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* 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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PReLU
Versioned name: PReLU-1
Category: Activation function
Short description: PReLU performs element-wise parametric ReLU operation with negative slope defined by the second input.
Attributes: operation has no attributes.
Inputs
-
1:
X- Input tensor of any supported floating point type T1. Required. -
2:
slope- Tensor with negative slope values of type T2. The shape of the tensor should be broadcastable to input 1. Required.
Outputs
- 1: The result of element-wise PReLU operation applied for tensor from input 1 with slope values from input 2. A tensor of type T1 and shape matching shape of input x tensor.
Types
-
T1: arbitrary supported floating point type.
-
T2: arbitrary supported floating point type.
Detailed description Before performing addition operation, input tensor 2 with slope values is broadcasted to input 1. The broadcasting rules are aligned with ONNX Broadcasting. Description is available in ONNX docs.
After broadcasting PReLU does the following for each input 1 element x:
f(x) = slope * x for x < 0; x for x >= 0