tensorflow save checkpoint-优快云博客

本文详细介绍如何使用TensorFlow将变量保存至文件及从文件中恢复。通过具体代码实例，展示了变量定义、初始化、保存及恢复的全过程。适用于深度学习模型训练过程中变量状态的持久化与读取。

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#save to file

import tensorflow as tf
import  numpy as np

##（1）Save to file 把相关变量存储到文件中
#remember to define the same dtype and shape when restore
W = tf.Variable([[1,2,3],[3,4,5]],dtype=tf.float32,name='weights')
b = tf.Variable([[1,2,3]],dtype=tf.float32,name='biases')

init = tf.initialize_all_variables()
saver = tf.train.Saver()

with tf.Session() as sess:
    sess.run(init)
    save_path = saver.save(sess,"my_net/save_net.ckpt")
    print("Save to path : ",save_path)

##（2）restore variables 从文件中取出相关变量
#redefine the same shape and same type for you variables
W = tf.Variable(np.arange(6).reshape((2,3)),dtype=tf.float32,name="weights")#reshape((2,3):2行3列
b = tf.Variable(np.arange(3).reshape((1,3)),dtype=tf.float32,name="biases")

#not need init step
saver = tf.train.Saver()
with tf.Session() as sess:
    saver.restore(sess,"my_net/save_net.ckpt")
    print("weights: ",sess.run(W))
    print("biases: ",sess.run(b))

转载于:https://my.oschina.net/u/2391658/blog/3067871