aksara_jawa_image_classification.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yudahendriawan/google-colab-projects/blob/main/aksara_jawa_image_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1> Javanese Script Image Classification"
      ],
      "metadata": {
        "id": "DyH32fNbKiAx"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2_BIkHfRM-tA"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import logging, os\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3mbryWs5M-tC"
      },
      "outputs": [],
      "source": [
        "path = '/Users/yudahendriawan/Downloads/Aksara Jawa Images/v3 copy/v3/train'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vRUkuitFM-tC",
        "outputId": "85df7e84-f0b6-4d4f-a2d4-2fd080719da8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total ba images : 101\n",
            "total ca images : 101\n",
            "total da images : 101\n",
            "total dha images : 101\n",
            "total ga images : 101\n",
            "total ha images : 101\n",
            "total ja images : 101\n",
            "total ka images : 101\n",
            "total la images : 101\n",
            "total ma images : 101\n",
            "total na images : 101\n",
            "total nga images : 101\n",
            "total nya images : 101\n",
            "total pa images : 101\n",
            "total ra images : 101\n",
            "total sa images : 101\n",
            "total ta images : 101\n",
            "total tha images : 101\n",
            "total wa images : 101\n",
            "total ya images : 100\n"
          ]
        }
      ],
      "source": [
        "aksara_jawa_labels = ['ba','ca','da','dha','ga','ha','ja','ka','la','ma','na','nga','nya','pa','ra','sa','ta','tha','wa','ya']\n",
        "\n",
        "for i in aksara_jawa_labels:\n",
        "    print(f'total {i} images :', len(os.listdir(f'{path}/{i}')))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3q3sgzP7M-tD",
        "outputId": "3f7a394b-297b-4f8f-8235-f51b6cea701e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from tensorflow.keras.preprocessing import image\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "%matplotlib inline\n",
        "img = image.load_img(f'{path}/ba/ba17.meki.b.png')\n",
        "imgplot = plt.imshow(img)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "au8jRnowM-tD",
        "outputId": "09ead957-e5a7-49aa-abed-080e08af5de3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 1600 images belonging to 20 classes.\n",
            "Found 400 images belonging to 20 classes.\n"
          ]
        }
      ],
      "source": [
        "img_pixel = 64\n",
        "\n",
        "train_dir = path\n",
        "train_datagen = ImageDataGenerator(rescale=1./255,\n",
        "    rotation_range=20,\n",
        "    zoom_range=0.2,\n",
        "    shear_range=0.2,\n",
        "    fill_mode = 'nearest',\n",
        "    validation_split=0.20)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=(img_pixel, img_pixel),\n",
        "    batch_size=8,\n",
        "    class_mode='categorical',\n",
        "    subset='training')\n",
        "\n",
        "validation_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=(img_pixel, img_pixel),\n",
        "    batch_size=16,\n",
        "    class_mode='categorical',\n",
        "    subset='validation')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N2Xyv4UoM-tD"
      },
      "outputs": [],
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(img_pixel, img_pixel, 3)),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Flatten(),\n",
        "\n",
        "    tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "\n",
        "    tf.keras.layers.Dense(20, activation='softmax')\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2YRXPC5sM-tD"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=0.0001),\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics = ['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jDfIKBVxM-tH"
      },
      "outputs": [],
      "source": [
        "class MyCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if (logs.get('accuracy') > 0.85 and logs.get('val_accuracy') > 0.85):\n",
        "      print(\"\\nAccuracy model telah mencapai 85% !!\")\n",
        "      self.model.stop_training = True\n",
        "\n",
        "callbacks = MyCallback()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d_KUr4sdM-tH",
