Egg_Img_Classification.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A2LiHWoFVJJo"
      },
      "source": [
        "# Fertile Egg Detector [CH2-PS266]\n",
        "\n",
        "Bangkit Batch 2 2023\n",
        "\n",
        "**Designing a Deep Learning-Based Mobile Application for Egg Candling**\n",
        "\n",
        "*Theme: Food Accessibility, Agribusiness, and Food Security*\n",
        "\n",
        "**Team Member**\n",
        "\n",
        "1.  (ML) M466BSX1032 – Brilian Herda – Universitas Sains Al-Qur’ an - [Active]\n",
        "2.  (ML) M466BSY1195 – Ahmad Ma'ruf – Universitas Sains Al-Qur’an - [Active]\n",
        "3.  (ML) M123BSY1353 – Habibi Ahmadi Muslim – Politeknik Elektronika Negeri Surabaya - [Active]\n",
        "4.  (CC) C182BSY3830 – Fathin Cahyo Ramadhan – Universitas Amikom Purwokerto - [Active]\n",
        "5.  (CC) C182BSY3969 – Yuntafa Ulkhaq – Universitas Amikom Purwokerto - [Active]\n",
        "6.  (MD) A182BSY2566 – Sandhya Nugraha Qusnur Aulia - Universitas Amikom Purwokerto - [Active]\n",
        "7.  (MD) A548BKY4459 - Zulfahmi M. Ardianto - UIN Sunan Kalijaga Yogyakarta - [Active]\n",
        "\n",
        "In this project, we've leveraged a cloud-based machine learning model for egg classification. This model, trained on diverse egg stage data, enables real-time analysis of images from the camera or gallery within the mobile app. It accurately categorizes eggs into early, mid, late, or non-viable stages, enhancing its functionality significantly.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1LOAInKPbk4W",
        "outputId": "26e88827-bdad-4fba-fca3-172155872ba3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflowjs\n",
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            "Installing collected packages: wurlitzer, tensorflow-decision-forests, tensorflowjs\n",
            "Successfully installed tensorflow-decision-forests-1.8.1 tensorflowjs-4.15.0 wurlitzer-3.0.3\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflowjs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4eR4PlthVDFU",
        "outputId": "901442e4-4b29-413f-dbf2-2cb4b03239bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From <ipython-input-2-40d48ecfa0e3>:20: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.config.list_physical_devices('GPU')` instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "• Using TensorFlow Version: 2.15.0\n",
            "• Using TensorFlow Hub Version:  0.15.0\n",
            "• GPU Device Found.\n"
          ]
        }
      ],
      "source": [
        "# Import lib\n",
        "import zipfile\n",
        "import os\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pylab as plt\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# There is bug in tensorflowjs. see: https://github.com/google/jax/issues/18978. cant import tensorflowjs, so cant convert to tfjs\n",
        "# import tensorflowjs as tfjs\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "from tqdm import tqdm\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "print(\"\\u2022 Using TensorFlow Version:\", tf.__version__)\n",
        "print(\"\\u2022 Using TensorFlow Hub Version: \", hub.__version__)\n",
        "print('\\u2022 GPU Device Found.' if tf.test.is_gpu_available() else '\\u2022 GPU Device Not Found. Running on CPU')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Hmcqfd2JVXnS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f24bb161-3527-45bb-9498-6fb44f3ad46d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4 with input size (224, 224) and output dimension 1280\n"
          ]
        }
      ],
      "source": [
        "module_selection = (\"mobilenet_v2\", 224, 1280) #@param [\"(\\\"mobilenet_v2\\\", 224, 1280)\", \"(\\\"inception_v3\\\", 299, 2048)\"] {type:\"raw\", allow-input: true}\n",
        "handle_base, pixels, FV_SIZE = module_selection\n",
        "MODULE_HANDLE =\"https://tfhub.dev/google/tf2-preview/{}/feature_vector/4\".format(handle_base)\n",
        "IMAGE_SIZE = (pixels, pixels)\n",
        "print(\"Using {} with input size {} and output dimension {}\".format(MODULE_HANDLE, IMAGE_SIZE, FV_SIZE))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "E6_OxAiSVkvR"
