Package smile.deep.layer

Class BatchNorm1dLayer

java.lang.Object

smile.deep.layer.BatchNorm1dLayer

All Implemented Interfaces:

Function<Tensor,Tensor>, Layer

public class BatchNorm1dLayer
extends Object
implements Layer

A batch normalization layer that re-centers and normalizes the output of one layer before feeding it to another. Centering and scaling the intermediate tensors has a number of beneficial effects, such as allowing higher learning rates without exploding/vanishing gradients.

Constructor Summary

Constructors

Constructor	Description
BatchNorm1dLayer(int channels)	Constructor.
BatchNorm1dLayer(int channels, double eps, double momentum, 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

BatchNorm1dLayer

public BatchNorm1dLayer(int channels)

Constructor.

Parameters:

channels - the number of input channels.

BatchNorm1dLayer

public BatchNorm1dLayer(int channels,
 double eps,
 double momentum,
 boolean affine)

Constructor.

Parameters:

channels - the number of input channels.

eps - a value added to the denominator for numerical stability.

momentum - the value used for the running_mean and running_var computation. Can be set to 0.0 for cumulative moving average (i.e. simple average).

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.