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)])       

[0.0, 0.0]
[5.0, 4.5]
[10.0, 4.5549998]
[10.0, 4.6094499]