Developer Guide for Intel® Data Analytics Acceleration Library 2018 Update 2
For given dimensions k 1 of size n k 1 , k 2 of size n k 2 , and f different from k 1 and k 2, the forward local contrast normalization layer normalizes the input p-dimensional tensor X ∈ R n 1 x n 2 x ... x n p . For more details, see Forward Local Contrast Normalization Layer.
The library supports four-dimensional input tensors X∈ R n 1 x n 2 x n 3 x n 4 .
Without loss of generality let's assume that backward local contrast normalization is applied to the last two dimensions. The backward local contrast normalization layer takes:
The layer computes the four-dimensional value tensor Z∈ R n 1 x n 2 x n 3 x n 4 :
The computation depends on whether the dimension f is set:
Dimension f is set; let n 2 be the sum dimension:
![]() |
![]() ![]() |
![]() ![]() |
![]() ![]() ![]() |
![]() ![]() |
![]() ![]() ![]() |
![]() ![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() ![]() |
![]() ![]() |
![]() ![]() |
Consequently:
Dimension f is not set:
|
![]() ![]() |
![]() ![]() |
![]() ![]() ![]() ![]() |
![]() ![]() |
![]() ![]() |
![]() |
![]() |
![]() |
![]() |
![]() ![]() |
![]() ![]() ![]() |
![]() ![]() |
![]() ![]() |
Consequently: