如何在MNIST上解决卷积神经网络中的形状不兼容问题？

import tensorflow as tf
from tensorflow.keras.datasets import mnist

# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 归一化像素值到[0, 1]
x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0

# 将图像数据从(28, 28)扩展到(28, 28, 1)，以匹配CNN的输入要求
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    Flatten(),
    Dense(64, activation='relu'),
    Dense(10, activation='softmax')
])

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

model.fit(x_train, y_train, epochs=5, batch_size=64, validation_split=0.1)