Tensorflow 实战google深度学习框架 07 滑动平均模型

import tensorflow as tf

#1. 定义变量及滑动平均类

v1 = tf.Variable(0, dtype=tf.float32)
step = tf.Variable(0, trainable=False)
ema = tf.train.ExponentialMovingAverage(0.99, step)
maintain_averages_op = ema.apply([v1])

# 2. 查看不同迭代中变量取值的变化
with tf.Session() as sess:

# 初始化
init_op = tf.global_variables_initializer()
sess.run(init_op)
print sess.run([v1, ema.average(v1)])

# 更新变量v1的取值
sess.run(tf.assign(v1, 5))
sess.run(maintain_averages_op)
print sess.run([v1, ema.average(v1)])

# 更新step和v1的取值
sess.run(tf.assign(step, 10000))
sess.run(tf.assign(v1, 10))
sess.run(maintain_averages_op)
print sess.run([v1, ema.average(v1)])

# 更新一次v1的滑动平均值
sess.run(maintain_averages_op)
print sess.run([v1, ema.average(v1)])