      },
      "outputs": [],
      "source": [
        "# PARSING CSV LABEL\n",
        "# Btw, label didalamnya tuh kek gini:\n",
        "\n",
        "# ' Pertengahan',\n",
        "# ' Tahap Akhir',\n",
        "# ' Tahap Awal',\n",
        "# ' Telur Mati',\n",
        "\n",
        "# Ada spasinya gitu, tapi ga masalah sih, cuma ngingetin aja\n",
        "# kalo ada bug, bisa jadi karena labelnya ini\n",
        "\n",
        "import zipfile\n",
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "# Extract the archive\n",
        "# ini file zip-nya di upload ke colab. biar simple aja. kalo mau pake drive ya sangat-sangat recomended.\n",
        "\n",
        "local_zip = '/content/egg-condition-classifications.v2i.multiclass (with test).zip'\n",
        "# local_zip = '/content/egg-condition-classifications.v2i.multiclass.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/content/data/')\n",
        "zip_ref.close()\n",
        "\n",
        "# Assign training and validation set directories\n",
        "base_dir = '/content/data'\n",
        "train_dir = os.path.join(base_dir, 'train')\n",
        "valid_dir = os.path.join(base_dir, 'valid')\n",
        "test_dir = os.path.join(base_dir, 'test')\n",
        "\n",
        "# Load the labels\n",
        "train_labels = pd.read_csv(\"/content/data/train/_classes.csv\")\n",
        "valid_labels = pd.read_csv(\"/content/data/valid/_classes.csv\")\n",
        "test_labels = pd.read_csv(\"/content/data/test/_classes.csv\")\n",
        "\n",
        "#parsing train_labels\n",
        "train_labels['combined_label'] = train_labels.apply(\n",
        "    lambda row: 'Pertengahan' if row[' Pertengahan'] == 1  else (\n",
        "        'Tahap_akhir' if row[' Tahap Akhir'] == 1 else (\n",
        "            'Tahap_awal' if row[' Tahap Awal'] == 1 else 'Telur_mati'\n",
        "        )\n",
        "    ),\n",
        "    axis=1\n",
        ")\n",
        "#parsing valid_labels\n",
        "valid_labels['combined_label'] = valid_labels.apply(\n",
        "    lambda row: 'Pertengahan' if row[' Pertengahan'] == 1  else (\n",
        "        'Tahap_akhir' if row[' Tahap Akhir'] == 1 else (\n",
        "            'Tahap_awal' if row[' Tahap Awal'] == 1 else 'Telur_mati'\n",
        "        )\n",
        "    ),\n",
        "    axis=1\n",
        ")\n",
        "#parsing test_labels\n",
        "test_labels['combined_label'] = test_labels.apply(\n",
        "    lambda row: 'Pertengahan' if row[' Pertengahan'] == 1  else (\n",
        "        'Tahap_akhir' if row[' Tahap Akhir'] == 1 else (\n",
        "            'Tahap_awal' if row[' Tahap Awal'] == 1 else 'Telur_mati'\n",
        "        )\n",
        "    ),\n",
        "    axis=1\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Jrs0nvNAW6-x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dde9e9a8-d692-4b90-dbf6-3a8e97ab1a66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "cyWrV4xFWcmt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbc1f221-fc9b-41e3-c3b6-f39267a8aec9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 429 validated image filenames belonging to 4 classes.\n",
            "Found 165 validated image filenames belonging to 4 classes.\n",
            "Found 68 validated image filenames belonging to 4 classes.\n"
          ]
        }
      ],
      "source": [
        "# PREPROCESSING BANGGG\n",
        "\n",
        "BATCH_SIZE = 13\n",
        "\n",
        "training_datagen = ImageDataGenerator(\n",
        "\t  rescale = 1./255,\n",
        "    rotation_range=40,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    fill_mode='nearest'\n",
        ") # with augmentation\n",
        "\n",
        "valid_datagen = ImageDataGenerator(\n",
        "    rescale = 1./255,\n",
        "    rotation_range=40,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    fill_mode='nearest'\n",
        "    )\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale = 1./255)\n",
        "\n",
        "train_generator = training_datagen.flow_from_dataframe(\n",
        "    train_labels,\n",
        "    directory=train_dir,\n",
