org.deeplearning4j.nn.layers.recurrent

Class BidirectionalLayer

java.lang.Object

org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

All Implemented Interfaces:

Serializable, Cloneable, Layer, RecurrentLayer, Model, Trainable

public class BidirectionalLayer
extends Object
implements RecurrentLayer

Bidirectional is a "wrapper" layer: it wraps any uni-directional RNN layer to make it bidirectional.
Note that multiple different modes are supported - these specify how the activations should be combined from the forward and backward RNN networks. See Bidirectional.Mode javadoc for more details.
Parameters are not shared here - there are 2 separate copies of the wrapped RNN layer, each with separate parameters.
Usage: .layer(new Bidirectional(new LSTM.Builder()....build())

Author:

Alex Black

See Also:

Serialized Form

Nested Class Summary

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

Layer.TrainingMode, Layer.Type

Constructor Summary

Constructors

Constructor and Description
BidirectionalLayer(@NonNull NeuralNetConfiguration conf, @NonNull Layer fwd, @NonNull Layer bwd, @NonNull INDArray paramsView)

Method Summary

Modifier and Type	Method and Description
INDArray	activate(boolean training, LayerWorkspaceMgr workspaceMgr) Perform forward pass and return the activations array with the last set input
INDArray	activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr) Perform forward pass and return the activations array with the specified input
void	addListeners(TrainingListener... listener) This method ADDS additional TrainingListener to existing listeners
void	allowInputModification(boolean allow) A performance optimization: mark whether the layer is allowed to modify its input array in-place.
void	applyConstraints(int iteration, int epoch) Apply any constraints to the model
Pair<Gradient,INDArray>	backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) Calculate the gradient relative to the error in the next layer
int	batchSize() The current inputs batch size
double	calcRegularizationScore(boolean backpropParamsOnly) Calculate the regularization component of the score, for the parameters in this layer For example, the L1, L2 and/or weight decay components of the loss function
void	clear() Clear input
void	clearNoiseWeightParams()
void	close()
void	computeGradientAndScore(LayerWorkspaceMgr workspaceMgr) Update the score
NeuralNetConfiguration	conf() The configuration for the neural network
Pair<INDArray,MaskState>	feedForwardMaskArray(INDArray maskArray, MaskState currentMaskState, int minibatchSize) Feed forward the input mask array, setting in the layer as appropriate.
void	fit() All models have a fit method
void	fit(INDArray data, LayerWorkspaceMgr workspaceMgr) Fit the model to the given data
TrainingConfig	getConfig()
int	getEpochCount()
INDArray	getGradientsViewArray()
LayerHelper	getHelper()
int	getIndex() Get the layer index.
int	getInputMiniBatchSize() Get current/last input mini-batch size, as set by setInputMiniBatchSize(int)
int	getIterationCount()
Collection<TrainingListener>	getListeners() Get the iteration listeners for this layer.
INDArray	getMaskArray()
ConvexOptimizer	getOptimizer() Returns this models optimizer
INDArray	getParam(String param) Get the parameter
Gradient	gradient() Get the gradient.
Pair<Gradient,Double>	gradientAndScore() Get the gradient and score
void	init() Init the model
INDArray	input() The input/feature matrix for the model
boolean	isPretrainLayer() Returns true if the layer can be trained in an unsupervised/pretrain manner (AE, VAE, etc)
long	numParams() the number of parameters for the model
long	numParams(boolean backwards) the number of parameters for the model
