Muhammad Pazrin Andreanor commited on
Commit Β·
508e2b6
1
Parent(s): 9200f4b
Add initial implementation of a Tiny NLP sentiment classification model
Browse files- Created a Jupyter notebook for a simple text classification task using TensorFlow and TensorFlow Datasets.
- Implemented data loading, preprocessing, model definition, training, and evaluation steps.
- Added functionality to convert the trained model to TensorFlow Lite format for deployment.
- Included a prediction function for testing custom input text.
- Saved the TensorFlow Lite model as 'tiny_sentiment_model_imdb.tflite'.
basic-clasification.ipynb
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| 1 |
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{
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| 2 |
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"cells": [
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{
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"cell_type": "markdown",
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| 5 |
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"id": "c201ca37",
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| 6 |
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"metadata": {},
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| 7 |
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"source": [
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| 8 |
+
"# Basic Image Classification (CNN)\n",
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| 9 |
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"\n",
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| 10 |
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"Notebook ini berisi contoh paling dasar untuk klasifikasi gambar menggunakan TensorFlow/Keras.\n",
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| 11 |
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"\n",
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"## Ide tugas klasifikasi\n",
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"1. Klasifikasi wireframe: `login`, `dashboard`, `product`, `form`, `table`.\n",
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"2. Klasifikasi style desain: `clean`, `dense`, `minimal`, `complex`.\n",
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| 15 |
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"3. Klasifikasi tipe komponen dominan: `card-heavy`, `table-heavy`, `form-heavy`.\n",
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"\n",
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"Struktur dataset yang disarankan:\n",
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"\n",
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"```text\n",
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| 20 |
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"my_dataset/\n",
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" train/\n",
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| 22 |
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" class_a/\n",
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| 23 |
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" class_b/\n",
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| 24 |
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" class_c/\n",
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| 25 |
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" val/\n",
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| 26 |
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" class_a/\n",
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| 27 |
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" class_b/\n",
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| 28 |
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" class_c/\n",
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| 29 |
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"```"
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]
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},
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{
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| 33 |
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"cell_type": "code",
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| 34 |
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"execution_count": null,
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| 35 |
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"id": "6067a559",
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| 36 |
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"metadata": {},
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| 37 |
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"outputs": [],
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| 38 |
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"source": [
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| 39 |
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"import tensorflow as tf\n",
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| 40 |
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"from tensorflow.keras import layers, models\n",
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"\n",
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| 42 |
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"# Ubah sesuai lokasi dataset Anda\n",
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| 43 |
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"data_dir_train = \"./my_dataset/train\"\n",
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| 44 |
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"data_dir_val = \"./my_dataset/val\"\n",
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| 45 |
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"\n",
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| 46 |
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"img_size = (128, 128)\n",
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| 47 |
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"batch_size = 32\n",
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| 48 |
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"\n",
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| 49 |
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"train_ds = tf.keras.utils.image_dataset_from_directory(\n",
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| 50 |
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" data_dir_train,\n",
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| 51 |
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" image_size=img_size,\n",
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| 52 |
