ema = tf.train.ExponentialMovingAverage(decay,num_updates) 

ema.apply([var0, var1]) 

sess.run(ema.average([var0, var1])) 

import tensorflow as tf 
if __name__ == "__main__": 

  v = tf.Variable(0.,name="v") 

  #設(shè)置滑動(dòng)平均模型的系數(shù) 

  ema = tf.train.ExponentialMovingAverage(0.99) 

  #設(shè)置變量v使用滑動(dòng)平均模型，tf.all_variables()設(shè)置所有變量 

  op = ema.apply([v]) 

  #獲取變量v的名字 

  print(v.name) 

  #v:0 

  #創(chuàng)建一個(gè)保存模型的對(duì)象 

  save = tf.train.Saver() 

  sess = tf.Session() 

  #初始化所有變量 

  init = tf.initialize_all_variables() 

  sess.run(init) 

  #給變量v重新賦值 

  sess.run(tf.assign(v,10)) 

  #應(yīng)用平均滑動(dòng)設(shè)置 

  sess.run(op) 

  #保存模型文件 

  save.save(sess,"./model.ckpt") 

  #輸出變量v之前的值和使用滑動(dòng)平均模型之后的值 

  print(sess.run([v,ema.average(v)])) 

  #[10.0, 0.099999905]  

v = tf.Variable(1.,name="v") 

#定義模型對(duì)象 

saver = tf.train.Saver({"v/ExponentialMovingAverage":v}) 

sess = tf.Session() 

saver.restore(sess,"./model.ckpt") 

print(sess.run(v)) 

#0.0999999  

v = tf.Variable(1.,name="v") 

#滑動(dòng)模型的參數(shù)的大小并不會(huì)影響v的值 

ema = tf.train.ExponentialMovingAverage(0.99) 

print(ema.variables_to_restore()) 

#{'v/ExponentialMovingAverage': <tf.Variable 'v:0' shape=() dtype=float32_ref>} 

sess = tf.Session() 

saver = tf.train.Saver(ema.variables_to_restore()) 

saver.restore(sess,"./model.ckpt") 

print(sess.run(v)) 

#0.0999999 

 | Example usage when creating a training model:
 | 
 | ```python
 | # 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)
 | 
 | with tf.control_dependencies([opt_op]):
 |   # Create the shadow variables, and add ops to maintain moving averages
 |   # of var0 and var1. This also creates 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.
 |   training_op = ema.apply([var0, var1])
 | 
 | ...train the model by running training_op...
 | ```

def batch_norm(x,beta,gamma,phase_train,scope='bn',decay=0.9,eps=1e-5):

  with tf.variable_scope(scope):

    # beta = tf.get_variable(name='beta', shape=[n_out], initializer=tf.constant_initializer(0.0), trainable=True)

    # gamma = tf.get_variable(name='gamma', shape=[n_out],

    #             initializer=tf.random_normal_initializer(1.0, stddev), trainable=True)

    batch_mean,batch_var = tf.nn.moments(x,[0,1,2],name='moments')

    ema = tf.train.ExponentialMovingAverage(decay=decay)

 

    def mean_var_with_update():

      ema_apply_op = ema.apply([batch_mean,batch_var])

      with tf.control_dependencies([ema_apply_op]):

        return tf.identity(batch_mean),tf.identity(batch_var)

        # identity之后會(huì)把Variable轉(zhuǎn)換為Tensor并入圖中，

        # 否則由于Variable是獨(dú)立于Session的，不會(huì)被圖控制control_dependencies限制

 

    mean,var = tf.cond(phase_train,

              mean_var_with_update,

              lambda: (ema.average(batch_mean),ema.average(batch_var)))

　　   normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, eps)

  return normed