INDArray	params() Parameters of the model (if any)
Map<String,INDArray>	paramTable() The param table
Map<String,INDArray>	paramTable(boolean backpropParamsOnly) Table of parameters by key, for backprop For many models (dense layers, etc) - all parameters are backprop parameters
INDArray	rnnActivateUsingStoredState(INDArray input, boolean training, boolean storeLastForTBPTT, LayerWorkspaceMgr workspaceMgr) Similar to rnnTimeStep, this method is used for activations using the state stored in the stateMap as the initialization.
void	rnnClearPreviousState() Reset/clear the stateMap for rnnTimeStep() and tBpttStateMap for rnnActivateUsingStoredState()
Map<String,INDArray>	rnnGetPreviousState() Returns a shallow copy of the RNN stateMap (that contains the stored history for use in methods such as rnnTimeStep
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 stateMap (stored history).
void	rnnSetTBPTTState(Map<String,INDArray> state) Set the RNN truncated backpropagations through time (TBPTT) state for the recurrent layer.
INDArray	rnnTimeStep(INDArray input, LayerWorkspaceMgr workspaceMgr) Do one or more time steps using the previous time step state stored in stateMap. Can be used to efficiently do forward pass one or n-steps at a time (instead of doing forward pass always from t=0) If stateMap is empty, default initialization (usually zeros) is used Implementations also update stateMap at the end of this method
double	score() The score for the model
void	setBackpropGradientsViewArray(INDArray gradients) Set the gradients array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
void	setCacheMode(CacheMode mode) This method sets given CacheMode for current layer
void	setConf(NeuralNetConfiguration conf) Setter for the configuration
void	setEpochCount(int epochCount) Set the current epoch count (number of epochs passed ) for the layer/network
void	setIndex(int index) Set the layer index.
void	setInput(INDArray input, LayerWorkspaceMgr layerWorkspaceMgr) Set the layer input.
void	setInputMiniBatchSize(int size) Set current/last input mini-batch size. Used for score and gradient calculations.
void	setIterationCount(int iterationCount) Set the current iteration count (number of parameter updates) for the layer/network
void	setListeners(Collection<TrainingListener> listeners) Set the TrainingListeners for this model.
void	setListeners(TrainingListener... listeners) Set the TrainingListeners for this model.
void	setMaskArray(INDArray maskArray) Set the mask array.
void	setParam(String key, INDArray val) Set the parameter with a new ndarray
void	setParams(INDArray params) Set the parameters for this model.
void	setParamsViewArray(INDArray params) Set the initial parameters array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
void	setParamTable(Map<String,INDArray> paramTable) Setter for the param table
Pair<Gradient,INDArray>	tbpttBackpropGradient(INDArray epsilon, int tbpttBackLength, LayerWorkspaceMgr workspaceMgr) Truncated BPTT equivalent of Layer.backpropGradient().
Layer.Type	type() Returns the layer type
void	update(Gradient gradient) Update layer weights and biases with gradient change
void	update(INDArray gradient, String paramType) Perform one update applying the gradient
boolean	updaterDivideByMinibatch(String paramName) DL4J layers typically produce the sum of the gradients during the backward pass for each layer, and if required (if minibatch=true) then divide by the minibatch size. However, there are some exceptions, such as the batch norm mean/variance estimate parameters: these "gradients" are actually not gradients, but are updates to be applied directly to the parameter vector.

