Plant_Leaf_Diseases_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Plant Leaf Diseases Classification.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNEuf2o2SxD0UsfdnlXrRoO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Vidi005/IDCamp-Dicoding-Challenge-SDGs-2020/blob/master/Plant_Leaf_Diseases_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fJEscDjOtgYH",
        "colab_type": "text"
      },
      "source": [
        "# **Farm Doctor**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KcHddQ17tyef",
        "colab_type": "text"
      },
      "source": [
        "Program ML untuk klasifikasi penyakit pada daun tanaman\n",
        "<br>\n",
        "Program ini dibuat menggunakan CNN dengan library Keras dan backend TensorFlow."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GFyhoUc4uD88",
        "colab_type": "text"
      },
      "source": [
        "## Memasang Kaggle untuk Mengimpor Dataset dari Kaggle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hj8zX4F02RhK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "5a78e797-cf8b-49c1-aa8f-da08ace32cf5"
      },
      "source": [
        "!pip install kaggle"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.6/dist-packages (1.5.6)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.0.1)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle) (2020.6.20)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.8.1)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.41.1)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (2.10)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle) (1.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XAoIQrUnvlur",
        "colab_type": "text"
      },
      "source": [
        "## Upload Kaggle API"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CXaTxXNu3W8_",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "1b21743a-3b2f-4f09-b4bd-5f87a9c1340c"
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-2556fa63-b8f1-42c8-a33a-db8b601d6f6d\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-2556fa63-b8f1-42c8-a33a-db8b601d6f6d\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"vidi005\",\"key\":\"9fce1c2f299c8e658ea0b78f1cce224f\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fNzNljrAvrCt",
        "colab_type": "text"
      },
      "source": [
        "## Menyimpan File Kaggle API untuk Download Datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FbXfW2P63oMC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pkjtuId6xpJB",
        "colab_type": "text"
      },
      "source": [
        "## Preprocessing Datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C_RLcltWv86V",
        "colab_type": "text"
      },
      "source": [
        "### Mendwonload Dataset Plant Diseases"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SSjTIHNx0uzV",
        "colab_type": "text"
      },
      "source": [
        "Dataset didownload dari link berikut:\n",
        "<br>\n",
        "https://www.kaggle.com/daoliu/plant-622"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NnFtBLlP3438",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "4116652b-9aa4-4a00-ec0a-85f2c2f20ca3"
      },
      "source": [
        "!kaggle datasets download -d daoliu/plant-622"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading plant-622.zip to /content\n",
            "100% 1.33G/1.34G [00:11<00:00, 47.7MB/s]\n",
            "100% 1.34G/1.34G [00:11<00:00, 122MB/s] \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jd2JMAlZwIbP",
        "colab_type": "text"
      },
      "source": [
        "### Menampilkan List Folder Content"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "moR0EgVk5GyW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c0fecb23-5797-4f07-d628-033cba63a9f5"
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "kaggle.json  plant-622.zip  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hxcu2NQ4wSl6",
        "colab_type": "text"
      },
      "source": [
        "### Mengekstrak Dataset yang Sudah didownload"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HpJ3OZ3l5ad2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Menyimpan hasil ekstrak ke dalam folder datasets\n",
        "import zipfile,os\n",
        "local_zip = \"plant-622.zip\"\n",
        "zip_ref = zipfile.ZipFile(local_zip, \"r\")\n",
        "zip_ref.extractall(\"/content/datasets\")\n",
        "zip_ref.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G39aprg3SKYB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Menghapus folder dataset pelatihan yang tidak diperlukan\n",
        "import shutil\n",
        "shutil.rmtree(\"/content/datasets/train/Blueberry___healthy\", ignore_errors=False, onerror=None)\n",
        "shutil.rmtree(\"/content/datasets/train/Orange___Haunglongbing_(Citrus_greening)\", ignore_errors=False, onerror=None)\n",
        "shutil.rmtree(\"/content/datasets/train/Peach___Bacterial_spot\", ignore_errors=False, onerror=None)\n",
