# chainer.functions.local_response_normalization¶

chainer.functions.local_response_normalization(x, n=5, k=2, alpha=0.0001, beta=0.75)[source]

Local response normalization across neighboring channels.

This function implements normalization across channels. Let $$x$$ an input image with $$N$$ channels. Then, this function computes an output image $$y$$ by following formula:

$y_i = {x_i \over \left( k + \ \alpha \sum_{j=\max{1, i - n/2}}^{\min{N, i + n/2}} \ x_j^2 \right)^\beta}.$
Parameters: x (Variable or N-dimensional array) – Input variable. n (int) – Normalization window width. k (float) – Smoothing parameter. alpha (float) – Normalizer scaling parameter. beta (float) – Normalizer power parameter. Output variable. Variable

See: Section 3.3 of ImageNet Classification with Deep Convolutional Neural Networks