Package org.deeplearning4j.nn.conf.layers

Class EmbeddingLayer.Builder

java.lang.Object

org.deeplearning4j.nn.conf.layers.Layer.Builder<T>

org.deeplearning4j.nn.conf.layers.BaseLayer.Builder<T>

org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder<EmbeddingLayer.Builder>

org.deeplearning4j.nn.conf.layers.EmbeddingLayer.Builder

Enclosing class:

EmbeddingLayer

public static class EmbeddingLayer.Builder
extends FeedForwardLayer.Builder<EmbeddingLayer.Builder>

Field Summary

Fields inherited from class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder

nIn, nOut

Fields inherited from class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

activationFn, biasInit, biasUpdater, gainInit, gradientNormalization, gradientNormalizationThreshold, iupdater, regularization, regularizationBias, weightInitFn, weightNoise

Fields inherited from class org.deeplearning4j.nn.conf.layers.Layer.Builder

allParamConstraints, biasConstraints, iDropout, layerName, weightConstraints

Constructor Summary

Constructors

Constructor	Description
Builder()

Method Summary

Modifier and Type	Method	Description
EmbeddingLayer	build()
EmbeddingLayer.Builder	hasBias(boolean hasBias)	If true: include bias parameters in the layer.
EmbeddingLayer.Builder	weightInit(EmbeddingInitializer embeddingInitializer)	Initialize the embedding layer using the specified EmbeddingInitializer - such as a Word2Vec instance
EmbeddingLayer.Builder	weightInit(IWeightInit weightInit)	Weight initialization scheme to use, for initial weight values
EmbeddingLayer.Builder	weightInit(INDArray vectors)	Initialize the embedding layer using values from the specified array.

Methods inherited from class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder

nIn, nIn, nOut, nOut, units

Methods inherited from class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

activation, activation, biasInit, biasUpdater, dist, gainInit, gradientNormalization, gradientNormalizationThreshold, l1, l1Bias, l2, l2Bias, regularization, regularizationBias, updater, updater, weightDecay, weightDecay, weightDecayBias, weightDecayBias, weightInit, weightInit, weightNoise

Methods inherited from class org.deeplearning4j.nn.conf.layers.Layer.Builder

constrainAllParameters, constrainBias, constrainWeights, dropOut, dropOut, name

Methods inherited from class java.lang.Object

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

Constructor Detail

Builder

public Builder()

Method Detail

hasBias

public EmbeddingLayer.Builder hasBias(boolean hasBias)

If true: include bias parameters in the layer. False (default): no bias.

Parameters:

hasBias - If true: include bias parameters in this layer

weightInit

public EmbeddingLayer.Builder weightInit(IWeightInit weightInit)

Description copied from class: BaseLayer.Builder

Weight initialization scheme to use, for initial weight values

Overrides:

weightInit in class BaseLayer.Builder<EmbeddingLayer.Builder>

See Also:

IWeightInit

weightInit

public EmbeddingLayer.Builder weightInit(EmbeddingInitializer embeddingInitializer)

Initialize the embedding layer using the specified EmbeddingInitializer - such as a Word2Vec instance

Parameters:

embeddingInitializer - Source of the embedding layer weights

weightInit

public EmbeddingLayer.Builder weightInit(INDArray vectors)

Initialize the embedding layer using values from the specified array. Note that the array should have shape [vocabSize, vectorSize]. After copying values from the array to initialize the network parameters, the input array will be discarded (so that, if necessary, it can be garbage collected)

Parameters:

vectors - Vectors to initialize the embedding layer with

build

public EmbeddingLayer build()

Specified by:

build in class Layer.Builder<EmbeddingLayer.Builder>