CNN_11.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CNN_11.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP8QnjR5nc8tLw7Nql12HS3",
      "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/auviera/ECE3336/blob/master/CNN_11.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": "DOVchM5fcsxM",
        "outputId": "ea894573-1147-4a53-b1e8-62994245c4e8"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jZbkMIc8dkMh",
        "outputId": "5dc966e3-80a4-4e8c-e1a4-51a0e81ab8e0"
      },
      "source": [
        "cd /content/drive/MyDrive/Colab Notebooks"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/Colab Notebooks\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4dx3ri7bdm06",
        "outputId": "2c18b61f-f0ee-49f7-cf00-406ccf74f27b"
      },
      "source": [
        "ls"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;36mdataset\u001b[0m@  Untitled0.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ffTPWqOdpE_"
      },
      "source": [
        "from keras.models import Sequential\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\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",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "import matplotlib.pyplot as plt\n",
        "from imutils import paths\n",
        "import numpy as np\n",
        "import os"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E7IGsvAYdtyL",
        "outputId": "346ae828-b7a2-42ba-f369-3d5f43caded7"
      },
      "source": [
        "print(\"[INFO] loading images...\")\n",
        "imagePaths = paths.list_images(\"dataset\")\n",
        "\n",
        "data = []\n",
        "labels = []"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] loading images...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fW02a-uqdx2n"
      },
      "source": [
        "# loop over our input images\n",
        "for imagePath in imagePaths:\n",
        "\t# 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",
        "\timage = Image.open(imagePath)\n",
        "  #preprocessing\n",
        "\timage = np.array(image.resize((128, 128))) / 255.0 #normalisasi\n",
        "\tdata.append(image)\n",
        "\n",
        "\t# extract the class label from the file path and update the\n",
        "\t# labels list\n",
        "\tlabel = imagePath.split(os.path.sep)[-2]\n",
        "\tlabels.append(label)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xm2JGlsZhJtI",
        "outputId": "3ea995ad-618b-4916-df34-585a1064ebe2"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_07', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 'GMB_09', 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'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_02', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_03', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_10', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_06', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'GMB_11', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB', 'nonGMB']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BnilJ-WAhNRo"
      },
      "source": [
        "lb = LabelBinarizer()\n",
        "labels = lb.fit_transform(labels)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mV_IPKlFhRhd",
        "outputId": "3cb684b2-a119-469f-fc5b-5a10ff0c26bd"
      },
      "source": [
        "print(labels)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 0 0 1]\n",
            " [0 0 0 ... 0 0 1]\n",
            " [0 0 0 ... 0 0 1]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2FICavZ6hVr7",
        "outputId": "e1b22c0c-386c-4c86-a70b-6192361a5456"
      },
      "source": [
        "(trainX, testX, trainY, testY) = train_test_split(np.array(data),\tnp.array(labels), test_size=0.25, shuffle=True)\n",
        "print(trainX.shape)\n",
        "print(testX.shape)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1054, 128, 128, 3)\n",
            "(352, 128, 128, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZXCXkbHOv5x"
      },
      "source": [
        "image_gen = ImageDataGenerator(rescale=1./255, horizontal_flip=True)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-bx1WKzGj8Pp",
        "outputId": "b6f71ed0-0a3f-45f3-c0ec-8191452bd178"
      },
      "source": [
        "from keras.layers import Convolution2D, MaxPooling2D\n",
        "from keras.models import Sequential\n",
        "from keras.layers.core import Flatten, Dense, Dropout\n",
        "\n",
        "model1 = Sequential()\n",
        "model1.add(Convolution2D(8, (3,3), activation='relu', input_shape=(128,128,3)))\n",
        "model1.add(MaxPooling2D(2,2))\n",
        "model1.add(Convolution2D(16, (3,3), activation='relu'))\n",
        "model1.add(MaxPooling2D(2,2))\n",
        "model1.add(Convolution2D(16, (3,3), activation='relu'))\n",
        "model1.add(MaxPooling2D(2,2))\n",
        "model1.add(Dropout(0.9))\n",
        "#fully connected layer\n",
        "model1.add(Flatten())\n",
        "model1.add(Dense(100, activation='relu'))\n",
        "model1.add(Dense(12, activation='softmax'))\n",
        "\n",
