tensorflow::ops::QuantizedBatchNormWithGlobalNormalization

#include <nn_ops.h>

Quantized Batch normalization.

Summary

This op is deprecated and will be removed in the future. Prefer tf.nn.batch_normalization.

Arguments:

• scope: A Scope object
• t: A 4D input Tensor.
• t_min: The value represented by the lowest quantized input.
• t_max: The value represented by the highest quantized input.
• m: A 1D mean Tensor with size matching the last dimension of t. This is the first output from tf.nn.moments, or a saved moving average thereof.
• m_min: The value represented by the lowest quantized mean.
• m_max: The value represented by the highest quantized mean.
• v: A 1D variance Tensor with size matching the last dimension of t. This is the second output from tf.nn.moments, or a saved moving average thereof.
• v_min: The value represented by the lowest quantized variance.
• v_max: The value represented by the highest quantized variance.
• beta: A 1D beta Tensor with size matching the last dimension of t. An offset to be added to the normalized tensor.
• beta_min: The value represented by the lowest quantized offset.
• beta_max: The value represented by the highest quantized offset.
• 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.
• gamma_min: The value represented by the lowest quantized gamma.
• gamma_max: The value represented by the highest quantized gamma.
• 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.

Returns:

• Output result
• Output result_min
• Output result_max

Public attributes

::tensorflow::Output result

::tensorflow::Output result_max

::tensorflow::Output result_min

Public functions

 QuantizedBatchNormWithGlobalNormalization(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input t,
  ::tensorflow::Input t_min,
  ::tensorflow::Input t_max,
  ::tensorflow::Input m,
  ::tensorflow::Input m_min,
  ::tensorflow::Input m_max,
  ::tensorflow::Input v,
  ::tensorflow::Input v_min,
  ::tensorflow::Input v_max,
  ::tensorflow::Input beta,
  ::tensorflow::Input beta_min,
  ::tensorflow::Input beta_max,
  ::tensorflow::Input gamma,
  ::tensorflow::Input gamma_min,
  ::tensorflow::Input gamma_max,
  DataType out_type,
  float variance_epsilon,
  bool scale_after_normalization
)