{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# import libraries\n",
"import tensorflow as tf\n",
"from tensorflow.keras import layers, models\n",
"from matplotlib import pyplot as plt\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1674 images belonging to 8 classes.\n",
"Found 157 images belonging to 8 classes.\n",
"Found 79 images belonging to 8 classes.\n"
]
}
],
"source": [
"TRAIN_DIR = 'dataset/train'\n",
"TEST_DIR = 'dataset/test'\n",
"VAL_DIR = 'dataset/val'\n",
"# Load dataset\n",
"datagen = ImageDataGenerator(rescale=1./255)\n",
"# Load data dari direktori menggunakan flow_from_directory\n",
"train_generator = datagen.flow_from_directory(\n",
" TRAIN_DIR,\n",
" target_size=(224, 224), # Sesuaikan dengan ukuran gambar input model\n",
" batch_size=32,\n",
" class_mode='categorical'\n",
")\n",
"\n",
"val_generator = datagen.flow_from_directory(\n",
" VAL_DIR,\n",
" target_size=(224, 224),\n",
" batch_size=32,\n",
" class_mode='categorical'\n",
")\n",
"\n",
"test_generator = datagen.flow_from_directory(\n",
" TEST_DIR,\n",
" target_size=(224, 224),\n",
" batch_size=32,\n",
" class_mode='categorical',\n",
" shuffle=False # Untuk testing, tidak perlu shuffle\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Daun Jambu Biji': 0,\n",
" 'Daun Kemangi': 1,\n",
" 'Daun Kunyit': 2,\n",
" 'Daun Mint': 3,\n",
" 'Daun Pepaya': 4,\n",
" 'Daun Sirih': 5,\n",
" 'Daun Sirsak': 6,\n",
" 'Lidah Buaya': 7}"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_generator.class_indices"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"model = models.Sequential()\n",
"\n",
"model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)))\n",
"model.add(layers.MaxPooling2D((2, 2)))\n",
"model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
"model.add(layers.MaxPooling2D((2, 2)))\n",
"model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
"model.add(layers.MaxPooling2D((2, 2)))\n",
"model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
"model.add(layers.MaxPooling2D((2, 2)))\n",
"\n",
"model.add(layers.Flatten())\n",
"model.add(layers.Dense(512, activation='relu'))\n",
"model.add(layers.Dense(8, activation='softmax'))\n",
"\n",
"model.compile(optimizer='adam',\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
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"Model: \"sequential_4\"\n",
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"┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
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"│ max_pooling2d_13 (MaxPooling2D) │ (None, 111, 111, 32) │ 0 │\n",
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"│ max_pooling2d_15 (MaxPooling2D) │ (None, 26, 26, 128) │ 0 │\n",
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"│ conv2d_16 (Conv2D) │ (None, 24, 24, 128) │ 147,584 │\n",
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"│ max_pooling2d_16 (MaxPooling2D) │ (None, 12, 12, 128) │ 0 │\n",
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"│ flatten_4 (Flatten) │ (None, 18432) │ 0 │\n",
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"│ dense_8 (Dense) │ (None, 512) │ 9,437,696 │\n",
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"│ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ max_pooling2d_13 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ conv2d_14 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ max_pooling2d_15 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ max_pooling2d_16 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
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"│ flatten_4 (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18432\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
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"│ dense_8 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m9,437,696\u001b[0m │\n",
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"│ dense_9 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m) │ \u001b[38;5;34m4,104\u001b[0m │\n",
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
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{
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" Total params: 9,682,632 (36.94 MB)\n",
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" Trainable params: 9,682,632 (36.94 MB)\n",
"\n"
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"source": [
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]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/20\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-10-14 11:16:05.231584: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled.\n",
"/Users/edoaurahman/development/anaconda/anaconda3/envs/tensorflow/lib/python3.10/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
" self._warn_if_super_not_called()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 353ms/step - accuracy: 0.2478 - loss: 2.1296 - val_accuracy: 0.5987 - val_loss: 1.1171\n",
"Epoch 2/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 339ms/step - accuracy: 0.6152 - loss: 1.0357 - val_accuracy: 0.6688 - val_loss: 0.8430\n",
"Epoch 3/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 322ms/step - accuracy: 0.7471 - loss: 0.7063 - val_accuracy: 0.7898 - val_loss: 0.6230\n",
"Epoch 4/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 370ms/step - accuracy: 0.8481 - loss: 0.4345 - val_accuracy: 0.8408 - val_loss: 0.5627\n",
