tf.train.Saver函数的用法之保存全部变量和模型

用于保存模型，以后再用就可以直接导入模型进行计算，方便。

例如：

import tensorflow as tf;  
import numpy as np;  
import matplotlib.pyplot as plt;  

v1 = tf.Variable(tf.constant(1, shape=[1]), name='v1')
v2 = tf.Variable(tf.constant(2, shape=[1]), name='v2')

result = v1 + v2

init = tf.initialize_all_variables()

saver = tf.train.Saver()

with tf.Session() as sess:
	sess.run(init)
	saver.save(sess, "/home/penglu/Desktop/lp/model.ckpt")
	# saver.restore(sess, "/home/penglu/Desktop/lp/model.ckpt")
	# print sess.run(result)

结果：

下次需要使用模型就可以用下面的代码：

import tensorflow as tf;  
import numpy as np;  
import matplotlib.pyplot as plt;  

v1 = tf.Variable(tf.constant(1, shape=[1]), name='v1')
v2 = tf.Variable(tf.constant(2, shape=[1]), name='v2')

result = v1 + v2

init = tf.initialize_all_variables()

saver = tf.train.Saver()

with tf.Session() as sess:
	saver.restore(sess, "/home/penglu/Desktop/lp/model.ckpt")
	print sess.run(result)

或者这个代码：

import tensorflow as tf;  
import numpy as np;  
import matplotlib.pyplot as plt;

saver = tf.train.import_meta_graph('/home/penglu/Desktop/lp/model.ckpt.meta')
with tf.Session() as sess:
	saver.restore(sess, "/home/penglu/Desktop/lp/model.ckpt")
	print sess.run(tf.get_default_graph().get_tensor_by_name('add:0'))

输出：

[3]