org.deeplearning4j.nn.conf.layers.recurrent

Class SimpleRnn

java.lang.Object

org.deeplearning4j.nn.conf.layers.Layer

org.deeplearning4j.nn.conf.layers.BaseLayer

org.deeplearning4j.nn.conf.layers.FeedForwardLayer

org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer

org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn

All Implemented Interfaces:

Serializable, Cloneable, TrainingConfig

public class SimpleRnn
extends BaseRecurrentLayer

Simple RNN - aka "vanilla" RNN is the simplest type of recurrent neural network layer. It implements out_t = activationFn( in_t * inWeight + out_(t-1) * recurrentWeights + bias). Note that other architectures (LSTM, etc) are usually more effective, especially for longer time series

Author:

Alex Black

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	SimpleRnn.Builder

Field Summary

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

distRecurrent, weightInitRecurrent

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

nIn, nOut

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

activationFn, biasInit, biasUpdater, dist, gradientNormalization, gradientNormalizationThreshold, iUpdater, l1, l1Bias, l2, l2Bias, weightInit, weightNoise

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

constraints, iDropout, layerName

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method and Description
double	getL1ByParam(String paramName) Get the L1 coefficient for the given parameter.
double	getL2ByParam(String paramName) Get the L2 coefficient for the given parameter.
LayerMemoryReport	getMemoryReport(InputType inputType) This is a report of the estimated memory consumption for the given layer
ParamInitializer	initializer()
Layer	instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, INDArray layerParamsView, boolean initializeParams)

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

getOutputType, getPreProcessorForInputType, isPretrain, setNIn

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

isPretrainParam

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

clone, getGradientNormalization, getUpdaterByParam, resetLayerDefaultConfig

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

initializeConstraints, setPretrain

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

getGradientNormalizationThreshold, getLayerName

Constructor Detail

SimpleRnn

protected SimpleRnn(SimpleRnn.Builder builder)

Method Detail

instantiate

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

Specified by:

instantiate in class Layer

initializer

public ParamInitializer initializer()

Specified by:

initializer in class Layer

Returns:

The parameter initializer for this model

getL1ByParam

public double getL1ByParam(String paramName)

Description copied from class: Layer

Get the L1 coefficient for the given parameter. Different parameters may have different L1 values, even for a single .l1(x) configuration. For example, biases generally aren't L1 regularized, even if weights are

Specified by:

getL1ByParam in interface TrainingConfig

Overrides:

getL1ByParam in class FeedForwardLayer

Parameters:

paramName - Parameter name

Returns:

L1 value for that parameter

getL2ByParam

public double getL2ByParam(String paramName)

Description copied from class: Layer

Get the L2 coefficient for the given parameter. Different parameters may have different L2 values, even for a single .l2(x) configuration. For example, biases generally aren't L1 regularized, even if weights are

Specified by:

getL2ByParam in interface TrainingConfig

Overrides:

getL2ByParam in class FeedForwardLayer

Parameters:

paramName - Parameter name

Returns:

L2 value for that parameter

getMemoryReport

public LayerMemoryReport getMemoryReport(InputType inputType)

Description copied from class: Layer

This is a report of the estimated memory consumption for the given layer

Specified by:

getMemoryReport in class Layer

Parameters:

inputType - Input type to the layer. Memory consumption is often a function of the input type

Returns:

Memory report for the layer