Package weka.classifiers.functions
Class MultilayerPerceptron
java.lang.Object
weka.classifiers.AbstractClassifier
weka.classifiers.functions.MultilayerPerceptron
- All Implemented Interfaces:
Serializable,Cloneable,Classifier,IterativeClassifier,BatchPredictor,CapabilitiesHandler,CapabilitiesIgnorer,CommandlineRunnable,OptionHandler,Randomizable,RevisionHandler,WeightedInstancesHandler
public class MultilayerPerceptron
extends AbstractClassifier
implements OptionHandler, WeightedInstancesHandler, Randomizable, IterativeClassifier
A classifier that uses backpropagation to learn a multi-layer perceptron to classify instances.
The network can be built by hand or set up using a simple heuristic.
The network parameters can also be monitored and modified during training time.
The nodes in this network are all sigmoid (except for when the class
is numeric, in which case the output nodes become unthresholded linear units).
Valid options are:
-L <learning rate> Learning rate for the backpropagation algorithm. (Value should be between 0 - 1, Default = 0.3).
-M <momentum> Momentum rate for the backpropagation algorithm. (Value should be between 0 - 1, Default = 0.2).
-N <number of epochs> Number of epochs to train through. (Default = 500).
-V <percentage size of validation set> Percentage size of validation set to use to terminate training (if this is non zero it can pre-empt num of epochs. (Value should be between 0 - 100, Default = 0).
-S <seed> The value used to seed the random number generator (Value should be >= 0 and and a long, Default = 0).
-E <threshold for number of consecutive errors> The number of consecutive increases of error allowed for validation testing before training terminates. (Value should be > 0, Default = 20).
-G GUI will be opened. (Use this to bring up a GUI).
-A Autocreation of the network connections will NOT be done. (This will be ignored if -G is NOT set)
-B A NominalToBinary filter will NOT automatically be used. (Set this to not use a NominalToBinary filter).
-H <comma separated numbers for nodes on each layer> The hidden layers to be created for the network. (Value should be a list of comma separated Natural numbers or the letters 'a' = (attribs + classes) / 2, 'i' = attribs, 'o' = classes, 't' = attribs .+ classes) for wildcard values, Default = a).
-C Normalizing a numeric class will NOT be done. (Set this to not normalize the class if it's numeric).
-I Normalizing the attributes will NOT be done. (Set this to not normalize the attributes).
-R Reseting the network will NOT be allowed. (Set this to not allow the network to reset).
-D Learning rate decay will occur. (Set this to cause the learning rate to decay).
- Version:
- $Revision: 15923 $
- Author:
- Malcolm Ware (mfw4@cs.waikato.ac.nz)
- See Also:
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Field Summary
Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionvoidblocker(boolean tf) A function used to stop the code that called buildclassifier from continuing on before the user has finished the decision tree.voidCall this function to build and train a neural network for the training data provided.double[]Call this function to predict the class of an instance once a classification model has been built with the buildClassifier call.voiddone()Signal end of iterating, useful for any house-keeping/cleanupbooleanReturns default capabilities of the classifier.booleangetDecay()booleangetGUI()doubledoublebooleanbooleanbooleanString[]Gets the current settings of NeuralNet.booleangetReset()booleanReturns true if the model is to be finalized (or has been finalized) after training.Returns the revision string.intgetSeed()Gets the seed for the random number generationsintintintThis will return a string describing the classifier.voidInitializes an iterative classifier.Returns an enumeration describing the available options.static voidMain method for testing this class.booleannext()Performs one iteration.Tool tip text for resume propertyvoidsetAutoBuild(boolean a) This will set whether the network is automatically built or if it is left up to the user.voidsetDecay(boolean d) voidsetGUI(boolean a) This will set whether A GUI is brought up to allow interaction by the user with the neural network during training.voidThis will set what the hidden layers are made up of when auto build is enabled.voidsetLearningRate(double l) The learning rate can be set using this command.voidsetMomentum(double m) The momentum can be set using this command.voidsetNominalToBinaryFilter(boolean f) voidsetNormalizeAttributes(boolean a) voidsetNormalizeNumericClass(boolean c) voidsetOptions(String[] options) Parses a given list of options.voidsetReset(boolean r) This sets the network up to be able to reset itself with the current settings and the learning rate at half of what it is currently.voidsetResume(boolean resume) If called with argument true, then the next time done() is called the model is effectively "frozen" and no further iterations can be performedvoidsetSeed(int l) This seeds the random number generator, that is used when a random number is needed for the network.voidsetTrainingTime(int n) Set the number of training epochs to perform.voidsetValidationSetSize(int a) This will set the size of the validation set.voidsetValidationThreshold(int t) This sets the threshold to use for when validation testing is being done.toString()Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlacesMethods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, waitMethods inherited from interface weka.classifiers.Classifier
classifyInstance
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Constructor Details
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MultilayerPerceptron
public MultilayerPerceptron()The constructor.
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Method Details
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main
Main method for testing this class.- Parameters:
argv- should contain command line options (see setOptions)
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setDecay
public void setDecay(boolean d) - Parameters:
d- True if the learning rate should decay.
