如何规范化BatchDataset并在模型中使用它？

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()

# 将图像数据转换为浮点数类型
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')

# 最小-最大规范化
x_train = (x_train - x_train.min()) / (x_train.max() - x_train.min())
x_test = (x_test - x_test.min()) / (x_test.max() - x_test.min())

# 将数据转换为4维张量（样本数，高度，宽度，通道数）
x_train = x_train.reshape((x_train.shape[0], 28, 28, 1))
x_test = x_test.reshape((x_test.shape[0], 28, 28, 1))

# 创建BatchDataset
batch_size = 32
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(batch_size)
test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(batch_size)

# 构建简单的CNN模型
model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

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

# 训练模型
model.fit(train_dataset, epochs=5, validation_data=test_dataset)