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" batch_size=batch_size,\n",
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| 53 |
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" label_mode=\"int\"\n",
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| 54 |
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")\n",
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| 55 |
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"\n",
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| 56 |
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"val_ds = tf.keras.utils.image_dataset_from_directory(\n",
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| 57 |
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" data_dir_val,\n",
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| 58 |
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" image_size=img_size,\n",
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| 59 |
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" batch_size=batch_size,\n",
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| 60 |
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" label_mode=\"int\"\n",
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| 61 |
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")\n",
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"\n",
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"class_names = train_ds.class_names\n",
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"num_classes = len(class_names)\n",
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| 65 |
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"print(\"Classes:\", class_names)\n",
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"\n",
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"# Optimasi pipeline input\n",
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"autotune = tf.data.AUTOTUNE\n",
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"train_ds = train_ds.shuffle(1000).prefetch(buffer_size=autotune)\n",
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"val_ds = val_ds.prefetch(buffer_size=autotune)"
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| 71 |
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]
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},
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{
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| 74 |
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"cell_type": "code",
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| 75 |
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"execution_count": null,
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| 76 |
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"id": "368bd39b",
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| 77 |
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"metadata": {},
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| 78 |
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"outputs": [],
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"source": [
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| 80 |
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"# Model CNN sederhana\n",
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| 81 |
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"model = models.Sequential([\n",
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| 82 |
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" layers.Rescaling(1.0 / 255, input_shape=(img_size[0], img_size[1], 3)),\n",
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| 83 |
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" layers.Conv2D(32, 3, activation=\"relu\"),\n",
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| 84 |
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" layers.MaxPooling2D(),\n",
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| 85 |
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" layers.Conv2D(64, 3, activation=\"relu\"),\n",
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| 86 |
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" layers.MaxPooling2D(),\n",
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| 87 |
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" layers.Conv2D(128, 3, activation=\"relu\"),\n",
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| 88 |
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" layers.MaxPooling2D(),\n",
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| 89 |
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" layers.Flatten(),\n",
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| 90 |
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" layers.Dense(128, activation=\"relu\"),\n",
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| 91 |
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" layers.Dropout(0.3),\n",
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| 92 |
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" layers.Dense(num_classes, activation=\"softmax\")\n",
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"])\n",
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"\n",
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| 95 |
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"model.compile(\n",
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| 96 |
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" optimizer=\"adam\",\n",
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| 97 |
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" loss=\"sparse_categorical_crossentropy\",\n",
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| 98 |
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" metrics=[\"accuracy\"]\n",
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")\n",
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"\n",
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"model.summary()"
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]
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},
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{
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| 105 |
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"cell_type": "code",
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| 106 |
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"execution_count": null,
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| 107 |
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"id": "b75d2ec6",
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| 108 |
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"metadata": {},
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| 109 |
