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: 15879 $
Author:
Malcolm Ware (mfw4@cs.waikato.ac.nz)
See Also:

  • Constructor Details

    • MultilayerPerceptron

      public MultilayerPerceptron()
      The constructor.

  • Method Details

    • main

      public static void main(String[] argv)
      Main method for testing this class.
      Parameters:
      argv - should contain command line options (see setOptions)

    • setDecay

      public void setDecay(boolean d)
      Parameters:
      d - True if the learning rate should decay.

    • getDecay

      public boolean getDecay()
      Returns:
      the flag for having the learning rate decay.

    • 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.

    • getReset

      public boolean getReset()
      Returns:
      The flag for reseting the network.

    • 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).

    • getNormalizeNumericClass

      public boolean getNormalizeNumericClass()
      Returns:
      The flag for normalizing a numeric class.

    • 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).

    • getNormalizeAttributes

      public boolean getNormalizeAttributes()
      Returns:
      The flag for normalizing attributes.

    • setNominalToBinaryFilter

      public void setNominalToBinaryFilter(boolean f)
      Parameters:
      f - True if a nominalToBinary filter should be used on the data.

    • getNominalToBinaryFilter

      public boolean getNominalToBinaryFilter()
      Returns:
      The flag for nominal to binary filter use.

    • 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:
      setSeed in interface Randomizable
      Parameters:
      l - The seed.

    • getSeed

      public int getSeed()
      Description copied from interface: Randomizable
      Gets the seed for the random number generations
      Specified by:
      getSeed in interface Randomizable
      Returns:
      The seed for the random number generator.

    • 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.

    • getValidationThreshold

      public int getValidationThreshold()
      Returns:
      The threshold used for validation testing.

    • 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.

    • getLearningRate

      public double getLearningRate()
      Returns:
      The learning rate for the nodes.

    • 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.

    • getMomentum

      public double getMomentum()
      Returns:
      The momentum for the nodes.

    • 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.

    • getAutoBuild

      public boolean getAutoBuild()
      Returns:
      The auto build state.

    • setHiddenLayers

      public void setHiddenLayers(String h)
      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.

    • getHiddenLayers

      public String getHiddenLayers()
      Returns:
      A string representing the hidden layers, each number is the number of nodes on a hidden layer.

    • 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.

    • getGUI

      public boolean getGUI()
      Returns:
      The true if should show gui.

    • 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.

    • getValidationSetSize

      public int getValidationSetSize()
      Returns:
      The percentage size of the validation set.

    • 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.

    • getTrainingTime

      public int getTrainingTime()
      Returns:
      The number of epochs to train through.

    • 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).

    • getCapabilities

      public Capabilities getCapabilities()
      Returns default capabilities of the classifier.
      Specified by:
      getCapabilities in interface CapabilitiesHandler
      Specified by:
      getCapabilities in interface Classifier
      Overrides:
      getCapabilities in class AbstractClassifier
      Returns:
      the capabilities of this classifier
      See Also:

    • initializeClassifier

      public void initializeClassifier(Instances data) throws Exception
      Initializes an iterative classifier.
      Specified by:
      initializeClassifier in interface IterativeClassifier
      Parameters:
      data - the instances to be used in induction
      Throws:
      Exception - if the model cannot be initialized

    • next

      public boolean next() throws Exception
      Performs one iteration.
      Specified by:
      next in interface IterativeClassifier
      Returns:
      false if no further iterations could be performed, true otherwise
      Throws:
      Exception - if this iteration fails for unexpected reasons

    • done

      public void done() throws Exception
      Signal end of iterating, useful for any house-keeping/cleanup
      Specified by:
      done in interface IterativeClassifier
      Throws:
      Exception - if cleanup fails

    • resumeTipText

      public String resumeTipText()
      Tool tip text for resume property
      Returns:
      the tool tip text for the finalize property

    • 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:
      setResume in interface IterativeClassifier
      Parameters:
      resume - true if the model is to be finalized after performing iterations

    • getResume

      public boolean getResume()
      Returns true if the model is to be finalized (or has been finalized) after training.
      Specified by:
      getResume in interface IterativeClassifier
      Returns:
      the current value of finalize

    • buildClassifier

      public void buildClassifier(Instances i) throws Exception
      Call this function to build and train a neural network for the training data provided.
      Specified by:
      buildClassifier in interface Classifier
      Parameters:
      i - The training data.
      Throws:
      Exception - if can't build classification properly.

    • distributionForInstance

      public double[] distributionForInstance(Instance i) throws Exception
      Call this function to predict the class of an instance once a classification model has been built with the buildClassifier call.
      Specified by:
      distributionForInstance in interface Classifier
      Overrides:
      distributionForInstance in class AbstractClassifier
      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.

    • listOptions

      public Enumeration<Option> listOptions()
      Returns an enumeration describing the available options.
      Specified by:
      listOptions in interface OptionHandler
      Overrides:
      listOptions in class AbstractClassifier
      Returns:
      an enumeration of all the available options.

    • setOptions

      public void setOptions(String[] options) throws Exception
      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:
      setOptions in interface OptionHandler
      Overrides:
      setOptions in class AbstractClassifier
      Parameters:
      options - the list of options as an array of strings
      Throws:
      Exception - if an option is not supported

    • getOptions

      public String[] getOptions()
      Gets the current settings of NeuralNet.
      Specified by:
      getOptions in interface OptionHandler
      Overrides:
      getOptions in class AbstractClassifier
      Returns:
      an array of strings suitable for passing to setOptions()

    • toString

      public String toString()
      Overrides:
      toString in class Object
      Returns:
      string describing the model.

    • globalInfo

      public String globalInfo()
      This will return a string describing the classifier.
      Returns:
      The string.

    • learningRateTipText

      public String learningRateTipText()
      Returns:
      a string to describe the learning rate option.

    • momentumTipText

      public String momentumTipText()
      Returns:
      a string to describe the momentum option.

    • autoBuildTipText

      public String autoBuildTipText()
      Returns:
      a string to describe the AutoBuild option.

    • seedTipText

      public String seedTipText()
      Returns:
      a string to describe the random seed option.

    • validationThresholdTipText

      public String validationThresholdTipText()
      Returns:
      a string to describe the validation threshold option.

    • GUITipText

      public String GUITipText()
      Returns:
      a string to describe the GUI option.

    • validationSetSizeTipText

      public String validationSetSizeTipText()
      Returns:
      a string to describe the validation size option.

    • trainingTimeTipText

      public String trainingTimeTipText()
      Returns:
      a string to describe the learning rate option.

    • nominalToBinaryFilterTipText

      public String nominalToBinaryFilterTipText()
      Returns:
      a string to describe the nominal to binary option.

    • hiddenLayersTipText

      public String hiddenLayersTipText()
      Returns:
      a string to describe the hidden layers in the network.

    • normalizeNumericClassTipText

      public String normalizeNumericClassTipText()
      Returns:
      a string to describe the nominal to binary option.

    • normalizeAttributesTipText

      public String normalizeAttributesTipText()
      Returns:
      a string to describe the nominal to binary option.

    • resetTipText

      public String resetTipText()
      Returns:
      a string to describe the Reset option.

    • decayTipText

      public String decayTipText()
      Returns:
      a string to describe the Decay option.

    • getRevision

      public String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Overrides:
      getRevision in class AbstractClassifier
      Returns:
      the revision