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>
</port>
</input>
<output>
<port id="2">
<dim>6</dim>
<dim>12</dim>
<dim>10</dim>
<dim>24</dim>
</port>
</output>
</layer>