        "model1.summary()"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_27\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_87 (Conv2D)           (None, 126, 126, 8)       224       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_86 (MaxPooling (None, 63, 63, 8)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_88 (Conv2D)           (None, 61, 61, 16)        1168      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_87 (MaxPooling (None, 30, 30, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_89 (Conv2D)           (None, 28, 28, 16)        2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_88 (MaxPooling (None, 14, 14, 16)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 14, 14, 16)        0         \n",
            "_________________________________________________________________\n",
            "flatten_24 (Flatten)         (None, 3136)              0         \n",
            "_________________________________________________________________\n",
            "dense_48 (Dense)             (None, 100)               313700    \n",
            "_________________________________________________________________\n",
            "dense_49 (Dense)             (None, 12)                1212      \n",
            "=================================================================\n",
            "Total params: 318,624\n",
            "Trainable params: 318,624\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6BzmgbZXkYIV",
        "outputId": "3d317e8d-b509-4985-ef40-339de9154ec0"
      },
      "source": [
        "print(\"[INFO] training network...\")\n",
        "opt = Adam(lr=1e-3, decay=1e-3 / 50)\n",
        "model1.compile(loss=\"categorical_crossentropy\", optimizer=opt, metrics=[\"accuracy\"])\n",
        "H = model1.fit(trainX, trainY, validation_data=(testX, testY), epochs=25, batch_size=40)"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] training network...\n",
            "Epoch 1/25\n",
            "27/27 [==============================] - 9s 347ms/step - loss: 0.3456 - accuracy: 0.8786 - val_loss: 0.7130 - val_accuracy: 0.7955\n",
            "Epoch 2/25\n",
            "27/27 [==============================] - 9s 334ms/step - loss: 0.2445 - accuracy: 0.9146 - val_loss: 0.6915 - val_accuracy: 0.7955\n",
            "Epoch 3/25\n",
            "27/27 [==============================] - 9s 330ms/step - loss: 0.2601 - accuracy: 0.9080 - val_loss: 0.6590 - val_accuracy: 0.8068\n",
            "Epoch 4/25\n",
            "27/27 [==============================] - 9s 333ms/step - loss: 0.2662 - accuracy: 0.8966 - val_loss: 0.6359 - val_accuracy: 0.7898\n",
            "Epoch 5/25\n",
            "27/27 [==============================] - 9s 335ms/step - loss: 0.3129 - accuracy: 0.8833 - val_loss: 0.7215 - val_accuracy: 0.7756\n",
            "Epoch 6/25\n",
            "27/27 [==============================] - 9s 332ms/step - loss: 0.2761 - accuracy: 0.8928 - val_loss: 0.7240 - val_accuracy: 0.7812\n",
            "Epoch 7/25\n",
            "27/27 [==============================] - 9s 334ms/step - loss: 0.2363 - accuracy: 0.9137 - val_loss: 0.6515 - val_accuracy: 0.8011\n",
            "Epoch 8/25\n",
            "27/27 [==============================] - 9s 332ms/step - loss: 0.2576 - accuracy: 0.9108 - val_loss: 0.6561 - val_accuracy: 0.8011\n",
            "Epoch 9/25\n",
            "27/27 [==============================] - 9s 332ms/step - loss: 0.2441 - accuracy: 0.9146 - val_loss: 0.6507 - val_accuracy: 0.7955\n",
            "Epoch 10/25\n",
            "27/27 [==============================] - 9s 346ms/step - loss: 0.2645 - accuracy: 0.9070 - val_loss: 0.7116 - val_accuracy: 0.7955\n",
            "Epoch 11/25\n",
            "27/27 [==============================] - 9s 349ms/step - loss: 0.2964 - accuracy: 0.8975 - val_loss: 0.7183 - val_accuracy: 0.7983\n",
            "Epoch 12/25\n",
            "27/27 [==============================] - 9s 330ms/step - loss: 0.2549 - accuracy: 0.9118 - val_loss: 0.6579 - val_accuracy: 0.7983\n",
            "Epoch 13/25\n",
            "27/27 [==============================] - 9s 331ms/step - loss: 0.2689 - accuracy: 0.9061 - val_loss: 0.6797 - val_accuracy: 0.8040\n",
            "Epoch 14/25\n",
            "27/27 [==============================] - 9s 331ms/step - loss: 0.2703 - accuracy: 0.8994 - val_loss: 0.6689 - val_accuracy: 0.8097\n",
            "Epoch 15/25\n",
            "27/27 [==============================] - 9s 334ms/step - loss: 0.2987 - accuracy: 0.8890 - val_loss: 0.7192 - val_accuracy: 0.7699\n",
            "Epoch 16/25\n",
            "27/27 [==============================] - 9s 335ms/step - loss: 0.2736 - accuracy: 0.9013 - val_loss: 0.6783 - val_accuracy: 0.8097\n",
            "Epoch 17/25\n",
            "27/27 [==============================] - 9s 344ms/step - loss: 0.2949 - accuracy: 0.8966 - val_loss: 0.6593 - val_accuracy: 0.8040\n",
            "Epoch 18/25\n",
            "27/27 [==============================] - 9s 343ms/step - loss: 0.3293 - accuracy: 0.8994 - val_loss: 0.6683 - val_accuracy: 0.7926\n",
            "Epoch 19/25\n",
