weka.dl4j

Class NeuralNetConfiguration

java.lang.Object

weka.dl4j.NeuralNetConfiguration

All Implemented Interfaces:

java.io.Serializable, weka.core.OptionHandler

public class NeuralNetConfiguration
extends java.lang.Object
implements java.io.Serializable, weka.core.OptionHandler

A version of DeepLearning4j's NeuralNetConfiguration that implements WEKA option handling.

The duplicate code of configuration parameters is necessary since the dl4j NeuralNetConfiguration.Builder object is not serializable which is necessary for the weka GUI.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
NeuralNetConfiguration() Constructor that provides default values for the settings.

Method Summary

Modifier and Type	Method and Description
org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder	builder() Deliver access to the internal builder
double	getBiasInit()
Updater	getBiasUpdater()
Distribution<? extends org.deeplearning4j.nn.conf.distribution.Distribution>	getDist()
AbstractDropout	getDropout()
GradientNormalization	getGradientNormalization()
double	getGradientNormalizationThreshold()
double	getL1()
double	getL2()
org.deeplearning4j.nn.api.OptimizationAlgorithm	getOptimizationAlgo()
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
long	getSeed()
Updater	getUpdater()
org.deeplearning4j.nn.weights.WeightInit	getWeightInit()
AbstractWeightNoise	getWeightNoise()
java.lang.String	globalInfo() Returns a string describing this search method
boolean	isMiniBatch()
boolean	isMinimize()
java.util.Enumeration<weka.core.Option>	listOptions() Returns an enumeration describing the available options.
void	setBiasInit(double biasInit)
void	setBiasUpdater(Updater biasUpdater)
void	setDist(Distribution<? extends org.deeplearning4j.nn.conf.distribution.Distribution> dist)
void	setDropout(AbstractDropout dropout)
void	setGradientNormalization(GradientNormalization gradientNormalization)
void	setGradientNormalizationThreshold(double gradientNormalizationThreshold)
void	setL1(double l1)
void	setL2(double l2)
void	setMiniBatch(boolean b)
void	setMinimize(boolean b)
void	setOptimizationAlgo(org.deeplearning4j.nn.api.OptimizationAlgorithm optimAlgorithm)
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setSeed(long n)
void	setUpdater(Updater updater)
void	setWeightInit(org.deeplearning4j.nn.weights.WeightInit weightInit)
void	setWeightNoise(AbstractWeightNoise weightNoise)

Methods inherited from class java.lang.Object

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

Methods inherited from interface weka.core.OptionHandler

makeCopy

Constructor Detail

NeuralNetConfiguration

public NeuralNetConfiguration()

Constructor that provides default values for the settings.

Method Detail

builder

public org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder builder()

Deliver access to the internal builder

Returns:

NeuralNetworkConfiguration

getOptimizationAlgo

@OptionMetadata(description="Optimization algorithm (LINE_GRADIENT_DESCENT, CONJUGATE_GRADIENT, HESSIAN_FREE, LBFGS, STOCHASTIC_GRADIENT_DESCENT)",
                displayName="optimization algorithm",
                commandLineParamName="algorithm",
                commandLineParamSynopsis="-algorithm <string>",
                displayOrder=1)
public org.deeplearning4j.nn.api.OptimizationAlgorithm getOptimizationAlgo()

setOptimizationAlgo

public void setOptimizationAlgo(org.deeplearning4j.nn.api.OptimizationAlgorithm optimAlgorithm)

isMinimize

@OptionMetadata(displayName="whether to minimize objective",
                description="Whether to minimize objective.",
                commandLineParamIsFlag=true,
                commandLineParamName="minimize",
                commandLineParamSynopsis="-minimize",
                displayOrder=7)
public boolean isMinimize()

setMinimize

public void setMinimize(boolean b)

getUpdater

@OptionMetadata(displayName="updater",
                description="The updater to use (default = SGD).",
                commandLineParamName="updater",
                commandLineParamSynopsis="-updater <string>",
                displayOrder=12)
public Updater getUpdater()

setUpdater

public void setUpdater(Updater updater)

getBiasUpdater

@OptionMetadata(displayName="biasUpdater",
                description="The updater to use for the bias (default = SGD).",
                commandLineParamName="biasUpdater",
                commandLineParamSynopsis="-biasUpdater <string>",
                displayOrder=13)
public Updater getBiasUpdater()

