Save and Load Models in TensorFlow

In TensorFlow, model saving and loading can be achieved using tf.train.Saver. Initially, during model training, tf.train.Saver can be used to save the model parameters. For example:

saver = tf.train.Saver()
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    # 训练模型
    saver.save(sess, "model.ckpt")

After saving the model, we can load the model parameters using tf.train.Saver. For example:

saver = tf.train.Saver()
with tf.Session() as sess:
    saver.restore(sess, "model.ckpt")
    # 使用加载的模型进行预测或继续训练

In this way, we can easily save and load TensorFlow model parameters, achieving model persistence and migration.