covid_detection_cnn.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "covid_detection_cnn.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPLZ9750DQWJsjESXhJb6M2",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/iqbalhanif/NN-with-Keras/blob/master/covid_detection_cnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLr71bhz9Etv",
        "outputId": "75ab7886-c048-4a0e-8b5b-5651aaff964a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66
        }
      },
      "source": [
        "#Koneksi ke Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "#ekstraksi Dataset\n",
        "!unzip \"/content/drive/My Drive/Health/covid-dataset/covid.zip\" -d \"/content/drive/My Drive/Health/covid-dataset/covid19/\"\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n",
            "Archive:  /content/drive/My Drive/Health/covid-dataset/covid.zip\n",
            "replace /content/drive/My Drive/Health/covid-dataset/covid19/covid/000001-12.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RU4H2S57iM48",
        "outputId": "87e17fee-8ea5-46be-f97e-39e08dd3fcb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "#ekstraksi Dataset\n",
        "!unzip \"/content/drive/My Drive/Health/covid-dataset/normal.zip\" -d \"/content/drive/My Drive/Health/covid-dataset/normal/\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/drive/My Drive/Health/covid-dataset/normal.zip\n",
            "replace /content/drive/My Drive/Health/covid-dataset/normal/normal/IM-0001-0001.jpeg? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Eq2KYgWUFl_",
        "outputId": "147093c9-6768-49d6-eb40-84d4795c1cd5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        }
      },
      "source": [
        "import os, re, glob, cv2, numpy as np\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "im = cv2.imread('/content/drive/My Drive/Health/covid-dataset/normal/normal/IM-0766-0001.jpeg')\n",
        "im = cv2.resize(im,(100,100))\n",
        "cv2_imshow(im)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=100x100 at 0x7FAE13347E48>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dt0z4zOVJcto",
        "outputId": "1fdb3fec-ffa9-46e5-f9a5-9f395b11322c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        }
      },
      "source": [
        "im = cv2.imread('/content/drive/My Drive/Health/covid-dataset/covid19/covid/yxppt-2020-02-19_00-51-27_287214-day12.jpg')\n",
        "im = cv2.resize(im,(100,100))\n",
        "cv2_imshow(im)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=100x100 at 0x7FADC32CF6A0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OfA9K6gliXbw",
        "outputId": "60c35de5-2afa-4b97-a109-36af0a049f18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "import os, re, glob, cv2, numpy as np\n",
        "\n",
        "dirs = os.listdir('/content/drive/My Drive/Health/covid-dataset/covid19/')\n",
        "label = 1\n",
        "im_arr = []\n",
        "lb_arr = []\n",
        "X = []\n",
        "y = []\n",
        "for i in dirs: #loop all directory\n",
        "    count = 0\n",
        "    for pic in glob.glob('/content/drive/My Drive/Health/covid-dataset/covid19/'+i+'/*'):\n",
        "        im = cv2.imread(pic)\n",
        "        im = cv2.resize(im,(70,70))\n",
        "        im = np.array(im)\n",
        "        count = count + 1\n",
        "        X.append(im)\n",
        "        y.append(label)\n",
        "        if(count <= 10):\n",
        "            im_arr.append({str(i):im})\n",
        "    print(\"Jumlah \"+str(i)+\" : \"+str(count))\n",
        "    label = label + 1\n",
        "    lb_arr.append(i)\n",
        "X_covid = np.array(X)\n",
        "y_covid = np.array(y);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Jumlah covid : 660\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6v-fGUwYl3Ze",
        "outputId": "1337a89b-8a83-46d4-f448-940382fb215e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        }
      },
      "source": [
        "y_covid"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PCpkzwUNmv81",
        "outputId": "b018b416-fde3-46bc-baa6-ff02cc0cd259",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "dirs = os.listdir('/content/drive/My Drive/Health/covid-dataset/normal/')\n",
        "label = 0\n",
        "im_arr = []\n",
        "lb_arr = []\n",
        "X = []\n",
        "y = []\n",
        "for i in dirs: #loop all directory\n",
        "    count = 0\n",
        "    for pic in glob.glob('/content/drive/My Drive/Health/covid-dataset/normal/'+i+'/*'):\n",
        "        im = cv2.imread(pic)\n",
        "        im = cv2.resize(im,(70,70))\n",
        "        im = np.array(im)\n",
        "        count = count + 1\n",
        "        X.append(im)\n",
        "        y.append(label)\n",
        "        if(count <= 10):\n",
        "            im_arr.append({str(i):im})\n",
        "    print(\"Jumlah \"+str(i)+\" : \"+str(count))\n",
        "    label = label + 1\n",
        "    lb_arr.append(i)\n",
        "X_normal = np.array(X)\n",
