import tensorflow as tf
from tensorflow.keras import layers, models
import numpy as np
import time

start_time = time.time()
# 변수 정의

# 사용 가능한 GPU 확인
gpus = tf.config.list_physical_devices('GPU')
print("Available GPUs:", gpus)

for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)
# 데이터셋 로드 및 전처리
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# 데이터 정규화 및 차원 확장
x_train = x_train / 255.0
x_test = x_test / 255.0
x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)

# 간단한 CNN 모델 정의
model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dense(10, activation='softmax')
])

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

# 모델 요약
model.summary()

# 모델 훈련
model.fit(x_train, y_train, epochs=500, batch_size=256, validation_split=0.2)

# 모델 평가
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
print(f"Test accuracy: {test_acc:.2f}")

# 모델 평가 종료 시간 기록 및 출력
end_time = time.time()
print(f"Evaluation time: {end_time - start_time:.2f} seconds")