        "    x_col=\"filename\",  # nama kolom yang berisi nama gambar pada csv train.\n",
        "    y_col=\"combined_label\",  # nama kolom yang berisi label pada csv train.\n",
        "    target_size=(224, 224),\n",
        "    class_mode='categorical',\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=len(train_labels) // 52\n",
        ")#.prefetch(tf.keras.data.experimental.AUTOTUNE)\n",
        "\n",
        "valid_generator = valid_datagen.flow_from_dataframe(\n",
        "    valid_labels,\n",
        "    directory=valid_dir,\n",
        "    x_col=\"filename\",  # nama kolom yang berisi nama gambar pada csv valid.\n",
        "    y_col=\"combined_label\",  # nama kolom yang berisi label pada csv valid.\n",
        "    target_size=(224, 224),\n",
        "    class_mode='categorical',\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=len(valid_labels) // 20\n",
        ")#.prefetch(tf.keras.data.experimental.AUTOTUNE)\n",
        "\n",
        "test_generator = test_datagen.flow_from_dataframe(\n",
        "    test_labels,\n",
        "    directory=test_dir,\n",
        "    x_col=\"filename\",  # nama kolom yang berisi nama gambar pada csv train.\n",
        "    y_col=\"combined_label\",  # nama kolom yang berisi label pada csv train.\n",
        "    target_size=(224, 224),\n",
        "    class_mode='categorical',\n",
        "    batch_size=1\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "Lf57g2TfWurf"
      },
      "outputs": [],
      "source": [
        "# Convert Datagen (FrameIterator) to tf.data.Dataset for Atribut .take()\n",
        "\n",
        "train_dataset = tf.data.Dataset.from_generator(\n",
        "    lambda: train_generator,\n",
        "    output_signature=(\n",
        "        tf.TensorSpec(shape=(BATCH_SIZE,224, 224, 3), dtype=tf.float32),\n",
        "        tf.TensorSpec(shape=(BATCH_SIZE, 4), dtype=tf.float32) # 4 for num of class\n",
        "    )\n",
        ")\n",
        "\n",
        "valid_dataset = tf.data.Dataset.from_generator(\n",
        "    lambda: valid_generator,\n",
        "    output_signature=(\n",
        "        tf.TensorSpec(shape=(BATCH_SIZE,224, 224, 3), dtype=tf.float32),\n",
        "        tf.TensorSpec(shape=(BATCH_SIZE, 4), dtype=tf.float32) # 4 for num of class\n",
        "    )\n",
        ")\n",
        "\n",
        "test_dataset = tf.data.Dataset.from_generator(\n",
        "    lambda: test_generator,\n",
        "    output_signature=(\n",
        "        tf.TensorSpec(shape=(1,224, 224, 3), dtype=tf.float32),\n",
        "        tf.TensorSpec(shape=(1, 4), dtype=tf.float32) # 4 for num of class\n",
        "    )\n",
        ")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "qaLpxUrmW8E2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb9ef2e6-1199-4679-fb24-19cdf6ecd3a9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([13, 224, 224, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Take one batch from the dataset\n",
        "for image_batch, label_batch in train_dataset.take(1):\n",
        "    pass\n",
        "\n",
        "# Now you can use image_batch and label_batch as NumPy arrays\n",
        "image_batch.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "d---5EEHW8vQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72f4f1f8-08b5-4a46-fddc-34e5e054c946"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Building model with https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\n",
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " keras_layer_2 (KerasLayer)  (None, 1280)              2257984   \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 1280)              0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 512)               655872    \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 4)                 2052      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2915908 (11.12 MB)\n",
            "Trainable params: 2881796 (10.99 MB)\n",
            "Non-trainable params: 34112 (133.25 KB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Defining the Model\n",
        "# All it takes is to put a linear classifier\n",