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getDecay
public boolean getDecay()- Returns:
- the flag for having the learning rate decay.
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setReset
public void setReset(boolean r) This sets the network up to be able to reset itself with the current settings and the learning rate at half of what it is currently. This will only happen if the network creates NaN or infinite errors. Also this will continue to happen until the network is trained properly. The learning rate will also get set back to it's original value at the end of this. This can only be set to true if the GUI is not brought up.- Parameters:
r- True if the network should restart with it's current options and set the learning rate to half what it currently is.
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getReset
public boolean getReset()- Returns:
- The flag for reseting the network.
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setNormalizeNumericClass
public void setNormalizeNumericClass(boolean c) - Parameters:
c- True if the class should be normalized (the class will only ever be normalized if it is numeric). (Normalization puts the range between -1 - 1).
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getNormalizeNumericClass
public boolean getNormalizeNumericClass()- Returns:
- The flag for normalizing a numeric class.
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setNormalizeAttributes
public void setNormalizeAttributes(boolean a) - Parameters:
a- True if the attributes should be normalized (even nominal attributes will get normalized here) (range goes between -1 - 1).
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getNormalizeAttributes
public boolean getNormalizeAttributes()- Returns:
- The flag for normalizing attributes.
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setNominalToBinaryFilter
public void setNominalToBinaryFilter(boolean f) - Parameters:
f- True if a nominalToBinary filter should be used on the data.
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getNominalToBinaryFilter
public boolean getNominalToBinaryFilter()- Returns:
- The flag for nominal to binary filter use.
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setSeed
public void setSeed(int l) This seeds the random number generator, that is used when a random number is needed for the network.- Specified by:
setSeedin interfaceRandomizable- Parameters:
l- The seed.
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getSeed
public int getSeed()Description copied from interface:RandomizableGets the seed for the random number generations- Specified by:
getSeedin interfaceRandomizable- Returns:
- The seed for the random number generator.
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setValidationThreshold
public void setValidationThreshold(int t) This sets the threshold to use for when validation testing is being done. It works by ending testing once the error on the validation set has consecutively increased a certain number of times.- Parameters:
t- The threshold to use for this.
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getValidationThreshold
public int getValidationThreshold()- Returns:
- The threshold used for validation testing.
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setLearningRate
public void setLearningRate(double l) The learning rate can be set using this command. NOTE That this is a static variable so it affect all networks that are running. Must be greater than 0 and no more than 1.- Parameters:
l- The New learning rate.
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getLearningRate
public double getLearningRate()- Returns:
- The learning rate for the nodes.
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setMomentum
public void setMomentum(double m) The momentum can be set using this command. THE same conditions apply to this as to the learning rate.- Parameters:
m- The new Momentum.
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getMomentum
public double getMomentum()- Returns:
- The momentum for the nodes.
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setAutoBuild
public void setAutoBuild(boolean a) This will set whether the network is automatically built or if it is left up to the user. (there is nothing to stop a user from altering an autobuilt network however).- Parameters:
a- True if the network should be auto built.
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getAutoBuild
public boolean getAutoBuild()- Returns:
- The auto build state.
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setHiddenLayers
This will set what the hidden layers are made up of when auto build is enabled. Note to have no hidden units, just put a single 0, Any more 0's will indicate that the string is badly formed and make it unaccepted. Negative numbers, and floats will do the same. There are also some wildcards. These are 'a' = (number of attributes + number of classes) / 2, 'i' = number of attributes, 'o' = number of classes, and 't' = number of attributes + number of classes.- Parameters:
h- A string with a comma seperated list of numbers. Each number is the number of nodes to be on a hidden layer.
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getHiddenLayers
- Returns:
- A string representing the hidden layers, each number is the number of nodes on a hidden layer.
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setGUI
public void setGUI(boolean a) This will set whether A GUI is brought up to allow interaction by the user with the neural network during training.- Parameters:
a- True if gui should be created.
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getGUI
public boolean getGUI()- Returns:
- The true if should show gui.
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setValidationSetSize
public void setValidationSetSize(int a) This will set the size of the validation set.- Parameters:
a- The size of the validation set, as a percentage of the whole.
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getValidationSetSize
public int getValidationSetSize()- Returns:
- The percentage size of the validation set.
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setTrainingTime
public void setTrainingTime(int n) Set the number of training epochs to perform. Must be greater than 0.- Parameters:
n- The number of epochs to train through.
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getTrainingTime
public int getTrainingTime()- Returns:
- The number of epochs to train through.
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blocker
public void blocker(boolean tf) A function used to stop the code that called buildclassifier from continuing on before the user has finished the decision tree.- Parameters:
tf- True to stop the thread, False to release the thread that is waiting there (if one).
