from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.utils import to_categorical

# Загрузка данных CIFAR-10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

# Нормализация и кодирование меток
x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

# Построение модели
model = Sequential([
    Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(32, 32, 3)),
    Conv2D(32, (3, 3), activation='relu', padding='same'),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu', padding='same'),
    Conv2D(64, (3, 3), activation='relu', padding='same'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(512, activation='relu'),
    Dense(10, activation='softmax')
])

# Компиляция и обучение
model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

model.fit(
    x_train, y_train,
    epochs=30,
    batch_size=64,
    validation_split=0.1,
    verbose=1
)