        "# on top of the feature_extractor_layer with the Hub module.\n",
        "\n",
        "# For speed, i start out with a non-trainable feature_extractor_layer,\n",
        "# but you can also enable fine-tuning for greater accuracy.\n",
        "\n",
        "do_fine_tuning = True\n",
        "\n",
        "feature_extractor = hub.KerasLayer(MODULE_HANDLE,\n",
        "                                   input_shape=IMAGE_SIZE + (3,),\n",
        "                                   output_shape=[FV_SIZE],\n",
        "                                   trainable=do_fine_tuning)\n",
        "\n",
        "print(\"Building model with\", MODULE_HANDLE)\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "        feature_extractor,\n",
        "        tf.keras.layers.Flatten(),\n",
        "        tf.keras.layers.Dense(512, activation='relu'),\n",
        "        # tf.keras.layers.Dropout(0.2),\n",
        "        tf.keras.layers.Dense(4, activation='softmax')\n",
        "])\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "rPsDfhnIXVoF"
      },
      "outputs": [],
      "source": [
        "# Optional Unfreeze some layers\n",
        "NUM_LAYERS = 1 # min 1, max 50\n",
        "\n",
        "if do_fine_tuning:\n",
        "    feature_extractor.trainable = True\n",
        "\n",
        "    for layer in model.layers[-NUM_LAYERS:]:\n",
        "        layer.trainable = True\n",
        "\n",
        "else:\n",
        "    feature_extractor.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "uWZHrKlEVjeX"
      },
      "outputs": [],
      "source": [
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    # Check the loss\n",
        "    if(logs.get('val_accuracy') > 0.90 and logs.get('accuracy') > 0.976 ):\n",
        "\n",
        "      # Stop if threshold is met\n",
        "      print(\"\\n val_acc > 90 & acc > 97. stop training\")\n",
        "      self.model.stop_training = True\n",
        "\n",
        "# Instantiate class\n",
        "callbacks = myCallback()\n",
        "\n",
        "\n",
        "lr_callback = tf.keras.callbacks.ReduceLROnPlateau(\n",
        "    monitor='val_loss',\n",
        "    factor=0.1,\n",
        "    patience=4,\n",
        "    min_lr=1e-6\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "oJfHvMoFXbgk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fdff5b92-b729-4a05-ee7f-3f4f10e0160a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "33/33 - 80s - loss: 1.2828 - accuracy: 0.4452 - val_loss: 1.3820 - val_accuracy: 0.3654 - lr: 0.0010 - 80s/epoch - 2s/step\n",
            "Epoch 2/200\n",
            "33/33 - 9s - loss: 0.9596 - accuracy: 0.7319 - val_loss: 1.1569 - val_accuracy: 0.5705 - lr: 0.0010 - 9s/epoch - 284ms/step\n",
            "Epoch 3/200\n",
            "33/33 - 10s - loss: 0.8095 - accuracy: 0.7879 - val_loss: 0.9850 - val_accuracy: 0.7115 - lr: 0.0010 - 10s/epoch - 298ms/step\n",
            "Epoch 4/200\n",
            "33/33 - 8s - loss: 0.7124 - accuracy: 0.7995 - val_loss: 0.8751 - val_accuracy: 0.7372 - lr: 0.0010 - 8s/epoch - 244ms/step\n",
            "Epoch 5/200\n",
            "33/33 - 8s - loss: 0.6513 - accuracy: 0.8135 - val_loss: 0.7292 - val_accuracy: 0.7692 - lr: 0.0010 - 8s/epoch - 244ms/step\n",
            "Epoch 6/200\n",
            "33/33 - 9s - loss: 0.5192 - accuracy: 0.8858 - val_loss: 0.6164 - val_accuracy: 0.8526 - lr: 0.0010 - 9s/epoch - 266ms/step\n",
            "Epoch 7/200\n",
            "33/33 - 8s - loss: 0.4889 - accuracy: 0.8998 - val_loss: 0.5958 - val_accuracy: 0.8654 - lr: 0.0010 - 8s/epoch - 241ms/step\n",
            "Epoch 8/200\n",
            "33/33 - 9s - loss: 0.4593 - accuracy: 0.9021 - val_loss: 0.5125 - val_accuracy: 0.8654 - lr: 0.0010 - 9s/epoch - 264ms/step\n",
            "Epoch 9/200\n",
            "33/33 - 9s - loss: 0.4458 - accuracy: 0.8928 - val_loss: 0.4615 - val_accuracy: 0.8782 - lr: 0.0010 - 9s/epoch - 286ms/step\n",
            "Epoch 10/200\n",
            "33/33 - 11s - loss: 0.3812 - accuracy: 0.9301 - val_loss: 0.4673 - val_accuracy: 0.8654 - lr: 0.0010 - 11s/epoch - 323ms/step\n",
            "Epoch 11/200\n",
            "33/33 - 7s - loss: 0.3589 - accuracy: 0.9347 - val_loss: 0.4505 - val_accuracy: 0.8782 - lr: 0.0010 - 7s/epoch - 222ms/step\n",