        "shutil.rmtree(\"/content/datasets/train/Peach___healthy\", ignore_errors=False, onerror=None)\n",
        "shutil.rmtree(\"/content/datasets/train/Raspberry___healthy\", ignore_errors=False, onerror=None)\n",
        "shutil.rmtree(\"/content/datasets/train/Soybean___healthy\", ignore_errors=False, onerror=None)\n",
        "shutil.rmtree(\"/content/datasets/train/Squash___Powdery_mildew\", ignore_errors=False, onerror=None)\n",
        "\n",
        "#Menghapus dataset validasi yang tidak diperlukan\n",
        "#Dataset validasi yang digunakan diambil 20% dari dataset pelatihan menggunakan image generator\n",
        "shutil.rmtree(\"/content/datasets/valid\", ignore_errors=False, onerror=None)\n",
        "\n",
        "#Menghapus dataset pengujian yang tidak diperlukan\n",
        "shutil.rmtree(\"/content/datasets/test/Blueberry___healthy\", ignore_errors=False, onerror=None)\n",
        "shutil.rmtree(\"/content/datasets/test/Orange___Haunglongbing_(Citrus_greening)\", ignore_errors=False, onerror=None)\n",
        "shutil.rmtree(\"/content/datasets/test/Peach___Bacterial_spot\", ignore_errors=False, onerror=None)\n",
        "shutil.rmtree(\"/content/datasets/test/Peach___healthy\", ignore_errors=False, onerror=None)\n",
        "shutil.rmtree(\"/content/datasets/test/Raspberry___healthy\", ignore_errors=False, onerror=None)\n",
        "shutil.rmtree(\"/content/datasets/test/Soybean___healthy\", ignore_errors=False, onerror=None)\n",
        "shutil.rmtree(\"/content/datasets/test/Squash___Powdery_mildew\", ignore_errors=False, onerror=None)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ML9_IgtoxI4V",
        "colab_type": "text"
      },
      "source": [
        "## Mengimpor Libraries yang dibutuhkan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YsFrRLdQ8QHA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "outputId": "32c70e78-2ff0-407e-ae2f-938fa8203480"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from sklearn.model_selection import train_test_split\n",
        "from skimage.io import imread\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "import numpy as np\n",
        "from skimage.transform import resize\n",
        "from keras.utils.np_utils import to_categorical\n",
        "\n",
        "# Mengihitung waktu lamanya eksekusi tiap sel di Google Colab\n",
        "!pip install ipython-autotime\n",
        "\n",
        "%load_ext autotime"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting ipython-autotime\n",
            "  Downloading https://files.pythonhosted.org/packages/e6/f9/0626bbdb322e3a078d968e87e3b01341e7890544de891d0cb613641220e6/ipython-autotime-0.1.tar.bz2\n",
            "Building wheels for collected packages: ipython-autotime\n",
            "  Building wheel for ipython-autotime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ipython-autotime: filename=ipython_autotime-0.1-cp36-none-any.whl size=1832 sha256=e053d151e4ec5222f407363bb7d9f1c27fa4f1957d56e537daeb5748631161dd\n",
            "  Stored in directory: /root/.cache/pip/wheels/d2/df/81/2db1e54bc91002cec40334629bc39cfa86dff540b304ebcd6e\n",
            "Successfully built ipython-autotime\n",
            "Installing collected packages: ipython-autotime\n",
            "Successfully installed ipython-autotime-0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_kFAzksXxVGu",
        "colab_type": "text"
      },
      "source": [
        "## Menampilkan Versi TensorFlow yang digunakan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k8MNFKcb8WBZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "0042401c-e7f3-46e9-c7cf-b6a26c6adac1"
      },
      "source": [
        "print(tf.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.3.0\n",
            "time: 2.45 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i52zugYyxd2h",
        "colab_type": "text"
      },
      "source": [
        "## Preprocessing Datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KI8DeSFEx2G5",
        "colab_type": "text"
      },
      "source": [
        "### Membuat Objek ImageDataGenerator & Data Augmentation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cU_ZuM4vyHdI",
        "colab_type": "text"
      },
      "source": [
        "Membuat obyek gambar dataset dengan ImageDataGenerator, Autmentasi dataset, dan Membagi dataset validasi (validation) sebesar 20% dan dataset pelatihan (training) sebesar 80%."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLR5yo1L7z6J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b743822b-607e-4a93-9af9-67826f401660"
      },
      "source": [
        "datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "    rescale=1./255, \n",
        "    validation_split = 0.2,\n",
        "    rotation_range = 20,\n",
        "    horizontal_flip = True,\n",
        "    shear_range = 0.2,\n",
        "    zoom_range = 0.2,    \n",
        "    vertical_flip = True,\n",