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"outputs": [],
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| 110 |
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"source": [
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| 111 |
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"epochs = 10\n",
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| 112 |
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"history = model.fit(\n",
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| 113 |
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" train_ds,\n",
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| 114 |
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" validation_data=val_ds,\n",
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| 115 |
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" epochs=epochs\n",
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| 116 |
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")\n",
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| 117 |
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"\n",
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| 118 |
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"loss, acc = model.evaluate(val_ds)\n",
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| 119 |
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"print(f\"Validation accuracy: {acc:.4f}\")\n",
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| 120 |
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"\n",
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| 121 |
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"model.save(\"basic_cnn_classification.h5\")"
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| 122 |
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]
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| 123 |
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},
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| 124 |
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{
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| 125 |
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"cell_type": "code",
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| 126 |
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"execution_count": null,
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| 127 |
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"id": "55fff896",
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| 128 |
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"metadata": {},
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| 129 |
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"outputs": [],
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| 130 |
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"source": [
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| 131 |
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"# Prediksi 1 gambar baru\n",
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| 132 |
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"import numpy as np\n",
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| 133 |
+
"from tensorflow.keras.preprocessing import image\n",
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| 134 |
+
"\n",
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| 135 |
+
"img_path = \"./sample.jpg\" # ganti ke file gambar Anda\n",
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| 136 |
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"img = image.load_img(img_path, target_size=img_size)\n",
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| 137 |
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"arr = image.img_to_array(img)\n",
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| 138 |
+
"arr = np.expand_dims(arr, axis=0) / 255.0\n",
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| 139 |
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"\n",
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| 140 |
+
"pred = model.predict(arr)\n",
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| 141 |
+
"pred_class = class_names[np.argmax(pred)]\n",
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| 142 |
+
"print(\"Predicted class:\", pred_class)"
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| 143 |
+
]
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| 144 |
+
}
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| 145 |
+
],
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| 146 |
+
"metadata": {
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| 147 |
+
"kernelspec": {
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| 148 |
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"display_name": "research",
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| 149 |
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"language": "python",
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| 150 |
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"name": "python3"
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| 151 |
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},
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| 152 |
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"language_info": {
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| 153 |
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"codemirror_mode": {
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| 154 |
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"name": "ipython",
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| 155 |
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"version": 3
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| 156 |
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},
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| 157 |
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"file_extension": ".py",
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| 158 |
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"mimetype": "text/x-python",
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| 159 |
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"name": "python",
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| 160 |
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"nbconvert_exporter": "python",
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| 161 |
+
"pygments_lexer": "ipython3",
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| 162 |
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"version": "3.10.20"
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| 163 |
+
}
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| 164 |