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getCapabilities
Returns default capabilities of the classifier.- Specified by:
getCapabilitiesin interfaceCapabilitiesHandler- Specified by:
getCapabilitiesin interfaceClassifier- Overrides:
getCapabilitiesin classAbstractClassifier- Returns:
- the capabilities of this classifier
- See Also:
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initializeClassifier
Initializes an iterative classifier.- Specified by:
initializeClassifierin interfaceIterativeClassifier- Parameters:
data- the instances to be used in induction- Throws:
Exception- if the model cannot be initialized
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next
Performs one iteration.- Specified by:
nextin interfaceIterativeClassifier- Returns:
- false if no further iterations could be performed, true otherwise
- Throws:
Exception- if this iteration fails for unexpected reasons
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done
Signal end of iterating, useful for any house-keeping/cleanup- Specified by:
donein interfaceIterativeClassifier- Throws:
Exception- if cleanup fails
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resumeTipText
Tool tip text for resume property- Returns:
- the tool tip text for the finalize property
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setResume
public void setResume(boolean resume) If called with argument true, then the next time done() is called the model is effectively "frozen" and no further iterations can be performed- Specified by:
setResumein interfaceIterativeClassifier- Parameters:
resume- true if the model is to be finalized after performing iterations
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getResume
public boolean getResume()Returns true if the model is to be finalized (or has been finalized) after training.- Specified by:
getResumein interfaceIterativeClassifier- Returns:
- the current value of finalize
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buildClassifier
Call this function to build and train a neural network for the training data provided.- Specified by:
buildClassifierin interfaceClassifier- Parameters:
i- The training data.- Throws:
Exception- if can't build classification properly.
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distributionForInstance
Call this function to predict the class of an instance once a classification model has been built with the buildClassifier call.- Specified by:
distributionForInstancein interfaceClassifier- Overrides:
distributionForInstancein classAbstractClassifier- Parameters:
i- The instance to classify.- Returns:
- A double array filled with the probabilities of each class type.
- Throws:
Exception- if can't classify instance.
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listOptions
Returns an enumeration describing the available options.- Specified by:
listOptionsin interfaceOptionHandler- Overrides:
listOptionsin classAbstractClassifier- Returns:
- an enumeration of all the available options.
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setOptions
Parses a given list of options. Valid options are:-L <learning rate> Learning rate for the backpropagation algorithm. (Value should be between 0 - 1, Default = 0.3).
-M <momentum> Momentum rate for the backpropagation algorithm. (Value should be between 0 - 1, Default = 0.2).
-N <number of epochs> Number of epochs to train through. (Default = 500).
-V <percentage size of validation set> Percentage size of validation set to use to terminate training (if this is non zero it can pre-empt num of epochs. (Value should be between 0 - 100, Default = 0).
-S <seed> The value used to seed the random number generator (Value should be >= 0 and and a long, Default = 0).
-E <threshold for number of consecutive errors> The number of consecutive increases of error allowed for validation testing before training terminates. (Value should be > 0, Default = 20).
-G GUI will be opened. (Use this to bring up a GUI).
-A Autocreation of the network connections will NOT be done. (This will be ignored if -G is NOT set)
-B A NominalToBinary filter will NOT automatically be used. (Set this to not use a NominalToBinary filter).
-H <comma separated numbers for nodes on each layer> The hidden layers to be created for the network. (Value should be a list of comma separated Natural numbers or the letters 'a' = (attribs + classes) / 2, 'i' = attribs, 'o' = classes, 't' = attribs .+ classes) for wildcard values, Default = a).
-C Normalizing a numeric class will NOT be done. (Set this to not normalize the class if it's numeric).
-I Normalizing the attributes will NOT be done. (Set this to not normalize the attributes).
-R Resetting the network will NOT be allowed. (Set this to not allow the network to reset).
-D Learning rate decay will occur. (Set this to cause the learning rate to decay).
- Specified by:
setOptionsin interfaceOptionHandler- Overrides:
setOptionsin classAbstractClassifier- Parameters:
options- the list of options as an array of strings- Throws:
Exception- if an option is not supported
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getOptions
Gets the current settings of NeuralNet.- Specified by:
getOptionsin interfaceOptionHandler- Overrides:
getOptionsin classAbstractClassifier- Returns:
- an array of strings suitable for passing to setOptions()
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toString
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globalInfo
This will return a string describing the classifier.- Returns:
- The string.
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learningRateTipText
- Returns:
- a string to describe the learning rate option.
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momentumTipText
- Returns:
- a string to describe the momentum option.
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autoBuildTipText
- Returns:
- a string to describe the AutoBuild option.
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seedTipText
- Returns:
- a string to describe the random seed option.
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validationThresholdTipText
- Returns:
- a string to describe the validation threshold option.
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GUITipText
- Returns:
- a string to describe the GUI option.
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validationSetSizeTipText
- Returns:
- a string to describe the validation size option.
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trainingTimeTipText
- Returns:
- a string to describe the learning rate option.
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nominalToBinaryFilterTipText
- Returns:
- a string to describe the nominal to binary option.
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normalizeNumericClassTipText
- Returns:
- a string to describe the nominal to binary option.
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normalizeAttributesTipText
- Returns:
- a string to describe the nominal to binary option.
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resetTipText
- Returns:
- a string to describe the Reset option.
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decayTipText
- Returns:
- a string to describe the Decay option.
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getRevision
Returns the revision string.- Specified by:
getRevisionin interfaceRevisionHandler- Overrides:
getRevisionin classAbstractClassifier- Returns:
- the revision
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