GroupNormLayer

java.lang.Object

smile.deep.layer.GroupNormLayer

All Implemented Interfaces:: Function<Tensor,Tensor>, Layer

public class GroupNormLayer extends Object implements Layer

Group normalization. The input channels are separated into groups. The mean and standard-deviation are calculated separately over each group.

Constructor Summary

Constructors

Constructor

Description

GroupNormLayer(int groups, int channels)

Constructor.

GroupNormLayer(int groups, int channels, double eps, boolean affine)

Constructor.
Method Summary

Modifier and Type

Method

Description

org.bytedeco.pytorch.Module

asTorch()

Returns the PyTorch Module object.

Tensor

forward(Tensor input)

Forward propagation (or forward pass) through the layer.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface java.util.function.Function
andThen, compose

Methods inherited from interface smile.deep.layer.Layer
apply, to, to

Constructor Details
- GroupNormLayer
  
  public GroupNormLayer(int groups, int channels)
  
  Constructor.
  
  Parameters:
  
  groups - the number of groups to separate the channels into. The number of channels must be divisible by the number of groups.
  
  channels - the number of input channels in (N,C,H,W).
- GroupNormLayer
  
  public GroupNormLayer(int groups, int channels, double eps, boolean affine)
  
  Constructor.
  
  Parameters:
  
  groups - the number of groups to separate the channels into. The number of channels must be divisible by the number of groups.
  
  channels - the number of input channels in (N,C,H,W).
  
  eps - a value added to the denominator for numerical stability.
  
  affine - when set to true, this layer has learnable affine parameters.
Method Details
- asTorch
  
  public org.bytedeco.pytorch.Module asTorch()
  
  Description copied from interface: Layer
  
  Returns the PyTorch Module object.
  
  Specified by:
  
  asTorch in interface Layer
  
  Returns:
  
  the PyTorch Module object.
- forward
  
  public Tensor forward(Tensor input)
  
  Description copied from interface: Layer
  
  Forward propagation (or forward pass) through the layer.
  
  Specified by:
  
  forward in interface Layer
  
  Parameters:
  
  input - the input tensor.
  
  Returns:
  
  the output tensor.