weka.classifiers.functions

Class Dl4jMlpClassifier

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.RandomizableClassifier

weka.classifiers.functions.Dl4jMlpClassifier

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, Randomizable, RevisionHandler

public class Dl4jMlpClassifier
extends RandomizableClassifier
implements BatchPredictor, CapabilitiesHandler

A wrapper for DeepLearning4j that can be used to train a multi-layer perceptron using that library.

Version:

$Revision: 11711 $

Author:

Christopher Beckham, Eibe Frank, Christopher Beckham

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
Dl4jMlpClassifier()

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances data) The method used to train the classifier.
double[]	distributionForInstance(Instance inst) The method to use when making predictions for a test instance.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
AbstractDataSetIterator	getDataSetIterator()
org.deeplearning4j.nn.conf.layers.Layer[]	getLayers()
java.io.File	getLogFile() Get the log file
int	getNumEpochs()
org.deeplearning4j.nn.api.OptimizationAlgorithm	getOptimizationAlgorithm()
java.lang.String	globalInfo()
static void	main(java.lang.String[] argv) The main method for running this class.
void	setDataSetIterator(AbstractDataSetIterator iterator)
void	setLayers(org.deeplearning4j.nn.conf.layers.Layer[] layers)
void	setLogFile(java.io.File logFile) Set the log file
void	setNumEpochs(int numEpochs)
void	setOptimizationAlgorithm(org.deeplearning4j.nn.api.OptimizationAlgorithm optimAlgorithm)
java.lang.String	toString() Returns a string describing the model.

Methods inherited from class weka.classifiers.RandomizableClassifier

getOptions, getSeed, listOptions, seedTipText, setOptions, setSeed

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, getRevision, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

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

Methods inherited from interface weka.core.BatchPredictor

distributionsForInstances, getBatchSize, implementsMoreEfficientBatchPrediction, setBatchSize

Constructor Detail

Dl4jMlpClassifier

public Dl4jMlpClassifier()

Method Detail

main

public static void main(java.lang.String[] argv)

The main method for running this class.

Parameters:

argv - the command-line arguments

globalInfo

public java.lang.String globalInfo()

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface Classifier

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class AbstractClassifier

Returns:

the capabilities of this classifier

getLogFile

public java.io.File getLogFile()

Get the log file

Returns:

the log file

setLogFile

@OptionMetadata(displayName="log file",
                description="The name of the log file to write loss information to (default = no log file).",
                commandLineParamName="logFile",
                commandLineParamSynopsis="-logFile <string>",
                displayOrder=1)
public void setLogFile(java.io.File logFile)

Set the log file

Parameters:

logFile - the log file

getLayers

public org.deeplearning4j.nn.conf.layers.Layer[] getLayers()

setLayers

@OptionMetadata(displayName="layer specification",
                description="The specification of the layers.",
                commandLineParamName="layers",
                commandLineParamSynopsis="-layers <string>",
                displayOrder=2)
public void setLayers(org.deeplearning4j.nn.conf.layers.Layer[] layers)

getNumEpochs

public int getNumEpochs()

setNumEpochs

@OptionMetadata(description="The number of epochs to perform",
                displayName="number of epochs",
                commandLineParamName="numEpochs",
                commandLineParamSynopsis="-numEpochs <int>",
                displayOrder=4)
public void setNumEpochs(int numEpochs)

getOptimizationAlgorithm

@OptionMetadata(description="Optimization algorithm (LINE_GRADIENT_DESCENT, CONJUGATE_GRADIENT, HESSIAN_FREE, LBFGS, STOCHASTIC_GRADIENT_DESCENT)",
                displayName="optimization algorithm",
                commandLineParamName="algorithm",
                commandLineParamSynopsis="-algorithm <string>",
                displayOrder=5)
public org.deeplearning4j.nn.api.OptimizationAlgorithm getOptimizationAlgorithm()

setOptimizationAlgorithm

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

getDataSetIterator

@OptionMetadata(description="The dataset iterator to use",
                displayName="dataset iterator",
                commandLineParamName="iterator",
                commandLineParamSynopsis="-iterator <string>",
                displayOrder=6)
public AbstractDataSetIterator getDataSetIterator()

setDataSetIterator

public void setDataSetIterator(AbstractDataSetIterator iterator)

buildClassifier

public void buildClassifier(Instances data)
                     throws java.lang.Exception

The method used to train the classifier.

Specified by:

buildClassifier in interface Classifier

Parameters:

data - set of instances serving as training data

Throws:

java.lang.Exception - if something goes wrong in the training process

distributionForInstance

public double[] distributionForInstance(Instance inst)
                                 throws java.lang.Exception

The method to use when making predictions for a test instance.

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

Parameters:

inst - the instance to get a prediction for

Returns:

the class probability estimates (if the class is nominal) or the numeric prediction (if it is numeric)

Throws:

java.lang.Exception - if something goes wrong at prediction time

toString

public java.lang.String toString()

Returns a string describing the model.

Overrides:

toString in class java.lang.Object

Returns:

the model string