Methods inherited from class java.lang.Object

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

Constructor Detail

BidirectionalLayer

public BidirectionalLayer(@NonNull
                          @NonNull NeuralNetConfiguration conf,
                          @NonNull
                          @NonNull Layer fwd,
                          @NonNull
                          @NonNull Layer bwd,
                          @NonNull
                          @NonNull INDArray paramsView)

Method Detail

rnnTimeStep

public INDArray rnnTimeStep(INDArray input,
                            LayerWorkspaceMgr workspaceMgr)

Description copied from interface: RecurrentLayer

Do one or more time steps using the previous time step state stored in stateMap.
Can be used to efficiently do forward pass one or n-steps at a time (instead of doing forward pass always from t=0)
If stateMap is empty, default initialization (usually zeros) is used
Implementations also update stateMap at the end of this method

Specified by:

rnnTimeStep in interface RecurrentLayer

Parameters:

input - Input to this layer

Returns:

activations

rnnGetPreviousState

public Map<String,INDArray> rnnGetPreviousState()

Description copied from interface: RecurrentLayer

Returns a shallow copy of the RNN stateMap (that contains the stored history for use in methods such as rnnTimeStep

Specified by:

rnnGetPreviousState in interface RecurrentLayer

rnnSetPreviousState

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

Description copied from interface: RecurrentLayer

Set the stateMap (stored history). Values set using this method will be used in next call to rnnTimeStep()

Specified by:

rnnSetPreviousState in interface RecurrentLayer

rnnClearPreviousState

public void rnnClearPreviousState()

Description copied from interface: RecurrentLayer

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

Specified by:

rnnClearPreviousState in interface RecurrentLayer

rnnActivateUsingStoredState

public INDArray rnnActivateUsingStoredState(INDArray input,
                                            boolean training,
                                            boolean storeLastForTBPTT,
                                            LayerWorkspaceMgr workspaceMgr)

Description copied from interface: RecurrentLayer

Similar to rnnTimeStep, this method is used for activations using the state stored in the stateMap as the initialization. However, unlike rnnTimeStep this method does not alter the stateMap; therefore, unlike rnnTimeStep, multiple calls to this method (with identical input) will:
(a) result in the same output
(b) leave the state maps (both stateMap and tBpttStateMap) in an identical state

Specified by:

rnnActivateUsingStoredState in interface RecurrentLayer

Parameters:

input - Layer input

training - if true: training. Otherwise: test

storeLastForTBPTT - If true: store the final state in tBpttStateMap for use in truncated BPTT training

Returns:

Layer activations

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

tbpttBackpropGradient

public Pair<Gradient,INDArray> tbpttBackpropGradient(INDArray epsilon,
                                                     int tbpttBackLength,
                                                     LayerWorkspaceMgr workspaceMgr)

Description copied from interface: RecurrentLayer

Truncated BPTT equivalent of Layer.backpropGradient(). Primary difference here is that forward pass in the context of BPTT is that we do forward pass using stored state for truncated BPTT vs. from zero initialization for standard BPTT.

Specified by:

tbpttBackpropGradient in interface RecurrentLayer

setCacheMode

public void setCacheMode(CacheMode mode)

Description copied from interface: Layer

This method sets given CacheMode for current layer

Specified by:

setCacheMode in interface Layer

calcRegularizationScore

public double calcRegularizationScore(boolean backpropParamsOnly)

Description copied from interface: Layer

Calculate the regularization component of the score, for the parameters in this layer
For example, the L1, L2 and/or weight decay components of the loss function

Specified by:

calcRegularizationScore in interface Layer

Parameters:

backpropParamsOnly - If true: calculate regularization score based on backprop params only. If false: calculate based on all params (including pretrain params, if any)

Returns:

the regularization score of

type

public Layer.Type type()

Description copied from interface: Layer

Returns the layer type

Specified by:

type in interface Layer

Returns:

backpropGradient

public Pair<Gradient,INDArray> backpropGradient(INDArray epsilon,
                                                LayerWorkspaceMgr workspaceMgr)

Description copied from interface: Layer

Calculate the gradient relative to the error in the next layer

Specified by:

backpropGradient in interface Layer

Parameters:

epsilon - w^(L+1)*delta^(L+1). Or, equiv: dC/da, i.e., (dC/dz)*(dz/da) = dC/da, where C is cost function a=sigma(z) is activation.

workspaceMgr - Workspace manager

Returns:

Pair where Gradient is gradient for this layer, INDArray is epsilon (activation gradient) needed by next layer, but before element-wise multiply by sigmaPrime(z). So for standard feed-forward layer, if this layer is L, then return.getSecond() == dL/dIn = (w^(L)*(delta^(L))^T)^T. Note that the returned array should be placed in the ArrayType.ACTIVATION_GRAD workspace via the workspace manager

activate

public INDArray activate(boolean training,
                         LayerWorkspaceMgr workspaceMgr)

Description copied from interface: Layer

Perform forward pass and return the activations array with the last set input

Specified by:

activate in interface Layer

Parameters:

training - training or test mode

workspaceMgr - Workspace manager

Returns:

the activation (layer output) of the last specified input. Note that the returned array should be placed in the ArrayType.ACTIVATIONS workspace via the workspace manager

activate

public INDArray activate(INDArray input,
                         boolean training,
                         LayerWorkspaceMgr workspaceMgr)

Description copied from interface: Layer

Perform forward pass and return the activations array with the specified input

Specified by:

activate in interface Layer

Parameters:

input - the input to use

training - train or test mode

workspaceMgr - Workspace manager.