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},
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| 165 |
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"nbformat": 4,
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| 166 |
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"nbformat_minor": 5
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}
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starter project Tiny NLP (klasifikasi teks sederhana: sentimen positif vs negatif).ipynb
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 2,
|
| 6 |
+
"id": "460f0c3f",
|
| 7 |
+
"metadata": {},
|
| 8 |
+
"outputs": [
|
| 9 |
+
{
|
| 10 |
+
"name": "stdout",
|
| 11 |
+
"output_type": "stream",
|
| 12 |
+
"text": [
|
| 13 |
+
"Requirement already satisfied: tensorflow in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (2.21.0)\n",
|
| 14 |
+
"Requirement already satisfied: tensorflow-datasets in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (4.9.9)\n",
|
| 15 |
+
"Requirement already satisfied: absl-py>=1.0.0 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (2.3.1)\n",
|
| 16 |
+
"Requirement already satisfied: astunparse>=1.6.0 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (1.6.3)\n",
|
| 17 |
+
"Requirement already satisfied: flatbuffers>=25.9.23 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (25.9.23)\n",
|
| 18 |
+
"Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (0.7.0)\n",
|
| 19 |
+
"Requirement already satisfied: google_pasta>=0.1.1 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (0.2.0)\n",
|
| 20 |
+
"Requirement already satisfied: libclang>=13.0.0 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (18.1.1)\n",
|
| 21 |
+
"Requirement already satisfied: opt_einsum>=2.3.2 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (3.3.0)\n",
|
| 22 |
+
"Requirement already satisfied: packaging in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (26.0)\n",
|
| 23 |
+
"Requirement already satisfied: protobuf<8.0.0,>=6.31.1 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (6.32.0)\n",
|
| 24 |
+
"Requirement already satisfied: requests<3,>=2.21.0 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (2.32.5)\n",
|
| 25 |
+
"Requirement already satisfied: setuptools in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (80.10.2)\n",
|
| 26 |
+
"Requirement already satisfied: six>=1.12.0 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (1.17.0)\n",
|
| 27 |
+
"Requirement already satisfied: termcolor>=1.1.0 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (3.2.0)\n",
|
| 28 |
+
"Requirement already satisfied: typing_extensions>=3.6.6 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (4.15.0)\n",
|
| 29 |
+
"Requirement already satisfied: wrapt>=1.11.0 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (2.0.1)\n",
|
| 30 |
+
"Requirement already satisfied: grpcio<2.0,>=1.24.3 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (1.78.0)\n",
|
| 31 |
+
"Requirement already satisfied: keras>=3.12.0 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (3.12.1)\n",
|
| 32 |
+
"Requirement already satisfied: numpy>=1.26.0 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (2.2.5)\n",
|
| 33 |
+
"Requirement already satisfied: h5py<3.15.0,>=3.11.0 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (3.14.0)\n",
|
| 34 |
+
"Requirement already satisfied: ml_dtypes<1.0.0,>=0.5.1 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow) (0.5.4)\n",
|
| 35 |
+
"Requirement already satisfied: charset_normalizer<4,>=2 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorflow) (3.4.4)\n",
|
| 36 |
+
"Requirement already satisfied: idna<4,>=2.5 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorflow) (3.11)\n",
|
| 37 |
+
"Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorflow) (2.6.3)\n",
|
| 38 |
+
"Requirement already satisfied: certifi>=2017.4.17 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorflow) (2026.1.4)\n",
|
| 39 |
+
"Requirement already satisfied: dm-tree in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow-datasets) (0.1.10)\n",
|
| 40 |
+
"Requirement already satisfied: etils>=1.6.0 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from etils[edc,enp,epath,epy,etree]>=1.6.0; python_version < \"3.11\"->tensorflow-datasets) (1.13.0)\n",
|
| 41 |
+
"Requirement already satisfied: immutabledict in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow-datasets) (4.3.1)\n",
|
| 42 |
+
"Requirement already satisfied: promise in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow-datasets) (2.3)\n",
|
| 43 |
+
"Requirement already satisfied: psutil in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow-datasets) (7.0.0)\n",
|
| 44 |
+
"Requirement already satisfied: pyarrow in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow-datasets) (23.0.1)\n",
|
| 45 |
+
"Requirement already satisfied: simple_parsing in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow-datasets) (0.1.8)\n",
|
| 46 |
+
"Requirement already satisfied: tensorflow-metadata in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow-datasets) (1.17.3)\n",
|
| 47 |
+
"Requirement already satisfied: toml in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow-datasets) (0.10.2)\n",
|
| 48 |
+
"Requirement already satisfied: tqdm in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from tensorflow-datasets) (4.67.3)\n",
|
| 49 |
+
"Requirement already satisfied: wheel<1.0,>=0.23.0 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from astunparse>=1.6.0->tensorflow) (0.46.3)\n",
|
| 50 |
+
"Requirement already satisfied: fsspec in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from etils[edc,enp,epath,epy,etree]>=1.6.0; python_version < \"3.11\"->tensorflow-datasets) (2026.3.0)\n",
|
| 51 |
+
"Requirement already satisfied: importlib_resources in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from etils[edc,enp,epath,epy,etree]>=1.6.0; python_version < \"3.11\"->tensorflow-datasets) (6.5.2)\n",
|
| 52 |
+
"Requirement already satisfied: zipp in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from etils[edc,enp,epath,epy,etree]>=1.6.0; python_version < \"3.11\"->tensorflow-datasets) (3.23.0)\n",
|
| 53 |
+
"Requirement already satisfied: einops in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from etils[edc,enp,epath,epy,etree]>=1.6.0; python_version < \"3.11\"->tensorflow-datasets) (0.8.2)\n",
|
| 54 |
+
"Requirement already satisfied: rich in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from keras>=3.12.0->tensorflow) (14.2.0)\n",
|
| 55 |
+
"Requirement already satisfied: namex in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from keras>=3.12.0->tensorflow) (0.1.0)\n",
