TensorFlow-关键字global_step使用

global_step变量用于保存全局训练步骤（global
training step）的数值。

经常在滑动平均，学习速率变化的时候用到这个参数。

使用optimizer.minimize()可以自动更新global_step。

# -*-coding: utf-8-*-

import tensorflow as tf
import numpy as np

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, 0.9, staircase=False)
loss = tf.pow(w*x - y, 2)

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

with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(10):
sess.run(train_op, 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)