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

 nn_group_norm R Documentation

## Group normalization

### Description

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

### Usage

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]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 

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. \gamma and \beta 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

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 June 7, 2023, 6:19 p.m.