tensorflow中的关键字global_step使用

global_step经常在滑动平均，学习速率变化的时候需要用到，这个参数在tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_steps)里面有，系统会自动更新这个参数的值，从1开始。

例如：

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

x = tf.placeholder(tf.float32, shape=[None, 1], name='x')
y = tf.placeholder(tf.float32, shape=[None, 1], name='y')
w = tf.Variable(tf.constant(0.0))

global_steps = tf.Variable(0, trainable=False)
learning_rate = tf.train.exponential_decay(0.1, global_steps, 10, 2, staircase=False)
loss = tf.pow(w*x-y, 2)

train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_steps)

with tf.Session() as sess:
	sess.run(tf.initialize_all_variables())
	for i in range(10):
		sess.run(train_step, feed_dict={x:np.linspace(1,2,10).reshape([10,1]),
			y:np.linspace(1,2,10).reshape([10,1])})
		print sess.run(learning_rate)
		print sess.run(global_steps)

输出：

0.107177
1
0.11487
2
0.123114
3
0.131951
4
0.141421
5
0.151572
6
0.16245
7
0.17411
8
0.186607
9
0.2
10

分析：学习速率第一次训练开始变化，global_steps每次自动加1