        "outputId": "72eee2a3-ea4c-4089-eb19-097025229044"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "200/200 [==============================] - 6s 27ms/step - loss: 3.0286 - accuracy: 0.0469 - val_loss: 2.9953 - val_accuracy: 0.0425\n",
            "Epoch 2/100\n",
            "200/200 [==============================] - 5s 26ms/step - loss: 3.0057 - accuracy: 0.0538 - val_loss: 2.9954 - val_accuracy: 0.0575\n",
            "Epoch 3/100\n",
            "200/200 [==============================] - 5s 27ms/step - loss: 3.0014 - accuracy: 0.0375 - val_loss: 2.9950 - val_accuracy: 0.0600\n",
            "Epoch 4/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 2.9975 - accuracy: 0.0456 - val_loss: 2.9951 - val_accuracy: 0.0550\n",
            "Epoch 5/100\n",
            "200/200 [==============================] - 5s 27ms/step - loss: 2.9999 - accuracy: 0.0494 - val_loss: 2.9956 - val_accuracy: 0.0500\n",
            "Epoch 6/100\n",
            "200/200 [==============================] - 5s 26ms/step - loss: 2.9986 - accuracy: 0.0481 - val_loss: 2.9950 - val_accuracy: 0.0525\n",
            "Epoch 7/100\n",
            "200/200 [==============================] - 5s 24ms/step - loss: 2.9946 - accuracy: 0.0575 - val_loss: 2.9951 - val_accuracy: 0.0500\n",
            "Epoch 8/100\n",
            "200/200 [==============================] - 5s 24ms/step - loss: 2.9971 - accuracy: 0.0487 - val_loss: 2.9940 - val_accuracy: 0.0775\n",
            "Epoch 9/100\n",
            "200/200 [==============================] - 5s 24ms/step - loss: 2.9939 - accuracy: 0.0619 - val_loss: 2.9931 - val_accuracy: 0.0650\n",
            "Epoch 10/100\n",
            "200/200 [==============================] - 5s 24ms/step - loss: 2.9904 - accuracy: 0.0725 - val_loss: 2.9896 - val_accuracy: 0.0850\n",
            "Epoch 11/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 2.9888 - accuracy: 0.0656 - val_loss: 2.9808 - val_accuracy: 0.1225\n",
            "Epoch 12/100\n",
            "200/200 [==============================] - 5s 26ms/step - loss: 2.9571 - accuracy: 0.0831 - val_loss: 2.9165 - val_accuracy: 0.1525\n",
            "Epoch 13/100\n",
            "200/200 [==============================] - 5s 24ms/step - loss: 2.7858 - accuracy: 0.1737 - val_loss: 2.5718 - val_accuracy: 0.2425\n",
            "Epoch 14/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 2.4004 - accuracy: 0.2731 - val_loss: 2.0699 - val_accuracy: 0.4450\n",
            "Epoch 15/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 2.0889 - accuracy: 0.3469 - val_loss: 1.7906 - val_accuracy: 0.4775\n",
            "Epoch 16/100\n",
            "200/200 [==============================] - 5s 24ms/step - loss: 1.7808 - accuracy: 0.4412 - val_loss: 1.5668 - val_accuracy: 0.5275\n",
            "Epoch 17/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 1.6589 - accuracy: 0.4663 - val_loss: 1.4656 - val_accuracy: 0.5575\n",
            "Epoch 18/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 1.5273 - accuracy: 0.5031 - val_loss: 1.4213 - val_accuracy: 0.5600\n",
            "Epoch 19/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 1.4202 - accuracy: 0.5381 - val_loss: 1.2763 - val_accuracy: 0.6150\n",
            "Epoch 20/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 1.3117 - accuracy: 0.5800 - val_loss: 1.1843 - val_accuracy: 0.6450\n",
            "Epoch 21/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 1.2071 - accuracy: 0.6119 - val_loss: 1.2137 - val_accuracy: 0.6400\n",
            "Epoch 22/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 1.1662 - accuracy: 0.6187 - val_loss: 1.1559 - val_accuracy: 0.6500\n",
            "Epoch 23/100\n",
            "200/200 [==============================] - 5s 24ms/step - loss: 1.0822 - accuracy: 0.6550 - val_loss: 1.0895 - val_accuracy: 0.6525\n",
            "Epoch 24/100\n",
            "200/200 [==============================] - 5s 24ms/step - loss: 1.0231 - accuracy: 0.6606 - val_loss: 1.0516 - val_accuracy: 0.6875\n",
            "Epoch 25/100\n",
            "200/200 [==============================] - 5s 24ms/step - loss: 0.9521 - accuracy: 0.6850 - val_loss: 1.0208 - val_accuracy: 0.6900\n",
            "Epoch 26/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 0.8885 - accuracy: 0.6950 - val_loss: 1.0786 - val_accuracy: 0.6775\n",