        "y_normal = np.array(y);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Jumlah normal : 666\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S41Y-6Q3m86U",
        "outputId": "b33a7258-7627-4008-8ac6-64c9c9cbe2c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        }
      },
      "source": [
        "y_normal"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       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,\n",
              "       0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Pg0TL_S9LV1"
      },
      "source": [
        "y1 = np.concatenate((y_covid, y_normal), axis=0)\n",
        "y2 = np.concatenate((y_normal, y_covid), axis=0)\n",
        "y = np.vstack((y1,y2)).transpose()\n",
        "X = np.concatenate((X_covid, X_normal), axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k7JD48CIDMBU",
        "outputId": "9ddc031d-ed5a-4ce3-e9a9-11e9b0f27b9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "X[10:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[[ 61,  61,  61],\n",
              "         [ 53,  53,  53],\n",
              "         [ 53,  53,  53],\n",
              "         ...,\n",
              "         [  3,   3,   3],\n",
              "         [  5,   5,   5],\n",
              "         [  6,   6,   6]],\n",
              "\n",
              "        [[  1,   1,   1],\n",
              "         [  1,   1,   1],\n",
              "         [  1,   1,   1],\n",
              "         ...,\n",
              "         [  1,   1,   1],\n",
              "         [  1,   1,   1],\n",
              "         [  1,   1,   1]],\n",
              "\n",
              "        [[  1,   1,   1],\n",
              "         [  1,   1,   1],\n",
              "         [  1,   1,   1],\n",
              "         ...,\n",
              "         [  2,   2,   2],\n",
              "         [  1,   1,   1],\n",
              "         [  1,   1,   1]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[  1,   1,   1],\n",
              "         [  1,   1,   1],\n",
              "         [  1,   1,   1],\n",
              "         ...,\n",
              "         [ 74,  74,  74],\n",
              "         [ 42,  42,  42],\n",
              "         [  7,   7,   7]],\n",
              "\n",
              "        [[  1,   1,   1],\n",
              "         [  1,   1,   1],\n",
              "         [  1,   1,   1],\n",
              "         ...,\n",
              "         [ 68,  68,  68],\n",
              "         [ 39,  39,  39],\n",
              "         [  9,   9,   9]],\n",
              "\n",
              "        [[  1,   1,   1],\n",
              "         [  1,   1,   1],\n",
              "         [  1,   1,   1],\n",
              "         ...,\n",
              "         [ 57,  57,  57],\n",
              "         [ 34,  34,  34],\n",
              "         [ 13,  13,  13]]],\n",
              "\n",
              "\n",
              "       [[[  6,   6,   6],\n",
              "         [  8,   8,   8],\n",
              "         [  8,   8,   8],\n",
              "         ...,\n",
              "         [ 86,  86,  86],\n",
              "         [ 76,  76,  76],\n",
              "         [ 68,  68,  68]],\n",
              "\n",
              "        [[  7,   7,   7],\n",
              "         [  7,   7,   7],\n",
              "         [  7,   7,   7],\n",
              "         ...,\n",
              "         [106, 106, 106],\n",
              "         [102, 102, 102],\n",
              "         [121, 121, 121]],\n",
              "\n",
              "        [[  7,   7,   7],\n",
              "         [  7,   7,   7],\n",
              "         [  7,   7,   7],\n",
              "         ...,\n",
              "         [140, 140, 140],\n",
              "         [168, 168, 168],\n",
              "         [138, 138, 138]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[ 11,  11,  11],\n",
              "         [ 11,  11,  11],\n",
              "         [ 11,  11,  11],\n",
              "         ...,\n",
              "         [  9,   9,   9],\n",
              "         [ 10,  10,  10],\n",
              "         [ 12,  12,  12]],\n",
              "\n",
              "        [[ 11,  11,  11],\n",
              "         [ 11,  11,  11],\n",
              "         [ 11,  11,  11],\n",
              "         ...,\n",
              "         [ 11,  11,  11],\n",
              "         [ 11,  11,  11],\n",
              "         [ 12,  12,  12]],\n",
              "\n",
              "        [[ 11,  11,  11],\n",
              "         [ 11,  11,  11],\n",
              "         [ 11,  11,  11],\n",
              "         ...,\n",
              "         [  9,   9,   9],\n",
              "         [ 11,  11,  11],\n",
              "         [ 11,  11,  11]]],\n",
              "\n",
              "\n",
              "       [[[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [ 84,  75,  59],\n",
              "         [  3,   3,   2],\n",
