TensorFlow

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np

# GPU 메모리 동적 할당 설정
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    for gpu in gpus:
        tf.config.experimental.set_memory_growth(gpu, True)

# 샘플 데이터셋 로드 (CIFAR-10)
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# CNN 모델 정의
model = keras.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dropout(0.5),
    layers.Dense(10, activation='softmax')
])

# 모델 컴파일
model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

# 콜백 설정
callbacks = [
    keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True),
    keras.callbacks.TensorBoard(log_dir='./logs'),
    keras.callbacks.ModelCheckpoint('best_model.keras', save_best_only=True)
]

# 모델 학습
history = model.fit(
    x_train, y_train,
    epochs=20,
    batch_size=64,
    validation_split=0.2,
    callbacks=callbacks
)

# 모델 평가
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
print(f'테스트 정확도: {test_acc:.4f}')

# 모델 저장 및 로드
model.save('cifar10_model.keras')
loaded_model = keras.models.load_model('cifar10_model.keras')

# TFLite 변환 (모바일 배포용)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
with open('model.tflite', 'wb') as f:
    f.write(tflite_model)