(inputs, is_training, scope, moments_dims, bn_decay)
return normed
Batch normalization on convolutional maps and beyond...
  Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
  
  Args:
      inputs:        Tensor, k-D input ... x C could be BC or BHWC or BDHWC
      is_training:   boolean tf.Varialbe, true indicates training phase
      scope:         string, variable scope
      moments_dims:  a list of ints, indicating dimensions for moments calculation
      bn_decay:      float or float tensor variable, controling moving average weight
  Return:
      normed:        batch-normalized maps
Batch normalization on convolutional maps and beyond...
  Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
  
  Args:
      inputs:        Tensor, k-D input ... x C could be BC or BHWC or BDHWC
      is_training:   boolean tf.Varialbe, true indicates training phase
      scope:         string, variable scope
      moments_dims:  a list of ints, indicating dimensions for moments calculation
      bn_decay:      float or float tensor variable, controling moving average weight
  Return:
      normed:        batch-normalized maps
Batch normalization on convolutional maps and beyond ... Ref . : http : // stackoverflow . com / questions / 33949786 / how - could - i - use - batch - normalization - in - tensorflow Args : inputs : Tensor k - D input ... x C could be BC or BHWC or BDHWC is_training : boolean tf . Varialbe true indicates training phase scope : string variable scope moments_dims : a list of ints indicating dimensions for moments calculation bn_decay : float or float tensor variable controling moving average weight Return : normed : batch - normalized maps
def batch_norm_template(inputs, is_training, scope, moments_dims, bn_decay):
    """ Batch normalization on convolutional maps and beyond...
  Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
  
  Args:
      inputs:        Tensor, k-D input ... x C could be BC or BHWC or BDHWC
      is_training:   boolean tf.Varialbe, true indicates training phase
      scope:         string, variable scope
      moments_dims:  a list of ints, indicating dimensions for moments calculation
      bn_decay:      float or float tensor variable, controling moving average weight
  Return:
      normed:        batch-normalized maps
  """
    with tf.variable_scope(scope) as sc:
        num_channels = inputs.get_shape()[-1].value
        beta = tf.Variable(
            tf.constant(0.0, shape=[num_channels]), name="beta", trainable=True
        )
        gamma = tf.Variable(
            tf.constant(1.0, shape=[num_channels]), name="gamma", trainable=True
        )
        batch_mean, batch_var = tf.nn.moments(inputs, moments_dims, name="moments")
        decay = bn_decay if bn_decay is not None else 0.9
        ema = tf.train.ExponentialMovingAverage(decay=decay)
        # Operator that maintains moving averages of variables.
        ema_apply_op = tf.cond(
            is_training, lambda: ema.apply([batch_mean, batch_var]), lambda: tf.no_op()
        )

        # Update moving average and return current batch's avg and var.
        def mean_var_with_update():
            with tf.control_dependencies([ema_apply_op]):
                return tf.identity(batch_mean), tf.identity(batch_var)

        # ema.average returns the Variable holding the average of var.
        mean, var = tf.cond(
            is_training,
            mean_var_with_update,
            lambda: (ema.average(batch_mean), ema.average(batch_var)),
        )
        normed = tf.nn.batch_normalization(inputs, mean, var, beta, gamma, 1e-3)
    return normed
def batch_norm_template ( inputs , is_training , scope , moments_dims , bn_decay ) : with tf . variable_scope ( scope ) as sc : num_channels = inputs . get_shape ( ) [ - 1 ] . value beta = tf . Variable ( tf . constant ( 0.0 , shape = [ num_channels ] ) , name = "beta" , trainable = True ) gamma = tf . Variable ( tf . constant ( 1.0 , shape = [ num_channels ] ) , name = "gamma" , trainable = True ) batch_mean , batch_var = tf . nn . moments ( inputs , moments_dims , name = "moments" ) decay = bn_decay if bn_decay is not None else 0.9 ema = tf . train . ExponentialMovingAverage ( decay = decay ) # Operator that maintains moving averages of variables. ema_apply_op = tf . cond ( is_training , lambda : ema . apply ( [ batch_mean , batch_var ] ) , lambda : tf . no_op ( ) ) # Update moving average and return current batch's avg and var. def mean_var_with_update ( ) : with tf . control_dependencies ( [ ema_apply_op ] ) : return tf . identity ( batch_mean ) , tf . identity ( batch_var ) # ema.average returns the Variable holding the average of var. mean , var = tf . cond ( is_training , mean_var_with_update , lambda : ( ema . average ( batch_mean ) , ema . average ( batch_var ) ) , ) normed = tf . nn . batch_normalization ( inputs , mean , var , beta , gamma , 1e-3 ) return normed