final_model_cnn_rena2019_dataset6000_sidangready.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "final_model_cnn_rena2019_dataset6000_sidangready.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOOFBP05ZVGzwkdO88752PJ",
      "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/WiryawanCEO/IndonesianBanknotes-ImageClassificationModel/blob/main/final_model_cnn_rena2019_dataset6000_sidangready.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5AfJ0aAmXNQi"
      },
      "source": [
        "Import library dan depedencies yang akan digunakan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ai4TMwb4WrVh"
      },
      "source": [
        "import tensorflow as tf\n",
        "import os\n",
        "import sys\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import itertools\n",
        "import math\n",
        "\n",
        "from google.colab import drive\n",
        "from pathlib import Path\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vT7-nU2NXUTN"
      },
      "source": [
        "Buat koneksi dengan Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wQK4YVAXXMOD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "c71cbdaa-6209-4254-a197-57be3eada5f6"
      },
      "source": [
        "drive.mount('/content/drive')\n",
        "base = Path('/content/drive/My Drive/')\n",
        "sys.path.append(str(base))\n",
        "\n",
        "zip_path = base/'Dataset73.zip'\n",
        "!cp '{zip_path}' .\n",
        "\n",
        "!unzip -q Dataset73.zip\n",
        "!rm Dataset73.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jtuv8OOqX12h"
      },
      "source": [
        "Definisikan direktori dataset di Google Drive, serta setup datagenerator dataset training & validasi"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kb0uI0scXj9D"
      },
      "source": [
        "train_dir = 'Dataset73/train'\n",
        "validation_dir = 'Dataset73/valid'\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale=1./255)\n",
        "val_datagen = ImageDataGenerator(rescale=1./255)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qmkOWVqGYaoh"
      },
      "source": [
        "Masukan dataset kedalam generator berdasarkan batch, kemudian resize ke ukuran 224x224 dengan mode kelas berdasarkan kategorikal"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6dDiZuytYNiE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "d7c9d452-fe19-4853-f5d4-6f618c868350"
      },
      "source": [
        "train_generator = train_datagen.flow_from_directory(\n",
        "        train_dir,\n",
        "        target_size=(224, 224),\n",
        "        batch_size=100,\n",
        "        class_mode='categorical')\n",
        "\n",
        "validation_generator = val_datagen.flow_from_directory(\n",
        "        validation_dir,\n",
        "        target_size=(224, 224),\n",
        "        batch_size=100,\n",
        "        shuffle=False,\n",
        "        class_mode='categorical')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 4200 images belonging to 6 classes.\n",
            "Found 1800 images belonging to 6 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mjw8QCGBa60H"
      },
      "source": [
        "Buat file labels.txt untuk referensi label kelas di implementasi nantinya sekaligus digunakan pada tahap evaluasi dengan confusion matrix dan classification report"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "97WY4RaBaita",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5b388be3-616a-4193-970f-5ea01f52d79d"
      },
      "source": [
        "print (train_generator.class_indices)\n",
        "\n",
        "labels = '\\n'.join(sorted(train_generator.class_indices.keys()))\n",
        "\n",
        "with open('labels.txt', 'w') as f:\n",
        "  f.write(labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'100rb': 0, '10rb': 1, '20rb': 2, '2rb': 3, '50rb': 4, '5rb': 5}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bitI-YTaZ_bY"
      },
      "source": [
        "Konstruksikan model sesuai dengan referensi skripsi rena(2019) dengan perubahan pada ukuran input dan lapisan fully-connected akhirnya"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "neBaDX5IZPGu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "fcb591be-97d1-458e-98fa-a7a3f0f3496d"
      },
      "source": [
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.Conv2D(32, kernel_size=(3, 3), \n",
        "                      activation='relu',input_shape=(224,224,3)))\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(tf.keras.layers.Conv2D(32, (3, 3),activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(tf.keras.layers.Dropout(0.5))\n",
