tensorflow学习笔记(三十三):ExponentialMovingAverage

import tensorflow as tf
w = tf.Variable(1.0)
ema = tf.train.ExponentialMovingAverage(0.9)
update = tf.assign_add(w, 1.0)

with tf.control_dependencies([update]):
    #返回一个op,这个op用来更新moving_average,i.e. shadow value
    ema_op = ema.apply([w])#这句和下面那句不能调换顺序
# 以 w 当作 key， 获取 shadow value 的值
ema_val = ema.average(w)#参数不能是list，有点蛋疼

with tf.Session() as sess:
    tf.global_variables_initializer().run()
    for i in range(3):
        sess.run(ema_op)
        print(sess.run(ema_val))
# 创建一个时间序列 1 2 3 4
#输出：
#1.1      =0.9*1 + 0.1*2
#1.29     =0.9*1.1+0.1*3
#1.561    =0.9*1.29+0.1*4

# Create variables.
var0 = tf.Variable(...)
var1 = tf.Variable(...)
# ... use the variables to build a training model...
...
# Create an op that applies the optimizer.  This is what we usually
# would use as a training op.
opt_op = opt.minimize(my_loss, [var0, var1])

# Create an ExponentialMovingAverage object
ema = tf.train.ExponentialMovingAverage(decay=0.9999)

# Create the shadow variables, and add ops to maintain moving averages
# of var0 and var1.
maintain_averages_op = ema.apply([var0, var1])

# Create an op that will update the moving averages after each training
# step.  This is what we will use in place of the usual training op.
with tf.control_dependencies([opt_op]):
    training_op = tf.group(maintain_averages_op)
    # run这个op获取当前时刻 ema_value
    get_var0_average_op = ema.average(var0)

# Create a Saver that loads variables from their saved shadow values.
shadow_var0_name = ema.average_name(var0)
shadow_var1_name = ema.average_name(var1)
saver = tf.train.Saver({shadow_var0_name: var0, shadow_var1_name: var1})
saver.restore(...checkpoint filename...)
# var0 and var1 now hold the moving average values

#Returns a map of names to Variables to restore.
variables_to_restore = ema.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
...
saver.restore(...checkpoint filename...)