            "Epoch 12/200\n",
            "33/33 - 7s - loss: 0.3624 - accuracy: 0.9207 - val_loss: 0.4227 - val_accuracy: 0.8846 - lr: 0.0010 - 7s/epoch - 222ms/step\n",
            "Epoch 13/200\n",
            "33/33 - 9s - loss: 0.3249 - accuracy: 0.9371 - val_loss: 0.4284 - val_accuracy: 0.8654 - lr: 0.0010 - 9s/epoch - 265ms/step\n",
            "Epoch 14/200\n",
            "33/33 - 11s - loss: 0.2928 - accuracy: 0.9510 - val_loss: 0.3937 - val_accuracy: 0.8526 - lr: 0.0010 - 11s/epoch - 329ms/step\n",
            "Epoch 15/200\n",
            "33/33 - 8s - loss: 0.2997 - accuracy: 0.9441 - val_loss: 0.4278 - val_accuracy: 0.8718 - lr: 0.0010 - 8s/epoch - 245ms/step\n",
            "Epoch 16/200\n",
            "33/33 - 9s - loss: 0.2867 - accuracy: 0.9417 - val_loss: 0.3977 - val_accuracy: 0.8654 - lr: 0.0010 - 9s/epoch - 287ms/step\n",
            "Epoch 17/200\n",
            "33/33 - 9s - loss: 0.2573 - accuracy: 0.9604 - val_loss: 0.3741 - val_accuracy: 0.8974 - lr: 0.0010 - 9s/epoch - 280ms/step\n",
            "Epoch 18/200\n",
            "33/33 - 7s - loss: 0.2561 - accuracy: 0.9534 - val_loss: 0.3662 - val_accuracy: 0.9231 - lr: 0.0010 - 7s/epoch - 226ms/step\n",
            "Epoch 19/200\n",
            "33/33 - 9s - loss: 0.2248 - accuracy: 0.9674 - val_loss: 0.3863 - val_accuracy: 0.9038 - lr: 0.0010 - 9s/epoch - 282ms/step\n",
            "Epoch 20/200\n",
            "33/33 - 9s - loss: 0.2567 - accuracy: 0.9510 - val_loss: 0.4296 - val_accuracy: 0.8462 - lr: 0.0010 - 9s/epoch - 279ms/step\n",
            "Epoch 21/200\n",
            "33/33 - 8s - loss: 0.2452 - accuracy: 0.9487 - val_loss: 0.3836 - val_accuracy: 0.8846 - lr: 0.0010 - 8s/epoch - 236ms/step\n",
            "Epoch 22/200\n",
            "33/33 - 8s - loss: 0.2436 - accuracy: 0.9604 - val_loss: 0.3811 - val_accuracy: 0.8910 - lr: 0.0010 - 8s/epoch - 250ms/step\n",
            "Epoch 23/200\n",
            "33/33 - 9s - loss: 0.2300 - accuracy: 0.9557 - val_loss: 0.3863 - val_accuracy: 0.8782 - lr: 1.0000e-04 - 9s/epoch - 265ms/step\n",
            "Epoch 24/200\n",
            "33/33 - 8s - loss: 0.2244 - accuracy: 0.9674 - val_loss: 0.3530 - val_accuracy: 0.9103 - lr: 1.0000e-04 - 8s/epoch - 245ms/step\n",
            "Epoch 25/200\n",
            "33/33 - 9s - loss: 0.2515 - accuracy: 0.9510 - val_loss: 0.3659 - val_accuracy: 0.9038 - lr: 1.0000e-04 - 9s/epoch - 287ms/step\n",
            "Epoch 26/200\n",
            "33/33 - 9s - loss: 0.1925 - accuracy: 0.9837 - val_loss: 0.3558 - val_accuracy: 0.8910 - lr: 1.0000e-04 - 9s/epoch - 285ms/step\n",
            "Epoch 27/200\n",
            "33/33 - 9s - loss: 0.2058 - accuracy: 0.9767 - val_loss: 0.3473 - val_accuracy: 0.8974 - lr: 1.0000e-04 - 9s/epoch - 273ms/step\n",
            "Epoch 28/200\n",
            "33/33 - 9s - loss: 0.2206 - accuracy: 0.9650 - val_loss: 0.3057 - val_accuracy: 0.9231 - lr: 1.0000e-04 - 9s/epoch - 267ms/step\n",
            "Epoch 29/200\n",
            "33/33 - 9s - loss: 0.2372 - accuracy: 0.9697 - val_loss: 0.3243 - val_accuracy: 0.8974 - lr: 1.0000e-04 - 9s/epoch - 263ms/step\n",
            "Epoch 30/200\n",
            "33/33 - 11s - loss: 0.2043 - accuracy: 0.9674 - val_loss: 0.3601 - val_accuracy: 0.9103 - lr: 1.0000e-04 - 11s/epoch - 332ms/step\n",
            "Epoch 31/200\n",
            "33/33 - 8s - loss: 0.1984 - accuracy: 0.9697 - val_loss: 0.3655 - val_accuracy: 0.9103 - lr: 1.0000e-04 - 8s/epoch - 241ms/step\n",
            "Epoch 32/200\n",
            "33/33 - 8s - loss: 0.1780 - accuracy: 0.9814 - val_loss: 0.3539 - val_accuracy: 0.8974 - lr: 1.0000e-04 - 8s/epoch - 252ms/step\n",
            "Epoch 33/200\n",
            "33/33 - 10s - loss: 0.2081 - accuracy: 0.9650 - val_loss: 0.3705 - val_accuracy: 0.8718 - lr: 1.0000e-05 - 10s/epoch - 289ms/step\n",
            "Epoch 34/200\n",
            "33/33 - 9s - loss: 0.2234 - accuracy: 0.9580 - val_loss: 0.3453 - val_accuracy: 0.9167 - lr: 1.0000e-05 - 9s/epoch - 277ms/step\n",
            "Epoch 35/200\n",
            "33/33 - 8s - loss: 0.2064 - accuracy: 0.9650 - val_loss: 0.3640 - val_accuracy: 0.8910 - lr: 1.0000e-05 - 8s/epoch - 248ms/step\n",