Returns:

Activations array. Note that the returned array should be placed in the ArrayType.ACTIVATIONS workspace via the workspace manager

getListeners

public Collection<TrainingListener> getListeners()

Description copied from interface: Layer

Get the iteration listeners for this layer.

Specified by:

getListeners in interface Layer

setListeners

public void setListeners(TrainingListener... listeners)

Description copied from interface: Layer

Set the TrainingListeners for this model. If any listeners have previously been set, they will be replaced by this method

Specified by:

setListeners in interface Layer

Specified by:

setListeners in interface Model

addListeners

public void addListeners(TrainingListener... listener)

Description copied from interface: Model

This method ADDS additional TrainingListener to existing listeners

Specified by:

addListeners in interface Model

fit

public void fit()

Description copied from interface: Model

All models have a fit method

Specified by:

fit in interface Model

update

public void update(Gradient gradient)

Description copied from interface: Model

Update layer weights and biases with gradient change

Specified by:

update in interface Model

update

public void update(INDArray gradient,
                   String paramType)

Description copied from interface: Model

Perform one update applying the gradient

Specified by:

update in interface Model

Parameters:

gradient - the gradient to apply

score

public double score()

Description copied from interface: Model

The score for the model

Specified by:

score in interface Model

Returns:

the score for the model

computeGradientAndScore

public void computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)

Description copied from interface: Model

Update the score

Specified by:

computeGradientAndScore in interface Model

params

public INDArray params()

Description copied from interface: Model

Parameters of the model (if any)

Specified by:

params in interface Model

Specified by:

params in interface Trainable

Returns:

the parameters of the model

getConfig

public TrainingConfig getConfig()

Specified by:

getConfig in interface Trainable

Returns:

Training configuration

numParams

public long numParams()

Description copied from interface: Model

the number of parameters for the model

Specified by:

numParams in interface Model

Specified by:

numParams in interface Trainable

Returns:

the number of parameters for the model

numParams

public long numParams(boolean backwards)

Description copied from interface: Model

the number of parameters for the model

Specified by:

numParams in interface Model

Returns:

the number of parameters for the model

setParams

public void setParams(INDArray params)

Description copied from interface: Model

Set the parameters for this model. This expects a linear ndarray which then be unpacked internally relative to the expected ordering of the model

Specified by:

setParams in interface Model

Parameters:

params - the parameters for the model

setParamsViewArray

public void setParamsViewArray(INDArray params)

Description copied from interface: Model

Set the initial parameters array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.

Specified by:

setParamsViewArray in interface Model

Parameters:

params - a 1 x nParams row vector that is a view of the larger (MLN/CG) parameters array

getGradientsViewArray

public INDArray getGradientsViewArray()

Specified by:

getGradientsViewArray in interface Model

Specified by:

getGradientsViewArray in interface Trainable

Returns:

1D gradients view array

setBackpropGradientsViewArray

public void setBackpropGradientsViewArray(INDArray gradients)

Description copied from interface: Model

Set the gradients array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.

Specified by:

setBackpropGradientsViewArray in interface Model

Parameters:

gradients - a 1 x nParams row vector that is a view of the larger (MLN/CG) gradients array

fit

public void fit(INDArray data,
                LayerWorkspaceMgr workspaceMgr)

Description copied from interface: Model

Fit the model to the given data

Specified by:

fit in interface Model

Parameters:

data - the data to fit the model to

gradient

public Gradient gradient()

Description copied from interface: Model

Get the gradient. Note that this method will not calculate the gradient, it will rather return the gradient that has been computed before. For calculating the gradient, see Model.computeGradientAndScore(LayerWorkspaceMgr) } .

