org.deeplearning4j.nn.layers.recurrent

Class LSTM

java.lang.Object

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

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

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

org.deeplearning4j.nn.layers.recurrent.LSTM

All Implemented Interfaces:

Serializable, Cloneable, Layer, RecurrentLayer, Model

public class LSTM
extends BaseRecurrentLayer<LSTM>

LSTM layer implementation. See also for full/vectorized equations (and a comparison to other LSTM variants): Greff et al. 2015, "LSTM: A Search Space Odyssey", pg11. This is the "no peephole" variant in said paper http://arxiv.org/pdf/1503.04069.pdf

Author:

Alex Black

See Also:

GravesLSTM class, for the version with peephole connections, 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 and Description
protected FwdPassReturn	cachedFwdPass
protected LSTMHelper	helper
static String	STATE_KEY_PREV_ACTIVATION
static String	STATE_KEY_PREV_MEMCELL

Fields inherited from class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer

stateMap, tBpttStateMap

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, dropoutApplied, dropoutMask, epochCount, index, input, iterationCount, maskArray, maskState, preOutput, trainingListeners

Constructor Summary

Constructors

Constructor and Description
LSTM(NeuralNetConfiguration conf)
LSTM(NeuralNetConfiguration conf, org.nd4j.linalg.api.ndarray.INDArray input)

Method Summary

Modifier and Type	Method and Description
org.nd4j.linalg.api.ndarray.INDArray	activate(boolean training, LayerWorkspaceMgr workspaceMgr) Perform forward pass and return the activations array with the last set input
org.nd4j.linalg.api.ndarray.INDArray	activate(org.nd4j.linalg.api.ndarray.INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr) Perform forward pass and return the activations array with the specified input
org.nd4j.linalg.primitives.Pair<Gradient,org.nd4j.linalg.api.ndarray.INDArray>	backpropGradient(org.nd4j.linalg.api.ndarray.INDArray epsilon, LayerWorkspaceMgr workspaceMgr) Calculate the gradient relative to the error in the next layer
org.nd4j.linalg.primitives.Pair<org.nd4j.linalg.api.ndarray.INDArray,MaskState>	feedForwardMaskArray(org.nd4j.linalg.api.ndarray.INDArray maskArray, MaskState currentMaskState, int minibatchSize) Feed forward the input mask array, setting in in the layer as appropriate.
Gradient	gradient() Get the gradient.
boolean	isPretrainLayer() Returns true if the layer can be trained in an unsupervised/pretrain manner (AE, VAE, etc)
org.nd4j.linalg.api.ndarray.INDArray	rnnActivateUsingStoredState(org.nd4j.linalg.api.ndarray.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.
org.nd4j.linalg.api.ndarray.INDArray	rnnTimeStep(org.nd4j.linalg.api.ndarray.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
org.nd4j.linalg.primitives.Pair<Gradient,org.nd4j.linalg.api.ndarray.INDArray>	tbpttBackpropGradient(org.nd4j.linalg.api.ndarray.INDArray epsilon, int tbpttBackwardLength, LayerWorkspaceMgr workspaceMgr) Truncated BPTT equivalent of Layer.backpropGradient().
Layer	transpose() Return a transposed copy of the weights/bias (this means reverse the number of inputs and outputs on the weights)
Layer.Type	type() Returns the layer type

Methods inherited from class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer

rnnClearPreviousState, rnnGetPreviousState, rnnGetTBPTTState, rnnSetPreviousState, rnnSetTBPTTState

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

accumulateScore, calcL1, calcL2, clear, clearNoiseWeightParams, clone, computeGradientAndScore, fit, fit, getGradientsViewArray, getOptimizer, getParam, getParamWithNoise, hasBias, initParams, layerConf, numParams, params, paramTable, paramTable, preOutput, score, setBackpropGradientsViewArray, setParam, setParams, setParams, setParamsViewArray, setParamTable, setScoreWithZ, toString, update, update