              "         [  0,   0,   0]],\n",
              "\n",
              "        [[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  1,   1,   1],\n",
              "         ...,\n",
              "         [  1,   1,   1],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]],\n",
              "\n",
              "        [[  0,   0,   0],\n",
              "         [  5,   5,   4],\n",
              "         [ 21,  19,  15],\n",
              "         ...,\n",
              "         [ 41,  36,  28],\n",
              "         [  8,   7,   7],\n",
              "         [  3,   2,   2]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[110,  98,  77],\n",
              "         [115, 101,  80],\n",
              "         [126, 111,  88],\n",
              "         ...,\n",
              "         [147, 130, 103],\n",
              "         [144, 128, 101],\n",
              "         [151, 134, 106]],\n",
              "\n",
              "        [[117, 104,  82],\n",
              "         [ 80,  71,  55],\n",
              "         [159, 141, 111],\n",
              "         ...,\n",
              "         [146, 129, 102],\n",
              "         [145, 128, 102],\n",
              "         [149, 132, 104]],\n",
              "\n",
              "        [[116, 103,  82],\n",
              "         [118, 104,  83],\n",
              "         [115, 101,  80],\n",
              "         ...,\n",
              "         [131, 116,  92],\n",
              "         [128, 113,  90],\n",
              "         [124, 110,  87]]],\n",
              "\n",
              "\n",
              "       ...,\n",
              "\n",
              "\n",
              "       [[[ 58,  58,  58],\n",
              "         [ 48,  48,  48],\n",
              "         [114, 114, 114],\n",
              "         ...,\n",
              "         [ 16,  16,  16],\n",
              "         [ 65,  65,  65],\n",
              "         [186, 186, 186]],\n",
              "\n",
              "        [[ 47,  47,  47],\n",
              "         [ 47,  47,  47],\n",
              "         [ 52,  52,  52],\n",
              "         ...,\n",
              "         [ 14,  14,  14],\n",
              "         [ 60,  60,  60],\n",
              "         [176, 176, 176]],\n",
              "\n",
              "        [[ 44,  44,  44],\n",
              "         [ 42,  42,  42],\n",
              "         [ 57,  57,  57],\n",
              "         ...,\n",
              "         [ 13,  13,  13],\n",
              "         [ 54,  54,  54],\n",
              "         [175, 175, 175]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [185, 185, 185],\n",
              "         [ 38,  38,  38],\n",
              "         [ 70,  70,  70]],\n",
              "\n",
              "        [[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [182, 182, 182],\n",
              "         [102, 102, 102],\n",
              "         [ 63,  63,  63]],\n",
              "\n",
              "        [[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [162, 162, 162],\n",
              "         [121, 121, 121],\n",
              "         [ 72,  72,  72]]],\n",
              "\n",
              "\n",
              "       [[[ 61,  61,  61],\n",
              "         [109, 109, 109],\n",
              "         [ 97,  97,  97],\n",
              "         ...,\n",
              "         [116, 116, 116],\n",
              "         [ 80,  80,  80],\n",
              "         [195, 195, 195]],\n",
              "\n",
              "        [[ 98,  98,  98],\n",
              "         [ 98,  98,  98],\n",
              "         [105, 105, 105],\n",
              "         ...,\n",
              "         [ 97,  97,  97],\n",
              "         [ 72,  72,  72],\n",
              "         [170, 170, 170]],\n",
              "\n",
              "        [[ 83,  83,  83],\n",
              "         [101, 101, 101],\n",
              "         [108, 108, 108],\n",
              "         ...,\n",
              "         [ 84,  84,  84],\n",
              "         [ 76,  76,  76],\n",
              "         [163, 163, 163]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [160, 160, 160],\n",
              "         [104, 104, 104],\n",
              "         [ 96,  96,  96]],\n",
              "\n",
              "        [[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [162, 162, 162],\n",
              "         [101, 101, 101],\n",
              "         [ 88,  88,  88]],\n",
              "\n",
              "        [[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  1,   1,   1],\n",
              "         ...,\n",
              "         [116, 116, 116],\n",
              "         [123, 123, 123],\n",
              "         [ 82,  82,  82]]],\n",
              "\n",
              "\n",
              "       [[[160, 160, 160],\n",
              "         [168, 168, 168],\n",