Specified by:

gradient in interface Model

Returns:

the gradient for this model, as calculated before

gradientAndScore

public Pair<Gradient,Double> gradientAndScore()

Description copied from interface: Model

Get the gradient and score

Specified by:

gradientAndScore in interface Model

Returns:

the gradient and score

batchSize

public int batchSize()

Description copied from interface: Model

The current inputs batch size

Specified by:

batchSize in interface Model

Returns:

the current inputs batch size

conf

public NeuralNetConfiguration conf()

Description copied from interface: Model

The configuration for the neural network

Specified by:

conf in interface Model

Returns:

the configuration for the neural network

setConf

public void setConf(NeuralNetConfiguration conf)

Description copied from interface: Model

Setter for the configuration

Specified by:

setConf in interface Model

input

public INDArray input()

Description copied from interface: Model

The input/feature matrix for the model

Specified by:

input in interface Model

Returns:

the input/feature matrix for the model

getOptimizer

public ConvexOptimizer getOptimizer()

Description copied from interface: Model

Returns this models optimizer

Specified by:

getOptimizer in interface Model

Returns:

this models optimizer

getParam

public INDArray getParam(String param)

Description copied from interface: Model

Get the parameter

Specified by:

getParam in interface Model

Parameters:

param - the key of the parameter

Returns:

the parameter vector/matrix with that particular key

paramTable

public Map<String,INDArray> paramTable()

Description copied from interface: Model

The param table

Specified by:

paramTable in interface Model

Returns:

paramTable

public Map<String,INDArray> paramTable(boolean backpropParamsOnly)

Description copied from interface: Model

Table of parameters by key, for backprop For many models (dense layers, etc) - all parameters are backprop parameters

Specified by:

paramTable in interface Model

Specified by:

paramTable in interface Trainable

Parameters:

backpropParamsOnly - If true, return backprop params only. If false: return all params (equivalent to paramsTable())

Returns:

Parameter table

updaterDivideByMinibatch

public boolean updaterDivideByMinibatch(String paramName)

Description copied from interface: Trainable

DL4J layers typically produce the sum of the gradients during the backward pass for each layer, and if required (if minibatch=true) then divide by the minibatch size.
However, there are some exceptions, such as the batch norm mean/variance estimate parameters: these "gradients" are actually not gradients, but are updates to be applied directly to the parameter vector. Put another way, most gradients should be divided by the minibatch to get the average; some "gradients" are actually final updates already, and should not be divided by the minibatch size.

Specified by:

updaterDivideByMinibatch in interface Trainable

Parameters:

paramName - Name of the parameter

Returns:

True if gradients should be divided by minibatch (most params); false otherwise (edge cases like batch norm mean/variance estimates)

setParamTable

public void setParamTable(Map<String,INDArray> paramTable)

Description copied from interface: Model

Setter for the param table

Specified by:

setParamTable in interface Model

setParam

public void setParam(String key,
                     INDArray val)

Description copied from interface: Model

Set the parameter with a new ndarray

Specified by:

setParam in interface Model

Parameters:

key - the key to se t

val - the new ndarray

clear

public void clear()

Description copied from interface: Model

Clear input

Specified by:

clear in interface Model

applyConstraints

public void applyConstraints(int iteration,
                             int epoch)

Description copied from interface: Model

Apply any constraints to the model

Specified by:

applyConstraints in interface Model

init

public void init()

Description copied from interface: Model

Init the model

Specified by:

init in interface Model

setListeners

public void setListeners(Collection<TrainingListener> listeners)

Description copied from interface: Layer

Set the TrainingListeners for this model. If any listeners have previously been set, they will be replaced by this method

Specified by:

setListeners in interface Layer

Specified by:

setListeners in interface Model

setIndex

public void setIndex(int index)

Description copied from interface: Layer

Set the layer index.

Specified by:

setIndex in interface Layer

getIndex

public int getIndex()

Description copied from interface: Layer

Get the layer index.