|
| 56 |
+
"Requirement already satisfied: optree in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from keras>=3.12.0->tensorflow) (0.18.0)\n",
|
| 57 |
+
"Requirement already satisfied: attrs>=18.2.0 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from dm-tree->tensorflow-datasets) (26.1.0)\n",
|
| 58 |
+
"Requirement already satisfied: markdown-it-py>=2.2.0 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from rich->keras>=3.12.0->tensorflow) (4.0.0)\n",
|
| 59 |
+
"Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from rich->keras>=3.12.0->tensorflow) (2.19.2)\n",
|
| 60 |
+
"Requirement already satisfied: mdurl~=0.1 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.12.0->tensorflow) (0.1.2)\n",
|
| 61 |
+
"Requirement already satisfied: docstring-parser~=0.15 in /Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages (from simple_parsing->tensorflow-datasets) (0.17.0)\n",
|
| 62 |
+
"TensorFlow version: 2.21.0\n"
|
| 63 |
+
]
|
| 64 |
+
}
|
| 65 |
+
],
|
| 66 |
+
"source": [
|
| 67 |
+
"# =========================\n",
|
| 68 |
+
"# 1. Install & Import\n",
|
| 69 |
+
"# =========================\n",
|
| 70 |
+
"!pip install tensorflow tensorflow-datasets\n",
|
| 71 |
+
"\n",
|
| 72 |
+
"import tensorflow as tf\n",
|
| 73 |
+
"import tensorflow_datasets as tfds\n",
|
| 74 |
+
"import numpy as np\n",
|
| 75 |
+
"\n",
|
| 76 |
+
"print(\"TensorFlow version:\", tf.__version__)"
|
| 77 |
+
]
|
| 78 |
+
},
|
| 79 |
+
{
|
| 80 |
+
"cell_type": "code",
|
| 81 |
+
"execution_count": 3,
|
| 82 |
+
"id": "7078b823",
|
| 83 |
+
"metadata": {},
|
| 84 |
+
"outputs": [
|
| 85 |
+
{
|
| 86 |
+
"name": "stderr",
|
| 87 |
+
"output_type": "stream",
|
| 88 |
+
"text": [
|
| 89 |
+
"WARNING:absl:Variant folder /Users/ryfazrin/tensorflow_datasets/imdb_reviews/plain_text/1.0.0 has no dataset_info.json\n"
|
| 90 |
+
]
|
| 91 |
+
},
|
| 92 |
+
{
|
| 93 |
+
"name": "stdout",
|
| 94 |
+
"output_type": "stream",
|
| 95 |
+
"text": [
|
| 96 |
+
"\u001b[1mDownloading and preparing dataset Unknown size (download: Unknown size, generated: Unknown size, total: Unknown size) to /Users/ryfazrin/tensorflow_datasets/imdb_reviews/plain_text/1.0.0...\u001b[0m\n"
|
| 97 |
+
]
|
| 98 |
+
},
|
| 99 |
+
{
|
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{
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"\u001b[1mDataset imdb_reviews downloaded and prepared to /Users/ryfazrin/tensorflow_datasets/imdb_reviews/plain_text/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n",
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"tfds.core.DatasetInfo(\n",
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| 231 |
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" name='imdb_reviews',\n",
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" full_name='imdb_reviews/plain_text/1.0.0',\n",
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" description=\"\"\"\n",
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" Large Movie Review Dataset. This is a dataset for binary sentiment\n",
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" classification containing substantially more data than previous benchmark\n",
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" datasets. We provide a set of 25,000 highly polar movie reviews for training,\n",
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" and 25,000 for testing. There is additional unlabeled data for use as well.\n",
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" \"\"\",\n",
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" config_description=\"\"\"\n",
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" Plain text\n",
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" \"\"\",\n",
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" homepage='http://ai.stanford.edu/~amaas/data/sentiment/',\n",
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| 243 |
+
" data_dir='/Users/ryfazrin/tensorflow_datasets/imdb_reviews/plain_text/1.0.0',\n",
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" file_format=tfrecord,\n",
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+
" download_size=80.23 MiB,\n",
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" dataset_size=129.83 MiB,\n",
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+
" features=FeaturesDict({\n",
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" 'label': ClassLabel(shape=(), dtype=int64, num_classes=2),\n",
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+
" 'text': Text(shape=(), dtype=string),\n",
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" }),\n",
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" disable_shuffling=False,\n",
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" nondeterministic_order=False,\n",
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" splits={\n",
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+
" 'test': <SplitInfo num_examples=25000, num_shards=1>,\n",
|
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+
" 'train': <SplitInfo num_examples=25000, num_shards=1>,\n",
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" 'unsupervised': <SplitInfo num_examples=50000, num_shards=1>,\n",
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" },\n",
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| 259 |
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" citation=\"\"\"@InProceedings{maas-EtAl:2011:ACL-HLT2011,\n",
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| 260 |
+
" author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},\n",
|
| 261 |
+
" title = {Learning Word Vectors for Sentiment Analysis},\n",
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| 262 |
+
" booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},\n",
|
| 263 |
+
" month = {June},\n",
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" year = {2011},\n",
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+
" address = {Portland, Oregon, USA},\n",
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" publisher = {Association for Computational Linguistics},\n",
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| 267 |
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" pages = {142--150},\n",
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" url = {http://www.aclweb.org/anthology/P11-1015}\n",
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" }\"\"\",\n",
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")\n"
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]
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}
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],
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"source": [
