本文为大家分享了TensorFLow用Saver保存和恢复变量的具体代码，供大家参考，具体内容如下
建立文件tensor_save.py, 保存变量v1,v2的tensor到checkpoint files中，名称分别设置为v3,v4。
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
|
import tensorflow as tf# Create some variables.v1 = tf.Variable(3, name="v1")v2 = tf.Variable(4, name="v2")# Create modely=tf.add(v1,v2)# Add an op to initialize the variables.init_op = tf.initialize_all_variables()# Add ops to save and restore all the variables.saver = tf.train.Saver({'v3':v1,'v4':v2})# Later, launch the model, initialize the variables, do some work, save the# variables to disk.with tf.Session() as sess: sess.run(init_op) print("v1 = ", v1.eval()) print("v2 = ", v2.eval()) # Save the variables to disk. save_path = saver.save(sess, "f:/tmp/model.ckpt") print ("Model saved in file: ", save_path) |
建立文件tensor_restror.py, 将checkpoint files中名称分别为v3,v4的tensor分别恢复到变量v3,v4中。
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
|
import tensorflow as tf# Create some variables.v3 = tf.Variable(0, name="v3")v4 = tf.Variable(0, name="v4")# Create modely=tf.mul(v3,v4)# Add ops to save and restore all the variables.saver = tf.train.Saver()# Later, launch the model, use the saver to restore variables from disk, and# do some work with the model.with tf.Session() as sess: # Restore variables from disk. saver.restore(sess, "f:/tmp/model.ckpt") print ("Model restored.") print ("v3 = ", v3.eval()) print ("v4 = ", v4.eval()) print ("y = ",sess.run(y)) |
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：http://blog.csdn.net/muyiyushan/article/details/68486497
