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openvino/docs/ops/normalization/LRN_1.md
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Updated from 2020.3 to 2020.4

Co-authored-by: domi2000 <domi2000@users.noreply.github.com>
2020-07-20 17:36:08 +03:00

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LRN

Versioned name: LRN-1

Category: Normalization

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: beta 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: Reference

Here is an example for 4D data input tensor and axes = [1]:

sqr_sum[a, b, c, d] =
    sum(input[a, b - local_size : b + local_size + 1, c, d] ** 2)
output = input / (bias + alpha * sqr_sum) ** beta

Example

<layer id="1" type="LRN" ...>
    <data alpha="1.0e-04" beta="0.75" size="5" bias="1"/>
    <input>
        <port id="0">
            <dim>6</dim>
            <dim>12</dim>
            <dim>10</dim>
            <dim>24</dim>
        </port>
        <port id="1">
            <dim>1</dim>         <!-- value is [1] that means independent normalization for each pixel along channels -->
        </port>
    </input>
    <output>
        <port id="2">
            <dim>6</dim>
            <dim>12</dim>
            <dim>10</dim>
            <dim>24</dim>
        </port>
    </output>
</layer>