# Caffe

Deep learning framework by the BVLC

Created by
Yangqing Jia
The local response normalization layer performs a kind of “lateral inhibition” by normalizing over local input regions. In ACROSS_CHANNELS mode, the local regions extend across nearby channels, but have no spatial extent (i.e., they have shape local_size x 1 x 1). In WITHIN_CHANNEL mode, the local regions extend spatially, but are in separate channels (i.e., they have shape 1 x local_size x local_size). Each input value is divided by $(1 + (\alpha/n) \sum_i x_i^2)^\beta$, where $n$ is the size of each local region, and the sum is taken over the region centered at that value (zero padding is added where necessary).