# nn_group_norm: Group normalization In torch: Tensors and Neural Networks with 'GPU' Acceleration

## Description

Applies Group Normalization over a mini-batch of inputs as described in the paper Group Normalization.

## Usage

 1 nn_group_norm(num_groups, num_channels, eps = 1e-05, affine = TRUE) 

## Arguments

 num_groups (int): number of groups to separate the channels into num_channels (int): number of channels expected in input eps a value added to the denominator for numerical stability. Default: 1e-5 affine a boolean value that when set to TRUE, this module has learnable per-channel affine parameters initialized to ones (for weights) and zeros (for biases). Default: TRUE.

## Details

y = \frac{x - \mathrm{E}[x]}{ √{\mathrm{Var}[x] + ε}} * γ + β

The input channels are separated into num_groups groups, each containing num_channels / num_groups channels. The mean and standard-deviation are calculated separately over the each group. γ and β are learnable per-channel affine transform parameter vectors of size num_channels if affine is TRUE. The standard-deviation is calculated via the biased estimator, equivalent to torch_var(input, unbiased=FALSE).

## Shape

• Input: (N, C, *) where C=\mbox{num\_channels}

• Output: (N, C, *)' (same shape as input)

## Note

This layer uses statistics computed from input data in both training and evaluation modes.

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 if (torch_is_installed()) { input <- torch_randn(20, 6, 10, 10) # Separate 6 channels into 3 groups m <- nn_group_norm(3, 6) # Separate 6 channels into 6 groups (equivalent with [nn_instance_morm]) m <- nn_group_norm(6, 6) # Put all 6 channels into a single group (equivalent with [nn_layer_norm]) m <- nn_group_norm(1, 6) # Activating the module output <- m(input) } 

torch documentation built on Oct. 7, 2021, 9:22 a.m.