              "         [139, 139, 139],\n",
              "         ...,\n",
              "         [  0,   0,   0],\n",
              "         [  2,   2,   2],\n",
              "         [  9,   9,   9]],\n",
              "\n",
              "        [[ 95,  95,  95],\n",
              "         [105, 105, 105],\n",
              "         [109, 109, 109],\n",
              "         ...,\n",
              "         [  0,   0,   0],\n",
              "         [  5,   5,   5],\n",
              "         [ 39,  39,  39]],\n",
              "\n",
              "        [[ 91,  91,  91],\n",
              "         [100, 100, 100],\n",
              "         [111, 111, 111],\n",
              "         ...,\n",
              "         [  0,   0,   0],\n",
              "         [  5,   5,   5],\n",
              "         [ 50,  50,  50]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [  7,   7,   7],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]],\n",
              "\n",
              "        [[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [ 12,  12,  12],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]],\n",
              "\n",
              "        [[  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [  7,   7,   7],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0]]]], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYSVPX8ADxCO",
        "outputId": "98e5fded-6029-4981-fb11-a79c8ffbf53b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133
        }
      },
      "source": [
        "y[10:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       ...,\n",
              "       [0, 1],\n",
              "       [0, 1],\n",
              "       [0, 1]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sRWagqUWCPta"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01n6dXuunju7"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense\n",
        "model = Sequential() #model = sequential \n",
        "model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',input_shape=(70,70,3))) #layer convolutional 2D\n",
        "model.add(MaxPooling2D(pool_size=(2,2))) #max pooling with stride (2,2)\n",
        "model.add(Conv2D(32, (3, 3), activation='relu')) #layer convolutional 2D\n",
        "model.add(MaxPooling2D(pool_size=(2,2))) #max pooling with stride (2,2)\n",
        "model.add(Dropout(0.25)) #delete neuron randomly while training and remain 75%\n",
        "model.add(Flatten()) #make layer flatten\n",
        "model.add(Dense(128, activation='relu')) #fully connected layer\n",
        "model.add(Dropout(0.5)) #delete neuron randomly and remain 50%\n",
        "model.add(Dense(2, activation='softmax')) #softmax works"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "02uDYGTaCeil",
        "outputId": "283ac943-1b68-4745-93ac-f0d5f52c8779",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        }
      },
      "source": [
        "from keras.optimizers import SGD\n",
        "epochs = 5\n",
        "lrate = 0.01\n",
        "decay = lrate/epochs\n",
        "sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)\n",
        "model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
        "print(model.summary())\n",
        "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=epochs, batch_size=32)\n",
        "scores = model.evaluate(X_test, y_test, verbose=0)\n",
        "print(\"Accuracy: %.2f%%\" % (scores[1]*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_7 (Conv2D)            (None, 68, 68, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 34, 34, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten_4 (Flatten)          (None, 8192)              0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 128)               1048704   \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 2)                 258       \n",
            "=================================================================\n",
            "Total params: 1,059,106\n",
            "Trainable params: 1,059,106\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Train on 928 samples, validate on 398 samples\n",
            "Epoch 1/5\n",
            "928/928 [==============================] - 5s 5ms/step - loss: nan - accuracy: 0.4849 - val_loss: nan - val_accuracy: 0.5226\n",
            "Epoch 2/5\n",
            "928/928 [==============================] - 5s 5ms/step - loss: nan - accuracy: 0.4935 - val_loss: nan - val_accuracy: 0.5226\n",
            "Epoch 3/5\n",
            "928/928 [==============================] - 5s 5ms/step - loss: nan - accuracy: 0.4935 - val_loss: nan - val_accuracy: 0.5226\n",
            "Epoch 4/5\n",
            "928/928 [==============================] - 5s 5ms/step - loss: nan - accuracy: 0.4935 - val_loss: nan - val_accuracy: 0.5226\n",
            "Epoch 5/5\n",
            "928/928 [==============================] - 5s 5ms/step - loss: nan - accuracy: 0.4935 - val_loss: nan - val_accuracy: 0.5226\n",
            "Accuracy: 52.26%\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}