        "    fill_mode = \"nearest\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 6.46 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fPtbXeyYyMHj",
        "colab_type": "text"
      },
      "source": [
        "### Menggunakan Obyek ImageDataGenerator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "spCgvam78OSs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "789a3e04-a0e0-4a73-b63a-a214456152e4"
      },
      "source": [
        "#Memuat dataset pelatihan & validasi\n",
        "base_dir = os.path.join(\"datasets/train\")\n",
        "\n",
        "img_size = 224\n",
        "batch_size = 128\n",
        "\n",
        "train_generator = datagen.flow_from_directory(\n",
        "    base_dir,\n",
        "    target_size=(img_size, img_size),\n",
        "    batch_size=batch_size, \n",
        "    subset='training',\n",
        "    class_mode= 'categorical')\n",
        "\n",
        "val_generator = datagen.flow_from_directory(\n",
        "    base_dir,\n",
        "    target_size=(img_size, img_size),\n",
        "    batch_size=batch_size, \n",
        "    subset='validation',\n",
        "    class_mode= 'categorical')\n",
        "\n",
        "#Memuat dataset pengujian\n",
        "X_test = []\n",
        "y_test = []\n",
        "labels = ['Apple___Apple_scab',\n",
        "          'Apple___Black_rot',\n",
        "          'Apple___Cedar_apple_rust',\n",
        "          'Apple___healthy',\n",
        "          'Cherry_(including_sour)___Powdery_mildew',\n",
        "          'Cherry_(including_sour)___healthy',\n",
        "          'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot',\n",
        "          'Corn_(maize)___Common_rust_',\n",
        "          'Corn_(maize)___Northern_Leaf_Blight',\n",
        "          'Corn_(maize)___healthy',\n",
        "          'Grape___Black_rot',\n",
        "          'Grape___Esca_(Black_Measles)',\n",
        "          'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',\n",
        "          'Grape___healthy',\n",
        "          'Pepper,_bell___Bacterial_spot',\n",
        "          'Pepper,_bell___healthy',\n",
        "          'Potato___Early_blight',\n",
        "          'Potato___Late_blight',\n",
        "          'Potato___healthy',\n",
        "          'Strawberry___Leaf_scorch',\n",
        "          'Strawberry___Leaf_scorch',\n",
        "          'Tomato___Bacterial_spot',\n",
        "          'Tomato___Early_blight',\n",
        "          'Tomato___Late_blight',\n",
        "          'Tomato___Leaf_Mold',\n",
        "          'Tomato___Septoria_leaf_spot',\n",
        "          'Tomato___Spider_mites Two-spotted_spider_mite',\n",
        "          'Tomato___Target_Spot',\n",
        "          'Tomato___Tomato_Yellow_Leaf_Curl_Virus',\n",
        "          'Tomato___Tomato_mosaic_virus',\n",
        "          'Tomato___healthy']\n",
        "\n",
        "for i,label in enumerate(labels):\n",
        "    folder = os.path.join(\"datasets/test\",label)\n",
        "    files = sorted(os.listdir(folder))\n",
        "    files = [x for x in files if x.endswith(\".jpg\")]\n",
        "    for k,file in enumerate(files):\n",
        "        image_path = os.path.join(folder, file)\n",
        "        \n",
        "        image = imread(image_path)/255.\n",
        "        image = resize(image,(224,224))\n",
        "        X_test.append(image)\n",
        "        category = os.path.split(folder)[-1]\n",
        "        y_test.append(i)\n",
        "\n",
        "X_test = np.array(X_test)\n",
        "y_test = np.array(y_test)\n",
        "\n",
        "#Menampilkan bentuk dari masing-masing dataset\n",
        "for image_batch, label_batch in train_generator:\n",
        "  break\n",
        "print(\"Bentuk array dari dataset train (pelatihan) adalah:\", image_batch.shape,label_batch.shape)\n",
        "for image_batch, label_batch in val_generator:\n",
        "  break\n",
        "print(\"Bentuk array dari dataset validation (validasi) adalah:\", image_batch.shape,label_batch.shape)\n",
        "print(\"Bentuk array dari dataset test (pengujian) adalah:\", X_test.shape,y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 34429 images belonging to 31 classes.\n",
            "Found 8595 images belonging to 31 classes.\n",
            "Bentuk array dari dataset train (pelatihan) adalah: (128, 224, 224, 3) (128, 31)\n",
            "Bentuk array dari dataset validation (validasi) adalah: (128, 224, 224, 3) (128, 31)\n",
            "Bentuk array dari dataset test (pengujian) adalah: (601, 224, 224, 3) (601,)\n",
            "time: 14.6 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cIdGru14yhrJ",
        "colab_type": "text"
      },
      "source": [
        "## Menyimpan Label Datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pDcFgxYu895P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "fabcbfa4-6507-4944-ea80-ff88bde11eaf"
      },
      "source": [
        "print (train_generator.class_indices)\n",
        "\n",
