如何在Tensorflow中恢复当前模型的预训练检查点？

import tensorflow as tf

# 假设我们有一个简单的卷积神经网络
def create_model():
    model = tf.keras.models.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')
    ])
    return model

model = create_model()

saver = tf.train.Checkpoint(model=model)

checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")

# 恢复检查点
saver.restore(tf.train.latest_checkpoint(checkpoint_dir))

checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")

if not tf.train.latest_checkpoint(checkpoint_dir):
    raise ValueError("No checkpoint found in directory: %s" % checkpoint_dir)

# 确保模型结构一致
model = create_model()

pip install tensorflow==<version>