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, IterativeClassifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, Randomizable, RevisionHandler

public class Dl4jMlpClassifier
extends RandomizableClassifier
implements BatchPredictor, CapabilitiesHandler, IterativeClassifier

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

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static int	FILTER_NONE filter: No normalization/standardization
static int	FILTER_NORMALIZE filter: Normalize training data
static int	FILTER_STANDARDIZE filter: Standardize training data
static Tag[]	TAGS_FILTER The filter to apply to the training data

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 a prediction for a test instance.
double[][]	distributionsForInstances(Instances insts) The method to use when making predictions for test instances.
void	done() Clean up after learning.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
AbstractDataSetIterator	getDataSetIterator()
SelectedTag	getFilterType()
org.deeplearning4j.nn.conf.layers.Layer[]	getLayers()
java.io.File	getLogFile() Get the log file
NeuralNetConfiguration	getNeuralNetConfiguration()
int	getNumEpochs()
int	getQueueSize()
java.lang.String	globalInfo()
boolean	implementsMoreEfficientBatchPrediction() Performs efficient batch prediction
void	initializeClassifier(Instances data) The method used to initialize the classifier.
static void	main(java.lang.String[] argv) The main method for running this class.
boolean	next() Perform another epoch.
void	setDataSetIterator(AbstractDataSetIterator iterator)
void	setFilterType(SelectedTag newType)
void	setLayers(org.deeplearning4j.nn.conf.layers.Layer[] layers)
void	setLogFile(java.io.File logFile) Set the log file
void	setNeuralNetConfiguration(NeuralNetConfiguration config)
void	setNumEpochs(int numEpochs)
void	setQueueSize(int QueueSize)
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, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, getRevision, 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

getBatchSize, setBatchSize

Methods inherited from interface weka.classifiers.Classifier

classifyInstance

Field Detail

FILTER_NORMALIZE

public static final int FILTER_NORMALIZE

filter: Normalize training data

See Also:

Constant Field Values

FILTER_STANDARDIZE

public static final int FILTER_STANDARDIZE

filter: Standardize training data

See Also:

Constant Field Values

FILTER_NONE

public static final int FILTER_NONE

filter: No normalization/standardization

See Also:

Constant Field Values

TAGS_FILTER

public static final Tag[] TAGS_FILTER

The filter to apply to the training data

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 a layer. This option can be used multiple times.",
                commandLineParamName="layer",
                commandLineParamSynopsis="-layer <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)

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)

getNeuralNetConfiguration

@OptionMetadata(description="The neural network configuration to use.",
                displayName="network configuration",
                commandLineParamName="config",
                commandLineParamSynopsis="-config <string>",
                displayOrder=7)
public NeuralNetConfiguration getNeuralNetConfiguration()

setNeuralNetConfiguration

public void setNeuralNetConfiguration(NeuralNetConfiguration config)

getFilterType

@OptionMetadata(description="The type of normalization to perform.",
                displayName="attribute normalization",
                commandLineParamName="normalization",
                commandLineParamSynopsis="-normalization <int>",
                displayOrder=8)
public SelectedTag getFilterType()

setFilterType

public void setFilterType(SelectedTag newType)

getQueueSize

public int getQueueSize()

setQueueSize

@OptionMetadata(description="The queue size for asynchronous data transfer (default: 0, synchronous transfer).",
                displayName="queue size for asynchronous data transfer",
                commandLineParamName="queueSize",
                commandLineParamSynopsis="-queueSize <int>",
                displayOrder=9)
public void setQueueSize(int QueueSize)

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

initializeClassifier

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

The method used to initialize the classifier.

Specified by:

initializeClassifier in interface IterativeClassifier

Parameters:

data - set of instances serving as training data

Throws:

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

next

public boolean next()
             throws java.lang.Exception

Perform another epoch.

Specified by:

next in interface IterativeClassifier

Throws:

java.lang.Exception

done

public void done()

Clean up after learning.

Specified by:

done in interface IterativeClassifier

implementsMoreEfficientBatchPrediction

public boolean implementsMoreEfficientBatchPrediction()

Performs efficient batch prediction

Specified by:

implementsMoreEfficientBatchPrediction in interface BatchPredictor

Overrides:

implementsMoreEfficientBatchPrediction in class AbstractClassifier

Returns:

true, as LogitBoost can perform efficient batch prediction

distributionForInstance

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

The method to use when making a prediction for a test instance. Use distributionsForInstances() instead for speed if possible.

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

distributionsForInstances

public double[][] distributionsForInstances(Instances insts)
                                     throws java.lang.Exception

The method to use when making predictions for test instances.

Specified by:

distributionsForInstances in interface BatchPredictor

Overrides:

distributionsForInstances in class AbstractClassifier

Parameters:

insts - the instances to get predictions for

Returns:

the class probability estimates (if the class is nominal) or the numeric predictions (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