nn_group_norm function

Group normalization

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

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

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)
}