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LRN operation specification refactoring (#5890)

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* LRN spec refactored against explicit type indication.

* Fix spelling.
This commit is contained in:
Jozef Daniecki
2021-06-01 12:01:43 +03:00
committed by GitHub
parent 7a239c3856
commit d40636f835
1 changed files with 48 additions and 44 deletions
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docs/ops/normalization/LRN_1.md
+48 -44
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@@ -6,50 +6,6 @@
**Short description**: Local response normalization.
**Attributes**:
* *alpha*
* **Description**: *alpha* represents the scaling attribute for the normalizing sum. For example, *alpha* equal 0.0001 means that the normalizing sum is multiplied by 0.0001.
* **Range of values**: no restrictions
* **Type**: float
* **Default value**: None
* **Required**: *yes*
* *beta*
* **Description**: *beta* represents the exponent for the normalizing sum. For example, *beta* equal 0.75 means that the normalizing sum is raised to the power of 0.75.
* **Range of values**: positive number
* **Type**: float
* **Default value**: None
* **Required**: *yes*
* *bias*
* **Description**: *bias* represents the offset. Usually positive number to avoid dividing by zero.
* **Range of values**: no restrictions
* **Type**: float
* **Default value**: None
* **Required**: *yes*
* *size*
* **Description**: *size* represents the side length of the region to be used for the normalization sum. The region can have one or more dimensions depending on the second input axes indices.
* **Range of values**: positive integer
* **Type**: int
* **Default value**: None
* **Required**: *yes*
**Inputs**
* **1**: `data` - input tensor of any floating point type and arbitrary shape. Required.
* **2**: `axes` - specifies indices of dimensions in `data` that define normalization slices. Required.
**Outputs**
* **1**: Output tensor of the same shape and type as the `data` input tensor.
**Detailed description**:
Local Response Normalization performs a normalization over local input regions.
Each input value is divided by
@@ -71,6 +27,54 @@ sqr_sum[a, b, c, d] =
output = data / (bias + (alpha / size ** len(axes)) * sqr_sum) ** beta
```
**Attributes**:
* *alpha*
* **Description**: *alpha* represents the scaling attribute for the normalizing sum. For example, *alpha* equal `0.0001` means that the normalizing sum is multiplied by `0.0001`.
* **Range of values**: no restrictions
* **Type**: `float`
* **Default value**: None
* **Required**: *yes*
* *beta*
* **Description**: *beta* represents the exponent for the normalizing sum. For example, *beta* equal `0.75` means that the normalizing sum is raised to the power of `0.75`.
* **Range of values**: positive number
* **Type**: `float`
* **Default value**: None
* **Required**: *yes*
* *bias*
* **Description**: *bias* represents the offset. Usually positive number to avoid dividing by zero.
* **Range of values**: no restrictions
* **Type**: `float`
* **Default value**: None
* **Required**: *yes*
* *size*
* **Description**: *size* represents the side length of the region to be used for the normalization sum. The region can have one or more dimensions depending on the second input axes indices.
* **Range of values**: positive integer
* **Type**: `int`
* **Default value**: None
* **Required**: *yes*
**Inputs**
* **1**: `data` - tensor of type `T` and arbitrary shape. **Required.**
* **2**: `axes` - 1D tensor of type `T_IND` which specifies indices of dimensions in `data` which define normalization slices. **Required.**
**Outputs**
* **1**: Output tensor of type `T` and the same shape as the `data` input tensor.
**Types**
* *T*: any supported floating point type.
* *T_IND*: any supported integer type.
**Example**
```xml