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

2D Locally-connected Backward Layer

The forward two-dimensional (2D) locally-connected layer computes the value tensor Y by applying a set of nKernels 2D kernels K of size m 1 x m 2 to the input argument x. The library supports four-dimensional input tensors XR n 1 x n 2 x n 3 x n 4 . Therefore, the following formula applies:



where i + a < n 1, j + b < n 2, and r is the kernel index.

A set of kernels is specific to the selected dimensions of the input argument x.

For more details, see Forward 2D Locally-connected Layer.

The backward 2D locally-connected layer computes the derivatives of the objective function E with respect to the input argument, weights, and biases.

Problem Statement

Without loss of generality, let's assume that convolution kernels are applied to the last two dimensions.

Given:

For the above tensors:

The problem is to compute:

In the above formulas: