvfCnxZ0pX5Z/zvIbwMs7p59lGzOknaFhFjGl0Os/3NTUNmZolzjcDMLHGuEZiZJc5BYGaWOAeBmVniHARmZolzEJiZJe7/A88j0xZUKy/AAAAAAElFTkSuQmCC\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": "a5c420b6-e264-4c60-b46e-9b0e21041ae1"
      },
      "source": [
        "predictions = model.predict(testX, batch_size=32)"
      ],
      "execution_count": 148,
      "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": "5163d66a-04af-40c0-82cc-bf1f603c670f"
      },
      "source": [
        "print(predictions.argmax(axis=1))"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 0 1 1 1 0 0 0 1 0 1 1 0 1\n",
            " 1 0 0 1 1 1 1 1 0 1 1 1 1 0 1 1 0 0 1 0 1 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 0\n",
            " 1 1 1 1 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 0 1 1 0 0 0 1 0\n",
            " 0 0 0 1 1 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 1 0 1 1 0 0 1 1 0 0 1 1 1 0 1 1\n",
            " 0 0 1 1 0 1 0 0 1 0 0 0 0 1 1 0 0 0 1 0 1 0 1 0 1 0 1 1 0 0 1 1 1 0 0 1 0\n",
            " 0 0 0 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 0 1 0 1 1 0\n",
            " 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 1 0 1 0 1 1 0 0 1 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6puOgflYR_e"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
      ],
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPAZkmBJVGCD",
        "outputId": "dce1cd79-6796-4882-b32b-2f79e63c3803"
      },
      "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": 151,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] evaluating network...\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Covid       1.00      1.00      1.00       123\n",
            "      Normal       1.00      1.00      1.00       127\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": "eddaa2bf-be80-4b39-8ae9-8cf22d2e3dcf"
      },
      "source": [
        "cf_matrix = confusion_matrix(testY,predictions.argmax(axis=1))\n",
        "print(cf_matrix)"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[123   0]\n",
            " [  0 127]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLp1ooZeY90p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "dfc80b68-5881-4665-afe2-d4da4e36438e"
      },
      "source": [
        "\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cf_matrix, display_labels=encoder.classes_)\n",
        "disp.plot(cmap=plt.cm.Blues)"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fbef587a350>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        },
        {
          "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"
          }
        }
      ]
    }
  ]
}