Package org.deeplearning4j.nn.layers.recurrent

Class BaseRecurrentLayer<LayerConfT extends BaseRecurrentLayer>

java.lang.Object

org.deeplearning4j.nn.layers.AbstractLayer<LayerConfT>

org.deeplearning4j.nn.layers.BaseLayer<LayerConfT>

org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer<LayerConfT>

All Implemented Interfaces:

Serializable, Cloneable, Layer, RecurrentLayer, Model, Trainable

Direct Known Subclasses:

GravesBidirectionalLSTM, GravesLSTM, LSTM, SimpleRnn

public abstract class BaseRecurrentLayer<LayerConfT extends BaseRecurrentLayer>
extends BaseLayer<LayerConfT>
implements RecurrentLayer

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface org.deeplearning4j.nn.api.Layer

Layer.TrainingMode, Layer.Type

Field Summary

Fields

Modifier and Type	Field	Description
protected int	helperCountFail
protected Map<String,INDArray>	stateMap	stateMap stores the INDArrays needed to do rnnTimeStep() forward pass.
protected Map<String,INDArray>	tBpttStateMap	State map for use specifically in truncated BPTT training.

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

gradient, gradientsFlattened, gradientViews, optimizer, params, paramsFlattened, score, solver, weightNoiseParams

Fields inherited from class org.deeplearning4j.nn.layers.AbstractLayer

cacheMode, conf, dataType, dropoutApplied, epochCount, index, input, inputModificationAllowed, iterationCount, maskArray, maskState, preOutput, trainingListeners

Constructor Summary

Constructors

Constructor	Description
BaseRecurrentLayer(NeuralNetConfiguration conf, DataType dataType)

Method Summary

Modifier and Type	Method	Description
RNNFormat	getDataFormat()
protected INDArray	permuteIfNWC(INDArray arr)
void	rnnClearPreviousState()	Reset/clear the stateMap for rnnTimeStep() and tBpttStateMap for rnnActivateUsingStoredState()
Map<String,INDArray>	rnnGetPreviousState()	Returns a shallow copy of the stateMap
Map<String,INDArray>	rnnGetTBPTTState()	Get the RNN truncated backpropagations through time (TBPTT) state for the recurrent layer.
void	rnnSetPreviousState(Map<String,INDArray> stateMap)	Set the state map.
void	rnnSetTBPTTState(Map<String,INDArray> state)	Set the RNN truncated backpropagations through time (TBPTT) state for the recurrent layer.

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

activate, backpropGradient, calcRegularizationScore, clear, clearNoiseWeightParams, clone, computeGradientAndScore, fit, fit, getGradientsViewArray, getOptimizer, getParam, getParamWithNoise, gradient, hasBias, hasLayerNorm, layerConf, numParams, params, paramTable, paramTable, preOutput, preOutputWithPreNorm, score, setBackpropGradientsViewArray, setParam, setParams, setParams, setParamsViewArray, setParamTable, setScoreWithZ, toString, update, update

Methods inherited from class org.deeplearning4j.nn.layers.AbstractLayer

activate, addListeners, allowInputModification, applyConstraints, applyDropOutIfNecessary, applyMask, assertInputSet, backpropDropOutIfPresent, batchSize, close, conf, feedForwardMaskArray, getConfig, getEpochCount, getHelper, getIndex, getInput, getInputMiniBatchSize, getListeners, getMaskArray, gradientAndScore, init, input, layerId, numParams, setCacheMode, setConf, setEpochCount, setIndex, setInput, setInputMiniBatchSize, setListeners, setListeners, setMaskArray, type, updaterDivideByMinibatch

Methods inherited from class java.lang.Object

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

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

activate, activate, allowInputModification, backpropGradient, calcRegularizationScore, clearNoiseWeightParams, feedForwardMaskArray, getEpochCount, getHelper, getIndex, getInputMiniBatchSize, getIterationCount, getListeners, getMaskArray, isPretrainLayer, setCacheMode, setEpochCount, setIndex, setInput, setInputMiniBatchSize, setIterationCount, setListeners, setListeners, setMaskArray, type

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

addListeners, applyConstraints, batchSize, clear, close, computeGradientAndScore, conf, fit, fit, getGradientsViewArray, getOptimizer, getParam, gradient, gradientAndScore, init, input, numParams, numParams, params, paramTable, paramTable, score, setBackpropGradientsViewArray, setConf, setParam, setParams, setParamsViewArray, setParamTable, update, update

Methods inherited from interface org.deeplearning4j.nn.api.layers.RecurrentLayer

rnnActivateUsingStoredState, rnnTimeStep, tbpttBackpropGradient

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

getConfig, getGradientsViewArray, numParams, params, paramTable, updaterDivideByMinibatch

Field Detail

stateMap

protected Map<String,INDArray> stateMap

stateMap stores the INDArrays needed to do rnnTimeStep() forward pass.

tBpttStateMap

protected Map<String,INDArray> tBpttStateMap

State map for use specifically in truncated BPTT training. Whereas stateMap contains the state from which forward pass is initialized, the tBpttStateMap contains the state at the end of the last truncated bptt

helperCountFail

protected int helperCountFail

Constructor Detail

BaseRecurrentLayer

public BaseRecurrentLayer(NeuralNetConfiguration conf,
                          DataType dataType)

Method Detail

rnnGetPreviousState

public Map<String,INDArray> rnnGetPreviousState()

Returns a shallow copy of the stateMap

Specified by:

rnnGetPreviousState in interface RecurrentLayer

rnnSetPreviousState

public void rnnSetPreviousState(Map<String,INDArray> stateMap)

Set the state map. Values set using this method will be used in next call to rnnTimeStep()

Specified by:

rnnSetPreviousState in interface RecurrentLayer

rnnClearPreviousState

public void rnnClearPreviousState()

Reset/clear the stateMap for rnnTimeStep() and tBpttStateMap for rnnActivateUsingStoredState()

Specified by:

rnnClearPreviousState in interface RecurrentLayer

rnnGetTBPTTState

public Map<String,INDArray> rnnGetTBPTTState()

Description copied from interface: RecurrentLayer

Get the RNN truncated backpropagations through time (TBPTT) state for the recurrent layer. The TBPTT state is used to store intermediate activations/state between updating parameters when doing TBPTT learning

Specified by:

rnnGetTBPTTState in interface RecurrentLayer

Returns:

State for the RNN layer

rnnSetTBPTTState

public void rnnSetTBPTTState(Map<String,INDArray> state)

Description copied from interface: RecurrentLayer

Set the RNN truncated backpropagations through time (TBPTT) state for the recurrent layer. The TBPTT state is used to store intermediate activations/state between updating parameters when doing TBPTT learning

Specified by:

rnnSetTBPTTState in interface RecurrentLayer

Parameters:

state - TBPTT state to set

getDataFormat

public RNNFormat getDataFormat()

permuteIfNWC

protected INDArray permuteIfNWC(INDArray arr)