layer_group_normalization: Group normalization layer

In tfaddons: Interface to 'TensorFlow SIG Addons'

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

tfaddons documentation built on July 2, 2020, 2:12 a.m.