MeanVarianceNormalization

MeanVarianceNormalization - 13

Version

This version of the operator has been available since version 13.

Summary

A MeanVarianceNormalization Function: Perform mean variance normalization on the input tensor X using formula: (X-EX)/sqrt(E(X-EX)^2)

Function Body

The function definition for this operator.

<
  domain: "",
  opset_import: ["" : 18]
>
MeanVarianceNormalization <axes>(X) => (Y)
{
   Exponent = Constant <value: tensor = float {2}> ()
   Epsilon = Constant <value: tensor = float {1e-09}> ()
   axes = Constant <value_ints: ints = @axes> ()
   X_RM = ReduceMean (X, axes)
   EX_squared = Pow (X_RM, Exponent)
   X_squared = Pow (X, Exponent)
   E_Xsquared = ReduceMean (X_squared, axes)
   Variance = Sub (E_Xsquared, EX_squared)
   STD = Sqrt (Variance)
   X_variance = Sub (X, X_RM)
   Processed_STD = Add (STD, Epsilon)
   Y = Div (X_variance, Processed_STD)
}

Attributes

  • axes - INTS (default is ['0', '2', '3']):

    A list of integers, along which to reduce. The default is to calculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.

Inputs

  • X (heterogeneous) - T:

    Input tensor

Outputs

  • Y (heterogeneous) - T:

    Output tensor

Type Constraints

  • T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16) ):

    Constrain input and output types to all numeric tensors.

MeanVarianceNormalization - 9

Version

This version of the operator has been available since version 9.

Summary

A MeanVarianceNormalization Function: Perform mean variance normalization on the input tensor X using formula:
(X-EX)/sqrt(E(X-EX)^2)

Function Body

The function definition for this operator.

<
  domain: "",
  opset_import: ["" : 9]
>
MeanVarianceNormalization <axes>(X) => (Y)
{
   Exponent = Constant <value: tensor = float {2}> ()
   Epsilon = Constant <value: tensor = float {1e-09}> ()
   X_RM = ReduceMean <axes: ints = @axes> (X)
   EX_squared = Pow (X_RM, Exponent)
   X_squared = Pow (X, Exponent)
   E_Xsquared = ReduceMean <axes: ints = @axes> (X_squared)
   Variance = Sub (E_Xsquared, EX_squared)
   STD = Sqrt (Variance)
   X_variance = Sub (X, X_RM)
   Processed_STD = Add (STD, Epsilon)
   Y = Div (X_variance, Processed_STD)
}

Attributes

  • axes - INTS (default is ['0', '2', '3']):

    A list of integers, along which to reduce. The default is to calculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.

Inputs

  • X (heterogeneous) - T:

    Input tensor

Outputs

  • Y (heterogeneous) - T:

    Output tensor

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(float16) ):

    Constrain input and output types to all numeric tensors.