        "labels_txt = '\\n'.join(sorted(train_generator.class_indices.keys()))\n",
        "\n",
        "with open('labels.txt', 'w') as f:\n",
        "  f.write(labels_txt)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'Apple___Apple_scab': 0, 'Apple___Black_rot': 1, 'Apple___Cedar_apple_rust': 2, 'Apple___healthy': 3, 'Cherry_(including_sour)___Powdery_mildew': 4, 'Cherry_(including_sour)___healthy': 5, 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot': 6, 'Corn_(maize)___Common_rust_': 7, 'Corn_(maize)___Northern_Leaf_Blight': 8, 'Corn_(maize)___healthy': 9, 'Grape___Black_rot': 10, 'Grape___Esca_(Black_Measles)': 11, 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)': 12, 'Grape___healthy': 13, 'Pepper,_bell___Bacterial_spot': 14, 'Pepper,_bell___healthy': 15, 'Potato___Early_blight': 16, 'Potato___Late_blight': 17, 'Potato___healthy': 18, 'Strawberry___Leaf_scorch': 19, 'Strawberry___healthy': 20, 'Tomato___Bacterial_spot': 21, 'Tomato___Early_blight': 22, 'Tomato___Late_blight': 23, 'Tomato___Leaf_Mold': 24, 'Tomato___Septoria_leaf_spot': 25, 'Tomato___Spider_mites Two-spotted_spider_mite': 26, 'Tomato___Target_Spot': 27, 'Tomato___Tomato_Yellow_Leaf_Curl_Virus': 28, 'Tomato___Tomato_mosaic_virus': 29, 'Tomato___healthy': 30}\n",
            "time: 6.23 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mKW3wxvGytma",
        "colab_type": "text"
      },
      "source": [
        "## Membuat Model dari Jaringan CNN yang Sudah dipelajari Sebelumnya (pre-trained convnets)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BsNzogMa9ZQx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "d1de81fb-4a79-46d1-813b-c63daf74fa90"
      },
      "source": [
        "IMG_SHAPE = (224, 224, 3)\n",
        "# Membuat model dasar (base model) dari pre-trained model MobileNet\n",
        "base_model = tf.keras.applications.MobileNet(input_shape=IMG_SHAPE,\n",
        "                                              include_top=False, \n",
        "                                              weights='imagenet')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_1_0_224_tf_no_top.h5\n",
            "17227776/17225924 [==============================] - 0s 0us/step\n",
            "time: 6.66 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nU4xTUdFy0FS",
        "colab_type": "text"
      },
      "source": [
        "### Feature Extraction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wDlRKJ6U9k9C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "49bb713e-9e10-46b4-f6b7-bfcb98e46f16"
      },
      "source": [
        "base_model.trainable = False\n",
        "base_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"mobilenet_1.00_224\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
            "_________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)    (None, 225, 225, 3)       0         \n",
            "_________________________________________________________________\n",
            "conv1 (Conv2D)               (None, 112, 112, 32)      864       \n",
            "_________________________________________________________________\n",
            "conv1_bn (BatchNormalization (None, 112, 112, 32)      128       \n",
            "_________________________________________________________________\n",
            "conv1_relu (ReLU)            (None, 112, 112, 32)      0         \n",
            "_________________________________________________________________\n",
            "conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)      288       \n",
            "_________________________________________________________________\n",
            "conv_dw_1_bn (BatchNormaliza (None, 112, 112, 32)      128       \n",
            "_________________________________________________________________\n",
            "conv_dw_1_relu (ReLU)        (None, 112, 112, 32)      0         \n",
            "_________________________________________________________________\n",
            "conv_pw_1 (Conv2D)           (None, 112, 112, 64)      2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_1_bn (BatchNormaliza (None, 112, 112, 64)      256       \n",
            "_________________________________________________________________\n",
            "conv_pw_1_relu (ReLU)        (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "conv_pad_2 (ZeroPadding2D)   (None, 113, 113, 64)      0         \n",
            "_________________________________________________________________\n",
            "conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)        576       \n",
            "_________________________________________________________________\n",
            "conv_dw_2_bn (BatchNormaliza (None, 56, 56, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv_dw_2_relu (ReLU)        (None, 56, 56, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv_pw_2 (Conv2D)           (None, 56, 56, 128)       8192      \n",
            "_________________________________________________________________\n",