            "27/27 [==============================] - 9s 333ms/step - loss: 0.2418 - accuracy: 0.9099 - val_loss: 0.6474 - val_accuracy: 0.8068\n",
            "Epoch 20/25\n",
            "27/27 [==============================] - 9s 336ms/step - loss: 0.2238 - accuracy: 0.9184 - val_loss: 0.6712 - val_accuracy: 0.8125\n",
            "Epoch 21/25\n",
            "27/27 [==============================] - 9s 333ms/step - loss: 0.3026 - accuracy: 0.8852 - val_loss: 0.6478 - val_accuracy: 0.8153\n",
            "Epoch 22/25\n",
            "27/27 [==============================] - 9s 333ms/step - loss: 0.2820 - accuracy: 0.8966 - val_loss: 0.6362 - val_accuracy: 0.7983\n",
            "Epoch 23/25\n",
            "27/27 [==============================] - 9s 331ms/step - loss: 0.2289 - accuracy: 0.9260 - val_loss: 0.6327 - val_accuracy: 0.7983\n",
            "Epoch 24/25\n",
            "27/27 [==============================] - 9s 332ms/step - loss: 0.2236 - accuracy: 0.9127 - val_loss: 0.6281 - val_accuracy: 0.8210\n",
            "Epoch 25/25\n",
            "27/27 [==============================] - 9s 330ms/step - loss: 0.2863 - accuracy: 0.8947 - val_loss: 0.6268 - val_accuracy: 0.8068\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "lgREz4wGMO7z",
        "outputId": "cc808fc8-a88d-473c-bdc3-0be38c3b823f"
      },
      "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": 121,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DXFKPe3mMtHo",
        "outputId": "7473b48a-98ba-456e-beaf-1eea53527711"
      },
      "source": [
        "# evaluate the network\n",
        "print(\"[INFO] evaluating network...\")\n",
        "predictions = model1.predict(testX, batch_size=32)\n",
        "print(classification_report(testY.argmax(axis=1),\n",
        "\tpredictions.argmax(axis=1), target_names=lb.classes_))"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] evaluating network...\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      GMB_01       0.82      0.82      0.82        34\n",
            "      GMB_02       0.83      0.86      0.84        28\n",
            "      GMB_03       0.85      0.79      0.81        14\n",
            "      GMB_04       0.74      0.87      0.80        30\n",
            "      GMB_05       0.82      0.93      0.87        29\n",
            "      GMB_06       0.77      0.57      0.66        35\n",
            "      GMB_07       0.87      0.61      0.71        33\n",
            "      GMB_08       0.75      0.86      0.80        28\n",
            "      GMB_09       0.94      1.00      0.97        31\n",
            "      GMB_10       0.86      0.81      0.83        31\n",
            "      GMB_11       0.68      0.77      0.72        35\n",
            "      nonGMB       0.84      0.88      0.86        24\n",
            "\n",
            "    accuracy                           0.81       352\n",
            "   macro avg       0.81      0.81      0.81       352\n",
            "weighted avg       0.81      0.81      0.80       352\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L7e1a38TMz8M",
        "outputId": "60e892a8-9b5f-4afb-e928-c0933b083d49"
      },
      "source": [
        "model1.save('nnmodel_dataset')"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "INFO:tensorflow:Assets written to: nnmodel_dataset/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nh0imUxrM8uo"
      },
      "source": [
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "image1='GMB_05_test.jpg'"
      ],
      "execution_count": 185,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "Xnl3jrprNCd9",
        "outputId": "940890f6-4f71-4b78-bcbb-3286cc65ce87"
      },
      "source": [
        "img_array = cv2.imread(image1)\n",
        "plt.imshow(img_array)\n",
        "plt.show()"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3zMqotHJYsAC",
        "outputId": "803a25f2-a057-4bb1-ae7b-fd5d25d4c0ce"
      },
      "source": [
        "image_testing = Image.open('GMB_05_test.jpg')\n",
        "image_testing = np.array(image_testing.resize((128, 128))) / 255.0\n",
        "image_testing.shape"
      ],
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(128, 128, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 188
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gADy7lK0Y05P",
        "outputId": "fc8fbb1a-1e12-4aa8-bbad-1ed8aa367332"
      },
      "source": [
        "image_testing = np.expand_dims(image_testing, axis=0)\n",
        "print(image_testing.shape)"
      ],
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 128, 128, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j-T06uleY4qm",
        "outputId": "26864e8e-b202-4cb4-94af-b73e038661a2"
      },
      "source": [
        "output = model1.predict(image_testing, 1)\n",
        "print(output)\n",
        "print(lb.classes_[output.argmax(axis=1)])"
      ],
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[5.8889033e-05 1.2091654e-05 1.4534261e-07 3.2683866e-04 9.9548316e-01\n",
            "  4.6093155e-05 4.6343758e-04 4.3398861e-05 2.6946006e-04 1.1607316e-08\n",
            "  3.2469365e-03 4.9706672e-05]]\n",
            "['GMB_05']\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}