如何保存神经网络中每个循环的结果

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# 构建一个简单的神经网络模型
model = Sequential([
    Dense(64, activation='relu', input_shape=(784,)),
    Dense(10, activation='softmax')
])

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

# 定义一个回调函数来保存每个epoch的结果
checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
    filepath='model_checkpoint_{epoch:02d}.h5',
    save_weights_only=False,  # 保存完整模型
    save_freq='epoch'  # 每个epoch保存一次
)

# 加载数据（以MNIST为例）
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train.reshape(-1, 784).astype('float32') / 255.0
x_test = x_test.reshape(-1, 784).astype('float32') / 255.0

# 训练模型并保存每个epoch的结果
model.fit(x_train, y_train,
          epochs=10,
          validation_data=(x_test, y_test),
          callbacks=[checkpoint_callback])