layer_instance_normalization: Instance normalization layer

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

Instance normalization layer

Description

Creates an instance normalization layer

Usage

layer_instance_normalization(
  object,
  axis = NULL,
  epsilon = 0.001,
  center = TRUE,
  scale = TRUE,
  betaInitializer = "zeros",
  gammaInitializer = "ones",
  betaRegularizer = NULL,
  gammaRegularizer = NULL,
  betaConstraint = NULL,
  gammaConstraint = NULL,
  trainable = TRUE
)

Arguments

object	Object to compose layer with. This is either a keras::keras_model_sequential to add the layer to, or another Layer which this layer will call.
axis	Integer specifying which axis should be normalized, typically the feature axis. For example, after a Conv2D layer with channels_first, set axis = 1. Setting axis=-1L will normalize all values in each instance of the batch. Axis 0 is the batch dimension for tensorflow backend so we throw an error if axis = 0.
epsilon	Small float added to the variance to avoid dividing by 0.
center	If TRUE, add beta offset to normalized tensor.
scale	If TRUE, multiply by gamma.
betaInitializer	Intializer for the beta weight.
gammaInitializer	Intializer for the gamma weight.
betaRegularizer	Regularizer for the beta weight.
gammaRegularizer	Regularizer for the gamma weight.
betaConstraint	Optional constraint for the beta weight.
gammaConstraint	Optional constraint for the gamma weight.
trainable	Whether the layer weights will be updated during training.

Value

a keras layer tensor

Author(s)

Tustison NJ

ANTsX/ANTsRNet documentation built on April 28, 2024, 12:16 p.m.