Shawn Lee Shawn Lee - 4 months ago 1021
Python Question

How could I use Batch Normalization in TensorFlow?

I would like to use Batch Normalization in TensorFlow, since I found it in the source code

. However, I did not find it documented on

BN has different semantics in MLP and CNN, so I am not sure what exactly this BN does.

I did not find a method called

The C++ code is copied here for reference:

.Input("t: T")
.Input("m: T")
.Input("v: T")
.Input("beta: T")
.Input("gamma: T")
.Output("result: T")
.Attr("T: numbertype")
.Attr("variance_epsilon: float")
.Attr("scale_after_normalization: bool")
Batch normalization.

t: A 4D input Tensor.
m: A 1D mean Tensor with size matching the last dimension of t.
This is the first output from MovingMoments.
v: A 1D variance Tensor with size matching the last dimension of t.
This is the second output from MovingMoments.
beta: A 1D beta Tensor with size matching the last dimension of t.
An offset to be added to the normalized tensor.
gamma: A 1D gamma Tensor with size matching the last dimension of t.
If "scale_after_normalization" is true, this tensor will be multiplied
with the normalized tensor.
variance_epsilon: A small float number to avoid dividing by 0.
scale_after_normalization: A bool indicating whether the resulted tensor
needs to be multiplied with gamma.

dga dga

The documentation string for this has improved since the release - see the docs comment in the master branch instead of the one you found. It clarifies, in particular, that it's the output from tf.nn.moments.

You can see a very simple example of its use in the batch_norm test code. For a more real-world use example, I've included below the helper class and use notes that I scribbled up for my own use (no warranty provided!):

"""A helper class for managing batch normalization state.                   

This class is designed to simplify adding batch normalization               
( to your model by                    
managing the state variables associated with it.                            

Important use note:  The function get_assigner() returns                    
an op that must be executed to save the updated state.                      
A suggested way to do this is to make execution of the                      
model optimizer force it, e.g., by:                                         

  update_assignments =,                         
  with tf.control_dependencies([optimizer]):                                
    optimizer =                                


import tensorflow as tf

class ConvolutionalBatchNormalizer(object):
  """Helper class that groups the normalization logic and variables.        

      ewma = tf.train.ExponentialMovingAverage(decay=0.99)                  
      bn = ConvolutionalBatchNormalizer(depth, 0.001, ewma, True)           
      update_assignments = bn.get_assigner()                                
      x = bn.normalize(y, train=training?)                                  
      (the output x will be batch-normalized).                              

  def __init__(self, depth, epsilon, ewma_trainer, scale_after_norm):
    self.mean = tf.Variable(tf.constant(0.0, shape=[depth]),
    self.variance = tf.Variable(tf.constant(1.0, shape=[depth]),
    self.beta = tf.Variable(tf.constant(0.0, shape=[depth]))
    self.gamma = tf.Variable(tf.constant(1.0, shape=[depth]))
    self.ewma_trainer = ewma_trainer
    self.epsilon = epsilon
    self.scale_after_norm = scale_after_norm

  def get_assigner(self):
    """Returns an EWMA apply op that must be invoked after optimization."""
    return self.ewma_trainer.apply([self.mean, self.variance])

  def normalize(self, x, train=True):
    """Returns a batch-normalized version of x."""
    if train:
      mean, variance = tf.nn.moments(x, [0, 1, 2])
      assign_mean = self.mean.assign(mean)
      assign_variance = self.variance.assign(variance)
      with tf.control_dependencies([assign_mean, assign_variance]):
        return tf.nn.batch_norm_with_global_normalization(
            x, mean, variance, self.beta, self.gamma,
            self.epsilon, self.scale_after_norm)
      mean = self.ewma_trainer.average(self.mean)
      variance = self.ewma_trainer.average(self.variance)
      local_beta = tf.identity(self.beta)
      local_gamma = tf.identity(self.gamma)
      return tf.nn.batch_norm_with_global_normalization(
          x, mean, variance, local_beta, local_gamma,
          self.epsilon, self.scale_after_norm)

Note that I called it a ConvolutionalBatchNormalizer because it pins the use of tf.nn.moments to sum across axes 0, 1, and 2, whereas for non-convolutional use you might only want axis 0.

Feedback appreciated if you use it.