我就废话不多说了，直接上代码吧！

import tensorflow as tfdef model_1(): with tf.variable_scope("var_a"): a = tf.Variable(initial_value=[1, 2, 3], name="a") vars = [var for var in tf.trainable_variables() if var.name.startswith("var_a")] print(len(vars)) return varsdef model_2(): vars1 = model_1() with tf.variable_scope("var_b"): a = tf.Variable(initial_value=[1, 2, 3], name="a") vars2 = [var for var in tf.trainable_variables() if var.name.startswith("var")] print(len(vars2)) return vars1def pretrain_model1(): print("-------- model 1 ------") vars = model_1() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) saver = tf.train.Saver() saver.save(sess, "./model.ckpt")def train_model2(): print("-------- model 2 ------") model1_vars = model_2() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) saver = tf.train.Saver(var_list=model1_vars) saver.restore(sess, "./model.ckpt") vars = sess.run([model1_vars]) for var in vars: print(var)step = 2if step == 1: pretrain_model1()else: train_model2()

以上这篇tensorflow 只恢复部分模型参数的实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。