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basic-classification

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basic-classification
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{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c201ca37",
   "metadata": {},
   "source": [
    "# Basic Image Classification (CNN)\n",
    "\n",
    "Notebook ini berisi contoh paling dasar untuk klasifikasi gambar menggunakan TensorFlow/Keras.\n",
    "\n",
    "## Ide tugas klasifikasi\n",
    "1. Klasifikasi wireframe: `login`, `dashboard`, `product`, `form`, `table`.\n",
    "2. Klasifikasi style desain: `clean`, `dense`, `minimal`, `complex`.\n",
    "3. Klasifikasi tipe komponen dominan: `card-heavy`, `table-heavy`, `form-heavy`.\n",
    "\n",
    "Struktur dataset yang disarankan:\n",
    "\n",
    "```text\n",
    "my_dataset/\n",
    "  train/\n",
    "    class_a/\n",
    "    class_b/\n",
    "    class_c/\n",
    "  val/\n",
    "    class_a/\n",
    "    class_b/\n",
    "    class_c/\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6067a559",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "\n",
    "# Ubah sesuai lokasi dataset Anda\n",
    "data_dir_train = \"./my_dataset/train\"\n",
    "data_dir_val = \"./my_dataset/val\"\n",
    "\n",
    "img_size = (128, 128)\n",
    "batch_size = 32\n",
    "\n",
    "train_ds = tf.keras.utils.image_dataset_from_directory(\n",
    "    data_dir_train,\n",
    "    image_size=img_size,\n",
    "    batch_size=batch_size,\n",
    "    label_mode=\"int\"\n",
    ")\n",
    "\n",
    "val_ds = tf.keras.utils.image_dataset_from_directory(\n",
    "    data_dir_val,\n",
    "    image_size=img_size,\n",
    "    batch_size=batch_size,\n",
    "    label_mode=\"int\"\n",
    ")\n",
    "\n",
    "class_names = train_ds.class_names\n",
    "num_classes = len(class_names)\n",
    "print(\"Classes:\", class_names)\n",
    "\n",
    "# Optimasi pipeline input\n",
    "autotune = tf.data.AUTOTUNE\n",
    "train_ds = train_ds.shuffle(1000).prefetch(buffer_size=autotune)\n",
    "val_ds = val_ds.prefetch(buffer_size=autotune)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "368bd39b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model CNN sederhana\n",
    "model = models.Sequential([\n",
    "    layers.Rescaling(1.0 / 255, input_shape=(img_size[0], img_size[1], 3)),\n",
    "    layers.Conv2D(32, 3, activation=\"relu\"),\n",
    "    layers.MaxPooling2D(),\n",
    "    layers.Conv2D(64, 3, activation=\"relu\"),\n",
    "    layers.MaxPooling2D(),\n",
    "    layers.Conv2D(128, 3, activation=\"relu\"),\n",
    "    layers.MaxPooling2D(),\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(128, activation=\"relu\"),\n",
    "    layers.Dropout(0.3),\n",
    "    layers.Dense(num_classes, activation=\"softmax\")\n",
    "])\n",
    "\n",
    "model.compile(\n",
    "    optimizer=\"adam\",\n",
    "    loss=\"sparse_categorical_crossentropy\",\n",
    "    metrics=[\"accuracy\"]\n",
    ")\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b75d2ec6",
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 10\n",
    "history = model.fit(\n",
    "    train_ds,\n",
    "    validation_data=val_ds,\n",
    "    epochs=epochs\n",
    ")\n",
    "\n",
    "loss, acc = model.evaluate(val_ds)\n",
    "print(f\"Validation accuracy: {acc:.4f}\")\n",
    "\n",
    "model.save(\"basic_cnn_classification.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55fff896",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prediksi 1 gambar baru\n",
    "import numpy as np\n",
    "from tensorflow.keras.preprocessing import image\n",
    "\n",
    "img_path = \"./sample.jpg\"  # ganti ke file gambar Anda\n",
    "img = image.load_img(img_path, target_size=img_size)\n",
    "arr = image.img_to_array(img)\n",
    "arr = np.expand_dims(arr, axis=0) / 255.0\n",
    "\n",
    "pred = model.predict(arr)\n",
    "pred_class = class_names[np.argmax(pred)]\n",
    "print(\"Predicted class:\", pred_class)"
   ]
  }
 ],
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