            "Epoch 36/200\n",
            "33/33 - 8s - loss: 0.2109 - accuracy: 0.9744 - val_loss: 0.3429 - val_accuracy: 0.9103 - lr: 1.0000e-05 - 8s/epoch - 247ms/step\n",
            "Epoch 37/200\n",
            "33/33 - 9s - loss: 0.2191 - accuracy: 0.9627 - val_loss: 0.3603 - val_accuracy: 0.8974 - lr: 1.0000e-06 - 9s/epoch - 270ms/step\n",
            "Epoch 38/200\n",
            "33/33 - 9s - loss: 0.2128 - accuracy: 0.9674 - val_loss: 0.3567 - val_accuracy: 0.8974 - lr: 1.0000e-06 - 9s/epoch - 267ms/step\n",
            "Epoch 39/200\n",
            "\n",
            " val_acc > 90 & acc > 97. stop training\n",
            "33/33 - 8s - loss: 0.2026 - accuracy: 0.9814 - val_loss: 0.3487 - val_accuracy: 0.9038 - lr: 1.0000e-06 - 8s/epoch - 245ms/step\n"
          ]
        }
      ],
      "source": [
        "# TRAINING\n",
        "\n",
        "if do_fine_tuning:\n",
        "    model.compile(\n",
        "                  optimizer=tf.keras.optimizers.SGD(learning_rate=0.001),\n",
        "                  # optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
        "                  loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "                  metrics=['accuracy'])\n",
        "else:\n",
        "    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
        "                  loss='categorical_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "\n",
        "steps_per_epoch = train_generator.n\n",
        "validation_steps = valid_generator.n\n",
        "\n",
        "EPOCHS = 200\n",
        "hist = model.fit(train_generator,\n",
        "                 epochs=EPOCHS,\n",
        "                 validation_data=valid_generator,\n",
        "                 steps_per_epoch=steps_per_epoch // 13,\n",
        "                 verbose=2,\n",
        "                 validation_steps=validation_steps // 13,\n",
        "                 callbacks=[callbacks, lr_callback])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ppr2tDA_XlYZ"
      },
      "source": [
        "# Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "p2hqurrfXn2C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "outputId": "85671f2d-1981-4b3b-d156-9f0cff698308"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot the results\n",
        "acc = hist.history['accuracy']\n",
        "val_acc = hist.history['val_accuracy']\n",
        "loss = hist.history['loss']\n",
        "val_loss = hist.history['val_loss']\n",
        "\n",
        "epochs = range(len(acc))\n",
        "\n",
        "plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, loss, 'r', label='Training Loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation Loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2M2_8rhrXwBG"
      },
      "source": [
        "# Export/Save Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "D5AxsVElX6_u"
      },
      "outputs": [],
      "source": [
        "EGG_CANDLING_SAVED_MODEL = \"exp_saved_model/1\"\n",
        "\n",
        "tf.saved_model.save(model, EGG_CANDLING_SAVED_MODEL)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert ke zip\n",
        "\n",
        "# Nama folder yang berisi hasil konversi TensorFlow.js\n",
        "folder_model = 'exp_saved_model'\n",
        "\n",
        "# Fungsi untuk membuat file zip dari folder hasil konversi\n",
        "def create_zip_file(folder_name):\n",
        "    with zipfile.ZipFile('exp_saved_model.zip', 'w', zipfile.ZIP_DEFLATED) as zipf:\n",
        "        for root, dirs, files in os.walk(folder_name):\n",
        "            for file in files:\n",
        "                file_path = os.path.join(root, file)\n",
        "                zipf.write(file_path, os.path.relpath(file_path, folder_name))\n",
        "\n",
        "# Panggil fungsi untuk membuat file zip\n",
        "create_zip_file(folder_model)\n",
        "\n",
        "try:\n",
        "    from google.colab import files\n",
        "    files.download('exp_saved_model.zip')\n",
        "except:\n",
        "    pass"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "qntfimQL-hWC",
