layer_instance_normalization: Instance normalization layer

In tfaddons: Interface to 'TensorFlow SIG Addons'

Description

Instance normalization layer

Usage

layer_instance_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

Instance Normalization is an specific case of “'GroupNormalizationsince“' it normalizes all features of one channel. The Groupsize is equal to the channel size. 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.

Value

A tensor

References

[Instance Normalization: The Missing Ingredient for Fast Stylization](https://arxiv.org/abs/1607.08022)

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