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import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.datasets import mnist
import matplotlib.pyplot as plt

# MNIST 데이터셋 로드
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# 데이터 전처리
train_images = train_images.reshape((60000, 28 * 28)).astype('float32') / 255
test_images = test_images.reshape((10000, 28 * 28)).astype('float32') / 255

# 모델 생성
model = models.Sequential([
    layers.Dense(128, activation='relu', input_shape=(28 * 28,)),
    layers.Dropout(0.2),
    layers.Dense(10, activation='softmax')
])

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

# 모델 훈련
model.fit(train_images, train_labels, epochs=5)

# 모델 평가
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print(f'\nTest Accuracy: {test_acc * 100:.2f} %')

# 예측 수행
predictions = model.predict(test_images)
predicted_labels = [tf.argmax(p).numpy() for p in predictions]

# 이미지 표시
plt.figure(figsize=(9, 9))
for i in range(25):
    plt.subplot(5, 5, i + 1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(test_images[i].reshape(28, 28), cmap=plt.cm.binary)  # 이미지를 2D로 변환
    plt.xlabel(f"Real: {test_labels[i]} - Pred: {predicted_labels[i]}")

plt.show()

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