tensorflow 只恢复部分模型参数

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 = 2
if step == 1:
    pretrain_model1()
else:
    train_model2()