MeanVarianceNormalization#
MeanVarianceNormalization - 13#
Version
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: <br/> ` (X-EX)/sqrt(E(X-EX)^2) `
Attributes
axes: A list of integers, along which to reduce. The default is to caculate 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. Default value is
[0 2 3].
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.
Examples
Differences
0 | 0 | A MeanVarianceNormalization Function: Perform mean variance normalization | A MeanVarianceNormalization Function: Perform mean variance normalization |
1 | 1 | on the input tensor X using formula: | on the input tensor X using formula: |
2 | 2 | | |
3 | 3 | **Attributes** | **Attributes** |
4 | 4 | | |
5 | 5 | * **axes**: | * **axes**: |
6 | 6 | A list of integers, along which to reduce. The default is to | A list of integers, along which to reduce. The default is to |
7 | 7 | caculate along axes [0,2,3] for calculating mean and variance along | caculate along axes [0,2,3] for calculating mean and variance along |
8 | 8 | each channel. Two variables with the same C-coordinate are | each channel. Two variables with the same C-coordinate are |
9 | 9 | associated with the same mean and variance. Default value is [0 2 3]. | associated with the same mean and variance. Default value is [0 2 3]. |
10 | 10 | | |
11 | 11 | **Inputs** | **Inputs** |
12 | 12 | | |
13 | 13 | * **X** (heterogeneous) - **T**: | * **X** (heterogeneous) - **T**: |
14 | 14 | Input tensor | Input tensor |
15 | 15 | | |
16 | 16 | **Outputs** | **Outputs** |
17 | 17 | | |
18 | 18 | * **Y** (heterogeneous) - **T**: | * **Y** (heterogeneous) - **T**: |
19 | 19 | Output tensor | Output tensor |
20 | 20 | | |
21 | 21 | **Type Constraints** | **Type Constraints** |
22 | 22 | | |
23 | 23 | * **T** in ( | * **T** in ( |
24 | tensor(bfloat16), | ||
24 | 25 | tensor(double), | tensor(double), |
25 | 26 | tensor(float), | tensor(float), |
26 | 27 | tensor(float16) | tensor(float16) |
27 | 28 | ): | ): |
28 | 29 | Constrain input and output types to all numeric tensors. | Constrain input and output types to all numeric tensors. |
MeanVarianceNormalization - 9#
Version
domain: main
since_version: 9
function: True
support_level: SupportType.COMMON
shape inference: False
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: <br/> ` (X-EX)/sqrt(E(X-EX)^2) `
Attributes
axes: A list of integers, along which to reduce. The default is to caculate 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. Default value is
[0 2 3].
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.