            "conv_pw_2_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv_pw_2_relu (ReLU)        (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_3 (DepthwiseConv2D)  (None, 56, 56, 128)       1152      \n",
            "_________________________________________________________________\n",
            "conv_dw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv_dw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_3 (Conv2D)           (None, 56, 56, 128)       16384     \n",
            "_________________________________________________________________\n",
            "conv_pw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv_pw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_pad_4 (ZeroPadding2D)   (None, 57, 57, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_4 (DepthwiseConv2D)  (None, 28, 28, 128)       1152      \n",
            "_________________________________________________________________\n",
            "conv_dw_4_bn (BatchNormaliza (None, 28, 28, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv_dw_4_relu (ReLU)        (None, 28, 28, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_4 (Conv2D)           (None, 28, 28, 256)       32768     \n",
            "_________________________________________________________________\n",
            "conv_pw_4_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv_pw_4_relu (ReLU)        (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_5 (DepthwiseConv2D)  (None, 28, 28, 256)       2304      \n",
            "_________________________________________________________________\n",
            "conv_dw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv_dw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_5 (Conv2D)           (None, 28, 28, 256)       65536     \n",
            "_________________________________________________________________\n",
            "conv_pw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv_pw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_pad_6 (ZeroPadding2D)   (None, 29, 29, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_6 (DepthwiseConv2D)  (None, 14, 14, 256)       2304      \n",
            "_________________________________________________________________\n",
            "conv_dw_6_bn (BatchNormaliza (None, 14, 14, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv_dw_6_relu (ReLU)        (None, 14, 14, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_6 (Conv2D)           (None, 14, 14, 512)       131072    \n",
            "_________________________________________________________________\n",
            "conv_pw_6_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_6_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_7 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_7 (Conv2D)           (None, 14, 14, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_8 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_8 (Conv2D)           (None, 14, 14, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_9 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_9 (Conv2D)           (None, 14, 14, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_10 (Conv2D)          (None, 14, 14, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_11 (Conv2D)          (None, 14, 14, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pad_12 (ZeroPadding2D)  (None, 15, 15, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 512)         4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_12_bn (BatchNormaliz (None, 7, 7, 512)         2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_12_relu (ReLU)       (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "conv_pw_12 (Conv2D)          (None, 7, 7, 1024)        524288    \n",
            "_________________________________________________________________\n",
            "conv_pw_12_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
            "_________________________________________________________________\n",
            "conv_pw_12_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
            "_________________________________________________________________\n",
            "conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 1024)        9216      \n",
            "_________________________________________________________________\n",
            "conv_dw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
            "_________________________________________________________________\n",
            "conv_dw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
            "_________________________________________________________________\n",
            "conv_pw_13 (Conv2D)          (None, 7, 7, 1024)        1048576   \n",
            "_________________________________________________________________\n",
            "conv_pw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
            "_________________________________________________________________\n",