            "Epoch 27/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 0.8619 - accuracy: 0.7244 - val_loss: 0.9217 - val_accuracy: 0.7525\n",
            "Epoch 28/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 0.8480 - accuracy: 0.7181 - val_loss: 0.8539 - val_accuracy: 0.7500\n",
            "Epoch 29/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 0.7216 - accuracy: 0.7519 - val_loss: 0.8194 - val_accuracy: 0.7825\n",
            "Epoch 30/100\n",
            "200/200 [==============================] - 6s 30ms/step - loss: 0.7400 - accuracy: 0.7462 - val_loss: 0.8643 - val_accuracy: 0.7525\n",
            "Epoch 31/100\n",
            "200/200 [==============================] - 5s 26ms/step - loss: 0.6437 - accuracy: 0.7869 - val_loss: 0.7946 - val_accuracy: 0.7700\n",
            "Epoch 32/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 0.6864 - accuracy: 0.7725 - val_loss: 0.7890 - val_accuracy: 0.7900\n",
            "Epoch 33/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 0.6264 - accuracy: 0.7900 - val_loss: 0.7471 - val_accuracy: 0.7725\n",
            "Epoch 34/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 0.5790 - accuracy: 0.8106 - val_loss: 0.7844 - val_accuracy: 0.7725\n",
            "Epoch 35/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 0.5361 - accuracy: 0.8213 - val_loss: 0.7914 - val_accuracy: 0.7625\n",
            "Epoch 36/100\n",
            "200/200 [==============================] - 5s 24ms/step - loss: 0.5488 - accuracy: 0.8119 - val_loss: 0.7133 - val_accuracy: 0.7900\n",
            "Epoch 37/100\n",
            "200/200 [==============================] - 5s 24ms/step - loss: 0.5157 - accuracy: 0.8263 - val_loss: 0.6652 - val_accuracy: 0.8300\n",
            "Epoch 38/100\n",
            "200/200 [==============================] - 5s 24ms/step - loss: 0.4753 - accuracy: 0.8481 - val_loss: 0.7337 - val_accuracy: 0.8025\n",
            "Epoch 39/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 0.4646 - accuracy: 0.8444 - val_loss: 0.7590 - val_accuracy: 0.7925\n",
            "Epoch 40/100\n",
            "200/200 [==============================] - 5s 24ms/step - loss: 0.4622 - accuracy: 0.8481 - val_loss: 0.7120 - val_accuracy: 0.7975\n",
            "Epoch 41/100\n",
            "200/200 [==============================] - 5s 24ms/step - loss: 0.4083 - accuracy: 0.8637 - val_loss: 0.7169 - val_accuracy: 0.8200\n",
            "Epoch 42/100\n",
            "200/200 [==============================] - 5s 26ms/step - loss: 0.4449 - accuracy: 0.8587 - val_loss: 0.7892 - val_accuracy: 0.7625\n",
            "Epoch 43/100\n",
            "200/200 [==============================] - 5s 24ms/step - loss: 0.4096 - accuracy: 0.8625 - val_loss: 0.6932 - val_accuracy: 0.8175\n",
            "Epoch 44/100\n",
            "200/200 [==============================] - 5s 24ms/step - loss: 0.3489 - accuracy: 0.8763 - val_loss: 0.6903 - val_accuracy: 0.8100\n",
            "Epoch 45/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 0.3476 - accuracy: 0.8856 - val_loss: 0.6413 - val_accuracy: 0.8100\n",
            "Epoch 46/100\n",
            "200/200 [==============================] - 5s 26ms/step - loss: 0.3585 - accuracy: 0.8737 - val_loss: 0.6594 - val_accuracy: 0.8300\n",
            "Epoch 47/100\n",
            "200/200 [==============================] - 5s 24ms/step - loss: 0.3371 - accuracy: 0.8825 - val_loss: 0.5706 - val_accuracy: 0.8200\n",
            "Epoch 48/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 0.3513 - accuracy: 0.8788 - val_loss: 0.6562 - val_accuracy: 0.8175\n",
            "Epoch 49/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 0.2950 - accuracy: 0.8994 - val_loss: 0.5342 - val_accuracy: 0.8475\n",
            "Epoch 50/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 0.2934 - accuracy: 0.8969 - val_loss: 0.6331 - val_accuracy: 0.8025\n",
            "Epoch 51/100\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 0.2954 - accuracy: 0.9006 - val_loss: 0.6046 - val_accuracy: 0.8175\n",
            "Epoch 52/100\n",
            "199/200 [============================>.] - ETA: 0s - loss: 0.2746 - accuracy: 0.9058\n",
            "Accuracy model telah mencapai 85% !!\n",
            "200/200 [==============================] - 5s 25ms/step - loss: 0.2749 - accuracy: 0.9056 - val_loss: 0.5073 - val_accuracy: 0.8525\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(train_generator,\n",
