org.neuroph.nnet.learning
Class DynamicBackPropagation
java.lang.Object
org.neuroph.core.learning.LearningRule
org.neuroph.core.learning.IterativeLearning
org.neuroph.core.learning.SupervisedLearning
org.neuroph.nnet.learning.LMS
org.neuroph.nnet.learning.SigmoidDeltaRule
org.neuroph.nnet.learning.BackPropagation
org.neuroph.nnet.learning.MomentumBackpropagation
org.neuroph.nnet.learning.DynamicBackPropagation
- All Implemented Interfaces:
- Serializable
public class DynamicBackPropagation
- extends MomentumBackpropagation
Backpropagation learning rule with dynamic learning rate and momentum
- Author:
- Zoran Sevarac
- See Also:
- Serialized Form
Methods inherited from class org.neuroph.core.learning.SupervisedLearning |
addToSquaredErrorSum, afterEpoch, beforeEpoch, calculateOutputError, doBatchWeightsUpdate, errorChangeStalled, getMaxError, getMinErrorChange, getMinErrorChangeIterationsCount, getMinErrorChangeIterationsLimit, getPreviousEpochError, getTotalNetworkError, hasReachedStopCondition, isInBatchMode, learn, learn, learnPattern, setBatchMode, setMaxError, setMinErrorChange, setMinErrorChangeIterationsLimit |
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
DynamicBackPropagation
public DynamicBackPropagation()
adjustLearningRate
protected void adjustLearningRate()
adjustMomentum
protected void adjustMomentum()
doLearningEpoch
public void doLearningEpoch(DataSet trainingSet)
- Description copied from class:
SupervisedLearning
- This method implements basic logic for one learning epoch for the
supervised learning algorithms. Epoch is the one pass through the
training set. This method iterates through the training set
and trains network for each element. It also sets flag if conditions
to stop learning has been reached: network error below some allowed
value, or maximum iteration count
- Overrides:
doLearningEpoch
in class SupervisedLearning
- Parameters:
trainingSet
- training set for training network
getLearningRateChange
public double getLearningRateChange()
setLearningRateChange
public void setLearningRateChange(double learningRateChange)
getMaxLearningRate
public double getMaxLearningRate()
setMaxLearningRate
public void setMaxLearningRate(double maxLearningRate)
getMaxMomentum
public double getMaxMomentum()
setMaxMomentum
public void setMaxMomentum(double maxMomentum)
getMinLearningRate
public double getMinLearningRate()
setMinLearningRate
public void setMinLearningRate(double minLearningRate)
getMinMomentum
public double getMinMomentum()
setMinMomentum
public void setMinMomentum(double minMomentum)
getMomentumChange
public double getMomentumChange()
setMomentumChange
public void setMomentumChange(double momentumChange)
getUseDynamicLearningRate
public boolean getUseDynamicLearningRate()
setUseDynamicLearningRate
public void setUseDynamicLearningRate(boolean useDynamicLearningRate)
getUseDynamicMomentum
public boolean getUseDynamicMomentum()
setUseDynamicMomentum
public void setUseDynamicMomentum(boolean useDynamicMomentum)
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