tensorflow 只恢复部分模型参数的实例
我就废话不多说了，直接上代码吧！
import tensorflow as tf
def 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 vars
def 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 vars1
def 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()
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我们。
赞 (0)