        "model.add(tf.keras.layers.Flatten())\n",
        "model.add(tf.keras.layers.Dense(1024, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(512, activation='relu'))\n",
        "model.add(tf.keras.layers.Dropout(0.5))\n",
        "model.add(tf.keras.layers.Dense(6, activation='softmax'))\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 222, 222, 32)      896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 111, 111, 32)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 109, 109, 32)      9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 54, 54, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 54, 54, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 93312)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1024)              95552512  \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 512)               524800    \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 6)                 3078      \n",
            "=================================================================\n",
            "Total params: 96,090,534\n",
            "Trainable params: 96,090,534\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LwQgTgMIbUN8"
      },
      "source": [
        "Konfigurasi parameter pelatihan dan mulai proses training-nya"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m2jS5pP9aOje",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        },
        "outputId": "77c1be0f-e58a-48ef-8735-fe11b0ba0644"
      },
      "source": [
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer=Adam(lr=0.0001),\n",
        "              metrics=['categorical_accuracy'])\n",
        "\n",
        "history = model.fit(\n",
        "      train_generator,\n",
        "      steps_per_epoch=42,  # 4200 images = batch_size * steps\n",
        "      epochs=20,\n",
        "      validation_data=validation_generator,\n",
        "      validation_steps=18,  # 1800 images = batch_size * steps\n",
        "      verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "42/42 [==============================] - 75s 2s/step - loss: 1.7626 - categorical_accuracy: 0.2464 - val_loss: 1.5120 - val_categorical_accuracy: 0.4550\n",
            "Epoch 2/20\n",
            "42/42 [==============================] - 76s 2s/step - loss: 1.2776 - categorical_accuracy: 0.5140 - val_loss: 1.0242 - val_categorical_accuracy: 0.6628\n",
            "Epoch 3/20\n",
            "42/42 [==============================] - 77s 2s/step - loss: 0.8952 - categorical_accuracy: 0.6798 - val_loss: 0.7095 - val_categorical_accuracy: 0.7778\n",
            "Epoch 4/20\n",
            "42/42 [==============================] - 75s 2s/step - loss: 0.6204 - categorical_accuracy: 0.7914 - val_loss: 0.5787 - val_categorical_accuracy: 0.8050\n",
            "Epoch 5/20\n",
            "42/42 [==============================] - 76s 2s/step - loss: 0.4932 - categorical_accuracy: 0.8336 - val_loss: 0.5123 - val_categorical_accuracy: 0.8256\n",
            "Epoch 6/20\n",
            "42/42 [==============================] - 75s 2s/step - loss: 0.4361 - categorical_accuracy: 0.8564 - val_loss: 0.4688 - val_categorical_accuracy: 0.8267\n",
            "Epoch 7/20\n",
            "42/42 [==============================] - 76s 2s/step - loss: 0.3716 - categorical_accuracy: 0.8776 - val_loss: 0.4287 - val_categorical_accuracy: 0.8511\n",
            "Epoch 8/20\n",
            "42/42 [==============================] - 76s 2s/step - loss: 0.3037 - categorical_accuracy: 0.9086 - val_loss: 0.3878 - val_categorical_accuracy: 0.8711\n",
            "Epoch 9/20\n",
            "42/42 [==============================] - 76s 2s/step - loss: 0.2680 - categorical_accuracy: 0.9143 - val_loss: 0.3835 - val_categorical_accuracy: 0.8572\n",
            "Epoch 10/20\n",
            "42/42 [==============================] - 76s 2s/step - loss: 0.2336 - categorical_accuracy: 0.9250 - val_loss: 0.3439 - val_categorical_accuracy: 0.8722\n",
            "Epoch 11/20\n",
            "42/42 [==============================] - 76s 2s/step - loss: 0.1677 - categorical_accuracy: 0.9555 - val_loss: 0.3180 - val_categorical_accuracy: 0.8889\n",
            "Epoch 12/20\n",
            "42/42 [==============================] - 75s 2s/step - loss: 0.1493 - categorical_accuracy: 0.9598 - val_loss: 0.3126 - val_categorical_accuracy: 0.8894\n",
            "Epoch 13/20\n",