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| 275 |
+
"# =========================\n",
|
| 276 |
+
"# 2. Load Dataset\n",
|
| 277 |
+
"# =========================\n",
|
| 278 |
+
"dataset, info = tfds.load(\n",
|
| 279 |
+
" \"imdb_reviews\",\n",
|
| 280 |
+
" with_info=True,\n",
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" as_supervised=True\n",
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")\n",
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"\n",
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"train_data, test_data = dataset['train'], dataset['test']\n",
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"\n",
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"print(info)"
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]
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},
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{
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"cell_type": "code",
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"id": "69c4d361",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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| 299 |
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"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
|
| 300 |
+
"I0000 00:00:1775131893.297532 71659 tf_record_dataset_op.cc:396] The default buffer size is 262144, which is overridden by the user specified `buffer_size` of 8388608\n"
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],
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"source": [
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| 305 |
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"# =========================\n",
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+
"# 3. Preprocessing (Tokenization)\n",
|
| 307 |
+
"# =========================\n",
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+
"vocab_size = 1000 # kecil β TinyML friendly\n",
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"max_length = 100\n",
|
| 310 |
+
"oov_tok = \"<OOV>\"\n",
|
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+
"\n",
|
| 312 |
+
"tokenizer = tf.keras.preprocessing.text.Tokenizer(\n",
|
| 313 |
+
" num_words=vocab_size,\n",
|
| 314 |
+
" oov_token=oov_tok\n",
|
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+
")\n",
|
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+
"\n",
|
| 317 |
+
"# ambil teks saja\n",
|
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+
"train_sentences = []\n",
|
| 319 |
+
"train_labels = []\n",
|
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+
"\n",
|
| 321 |
+
"for sentence, label in train_data:\n",
|
| 322 |
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" train_sentences.append(sentence.numpy().decode('utf-8'))\n",
|
| 323 |
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" train_labels.append(label.numpy())\n",
|
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"\n",
|
| 325 |
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"tokenizer.fit_on_texts(train_sentences)\n",
|
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+
"\n",
|
| 327 |
+
"# convert ke sequence\n",
|
| 328 |
+
"train_sequences = tokenizer.texts_to_sequences(train_sentences)\n",
|
| 329 |
+
"train_padded = tf.keras.preprocessing.sequence.pad_sequences(\n",
|
| 330 |
+
" train_sequences,\n",
|
| 331 |
+
" maxlen=max_length,\n",
|
| 332 |
+
" padding='post',\n",
|
| 333 |
+
" truncating='post'\n",
|
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")\n",
|
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"\n",
|
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"train_labels = np.array(train_labels)"
|
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]
|
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},
|
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{
|
| 340 |
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"cell_type": "code",
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"execution_count": 5,
|
| 342 |
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"id": "aadbfb8a",
|
| 343 |
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"metadata": {},
|
| 344 |
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"outputs": [],
|
| 345 |
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"source": [
|
| 346 |
+
"# =========================\n",
|
| 347 |
+
"# 4. Test Data Preprocessing\n",
|
| 348 |
+
"# =========================\n",
|
| 349 |
+
"test_sentences = []\n",
|
| 350 |
+
"test_labels = []\n",
|
| 351 |
+
"\n",
|
| 352 |
+
"for sentence, label in test_data:\n",
|
| 353 |
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" test_sentences.append(sentence.numpy().decode('utf-8'))\n",
|
| 354 |
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" test_labels.append(label.numpy())\n",
|
| 355 |
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"\n",
|
| 356 |
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"test_sequences = tokenizer.texts_to_sequences(test_sentences)\n",
|
| 357 |
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"test_padded = tf.keras.preprocessing.sequence.pad_sequences(\n",
|
| 358 |
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" test_sequences,\n",
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| 359 |
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|
| 360 |
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|
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" truncating='post'\n",
|
| 362 |
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")\n",
|
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"\n",
|
| 364 |
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]
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},
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"/Users/ryfazrin/miniconda3/envs/research/lib/python3.10/site-packages/keras/src/layers/core/embedding.py:97: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
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"β embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>) β ? β <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) β\n",
|
| 401 |
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"βββββββββββββββββββββββββββββββββββΌβββββββββββββββββββββββββΌββββββββββββββββ€\n",
|
| 402 |
+
"β global_average_pooling1d β ? β <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> β\n",
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"β (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling1D</span>) β β β\n",
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| 404 |
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"βββββββββββββββββββββββββββββββββββΌβββββββββββββββββββββββββΌββββββββββββββββ€\n",
|