Specified by:

getIndex in interface Layer

getIterationCount

public int getIterationCount()

Specified by:

getIterationCount in interface Layer

Returns:

The current iteration count (number of parameter updates) for the layer/network

getEpochCount

public int getEpochCount()

Specified by:

getEpochCount in interface Layer

Returns:

The current epoch count (number of training epochs passed) for the layer/network

setIterationCount

public void setIterationCount(int iterationCount)

Description copied from interface: Layer

Set the current iteration count (number of parameter updates) for the layer/network

Specified by:

setIterationCount in interface Layer

setEpochCount

public void setEpochCount(int epochCount)

Description copied from interface: Layer

Set the current epoch count (number of epochs passed ) for the layer/network

Specified by:

setEpochCount in interface Layer

setInput

public void setInput(INDArray input,
                     LayerWorkspaceMgr layerWorkspaceMgr)

Description copied from interface: Layer

Set the layer input.

Specified by:

setInput in interface Layer

setInputMiniBatchSize

public void setInputMiniBatchSize(int size)

Description copied from interface: Layer

Set current/last input mini-batch size.
Used for score and gradient calculations. Mini batch size may be different from getInput().size(0) due to reshaping operations - for example, when using RNNs with DenseLayer and OutputLayer. Called automatically during forward pass.

Specified by:

setInputMiniBatchSize in interface Layer

getInputMiniBatchSize

public int getInputMiniBatchSize()

Description copied from interface: Layer

Get current/last input mini-batch size, as set by setInputMiniBatchSize(int)

Specified by:

getInputMiniBatchSize in interface Layer

See Also:

Layer.setInputMiniBatchSize(int)

setMaskArray

public void setMaskArray(INDArray maskArray)

Description copied from interface: Layer

Set the mask array. Note: In general, Layer.feedForwardMaskArray(INDArray, MaskState, int) should be used in preference to this.

Specified by:

setMaskArray in interface Layer

Parameters:

maskArray - Mask array to set

getMaskArray

public INDArray getMaskArray()

Specified by:

getMaskArray in interface Layer

isPretrainLayer

public boolean isPretrainLayer()

Description copied from interface: Layer

Returns true if the layer can be trained in an unsupervised/pretrain manner (AE, VAE, etc)

Specified by:

isPretrainLayer in interface Layer

Returns:

true if the layer can be pretrained (using fit(INDArray), false otherwise

clearNoiseWeightParams

public void clearNoiseWeightParams()

Specified by:

clearNoiseWeightParams in interface Layer

allowInputModification

public void allowInputModification(boolean allow)

Description copied from interface: Layer

A performance optimization: mark whether the layer is allowed to modify its input array in-place. In many cases, this is totally safe - in others, the input array will be shared by multiple layers, and hence it's not safe to modify the input array. This is usually used by ops such as dropout.

Specified by:

allowInputModification in interface Layer

Parameters:

allow - If true: the input array is safe to modify. If false: the input array should be copied before it is modified (i.e., in-place modifications are un-safe)

feedForwardMaskArray

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

Description copied from interface: Layer

Feed forward the input mask array, setting in the layer as appropriate. This allows different layers to handle masks differently - for example, bidirectional RNNs and normal RNNs operate differently with masks (the former sets activations to 0 outside of the data present region (and keeps the mask active for future layers like dense layers), whereas normal RNNs don't zero out the activations/errors )instead relying on backpropagated error arrays to handle the variable length case.
This is also used for example for networks that contain global pooling layers, arbitrary preprocessors, etc.

Specified by:

feedForwardMaskArray in interface Layer

Parameters:

maskArray - Mask array to set

currentMaskState - Current state of the mask - see MaskState

minibatchSize - Current minibatch size. Needs to be known as it cannot always be inferred from the activations array due to reshaping (such as a DenseLayer within a recurrent neural network)

Returns:

New mask array after this layer, along with the new mask state.

getHelper

public LayerHelper getHelper()

Specified by:

getHelper in interface Layer

Returns:

Get the layer helper, if any

close

public void close()

Specified by:

close in interface Model