            "42/42 [==============================] - 76s 2s/step - loss: 0.1305 - categorical_accuracy: 0.9674 - val_loss: 0.3047 - val_categorical_accuracy: 0.8933\n",
            "Epoch 14/20\n",
            "42/42 [==============================] - 75s 2s/step - loss: 0.1092 - categorical_accuracy: 0.9719 - val_loss: 0.3514 - val_categorical_accuracy: 0.8756\n",
            "Epoch 15/20\n",
            "42/42 [==============================] - 76s 2s/step - loss: 0.1073 - categorical_accuracy: 0.9731 - val_loss: 0.3241 - val_categorical_accuracy: 0.8856\n",
            "Epoch 16/20\n",
            "42/42 [==============================] - 76s 2s/step - loss: 0.0925 - categorical_accuracy: 0.9760 - val_loss: 0.2821 - val_categorical_accuracy: 0.9050\n",
            "Epoch 17/20\n",
            "42/42 [==============================] - 76s 2s/step - loss: 0.0666 - categorical_accuracy: 0.9883 - val_loss: 0.3038 - val_categorical_accuracy: 0.9017\n",
            "Epoch 18/20\n",
            "42/42 [==============================] - 76s 2s/step - loss: 0.0513 - categorical_accuracy: 0.9929 - val_loss: 0.3226 - val_categorical_accuracy: 0.8894\n",
            "Epoch 19/20\n",
            "42/42 [==============================] - 76s 2s/step - loss: 0.0539 - categorical_accuracy: 0.9888 - val_loss: 0.2682 - val_categorical_accuracy: 0.9083\n",
            "Epoch 20/20\n",
            "42/42 [==============================] - 76s 2s/step - loss: 0.0494 - categorical_accuracy: 0.9907 - val_loss: 0.3165 - val_categorical_accuracy: 0.8983\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XcGQcp_9cKjM"
      },
      "source": [
        "Evaluasi hasil pelatihan dengan grafik visualisasi proses training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uWCl8K0Ib8cE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "outputId": "58da352d-8811-486f-bf67-64e96cb346d6"
      },
      "source": [
        "# Retrieve a list of accuracy results on training and validation data\n",
        "# sets for each training epoch\n",
        "acc = history.history['categorical_accuracy']\n",
        "val_acc = history.history['val_categorical_accuracy']\n",
        "\n",
        "# Retrieve a list of list results on training and validation data\n",
        "# sets for each training epoch\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "# Get number of epochs\n",
        "epochs = range(len(acc))\n",
        "\n",
        "# Plot training and validation accuracy per epoch\n",
        "plt.plot(epochs, acc, label = 'Training Accuracy')\n",
        "plt.plot(epochs, val_acc, label = 'Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and validation accuracy')\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "# Plot training and validation loss per epoch\n",
        "plt.plot(epochs, loss, label = 'Training Loss')\n",
        "plt.plot(epochs, val_loss, label = 'Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and validation loss')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Training and validation loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "BP4Et9txb2eK"
      },
      "source": [
        "Evaluasi akurasi akhir model pada kumpulan dataset validasi"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FmpV05w8bi2P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "a1b7ec30-2479-41fe-aa6e-b2b7634c323a"
      },
      "source": [
        "validation_generator.reset()\n",
        "STEP_SIZE_VALID=validation_generator.n//validation_generator.batch_size\n",
        "model.evaluate(validation_generator, steps=STEP_SIZE_VALID)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "18/18 [==============================] - 21s 1s/step - loss: 0.3165 - categorical_accuracy: 0.8983\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.31647342443466187, 0.8983333110809326]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iE4vdNqRedCu"
      },
      "source": [
        "Definisikan plotting confusion matrix terlebih dahulu"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DeAg7D2Bdh3N"
      },
      "source": [
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\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 normalize:\n",
        "            cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "            title='Normalized confusion matrix'\n",
        "        else:\n",
        "            title='Confusion matrix'\n",
        "\n",
        "        # Calculate chart area size\n",
        "        leftmargin = 0.5 # inches\n",
        "        rightmargin = 0.5 # inches\n",
        "        categorysize = 0.5 # inches\n",