        "                    validation_data=validation_generator,\n",
        "                    epochs=100,\n",
        "                    verbose=1,\n",
        "                    callbacks=[callbacks]\n",
        "                    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "We64JnJpM-tI",
        "outputId": "526f112c-7c18-4f25-9610-13b41c5237ef"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Akurasi Model')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zamINE7jM-tI",
        "outputId": "0b6dd6d5-5a23-45f0-c605-c292822d6474"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Loss Model')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bu4NEDG-M-tI",
        "outputId": "df906159-8a3a-4bec-8197-1991f9080319"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: saved_model/assets\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: saved_model/assets\n",
            "2024-02-02 10:22:45.855061: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:378] Ignored output_format.\n",
            "2024-02-02 10:22:45.855210: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:381] Ignored drop_control_dependency.\n",
            "2024-02-02 10:22:45.856243: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: saved_model/\n",
            "2024-02-02 10:22:45.857607: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }\n",
            "2024-02-02 10:22:45.857611: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: saved_model/\n",
            "2024-02-02 10:22:45.863840: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle.\n",
            "2024-02-02 10:22:45.979763: I tensorflow/cc/saved_model/loader.cc:217] Running initialization op on SavedModel bundle at path: saved_model/\n",
            "2024-02-02 10:22:45.993586: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 137340 microseconds.\n",
            "Summary on the non-converted ops:\n",
            "---------------------------------\n",
            " * Accepted dialects: tfl, builtin, func\n",
            " * Non-Converted Ops: 13, Total Ops 27, % non-converted = 48.15 %\n",
            " * 13 ARITH ops\n",
            "\n",
            "- arith.constant:   13 occurrences  (f32: 12, i32: 1)\n",
            "\n",
            "\n",
            "\n",
            "  (f32: 3)\n",
            "  (f32: 3)\n",
            "  (f32: 3)\n",
            "  (f32: 1)\n",
            "  (f32: 1)\n",
            "2024-02-02 10:22:46.386589: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:378] Ignored output_format.\n",
            "2024-02-02 10:22:46.386604: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:381] Ignored drop_control_dependency.\n",
            "2024-02-02 10:22:46.386829: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: saved_model/\n",
            "2024-02-02 10:22:46.388206: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }\n",
            "2024-02-02 10:22:46.388215: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: saved_model/\n",
            "2024-02-02 10:22:46.396619: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle.\n",
            "2024-02-02 10:22:46.447819: I tensorflow/cc/saved_model/loader.cc:217] Running initialization op on SavedModel bundle at path: saved_model/\n",
            "2024-02-02 10:22:46.461691: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 74863 microseconds.\n",
            "Summary on the non-converted ops:\n",
            "---------------------------------\n",
            " * Accepted dialects: tfl, builtin, func\n",
            " * Non-Converted Ops: 13, Total Ops 27, % non-converted = 48.15 %\n",
            " * 13 ARITH ops\n",
            "\n",
            "- arith.constant:   13 occurrences  (f32: 12, i32: 1)\n",
            "\n",
            "\n",
            "\n",
            "  (f32: 3)\n",
            "  (f32: 3)\n",
            "  (f32: 3)\n",
            "  (f32: 1)\n",
            "  (f32: 1)\n"
          ]
        }
      ],
      "source": [
        "export_dir = 'saved_model/'\n",
        "tf.saved_model.save(model, export_dir)\n",
        "\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model(\"saved_model/\")\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "with tf.io.gfile.GFile('aksara_jawa_classification.tflite', 'wb') as f:\n",
        "    f.write(tflite_model)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.10"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}