layer_group_normalization {tfaddons}

Group normalization layer

Description

Group normalization layer

Usage

layer_group_normalization(
  object,
  groups = 2,
  axis = -1,
  epsilon = 0.001,
  center = TRUE,
  scale = TRUE,
  beta_initializer = "zeros",
  gamma_initializer = "ones",
  beta_regularizer = NULL,
  gamma_regularizer = NULL,
  beta_constraint = NULL,
  gamma_constraint = NULL,
  ...
)

Arguments

object

Model or layer object

groups

Integer, the number of groups for Group Normalization. Can be in the range [1, N] where N is the input dimension. The input dimension must be divisible by the number of groups.

axis

Integer, the axis that should be normalized.

epsilon

Small float added to variance to avoid dividing by zero.

center

If TRUE, add offset of beta to normalized tensor. If False, beta is ignored.

scale

If TRUE, multiply by gamma. If False, gamma is not used.

beta_initializer

Initializer for the beta weight.

gamma_initializer

Initializer for the gamma weight.

beta_regularizer

Optional regularizer for the beta weight.

gamma_regularizer

Optional regularizer for the gamma weight.

beta_constraint

Optional constraint for the beta weight.

gamma_constraint

Optional constraint for the gamma weight.

...

additional parameters to pass

Details

Group Normalization divides the channels into groups and computes within each group the mean and variance for normalization. Empirically, its accuracy is more stable than batch norm in a wide range of small batch sizes, if learning rate is adjusted linearly with batch sizes. Relation to Layer Normalization: If the number of groups is set to 1, then this operation becomes identical to Layer Normalization. Relation to Instance Normalization: If the number of groups is set to the input dimension (number of groups is equal to number of channels), then this operation becomes identical to Instance Normalization.

Value

A tensor