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"β dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) β ? β <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) β\n",
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"βββββββββββββββββββββββββββββββββββΌβββββββββββββββββββββββββΌββββββββββββββββ€\n",
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"β dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) β ? β <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) β\n",
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"β‘βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ©\n",
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| 415 |
+
"β embedding (\u001b[38;5;33mEmbedding\u001b[0m) β ? β \u001b[38;5;34m0\u001b[0m (unbuilt) β\n",
|
| 416 |
+
"βββββββββββββββββββββββββββββββββββΌβββββββββββββββββββββββββΌββββββββββββββββ€\n",
|
| 417 |
+
"β global_average_pooling1d β ? β \u001b[38;5;34m0\u001b[0m β\n",
|
| 418 |
+
"β (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m) β β β\n",
|
| 419 |
+
"βββββββββββββββββββββββββββββββββββΌβββββββββββββββββββββββββΌββββββββββββββββ€\n",
|
| 420 |
+
"β dense (\u001b[38;5;33mDense\u001b[0m) β ? β \u001b[38;5;34m0\u001b[0m (unbuilt) β\n",
|
| 421 |
+
"βββββββββββββββββββββββββββββββββββΌβββββββββββββββββββββββββΌββββββββββββββββ€\n",
|
| 422 |
+
"β dense_1 (\u001b[38;5;33mDense\u001b[0m) β ? β \u001b[38;5;34m0\u001b[0m (unbuilt) β\n",
|
| 423 |
+
"βββββββββββββββββββββββββββββββββββ΄βββββββββββββββββββββββββ΄ββββββββββββββββ\n"
|
| 424 |
+
]
|
| 425 |
+
},
|
| 426 |
+
"metadata": {},
|
| 427 |
+
"output_type": "display_data"
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},
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+
{
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+
"data": {
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"text/html": [
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
|
| 433 |
+
"</pre>\n"
|
| 434 |
+
],
|
| 435 |
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"text/plain": [
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"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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| 437 |
+
]
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| 438 |
+
},
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"metadata": {},
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"output_type": "display_data"
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},
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+
{
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"data": {
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| 444 |
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"text/html": [
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+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
|
| 446 |
+
"</pre>\n"
|
| 447 |
+
],
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| 448 |
+
"text/plain": [
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+
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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| 450 |
+
]
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+
},
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"metadata": {},
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"output_type": "display_data"
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+
},
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+
{
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+
"data": {
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| 457 |
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"text/html": [
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| 458 |
+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
|
| 459 |
+
"</pre>\n"
|
| 460 |
+
],
|
| 461 |
+
"text/plain": [
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+
"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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| 463 |
+
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+
},
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"metadata": {},
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+
"output_type": "display_data"
|
| 467 |
+
}
|
| 468 |
+
],
|
| 469 |
+
"source": [
|
| 470 |
+
"# =========================\n",
|
| 471 |
+
"# 5. Model Tiny (Lightweight)\n",
|
| 472 |
+
"# =========================\n",
|
| 473 |
+
"model = tf.keras.Sequential([\n",
|
| 474 |
+
" tf.keras.layers.Embedding(vocab_size, 16, input_length=max_length),\n",
|
| 475 |
+
" tf.keras.layers.GlobalAveragePooling1D(),\n",
|
| 476 |
+
" tf.keras.layers.Dense(16, activation='relu'),\n",
|
| 477 |
+
" tf.keras.layers.Dense(1, activation='sigmoid')\n",
|
| 478 |
+
"])\n",
|
| 479 |
+
"\n",
|
| 480 |
+
"model.compile(\n",
|
| 481 |
+
" loss='binary_crossentropy',\n",
|
| 482 |
+
" optimizer='adam',\n",
|
| 483 |
+
" metrics=['accuracy']\n",
|
| 484 |
+
")\n",
|
| 485 |
+
"\n",
|
| 486 |
+
"model.summary()"
|
| 487 |
+
]
|
| 488 |
+
},
|
| 489 |
+
{
|
| 490 |
+
"cell_type": "code",
|
| 491 |
+
"execution_count": 7,
|
| 492 |
+
"id": "21c6cb46",
|
| 493 |
+
"metadata": {},
|
| 494 |
+
"outputs": [
|
| 495 |
+
{
|
| 496 |
+
"name": "stdout",
|
| 497 |
+
"output_type": "stream",
|
| 498 |
+
"text": [
|
| 499 |
+
"Epoch 1/5\n",
|
| 500 |
+
"\u001b[1m782/782\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.6960 - loss: 0.5796 - val_accuracy: 0.7796 - val_loss: 0.4684\n",
|
| 501 |
+
"Epoch 2/5\n",
|
| 502 |
+
"\u001b[1m782/782\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.7903 - loss: 0.4446 - val_accuracy: 0.7908 - val_loss: 0.4400\n",
|
| 503 |
+
"Epoch 3/5\n",
|
| 504 |
+
"\u001b[1m782/782\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8042 - loss: 0.4221 - val_accuracy: 0.7971 - val_loss: 0.4329\n",
|
| 505 |
+
"Epoch 4/5\n",
|
| 506 |
+
"\u001b[1m782/782\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8093 - loss: 0.4146 - val_accuracy: 0.7968 - val_loss: 0.4296\n",
|
| 507 |
+
"Epoch 5/5\n",
|
| 508 |
+
"\u001b[1m782/782\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8081 - loss: 0.4121 - val_accuracy: 0.7958 - val_loss: 0.4364\n"
|
| 509 |
+
]
|
| 510 |
+
}
|
| 511 |
+
],
|
| 512 |
+
"source": [
|
| 513 |
+
"# =========================\n",
|
| 514 |
+
"# 6. Training\n",
|
| 515 |
+
"# =========================\n",
|
| 516 |
+
"history = model.fit(\n",
|
| 517 |
+
" train_padded,\n",
|
| 518 |
+
" train_labels,\n",
|
| 519 |
+
" epochs=5,\n",
|
| 520 |
+
" validation_data=(test_padded, test_labels)\n",
|
| 521 |
+
")"
|
| 522 |
+
]
|
| 523 |
+
},
|
| 524 |
+
{
|
| 525 |
+
"cell_type": "code",
|
| 526 |
+
"execution_count": 8,
|
| 527 |
+
"id": "7c98aa50",
|
| 528 |
+
"metadata": {},
|
| 529 |