        "        figwidth = leftmargin + rightmargin + (len(classes) * categorysize)           \n",
        "\n",
        "        f = plt.figure(figsize=(figwidth, figwidth))\n",
        "\n",
        "        # Create an axes instance and ajust the subplot size\n",
        "        ax = f.add_subplot(111)\n",
        "        ax.set_aspect(1)\n",
        "        f.subplots_adjust(left=leftmargin/figwidth, right=1-rightmargin/figwidth, top=0.94, bottom=0.1)\n",
        "\n",
        "        res = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "\n",
        "        plt.title(title)\n",
        "        plt.colorbar(res)\n",
        "        ax.set_xticks(range(len(classes)))\n",
        "        ax.set_yticks(range(len(classes)))\n",
        "        ax.set_xticklabels(classes, rotation=45, ha='right')\n",
        "        ax.set_yticklabels(classes)\n",
        "\n",
        "        fmt = '.2f' if normalize else 'd'\n",
        "        thresh = cm.max() / 2.\n",
        "        for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "            ax.text(j, i, format(cm[i, j], fmt),\n",
        "                    horizontalalignment=\"center\",\n",
        "                    color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "        # plt.tight_layout()\n",
        "        plt.ylabel('True label')\n",
        "        plt.xlabel('Predicted label')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YAtTFzLKej3_"
      },
      "source": [
        "Evaluasi klasifikasi dengan menggunakan confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Npc8dSLJdqg5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "outputId": "4b4715de-cac3-4d55-df83-bbbf01dbe150"
      },
      "source": [
        "validation_generator.reset()\n",
        "Y_pred = model.predict(validation_generator, steps=STEP_SIZE_VALID)\n",
        "y_pred = np.argmax(Y_pred, axis=1)\n",
        "target_names = !cat labels.txt\n",
        "\n",
        "cm = confusion_matrix(validation_generator.classes, y_pred)\n",
        "\n",
        "# Compute confusion matrix\n",
        "cnf_matrix = cm\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "# Plot non-normalized confusion matrix\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cnf_matrix, classes=target_names, title='Confusion matrix, without normalization')\n",
        "\n",
        "# Plot normalized confusion matrix\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cnf_matrix, classes=target_names, normalize=True, title='Normalized confusion matrix')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v2Cr2Th6epsR"
      },
      "source": [
        "Evaluasi klasifikasi dengan laporan klasifikasi (classification report)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I4lHYM-HdXBY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "52475d82-4562-4c41-c483-ab8926f1ebfc"
      },
      "source": [
        "print('Classification Report')\n",
        "target_names = !cat labels.txt\n",
        "print(classification_report(validation_generator.classes, y_pred, target_names=target_names))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       100rb       0.92      0.90      0.91       300\n",
            "        10rb       0.92      0.91      0.91       300\n",
            "        20rb       0.84      0.99      0.91       300\n",
            "         2rb       0.84      0.81      0.82       300\n",
            "        50rb       0.93      0.92      0.92       300\n",
            "         5rb       0.96      0.87      0.91       300\n",
            "\n",
            "    accuracy                           0.90      1800\n",
            "   macro avg       0.90      0.90      0.90      1800\n",
            "weighted avg       0.90      0.90      0.90      1800\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vMwHBsEye9hR"
      },
      "source": [
        "Simpan model dan konversikan kedalam format TensorFlow Lite"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XAhAVd_Me8nO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c241fe1c-a7e8-46dd-9889-bf3b7390c0a8"
      },
      "source": [
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "tflite_model = converter.convert()\n",
        "open (\"cnn_rena2019_dataset6000.tflite\" , \"wb\") .write(tflite_model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "384364796"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gwFrUivFT_Uk"
      },
      "source": [
        "model.save(\"cnn_rena2019_dataset6000.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}