setBiasUpdater

public void setBiasUpdater(Updater biasUpdater)

getDropout

@OptionMetadata(displayName="dropout",
                description="The dropout method to use (default = Dropout(0.0).",
                commandLineParamName="dropout",
                commandLineParamSynopsis="-dropout <Dropout>",
                displayOrder=25)
public AbstractDropout getDropout()

setDropout

public void setDropout(AbstractDropout dropout)

getWeightNoise

@OptionMetadata(displayName="weightNoise",
                description="The weight noise method to use (default = None).",
                commandLineParamName="weightNoise",
                commandLineParamSynopsis="-weightNoise <WeightNoise>",
                displayOrder=26)
public AbstractWeightNoise getWeightNoise()

setWeightNoise

public void setWeightNoise(AbstractWeightNoise weightNoise)

getL1

@OptionMetadata(displayName="l1 regularization factor",
                description="L1 regularization factor (default = 0.00).",
                commandLineParamName="l1",
                commandLineParamSynopsis="-l1 <double>",
                displayOrder=14)
public double getL1()

setL1

public void setL1(double l1)

getL2

@OptionMetadata(displayName="l2 regularization factor",
                description="L2 regularization factor (default = 0.00).",
                commandLineParamName="l2",
                commandLineParamSynopsis="-l2 <double>",
                displayOrder=15)
public double getL2()

setL2

public void setL2(double l2)

getWeightInit

@OptionMetadata(displayName="weight initialization method",
                description="The method for weight initialization (default = XAVIER).",
                commandLineParamName="weightInit",
                commandLineParamSynopsis="-weightInit <specification>",
                displayOrder=18)
public org.deeplearning4j.nn.weights.WeightInit getWeightInit()

setWeightInit

public void setWeightInit(org.deeplearning4j.nn.weights.WeightInit weightInit)

getGradientNormalization

@OptionMetadata(displayName="gradient normalization method",
                description="The gradient normalization method (default = None).",
                commandLineParamName="gradientNormalization",
                commandLineParamSynopsis="-gradientNormalization <specification>",
                displayOrder=22)
public GradientNormalization getGradientNormalization()

setGradientNormalization

public void setGradientNormalization(GradientNormalization gradientNormalization)

getGradientNormalizationThreshold

@OptionMetadata(displayName="gradient normalization threshold",
                description="The gradient normalization threshold (default = 1).",
                commandLineParamName="gradNormThreshold",
                commandLineParamSynopsis="-gradNormThreshold <double>",
                displayOrder=23)
public double getGradientNormalizationThreshold()

setGradientNormalizationThreshold

public void setGradientNormalizationThreshold(double gradientNormalizationThreshold)

getDist

@OptionMetadata(displayName="distribution",
                description="The weight init distribution type. Only applies when weightinit=DISTRIBUTION (default = Disabled).",
                commandLineParamName="dist",
                commandLineParamSynopsis="-dist <specification>",
                displayOrder=19)
public Distribution<? extends org.deeplearning4j.nn.conf.distribution.Distribution> getDist()

setDist

public void setDist(Distribution<? extends org.deeplearning4j.nn.conf.distribution.Distribution> dist)

getBiasInit

@OptionMetadata(displayName="bias initialization",
                description="The bias initialization (default = 0.0).",
                commandLineParamName="biasInit",
                commandLineParamSynopsis="-biasInit <double>",
                displayOrder=20)
public double getBiasInit()

setBiasInit

public void setBiasInit(double biasInit)

getSeed

@ProgrammaticProperty
public long getSeed()

setSeed

public void setSeed(long n)

isMiniBatch

@ProgrammaticProperty
public boolean isMiniBatch()

setMiniBatch

public void setMiniBatch(boolean b)

listOptions

public java.util.Enumeration<weka.core.Option> listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface weka.core.OptionHandler

Returns:

an enumeration of all the available options.

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the Classifier.

Specified by:

getOptions in interface weka.core.OptionHandler

Returns:

an array of strings suitable for passing to setOptions

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options.

Specified by:

setOptions in interface weka.core.OptionHandler

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

globalInfo

public java.lang.String globalInfo()

Returns a string describing this search method

Returns:

a description of the search method suitable for displaying in the explorer/experimenter gui