Python tensorflow.python.ops.nn.fused_batch_norm() Examples

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Example #1
Source File: normalization.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 4 votes vote down vote up 
def _fused_batch_norm(self, inputs, training):
    """Returns the output of fused batch norm."""
    # TODO(reedwm): Add support for fp16 inputs.
    beta = self.beta if self.center else self._beta_const
    gamma = self.gamma if self.scale else self._gamma_const

    def _fused_batch_norm_training():
      return nn.fused_batch_norm(
          inputs,
          gamma,
          beta,
          epsilon=self.epsilon,
          data_format=self._data_format)

    def _fused_batch_norm_inference():
      return nn.fused_batch_norm(
          inputs,
          gamma,
          beta,
          mean=self.moving_mean,
          variance=self.moving_variance,
          epsilon=self.epsilon,
          is_training=False,
          data_format=self._data_format)

    output, mean, variance = utils.smart_cond(
        training, _fused_batch_norm_training, _fused_batch_norm_inference)
    if not self._bessels_correction_test_only:
      # Remove Bessel's correction to be consistent with non-fused batch norm.
      # Note that the variance computed by fused batch norm is
      # with Bessel's correction.
      sample_size = math_ops.cast(
          array_ops.size(inputs) / array_ops.size(variance), variance.dtype)
      factor = (sample_size - math_ops.cast(1.0, variance.dtype)) / sample_size
      variance *= factor

    training_value = utils.constant_value(training)
    if training_value is None:
      one_minus_decay = _smart_select(training,
                                      lambda: self._one_minus_decay,
                                      lambda: 0.)
    else:
      one_minus_decay = ops.convert_to_tensor(self._one_minus_decay)
    if training_value or training_value is None:
      mean_update = self._assign_moving_average(self.moving_mean, mean,
                                                one_minus_decay)
      variance_update = self._assign_moving_average(self.moving_variance,
                                                    variance, one_minus_decay)
      if context.in_graph_mode():
        # Note that in Eager mode, the updates are already executed when running
        # assign_moving_averages. So we do not need to put them into
        # collections.
        self.add_update(mean_update, inputs=inputs)
        self.add_update(variance_update, inputs=inputs)

    return output