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).

## Note

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

## Shape

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

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

## 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)
}
`