meka.classifiers.multilabel

Class BPNN

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

meka.classifiers.multilabel.ProblemTransformationMethod

meka.classifiers.multilabel.NN.AbstractNeuralNet

meka.classifiers.multilabel.BPNN

All Implemented Interfaces:

Serializable, Cloneable, MultiLabelClassifier, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.Randomizable, weka.core.RevisionHandler

public class BPNN
extends AbstractNeuralNet

BPNN.java - Back Propagation Neural Network. This is a standard back-propagated Neural Network with multiple outputs that correspond to multiple labels.
If trained 'from scratch' only 1 layer is possible, but if you initialise it (from another method) with pre-trained weight matrices, the number of layers is inferred from that.

Version:

March 2013

Author:

Jesse Read

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected Jama.Matrix[]	dW_
protected Random	r
Jama.Matrix[]	W Weight Matrix

Fields inherited from class meka.classifiers.multilabel.NN.AbstractNeuralNet

m_E, m_H, m_M, m_R, m_Seed

Fields inherited from class meka.classifiers.multilabel.ProblemTransformationMethod

m_InstancesTemplate

Fields inherited from class weka.classifiers.SingleClassifierEnhancer

m_Classifier

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
BPNN()

Method Summary

Modifier and Type	Method and Description
double	backPropagate(double[][] X_, double[][] Y_) Back Propagate - Do one round of Back Propagation on batch X_,Y_.
void	buildClassifier(weka.core.Instances D)
double[]	distributionForInstance(weka.core.Instance xy)
Jama.Matrix[]	forwardPass(double[][] X_) Forward Pass - Given input X_, get output of all layers Z[0]...
void	initWeights(int d, int L, int[] H) InitWeights - Initialize a BPNN of H.length hidden layers with H[0], H[1], etc hidden units in each layer (W will be random, and of the corresponding dimensions).
static void	main(String[] args)
double[]	popy(double[] x_) Forward Pass - Given input x_, get output y_.
double[][]	popY(double[][] X_) Forward Pass - Given input X_, get output Y_.
void	presetWeights(Jama.Matrix[] W, int L) Preset Weights - Initialize a BPNN with (pre-trained) weight matrices W (which also determines X dimensions).
double	train(double[][] X_, double[][] Y_)
double	train(double[][] X_, double[][] Y_, int I) Train - Train for I iterations.
double	update(double[][] X_, double[][] Y_) Update - A single training epoch.

Methods inherited from class meka.classifiers.multilabel.NN.AbstractNeuralNet

eTipText, getE, getH, getLearningRate, getMomentum, getOptions, getSeed, hTipText, learningRateTipText, listOptions, momentumTipText, seedTipText, setE, setH, setLearningRate, setMomentum, setOptions, setSeed, toString

Methods inherited from class meka.classifiers.multilabel.ProblemTransformationMethod

defaultClassifierString, evaluation, getCapabilities, getModel, getRevision, getTemplate, globalInfo, makeCopies, runClassifier, testCapabilities

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, defaultClassifierOptions, getClassifier, getClassifierSpec, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

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

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface meka.classifiers.multilabel.MultiLabelClassifier

debugTipText, getDebug, setDebug

Methods inherited from interface weka.classifiers.Classifier

classifyInstance

Field Detail

W

public Jama.Matrix[] W

Weight Matrix

r

protected Random r

dW_

protected Jama.Matrix[] dW_

Constructor Detail

BPNN

public BPNN()

Method Detail

buildClassifier

public void buildClassifier(weka.core.Instances D)
                     throws Exception

Specified by:

buildClassifier in interface weka.classifiers.Classifier

Specified by:

buildClassifier in class ProblemTransformationMethod

Throws:

Exception

distributionForInstance

public double[] distributionForInstance(weka.core.Instance xy)
                                 throws Exception

Specified by:

distributionForInstance in interface weka.classifiers.Classifier

Specified by:

distributionForInstance in class ProblemTransformationMethod

Throws:

Exception

presetWeights

public void presetWeights(Jama.Matrix[] W,
                          int L)
                   throws Exception

Preset Weights - Initialize a BPNN with (pre-trained) weight matrices W (which also determines X dimensions).

Parameters:

W - pre-trained weight matrix (should include bias weights, assume W[-1]-1 hidden units in penultimate layer not including bias])

L - the number of labels (for making the final matrix)

Throws:

Exception

initWeights

public void initWeights(int d,
                        int L,
                        int[] H)
                 throws Exception

InitWeights - Initialize a BPNN of H.length hidden layers with H[0], H[1], etc hidden units in each layer (W will be random, and of the corresponding dimensions).

Parameters:

d - number of visible units

L - number of labels (output units)

H - number of units in hidden layers, H.length = number of hidden layers. CURRENTLY LIMITED TO 1.

Throws:

Exception

train

public double train(double[][] X_,
                    double[][] Y_)
             throws Exception

Throws:

Exception

train

public double train(double[][] X_,
                    double[][] Y_,
                    int I)
             throws Exception

Train - Train for I iterations. I is not necessarily m_E (yet)!

Throws:

Exception

update

public double update(double[][] X_,
                     double[][] Y_)
              throws Exception

Update - A single training epoch.

Throws:

Exception

popy

public double[] popy(double[] x_)

Forward Pass - Given input x_, get output y_.

Parameters:

x_ - input

Returns:

y_ output

popY

public double[][] popY(double[][] X_)

Forward Pass - Given input X_, get output Y_.

Parameters:

X_ - input

Returns:

Y_ output

forwardPass

public Jama.Matrix[] forwardPass(double[][] X_)

Forward Pass - Given input X_, get output of all layers Z[0]...

Parameters:

X_ - input (no bias included)

Returns:

output Z[] = {X,Z1,Z2,...,Y}

backPropagate

public double backPropagate(double[][] X_,
                            double[][] Y_)
                     throws Exception

Back Propagate - Do one round of Back Propagation on batch X_,Y_.

Parameters:

X_ - input

Y_ - teacher values

Throws:

Exception

main

public static void main(String[] args)
                 throws Exception

Throws:

Exception