ef45b5da8d
* Doc Migration from Gitlab (#1289) * doc migration * fix * Update FakeQuantize_1.md * Update performance_benchmarks.md * Updates graphs for FPGA * Update performance_benchmarks.md * Change DL Workbench structure (#1) * Changed DL Workbench structure * Fixed tags * fixes * Update ie_docs.xml * Update performance_benchmarks_faq.md * Fixes in DL Workbench layout * Fixes for CVS-31290 * [DL Workbench] Minor correction * Fix for CVS-30955 * Added nGraph deprecation notice as requested by Zoe * fix broken links in api doxy layouts * CVS-31131 fixes * Additional fixes * Fixed POT TOC * Update PAC_Configure.md PAC DCP 1.2.1 install guide. * Update inference_engine_intro.md * fix broken link * Update opset.md * fix * added opset4 to layout * added new opsets to layout, set labels for them * Update VisionAcceleratorFPGA_Configure.md Updated from 2020.3 to 2020.4 Co-authored-by: domi2000 <domi2000@users.noreply.github.com>
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1.3 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 by 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 variance.
- Range of values: a non-negative floating point value
- Type:
float - Default value: None
- Required: yes
Inputs
- 1: Input tensor with element of any floating point type and
2 <= rank <=4. Required.
Outputs
- 1: Output tensor of the same type and shape as the input tensor.
Example
<layer id="5" name="normalization" 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>