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* Update detailed description * Update attrs description * Add Types section and Inputs Outputs description * Update example
1.4 KiB
1.4 KiB
GRN
Versioned name: GRN-1
Category: Normalization
Short description: GRN is the Global Response Normalization with L2 norm (across channels only).
Detailed description:
GRN computes the L2 norm across channels for input tensor with shape [N, C, ...]. GRN does the following with the input tensor:
output[i0, i1, ..., iN] = x[i0, i1, ..., iN] / sqrt(sum[j = 0..C-1](x[i0, j, ..., iN]**2) + bias)
Attributes:
-
bias
- Description: bias is added to the sum of squares.
- Range of values: a positive floating-point number
- Type:
float - Required: yes
Inputs
- 1:
data- A tensor of type T and2 <= rank <= 4. Required.
Outputs
- 1: The result of GRN function applied to
datainput tensor. Normalized tensor of the same type and shape as the data input.
Types
- T: arbitrary supported floating-point type.
Example
<layer ... type="GRN">
<data bias="1e-4"/>
<input>
<port id="0">
<dim>1</dim>
<dim>20</dim>
<dim>224</dim>
<dim>224</dim>
</port>
</input>
<output>
<port id="0" precision="f32">
<dim>1</dim>
<dim>20</dim>
<dim>224</dim>
<dim>224</dim>
</port>
</output>
</layer>