- Direct Known Subclasses:
- Backpropagation, ManhattanPropagation, QuickPropagation, ResilientPropagation, ScaledConjugateGradient
public abstract class Propagationextends BasicTrainingimplements TrainImplements basic functionality that is needed by each of the propagation methods. The specifics of each of the propagation methods is implemented inside of the PropagationMethod interface implementors.
Constructors Constructor and Description
Propagation(ContainsFlat network, MLDataSet training)Construct a propagation object.
Methods Modifier and Type Method and Description
finishTraining()Should be called after training has completed and the iteration method will not be called any further.
fixFlatSpot(boolean b)Default is true.
getMethod()Get the current best machine learning method from the training.
iteration()Perform one training iteration.
iteration(int count)Perform the specified number of training iterations.
setNumThreads(int numThreads)Set the number of threads.
Methods inherited from class org.encog.ml.train.BasicTraining
addStrategy, getError, getImplementationType, getIteration, getStrategies, getTraining, isTrainingDone, postIteration, preIteration, setError, setIteration, setTraining
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
public final void finishTraining()Should be called after training has completed and the iteration method will not be called any further.
public final FlatNetwork getCurrentFlatNetwork()
- the currentFlatNetwork
public final TrainFlatNetwork getFlatTraining()
- the flatTraining
public final MLMethod getMethod()Get the current best machine learning method from the training.
public final int getNumThreads()
- The number of threads.
public final void iteration()Perform one training iteration.
public final void iteration(int count)Perform the specified number of training iterations. This can be more efficient than single training iterations. This is particularly true if you are training with a GPU.
public final void setFlatTraining(TrainFlatNetwork flatTraining)
flatTraining- the flatTraining to set
public final void setNumThreads(int numThreads)Set the number of threads. Specify zero to tell Encog to automatically determine the best number of threads for the processor. If OpenCL is used as the target device, then this value is not used.
numThreads- The number of threads.
public void fixFlatSpot(boolean b)Default is true. Call this with false to disable flat spot fix. For more info on flat spot: http://www.heatonresearch.com/wiki/Flat_Spot
b- True to fix flat spots, false otherwise.
public void setErrorFunction(ErrorFunction ef)
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