        "outputId": "aea2ed80-7049-458f-d545-b83a3f5473d2"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_2410cd80-7ed0-4f38-80e1-1e5b346ceeb6\", \"exp_saved_model.zip\", 21782991)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mBXqCvgJZHB5"
      },
      "source": [
        "# Convert to TFLite"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "BoZsGrK1YFzC"
      },
      "outputs": [],
      "source": [
        "# INI KALO MAU CONVERT KE TFLITE\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model(EGG_CANDLING_SAVED_MODEL)\n",
        "\n",
        "\n",
        "\n",
        "# Post-Training Quantization\n",
        "# ----------------------------\n",
        "# The simplest form of post-training quantization quantizes weights\n",
        "# from floating point to 8-bits of precision.\n",
        "# This technique is enabled as an option in the TensorFlow Lite converter.\n",
        "# At inference, weights are converted from 8-bits of precision to floating point\n",
        "# and computed using floating-point kernels. This conversion is done once\n",
        "# and cached to reduce latency.\n",
        "\n",
        "# To further improve latency, hybrid operators dynamically quantize activations\n",
        "# to 8-bits and perform computations with 8-bit weights and activations.\n",
        "# This optimization provides latencies close to fully fixed-point inference.\n",
        "# However, the outputs are still stored using floating point,\n",
        "# so that the speedup with hybrid ops is less than a full fixed-point computation.\n",
        "\n",
        "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
        "\n",
        "\n",
        "# Post-Training Integer Quantization\n",
        "# ----------------------------\n",
        "# We can get further latency improvements, reductions in peak memory usage,\n",
        "# and access to integer only hardware accelerators\n",
        "# by making sure all model math is quantized. To do this,\n",
        "# we need to measure the dynamic range of activations and inputs\n",
        "# with a representative data set. You can simply create an input data generator\n",
        "# and provide it to our converter.\n",
        "\n",
        "def representative_data_gen():\n",
        "    for input_value, _ in test_dataset.take(67):\n",
        "        yield [input_value]\n",
        "\n",
        "converter.representative_dataset = representative_data_gen\n",
        "\n",
        "# The resulting model will be fully quantized but still take float input and output for convenience.\n",
        "\n",
        "# Ops that do not have quantized implementations will automatically be left in floating point.\n",
        "# This allows conversion to occur smoothly but may restrict deployment to accelerators that support float.\n",
        "\n",
        "\n",
        "# Full Integer Quantization\n",
        "# ----------------------------\n",
        "# To require the converter to only output integer operations, one can specify:\n",
        "\n",
        "converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\n",
        "\n",
        "\n",
        "# FINALLY CONVERT THE MODEL\n",
        "tflite_model = converter.convert()\n",
        "tflite_model_file = 'converted_model.tflite'\n",
        "\n",
        "with open(tflite_model_file, \"wb\") as f:\n",
        "    f.write(tflite_model)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "tjWysErrZqr-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "59e8795e-5e5b-4b52-92cd-c544917da1da"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_62b8f6d4-fcc2-4d86-8111-5270c7a43c68\", \"converted_model.tflite\", 3370456)"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# KALO MAU DOWNLOAD HASIL CONVERT DI COLLAB\n",
        "try:\n",
        "    from google.colab import files\n",
        "    files.download('converted_model.tflite')\n",
        "except:\n",
        "    pass"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c8kkXYALZ5hx"
      },
      "source": [
        "# Convert to TFJS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JkFUARriZ8XI"
      },
      "outputs": [],
      "source": [
