Package smile.deep.layer

Class EmbeddingLayer

java.lang.Object

smile.deep.layer.EmbeddingLayer

All Implemented Interfaces:

Function<Tensor,Tensor>, Layer

public class EmbeddingLayer
extends Object
implements Layer

An embedding layer that is a simple lookup table that stores embeddings of a fixed dictionary and size.

This layer is often used to store word embeddings and retrieve them using indices. The input to the module is a list of indices, and the output is the corresponding word embeddings.

Constructor Summary

Constructors

Constructor	Description
EmbeddingLayer(int numTokens, int dim)	Constructor.
EmbeddingLayer(int numTokens, int dim, double alpha)	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, isTraining

Constructor Details

EmbeddingLayer

public EmbeddingLayer(int numTokens,
 int dim)

Constructor.

Parameters:

numTokens - the size of the dictionary of embeddings.

dim - the size of each embedding vector.

EmbeddingLayer

public EmbeddingLayer(int numTokens,
 int dim,
 double alpha)

Constructor.

Parameters:

numTokens - the size of the dictionary of embeddings.

dim - the size of each embedding vector.

alpha - optional scaling factor.

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.