+
"outputs": [
|
| 530 |
+
{
|
| 531 |
+
"name": "stdout",
|
| 532 |
+
"output_type": "stream",
|
| 533 |
+
"text": [
|
| 534 |
+
"\u001b[1m782/782\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 454us/step - accuracy: 0.7958 - loss: 0.4364\n",
|
| 535 |
+
"Test Accuracy: 0.7957599759101868\n"
|
| 536 |
+
]
|
| 537 |
+
}
|
| 538 |
+
],
|
| 539 |
+
"source": [
|
| 540 |
+
"# =========================\n",
|
| 541 |
+
"# 7. Evaluation\n",
|
| 542 |
+
"# =========================\n",
|
| 543 |
+
"loss, acc = model.evaluate(test_padded, test_labels)\n",
|
| 544 |
+
"print(\"Test Accuracy:\", acc)"
|
| 545 |
+
]
|
| 546 |
+
},
|
| 547 |
+
{
|
| 548 |
+
"cell_type": "code",
|
| 549 |
+
"execution_count": null,
|
| 550 |
+
"id": "2e979268",
|
| 551 |
+
"metadata": {},
|
| 552 |
+
"outputs": [
|
| 553 |
+
{
|
| 554 |
+
"name": "stdout",
|
| 555 |
+
"output_type": "stream",
|
| 556 |
+
"text": [
|
| 557 |
+
"INFO:tensorflow:Assets written to: /var/folders/d6/03s1zvdj5wg1gwyhbzrcn8gr0000gn/T/tmpfhvqfrf5/assets\n"
|
| 558 |
+
]
|
| 559 |
+
},
|
| 560 |
+
{
|
| 561 |
+
"name": "stderr",
|
| 562 |
+
"output_type": "stream",
|
| 563 |
+
"text": [
|
| 564 |
+
"INFO:tensorflow:Assets written to: /var/folders/d6/03s1zvdj5wg1gwyhbzrcn8gr0000gn/T/tmpfhvqfrf5/assets\n"
|
| 565 |
+
]
|
| 566 |
+
},
|
| 567 |
+
{
|
| 568 |
+
"name": "stdout",
|
| 569 |
+
"output_type": "stream",
|
| 570 |
+
"text": [
|
| 571 |
+
"Saved artifact at '/var/folders/d6/03s1zvdj5wg1gwyhbzrcn8gr0000gn/T/tmpfhvqfrf5'. The following endpoints are available:\n",
|
| 572 |
+
"\n",
|
| 573 |
+
"* Endpoint 'serve'\n",
|
| 574 |
+
" args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 100), dtype=tf.float32, name='keras_tensor')\n",
|
| 575 |
+
"Output Type:\n",
|
| 576 |
+
" TensorSpec(shape=(None, 1), dtype=tf.float32, name=None)\n",
|
| 577 |
+
"Captures:\n",
|
| 578 |
+
" 6045205776: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
|
| 579 |
+
" 13165568320: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
|
| 580 |
+
" 13127621040: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
|
| 581 |
+
" 13127623152: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
|
| 582 |
+
" 13127625440: TensorSpec(shape=(), dtype=tf.resource, name=None)\n"
|
| 583 |
+
]
|
| 584 |
+
},
|
| 585 |
+
{
|
| 586 |
+
"name": "stderr",
|
| 587 |
+
"output_type": "stream",
|
| 588 |
+
"text": [
|
| 589 |
+
"W0000 00:00:1775131910.106384 68307 tf_tfl_flatbuffer_helpers.cc:364] Ignored output_format.\n",
|
| 590 |
+
"W0000 00:00:1775131910.106397 68307 tf_tfl_flatbuffer_helpers.cc:367] Ignored drop_control_dependency.\n"
|
| 591 |
+
]
|
| 592 |
+
},
|
| 593 |
+
{
|
| 594 |
+
"name": "stdout",
|
| 595 |
+
"output_type": "stream",
|
| 596 |
+
"text": [
|
| 597 |
+
"Model TFLite berhasil disimpan!\n"
|
| 598 |
+
]
|
| 599 |
+
},
|
| 600 |
+
{
|
| 601 |
+
"name": "stderr",
|
| 602 |
+
"output_type": "stream",
|
| 603 |
+
"text": [
|
| 604 |
+
"I0000 00:00:1775131910.106825 68307 reader.cc:83] Reading SavedModel from: /var/folders/d6/03s1zvdj5wg1gwyhbzrcn8gr0000gn/T/tmpfhvqfrf5\n",
|
| 605 |
+
"I0000 00:00:1775131910.107216 68307 reader.cc:52] Reading meta graph with tags { serve }\n",
|
| 606 |
+
"I0000 00:00:1775131910.107221 68307 reader.cc:147] Reading SavedModel debug info (if present) from: /var/folders/d6/03s1zvdj5wg1gwyhbzrcn8gr0000gn/T/tmpfhvqfrf5\n",
|
| 607 |
+
"I0000 00:00:1775131910.110084 68307 mlir_graph_optimization_pass.cc:437] MLIR V1 optimization pass is not enabled\n",
|
| 608 |
+
"I0000 00:00:1775131910.110559 68307 loader.cc:236] Restoring SavedModel bundle.\n",
|
| 609 |
+
"I0000 00:00:1775131910.128073 68307 loader.cc:220] Running initialization op on SavedModel bundle at path: /var/folders/d6/03s1zvdj5wg1gwyhbzrcn8gr0000gn/T/tmpfhvqfrf5\n",
|
| 610 |
+
"I0000 00:00:1775131910.134243 68307 loader.cc:471] SavedModel load for tags { serve }; Status: success: OK. Took 27421 microseconds.\n",
|
| 611 |
+
"I0000 00:00:1775131910.146179 68307 dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n"
|
| 612 |
+
]
|
| 613 |
+
}
|
| 614 |
+
],
|
| 615 |
+
"source": [
|
| 616 |
+
"# =========================\n",
|
| 617 |
+
"# 8. Convert ke TensorFlow Lite (TinyML Step)\n",
|
| 618 |
+
"# =========================\n",
|
| 619 |
+
"converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
|
| 620 |
+
"\n",
|
| 621 |
+
"# Quantization (WAJIB untuk TinyML)\n",
|
| 622 |
+
"converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
|
| 623 |
+
"\n",
|
| 624 |
+
"tflite_model = converter.convert()\n",
|
| 625 |
+
"\n",
|
| 626 |
+
"# simpan\n",
|
| 627 |
+
"with open(\"tiny_sentiment_model_imdb.tflite\", \"wb\") as f:\n",
|
| 628 |
+
" f.write(tflite_model)\n",
|
| 629 |
+
"\n",
|
| 630 |
+
"print(\"Model TFLite berhasil disimpan!\")"
|
| 631 |
+
]
|
| 632 |
+
},
|
| 633 |
+
{
|
| 634 |
+
"cell_type": "code",
|
| 635 |
+
"execution_count": 12,
|
| 636 |
+
"id": "e89e8714",
|
| 637 |
+
"metadata": {},
|
| 638 |
+
"outputs": [
|
| 639 |
+
{
|
| 640 |
+
"name": "stdout",
|
| 641 |
+
"output_type": "stream",
|
| 642 |
+
"text": [
|
| 643 |
+
"\u001b[1m1/1\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
|
| 644 |
+
"Negative\n",
|
| 645 |
+
"\u001b[1m1/1\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
|
| 646 |
+
"Negative\n"
|
| 647 |
+
]
|
| 648 |
+
}
|
| 649 |
+
],
|
| 650 |
+
"source": [
|
| 651 |
+
"# =========================\n",
|
| 652 |
+
"# 9. Test Custom Input\n",
|
| 653 |
+
"# =========================\n",
|
| 654 |
+
"def predict(text):\n",
|
| 655 |
+
" seq = tokenizer.texts_to_sequences([text])\n",
|
| 656 |
+
" padded = tf.keras.preprocessing.sequence.pad_sequences(\n",
|
| 657 |
+
" seq, maxlen=max_length, padding='post'\n",
|
| 658 |
+
" )\n",
|
| 659 |
+
" pred = model.predict(padded)[0][0]\n",
|
| 660 |
+
" return \"Positive\" if pred > 0.5 else \"Negative\"\n",
|
| 661 |
+
"\n",
|
| 662 |
+
"print(predict(\"this movie is ugly\"))\n",
|
| 663 |
+
"print(predict(\"bad film ever\"))"
|
| 664 |
+
]
|
| 665 |
+
}
|
| 666 |
+
],
|
| 667 |
+
"metadata": {
|
| 668 |
+
"kernelspec": {
|
| 669 |
+
"display_name": "research",
|
| 670 |
+
"language": "python",
|
| 671 |
+
"name": "python3"
|
| 672 |
+
},
|
| 673 |
+
"language_info": {
|
| 674 |
+
"codemirror_mode": {
|
| 675 |
+
"name": "ipython",
|
| 676 |
+
"version": 3
|
| 677 |
+
},
|
| 678 |
+
"file_extension": ".py",
|
| 679 |
+
"mimetype": "text/x-python",
|
| 680 |
+
"name": "python",
|
| 681 |
+
"nbconvert_exporter": "python",
|
| 682 |
+
"pygments_lexer": "ipython3",
|
| 683 |
+
"version": "3.10.20"
|
| 684 |
+
}
|
| 685 |
+
},
|
| 686 |
+
"nbformat": 4,
|
| 687 |
+
"nbformat_minor": 5
|
| 688 |
+
}
|
tiny_sentiment_model_imdb.tflite
ADDED
|
Binary file (20.7 kB). View file
|
|
|