            "conv_pw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
            "=================================================================\n",
            "Total params: 3,228,864\n",
            "Trainable params: 0\n",
            "Non-trainable params: 3,228,864\n",
            "_________________________________________________________________\n",
            "time: 35.1 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ex7kSTzXzE2I",
        "colab_type": "text"
      },
      "source": [
        "## Membuat Model Arsitektur CNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "csfkNloMzQWQ",
        "colab_type": "text"
      },
      "source": [
        "Membuat arsitektur CNN mulai dari Lapisan Masukkan (Input Layer), Lapisan Konvolusi (Convolution Layer), Lapisan aktivasi (Activation Layer), Pooling Layer, sampai Lapisan terhubung erat (Fully Connected Layer/Densely Connected Layer)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Ji1Xs0D9or9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0352f8dd-6d47-4ad0-c0eb-bf83343e3484"
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    base_model,    \n",
        "    tf.keras.layers.Conv2D(32, 3, activation='relu'),\n",
        "    #tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.GlobalAveragePooling2D(),\n",
        "    tf.keras.layers.Dense(31, activation='softmax')\n",
        "])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 192 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fxbh9zgRzT5h",
        "colab_type": "text"
      },
      "source": [
        "### Mengkompilasi Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GdlCU1DL99RC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "6e8910b9-4851-41a9-bfe3-bc79e4f07eeb"
      },
      "source": [
        "model.compile(\"adam\",loss=\"categorical_crossentropy\",metrics=[\"acc\"])\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "mobilenet_1.00_224 (Function (None, 7, 7, 1024)        3228864   \n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 5, 5, 32)          294944    \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 31)                1023      \n",
            "=================================================================\n",
            "Total params: 3,524,831\n",
            "Trainable params: 295,967\n",
            "Non-trainable params: 3,228,864\n",
            "_________________________________________________________________\n",
            "time: 26.5 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GRkcGbsKzbSJ",
        "colab_type": "text"
      },
      "source": [
        "### Melathi Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I_nEgtEG-KVm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "outputId": "2a027efa-892a-492e-d519-7e93ff6463d3"
      },
      "source": [
        "#es = EarlyStopping(monitor=\"val_loss\", patience=7, verbose=1, min_delta=0.09, mode=\"auto\")\n",
        "history = model.fit(train_generator, \n",
        "                    epochs=20, \n",
        "                    validation_data=val_generator)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "269/269 [==============================] - 484s 2s/step - loss: 1.2826 - acc: 0.6283 - val_loss: 0.5233 - val_acc: 0.8361\n",
            "Epoch 2/20\n",
            "269/269 [==============================] - 483s 2s/step - loss: 0.3888 - acc: 0.8723 - val_loss: 0.3859 - val_acc: 0.8734\n",
            "Epoch 3/20\n",
            "269/269 [==============================] - 481s 2s/step - loss: 0.3044 - acc: 0.8981 - val_loss: 0.3542 - val_acc: 0.8844\n",
            "Epoch 4/20\n",
            "269/269 [==============================] - 479s 2s/step - loss: 0.2640 - acc: 0.9116 - val_loss: 0.3066 - val_acc: 0.9020\n",
            "Epoch 5/20\n",
            "269/269 [==============================] - 494s 2s/step - loss: 0.2331 - acc: 0.9215 - val_loss: 0.3093 - val_acc: 0.9025\n",
            "Epoch 6/20\n",
            "269/269 [==============================] - 497s 2s/step - loss: 0.2244 - acc: 0.9242 - val_loss: 0.2747 - val_acc: 0.9109\n",
            "Epoch 7/20\n",
            "269/269 [==============================] - 504s 2s/step - loss: 0.2136 - acc: 0.9288 - val_loss: 0.2946 - val_acc: 0.9047\n",
            "Epoch 8/20\n",
            "269/269 [==============================] - 504s 2s/step - loss: 0.2019 - acc: 0.9330 - val_loss: 0.2697 - val_acc: 0.9153\n",
            "Epoch 9/20\n",
            "269/269 [==============================] - 498s 2s/step - loss: 0.1949 - acc: 0.9341 - val_loss: 0.2626 - val_acc: 0.9198\n",
            "Epoch 10/20\n",
            "269/269 [==============================] - 497s 2s/step - loss: 0.1814 - acc: 0.9396 - val_loss: 0.2463 - val_acc: 0.9211\n",
            "Epoch 11/20\n",
            "269/269 [==============================] - 508s 2s/step - loss: 0.1892 - acc: 0.9362 - val_loss: 0.2903 - val_acc: 0.9086\n",
            "Epoch 12/20\n",