"Epoch 5/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 471ms/step - accuracy: 0.9096 - loss: 0.2562 - val_accuracy: 0.8408 - val_loss: 0.5344\n",
"Epoch 6/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 363ms/step - accuracy: 0.9161 - loss: 0.2274 - val_accuracy: 0.8408 - val_loss: 0.8011\n",
"Epoch 7/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 367ms/step - accuracy: 0.9671 - loss: 0.0961 - val_accuracy: 0.8408 - val_loss: 0.6227\n",
"Epoch 8/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 403ms/step - accuracy: 0.9832 - loss: 0.0657 - val_accuracy: 0.7898 - val_loss: 0.9990\n",
"Epoch 9/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 420ms/step - accuracy: 0.9750 - loss: 0.0758 - val_accuracy: 0.8344 - val_loss: 0.8001\n",
"Epoch 10/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 378ms/step - accuracy: 0.9909 - loss: 0.0312 - val_accuracy: 0.8344 - val_loss: 1.0499\n",
"Epoch 11/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 359ms/step - accuracy: 0.9803 - loss: 0.0627 - val_accuracy: 0.8599 - val_loss: 0.8847\n",
"Epoch 12/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 369ms/step - accuracy: 0.9984 - loss: 0.0089 - val_accuracy: 0.8280 - val_loss: 1.0634\n",
"Epoch 13/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 377ms/step - accuracy: 0.9980 - loss: 0.0106 - val_accuracy: 0.8217 - val_loss: 1.2077\n",
"Epoch 14/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 333ms/step - accuracy: 0.9768 - loss: 0.0614 - val_accuracy: 0.8535 - val_loss: 0.8965\n",
"Epoch 15/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 345ms/step - accuracy: 0.9867 - loss: 0.0368 - val_accuracy: 0.7962 - val_loss: 1.3721\n",
"Epoch 16/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 408ms/step - accuracy: 0.9825 - loss: 0.0534 - val_accuracy: 0.8153 - val_loss: 1.1506\n",
"Epoch 17/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 372ms/step - accuracy: 0.9965 - loss: 0.0116 - val_accuracy: 0.8471 - val_loss: 1.2062\n",
"Epoch 18/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 376ms/step - accuracy: 1.0000 - loss: 0.0027 - val_accuracy: 0.8408 - val_loss: 1.2559\n",
"Epoch 19/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 415ms/step - accuracy: 1.0000 - loss: 2.3890e-04 - val_accuracy: 0.8535 - val_loss: 1.3033\n",
"Epoch 20/20\n",
"\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 411ms/step - accuracy: 1.0000 - loss: 1.3011e-04 - val_accuracy: 0.8471 - val_loss: 1.2932\n"
]
}
],
"source": [
"# Melatih model dengan data train, validasi dilakukan dengan data validation\n",
"history = model.fit(\n",
" train_generator,\n",
" epochs=10, # Sesuaikan jumlah epoch\n",
" validation_data=val_generator\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
}
],
"source": [
"# save model\n",
"model.save('model.h5')"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"# save history\n",
"import pickle\n",
"with open('history.pkl', 'wb') as file_pi:\n",
" pickle.dump(history.history, file_pi)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-10-14 11:41:05.053908: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1\n",
"2024-10-14 11:41:05.053947: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 8.00 GB\n",
"2024-10-14 11:41:05.053957: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 2.67 GB\n",
"2024-10-14 11:41:05.054262: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
"2024-10-14 11:41:05.054278: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: )\n",
"WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"\n",
"# Load model .h5\n",
"model = tf.keras.models.load_model('model.h5')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import numpy as np\n",
"from tensorflow.keras.preprocessing.image import img_to_array\n",
"\n",
"def preprocess_image(image_path, img_size):\n",
" # Baca gambar\n",
" img = cv2.imread(image_path)\n",
" \n",
" # Resize gambar sesuai dengan input model\n",
" img = cv2.resize(img, (img_size, img_size))\n",
" \n",
" # Konversi gambar ke array dan normalisasi\n",
" \n",
" # Tambahkan dimensi batch: (height, width, channels) -> (1, height, width, channels)\n",
" img = np.expand_dims(img, axis=0)\n",
" \n",
" return img\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step\n",
"Predictions: [[0. 0. 0. 1. 0. 0. 0. 0.]]\n",
"Predicted class: [3]\n",
"Predicted class: Daun Mint\n"
]
}
],
"source": [
"# Path ke gambar yang ingin diprediksi\n",
"image_path = 'lidah-buaya.jpg'\n",
"\n",
"# Preprocessing gambar (misalnya ukuran gambar input yang diharapkan model adalah 224x224)\n",
"img_size = 224\n",
"preprocessed_image = preprocess_image(image_path, img_size)\n",
"\n",
"# Prediksi menggunakan model\n",
"predictions = model.predict(preprocessed_image)\n",
"\n",
"# Tampilkan hasil prediksi\n",
"print(\"Predictions:\", predictions)\n",
"\n",
"# Ambil kelas dengan probabilitas tertinggi\n",
"predicted_class = np.argmax(predictions, axis=1)\n",
"\n",
"# Cetak kelas yang diprediksi\n",
"print(\"Predicted class:\", predicted_class)\n",
"\n",
"class_names = ['Daun Jambu Biji',\n",
" 'Daun Kemangi',\n",
" 'Daun Kunyit',\n",
" 'Daun Mint',\n",
" 'Daun Pepaya',\n",
" 'Daun Sirih',\n",
" 'Daun Sirsak',\n",
" 'Lidah Buaya']\n",
"# Konversi indeks prediksi menjadi nama kelas\n",
"predicted_class_name = class_names[predicted_class[0]]\n",
"\n",
"print(\"Predicted class:\", predicted_class_name)\n"
]
}
],
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"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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