org.deeplearning4j.nn.conf.layers.samediff

Class SameDiffLayer

java.lang.Object

org.deeplearning4j.nn.conf.layers.Layer

org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer

All Implemented Interfaces:

Serializable, Cloneable, TrainingConfig

Direct Known Subclasses:

CapsuleLayer, LearnedSelfAttentionLayer, LocallyConnected1D, LocallyConnected2D, PrimaryCapsules, RecurrentAttentionLayer, SameDiffLambdaLayer, SelfAttentionLayer

public abstract class SameDiffLayer
extends AbstractSameDiffLayer

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	SameDiffLayer.Builder<T extends SameDiffLayer.Builder<T>>

Field Summary

Fields

Modifier and Type	Field and Description
protected Map<String,IWeightInit>	paramWeightInit
protected WeightInit	weightInit

Fields inherited from class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

biasUpdater, gradientNormalization, gradientNormalizationThreshold, regularization, regularizationBias, updater

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

constraints, iDropout, layerName

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	SameDiffLayer()
protected	SameDiffLayer(SameDiffLayer.Builder builder)

Method Summary

Modifier and Type	Method and Description
abstract SDVariable	defineLayer(SameDiff sameDiff, SDVariable layerInput, Map<String,SDVariable> paramTable, SDVariable mask) Define the layer
Pair<INDArray,MaskState>	feedForwardMaskArray(INDArray maskArray, MaskState currentMaskState, int minibatchSize)
Layer	instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType)
void	validateInput(INDArray input) Validate input arrays to confirm that they fulfill the assumptions of the layer.

Methods inherited from class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

applyGlobalConfig, applyGlobalConfigToLayer, defineParameters, getLayerParams, getMemoryReport, getPreProcessorForInputType, getRegularizationByParam, getUpdaterByParam, initializeParameters, initializer, initWeights, isPretrainParam, onesMaskForInput, paramReshapeOrder, setNIn

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

clone, getOutputType, initializeConstraints, resetLayerDefaultConfig, setDataType

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.deeplearning4j.nn.api.TrainingConfig

getGradientNormalization, getGradientNormalizationThreshold, getLayerName

Field Detail

weightInit

protected WeightInit weightInit

paramWeightInit

protected Map<String,IWeightInit> paramWeightInit

Constructor Detail

SameDiffLayer

protected SameDiffLayer(SameDiffLayer.Builder builder)

SameDiffLayer

protected SameDiffLayer()

Method Detail

defineLayer

public abstract SDVariable defineLayer(SameDiff sameDiff,
                                       SDVariable layerInput,
                                       Map<String,SDVariable> paramTable,
                                       SDVariable mask)

Define the layer

Parameters:

sameDiff - SameDiff instance

layerInput - Input to the layer

paramTable - Parameter table - keys as defined by AbstractSameDiffLayer.defineParameters(SDLayerParams)

mask - Optional, maybe null. Mask to apply if supported

Returns:

The final layer variable corresponding to the activations/output from the forward pass

feedForwardMaskArray

public Pair<INDArray,MaskState> feedForwardMaskArray(INDArray maskArray,
                                                     MaskState currentMaskState,
                                                     int minibatchSize)

See Also:

Layer.feedForwardMaskArray(INDArray, MaskState, int)

validateInput

public void validateInput(INDArray input)

Validate input arrays to confirm that they fulfill the assumptions of the layer. If they don't, throw an exception.

Parameters:

input - input to the layer

instantiate

public Layer instantiate(NeuralNetConfiguration conf,
                         Collection<TrainingListener> trainingListeners,
                         int layerIndex,
                         INDArray layerParamsView,
                         boolean initializeParams,
                         DataType networkDataType)

Specified by:

instantiate in class AbstractSameDiffLayer