            "269/269 [==============================] - 500s 2s/step - loss: 0.1818 - acc: 0.9366 - val_loss: 0.2765 - val_acc: 0.9158\n",
            "Epoch 13/20\n",
            "269/269 [==============================] - 492s 2s/step - loss: 0.1675 - acc: 0.9426 - val_loss: 0.2815 - val_acc: 0.9139\n",
            "Epoch 14/20\n",
            "269/269 [==============================] - 499s 2s/step - loss: 0.1880 - acc: 0.9367 - val_loss: 0.2411 - val_acc: 0.9262\n",
            "Epoch 15/20\n",
            "269/269 [==============================] - 504s 2s/step - loss: 0.1790 - acc: 0.9407 - val_loss: 0.2388 - val_acc: 0.9265\n",
            "Epoch 16/20\n",
            "269/269 [==============================] - 504s 2s/step - loss: 0.1725 - acc: 0.9423 - val_loss: 0.2552 - val_acc: 0.9208\n",
            "Epoch 17/20\n",
            "269/269 [==============================] - 501s 2s/step - loss: 0.1666 - acc: 0.9439 - val_loss: 0.2325 - val_acc: 0.9291\n",
            "Epoch 18/20\n",
            "269/269 [==============================] - 497s 2s/step - loss: 0.1582 - acc: 0.9464 - val_loss: 0.3131 - val_acc: 0.9039\n",
            "Epoch 19/20\n",
            "269/269 [==============================] - 499s 2s/step - loss: 0.1522 - acc: 0.9484 - val_loss: 0.2384 - val_acc: 0.9257\n",
            "Epoch 20/20\n",
            "269/269 [==============================] - 499s 2s/step - loss: 0.1506 - acc: 0.9482 - val_loss: 0.2571 - val_acc: 0.9225\n",
            "time: 2h 46min 4s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0VjNo8EqzvyB",
        "colab_type": "text"
      },
      "source": [
        "### Menampilkan Grafik Model Hasil Pelatihan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6wJ5S8ih-8_x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "978d94e7-bfe7-4da9-9b37-550df2b83873"
      },
      "source": [
        "fig = plt.figure(figsize=(7, 4))\n",
        "fig.set_figheight(10)\n",
        "fig.set_figwidth(15)\n",
        "\n",
        "plt.subplot(2, 2, 1)\n",
        "plt.plot(history.history[\"acc\"],label = \"Training Accuracy\")\n",
        "plt.plot(history.history[\"val_acc\"],label = \"Validation Accuracy\")\n",
        "plt.legend()\n",
        "plt.title(\"Kurva Tingkat Akurasi\", size=15)\n",
        "plt.grid(zorder = 0)\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "plt.plot(history.history[\"loss\"],label = \"Training Loss\")\n",
        "plt.plot(history.history[\"val_loss\"],label = \"Validation Loss\")\n",
        "plt.legend()\n",
        "plt.title(\"Kurva Tingkat Error\", size=15)\n",
        "plt.grid(zorder = 0)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "time: 339 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dBhuf_mvz5kf",
        "colab_type": "text"
      },
      "source": [
        "## Memuat Dataset Pengujian"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GsmiaoRRR2lS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4fcd1150-3a20-4ee3-be0a-49f86540b599"
      },
      "source": [
        "y_test2 = to_categorical(y_test)\n",
        "X_test3, y_test3 = (X_test, y_test2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 1.46 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IpsC9mrI0H9X",
        "colab_type": "text"
      },
      "source": [
        "## Evaluasi Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPAc4PomSAAO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 918
        },
        "outputId": "e36aadbc-06a6-4f42-a5a3-95e25c84ef36"
      },
      "source": [
        "#Menampilkan matriks yang benar dan matriks hasil prediksi\n",
        "\n",
        "#Label yang benar\n",
        "y_true = np.argmax(y_test2,axis=1)\n",
        "\n",
        "#Label prediksi\n",
        "Y_pred = model.predict(X_test)\n",
        "y_pred = np.argmax(Y_pred, axis=1)\n",
        "\n",
        "print(y_true)\n",
        "print(y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6\n",
            "  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6\n",
            "  6  6  6  6  6  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8\n",
            "  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8\n",
            "  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9 23 23 23 23 23\n",
            " 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23\n",
            " 23]\n",
            "[ 6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6\n",
            "  8  9  9  6  6  6 28  6  8  6  6  6  6  6  6  8  6  6  6  6  6  6  6  8\n",
            "  6  6  6  6  2  8  8  8  8  8  8  8  8  8 21  8  8  8  6  8  8  8  8  8\n",
            "  8  8  8  8  8  8  8  8  8  8  8  6  6  8  6  8  8  8  8  8  8  8  8  8\n",
            "  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9 23 23 23 23 23\n",
            " 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23\n",
            " 23]\n",
            "time: 1.65 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s_9daykG0MAK",