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

addListeners, applyConstraints, applyDropOutIfNecessary, applyMask, assertInputSet, batchSize, conf, getIndex, getInput, getInputMiniBatchSize, getListeners, getMaskArray, gradientAndScore, init, input, layerId, numParams, setCacheMode, setConf, setIndex, setInput, setInputMiniBatchSize, setListeners, setListeners, setMaskArray, validateInput

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

calcL1, calcL2, clearNoiseWeightParams, clone, getEpochCount, getIndex, getInputMiniBatchSize, getIterationCount, getListeners, getMaskArray, setCacheMode, setEpochCount, setIndex, setInput, setInputMiniBatchSize, setIterationCount, setListeners, setListeners, setMaskArray

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

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

Field Detail

STATE_KEY_PREV_ACTIVATION

public static final String STATE_KEY_PREV_ACTIVATION

See Also:

Constant Field Values

STATE_KEY_PREV_MEMCELL

public static final String STATE_KEY_PREV_MEMCELL

See Also:

Constant Field Values

helper

protected LSTMHelper helper

cachedFwdPass

protected FwdPassReturn cachedFwdPass

Constructor Detail

LSTM

public LSTM(NeuralNetConfiguration conf)

LSTM

public LSTM(NeuralNetConfiguration conf,
            org.nd4j.linalg.api.ndarray.INDArray input)

Method Detail

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() .

Specified by:

gradient in interface Model

Overrides:

gradient in class BaseLayer<LSTM>

Returns:

the gradient for this model, as calculated before

backpropGradient

public org.nd4j.linalg.primitives.Pair<Gradient,org.nd4j.linalg.api.ndarray.INDArray> backpropGradient(org.nd4j.linalg.api.ndarray.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

Overrides:

backpropGradient in class BaseLayer<LSTM>

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

tbpttBackpropGradient

public org.nd4j.linalg.primitives.Pair<Gradient,org.nd4j.linalg.api.ndarray.INDArray> tbpttBackpropGradient(org.nd4j.linalg.api.ndarray.INDArray epsilon,
                                                                                                            int tbpttBackwardLength,
                                                                                                            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.

activate

public org.nd4j.linalg.api.ndarray.INDArray activate(org.nd4j.linalg.api.ndarray.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

Overrides:

activate in class AbstractLayer<LSTM>

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

activate

public org.nd4j.linalg.api.ndarray.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

Overrides:

activate in class BaseLayer<LSTM>

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

type

public Layer.Type type()

Description copied from interface: Layer

Returns the layer type

Specified by:

type in interface Layer

Overrides:

type in class AbstractLayer<LSTM>

Returns:

transpose

public Layer transpose()

Description copied from interface: Layer

Return a transposed copy of the weights/bias (this means reverse the number of inputs and outputs on the weights)

Specified by:

transpose in interface Layer

Overrides:

transpose in class BaseLayer<LSTM>

Returns:

the transposed 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)

Returns:

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

feedForwardMaskArray

public org.nd4j.linalg.primitives.Pair<org.nd4j.linalg.api.ndarray.INDArray,MaskState> feedForwardMaskArray(org.nd4j.linalg.api.ndarray.INDArray maskArray,
                                                                                                            MaskState currentMaskState,
                                                                                                            int minibatchSize)

Description copied from interface: Layer

Feed forward the input mask array, setting in 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

Overrides:

feedForwardMaskArray in class AbstractLayer<LSTM>

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.

rnnTimeStep

public org.nd4j.linalg.api.ndarray.INDArray rnnTimeStep(org.nd4j.linalg.api.ndarray.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

Parameters:

input - Input to this layer

Returns:

activations

rnnActivateUsingStoredState

public org.nd4j.linalg.api.ndarray.INDArray rnnActivateUsingStoredState(org.nd4j.linalg.api.ndarray.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

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