Developer Guide for Intel® Data Analytics Acceleration Library 2018 Update 3

Fully-connected Backward Layer

The forward fully-connected layer computes values

for n input arguments x k , weights w ki , weights mask s ki , and biases b i , where k ∈ {1, ..., n}, i ∈ {1, ..., m}, and m is the number of layer outputs. For more details, see Forward Fully-connected Layer.

The backward fully-connected layer computes the following values:

where

E is the objective function used at the training stage, and g j is the input gradient computed on the preceding layer.

Problem Statement

Given:

The problem is to compute:

In the above formulas: