我就废话不多说了,直接上代码吧!
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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()
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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服务器之家。
原文链接:https://www.cnblogs.com/huwtylv/p/10204295.html