        "colab_type": "text"
      },
      "source": [
        "## Membuat Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GhoUQvs1SYko",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "outputId": "8e05cfd8-e0ca-40af-8c88-1b83c73110dc"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.utils.multiclass import unique_labels\n",
        "\n",
        "def plot_confusion_matrix(y_true, y_pred, classes,\n",
        "                          normalize=False,\n",
        "                          title=None,\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if not title:\n",
        "        if normalize:\n",
        "            title = 'Normalized confusion matrix'\n",
        "        else:\n",
        "            title = 'Confusion matrix, without normalization'\n",
        "\n",
        "    # Compute confusion matrix\n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "    # Only use the labels that appear in the data\n",
        "    #classes = classes[unique_labels(y_true, y_pred)]\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(5,5))\n",
        "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    #ax.figure.colorbar(im, ax=ax)\n",
        "    # We want to show all ticks...\n",
        "    ax.set(xticks=np.arange(cm.shape[1]),\n",
        "           yticks=np.arange(cm.shape[0]),\n",
        "           # ... and label them with the respective list entries\n",
        "           xticklabels=classes, yticklabels=classes,\n",
        "           title=title,\n",
        "           ylabel='Label Benar',\n",
        "           xlabel='Label Prediksi')\n",
        "\n",
        "    # Rotate the tick labels and set their alignment.\n",
        "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
        "             rotation_mode=\"anchor\")\n",
        "\n",
        "    # Loop over data dimensions and create text annotations.\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i in range(cm.shape[0]):\n",
        "        for j in range(cm.shape[1]):\n",
        "            ax.text(j, i, format(cm[i, j], fmt),\n",
        "                    ha=\"center\", va=\"center\",\n",
        "                    color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "    fig.tight_layout()\n",
        "    return ax\n",
        "\n",
        "\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "plot_confusion_matrix(y_true, y_pred, classes=labels, normalize=True,\n",
        "                      title='Normalized confusion matrix')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normalized confusion matrix\n",
            "[[ nan  nan  nan  nan  nan  nan  nan]\n",
            " [0.02 0.85 0.08 0.04 0.   0.   0.02]\n",
            " [0.   0.06 0.92 0.   0.02 0.   0.  ]\n",
            " [0.   0.   0.   1.   0.   0.   0.  ]\n",
            " [ nan  nan  nan  nan  nan  nan  nan]\n",
            " [0.   0.   0.   0.   0.   1.   0.  ]\n",
            " [ nan  nan  nan  nan  nan  nan  nan]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in true_divide\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe525fa34a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "time: 442 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "99nYLupr0eAs",
        "colab_type": "text"
      },
      "source": [
        "## Menyimpan dan Konversi Model ke \".tflite\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NxIkRiZJ0hVH",
        "colab_type": "text"
      },
      "source": [
        "Menyimpan model menggunakan tf.saved_model.save dan kemudian mengonversi model tersimpan ke format yang kompatibel tf lite."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DnpPEwsm_N10",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "df8f6abb-d769-48ab-ec6a-e8fcc55101af"
      },
      "source": [
        "saved_model_dir = 'save/model'\n",
        "tf.saved_model.save(model, saved_model_dir)\n",
        "\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "with open('Plant_Leaf_Diseases_Classification_MobileNet.tflite', 'wb') as f:\n",
        "  f.write(tflite_model)"
      ],
      "execution_count": null,
      "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: save/model/assets\n",
            "time: 14.9 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LyEPqGw1ybSO",
        "colab_type": "text"
      },
      "source": [
        "Menyimpan model tflite versi kompatibel quantized"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S8A00bMzyHPF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "saved_model_dir = 'save/model'\n",
        "tf.saved_model.save(model, saved_model_dir)\n",
        "\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)\n",
        "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
        "tflite_quant_model = converter.convert()\n",
        "\n",
        "with open('Plant_Leaf_Diseases_Classification_MobileNet_Quantized.tflite', 'wb') as f:\n",
        "  f.write(tflite_model)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}