org.encog.neural.prune
Class PruneIncremental

java.lang.Object
  org.encog.util.concurrency.job.ConcurrentJob
      org.encog.neural.prune.PruneIncremental

All Implemented Interfaces:

Runnable, MultiThreadable

public class PruneIncremental
extends ConcurrentJob

This class is used to help determine the optimal configuration for the hidden layers of a neural network. It can accept a pattern, which specifies the type of neural network to create, and a list of the maximum and minimum hidden layer neurons. It will then attempt to train the neural network at all configurations and see which hidden neuron counts work the best. This method does not simply choose the network with the lowest error rate. A specifiable number of best networks are kept, which represent the networks with the lowest error rates. From this collection of networks, the best network is defined to be the one with the fewest number of connections. Not all starting random weights are created equal. Because of this, an option is provided to allow you to choose how many attempts you want the process to make, with different weights. All random weights are created using the default Nguyen-Widrow method normally used by Encog.

Constructor Summary

PruneIncremental(MLDataSet training, NeuralNetworkPattern pattern, int iterations, int weightTries, int numTopResults, StatusReportable report)
Construct an object to determine the optimal number of hidden layers and neurons for the specified training data and pattern.

Method Summary

void	addHiddenLayer(int min, int max) Add a hidden layer's min and max.
BasicNetwork	getBestNetwork()
List<HiddenLayerParams>	getHidden()
int	getHidden1Size()
int	getHidden2Size()
double	getHigh()
int	getIterations()
double	getLow()
NeuralNetworkPattern	getPattern()
double[][]	getResults()
double[]	getTopErrors()
BasicNetwork[]	getTopNetworks()
MLDataSet	getTraining()
void	init() Init for prune.
int	loadWorkload() Get the next workload.
static String	networkToString(BasicNetwork network) Format the network as a human readable string that lists the hidden layers.
void	performJobUnit(JobUnitContext context) Perform an individual job unit, which is a single network to train and evaluate.
void	process() Begin the prune process.
Object	requestNextTask() Request the next task.

Methods inherited from class org.encog.util.concurrency.job.ConcurrentJob

getShouldStop, getThreadCount, isRunning, processBackground, reportStatus, run, setReport, setThreadCount, stop

Methods inherited from class java.lang.Object

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

Constructor Detail

PruneIncremental

public PruneIncremental(MLDataSet training,
                        NeuralNetworkPattern pattern,
                        int iterations,
                        int weightTries,
                        int numTopResults,
                        StatusReportable report)

Construct an object to determine the optimal number of hidden layers and neurons for the specified training data and pattern.

Parameters:

training - The training data to use.

pattern - The network pattern to use to solve this data.

iterations - How many iterations to try per network.

weightTries - The number of random weights to use.

numTopResults - The number of "top networks" to choose the most simple "best network" from.

report - Object used to report status to.

Method Detail

networkToString

public static String networkToString(BasicNetwork network)

Format the network as a human readable string that lists the hidden layers.

Parameters:

network - The network to format.

Returns:

A human readable string.

addHiddenLayer

public final void addHiddenLayer(int min,
                                 int max)

Add a hidden layer's min and max. Call this once per hidden layer. Specify a zero min if it is possible to remove this hidden layer.

Parameters:

min - The minimum number of neurons for this layer.

max - The maximum number of neurons for this layer.

getBestNetwork

public final BasicNetwork getBestNetwork()

Returns:

The network being processed.

getHidden

public final List<HiddenLayerParams> getHidden()

Returns:

The hidden layer max and min.

getHidden1Size

public final int getHidden1Size()

Returns:

The size of the first hidden layer.

getHidden2Size

public final int getHidden2Size()

Returns:

The size of the second hidden layer.

getHigh

public final double getHigh()

Returns:

The higest error so far.

getIterations

public final int getIterations()

Returns:

The number of training iterations to try for each network.

getLow

public final double getLow()

Returns:

The lowest error so far.

getPattern

public final NeuralNetworkPattern getPattern()

Returns:

The network pattern to use.

getResults

public final double[][] getResults()

Returns:

The error results.

getTopErrors

public final double[] getTopErrors()

Returns:

the topErrors

getTopNetworks

public final BasicNetwork[] getTopNetworks()

Returns:

the topNetworks

getTraining

public final MLDataSet getTraining()

Returns:

The training set to use.

init

public final void init()

Init for prune.

loadWorkload

public final int loadWorkload()

Get the next workload. This is the number of hidden neurons. This is the total amount of work to be processed.

Specified by:

loadWorkload in class ConcurrentJob

Returns:

The amount of work to be processed by this.

performJobUnit

public final void performJobUnit(JobUnitContext context)

Perform an individual job unit, which is a single network to train and evaluate.

Specified by:

performJobUnit in class ConcurrentJob

Parameters:

context - Contains information about the job unit.

process

public final void process()

Begin the prune process.

Overrides:

process in class ConcurrentJob

requestNextTask

public final Object requestNextTask()

Request the next task. This is the next network to attempt to train.

Specified by:

requestNextTask in class ConcurrentJob

Returns:

The next network to train.