org.kramerlab.autoencoder.neuralnet.rbm

TournamentTrainingStrategy

class TournamentTrainingStrategy extends RbmTrainingStrategy

This training strategy generates multiple Rbm training configurations at random, and then trains multiple Rbm's with these configurations in rounds with specified number of epochs. After each round, a fraction of best Rbm's is selected, and the training continues with the next round, until there is only one Rbm left. This rbm is trained until the error on the validation set does not decrease any more.

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Instance Constructors

  1. new TournamentTrainingStrategy(initialNumberOfCandidates: Int, epochsPerRound: Int, fraction: Double, relativeValidationSetSize: Double)

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  1. final def !=(arg0: AnyRef): Boolean

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  11. final def getClass(): Class[_]

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  13. final def isInstanceOf[T0]: Boolean

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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  17. final def synchronized[T0](arg0: ⇒ T0): T0

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  18. def toString(): String

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  19. def train(rbm: Rbm, data: Mat, trainingObservers: List[TrainingObserver]): Rbm

    Definition Classes
    TournamentTrainingStrategy → RbmTrainingStrategy

  20. final def wait(): Unit

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  21. final def wait(arg0: Long, arg1: Int): Unit

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  22. final def wait(arg0: Long): Unit

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Inherited from RbmTrainingStrategy

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