MeanVarianceNormalization¶

MeanVarianceNormalization - 13¶

Version¶

name: MeanVarianceNormalization (GitHub)
domain: main
since_version: 13
function: True
support_level: SupportType.COMMON
shape inference: False

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 vs 13

MeanVarianceNormalization - 9¶