        "# Convert the SavedModel to TensorFlow.js format\n",
        "tfjs.converters.convert_tf_saved_model(EGG_CANDLING_SAVED_MODEL, 'tfjs_model')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HGz35yL0a2it"
      },
      "outputs": [],
      "source": [
        "# convert ke zip\n",
        "\n",
        "# Nama folder yang berisi hasil konversi TensorFlow.js\n",
        "folder_model = 'tfjs_model'\n",
        "\n",
        "# Fungsi untuk membuat file zip dari folder hasil konversi\n",
        "def create_zip_file(folder_name):\n",
        "    with zipfile.ZipFile('tfjs_model.zip', 'w', zipfile.ZIP_DEFLATED) as zipf:\n",
        "        for root, dirs, files in os.walk(folder_name):\n",
        "            for file in files:\n",
        "                file_path = os.path.join(root, file)\n",
        "                zipf.write(file_path, os.path.relpath(file_path, folder_name))\n",
        "\n",
        "# Panggil fungsi untuk membuat file zip\n",
        "create_zip_file(folder_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1z0sAm2oFNQM"
      },
      "outputs": [],
      "source": [
        "# KALO MAU DOWNLOAD HASIL CONVERT DI COLLAB\n",
        "try:\n",
        "    from google.colab import files\n",
        "    files.download('tfjs_model.zip')\n",
        "except:\n",
        "    pass"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test TFlite Model"
      ],
      "metadata": {
        "id": "jiGsanQ9slJD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "vdoneesUZDNn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "outputId": "2bf2c3b6-a20a-4549-d04b-157b6117d957"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100it [00:02, 39.90it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# TEST HASIL CONVERT KE TFLITE\n",
        "\n",
        "# Load TFLite model and allocate tensors.\n",
        "\n",
        "interpreter = tf.lite.Interpreter(model_path=tflite_model_file)\n",
        "interpreter.allocate_tensors()\n",
        "\n",
        "input_index = interpreter.get_input_details()[0][\"index\"]\n",
        "output_index = interpreter.get_output_details()[0][\"index\"]\n",
        "\n",
        "# Gather results for the randomly sampled test images\n",
        "predictions = []\n",
        "\n",
        "test_labels, test_imgs = [], []\n",
        "for img, label in tqdm(test_dataset.take(100)):\n",
        "    interpreter.set_tensor(input_index, img)\n",
        "    interpreter.invoke()\n",
        "    predictions.append(interpreter.get_tensor(output_index))\n",
        "\n",
        "    test_labels.append(label.numpy()[0])\n",
        "    test_imgs.append(img)\n",
        "\n",
        "\n",
        "# Utility functions for plotting\n",
        "# Utilities for plotting\n",
        "\n",
        "class_names = ['pertengahan', 'akhir', 'tahap awal', 'telur mati']\n",
        "\n",
        "def plot_image(i, predictions_array, true_label, img):\n",
        "    predictions_array, true_label, img = predictions_array[i], np.argmax(true_label[i]), img[i]\n",
        "    plt.grid(False)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "\n",
        "    img = np.squeeze(img)\n",
        "\n",
        "    plt.imshow(img, cmap=plt.cm.binary)\n",
        "\n",
        "    predicted_label = np.argmax(predictions_array)\n",
        "\n",
        "    if predicted_label == true_label:\n",
        "        color = 'green'\n",
        "    else:\n",
        "        color = 'red'\n",
        "\n",
        "    plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
        "                                         100*np.max(predictions_array),\n",
        "                                         class_names[true_label]), color=color)\n",
        "\n",
        "\n",
        "# Visualize the outputs { run: \"auto\" }\n",
        "index = 61\n",
        "plt.figure(figsize=(7,6))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(index, predictions, test_labels, test_imgs)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ee_qhUAERXMv"
      },
      "outputs": [],
      "source": [
        "# while True:pass"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}