Class BatchNorm1dLayer

java.lang.Object

smile.deep.layer.ModuleLayer

smile.deep.layer.BatchNorm1dLayer

All Implemented Interfaces:

Function<Tensor,Tensor>, Layer

public class BatchNorm1dLayer
extends ModuleLayer

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
Tensor	forward(Tensor input)	Forward propagation (or forward pass) through the layer.

Methods inherited from class ModuleLayer

module, toString

Methods inherited from class Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface Function

andThen, compose

Methods inherited from interface Layer

apply, name, 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

forward

public Tensor forward(Tensor input)

Description copied from interface: Layer

Forward propagation (or forward pass) through the layer.

Parameters:

input - the input tensor.

Returns:

the output tensor.