org.kramerlab.autoencoder.neuralnet.rbm

CompetitiveRetryTrainingStrategy

class CompetitiveRetryTrainingStrategy extends RbmTrainingStrategy

This training strategy generates multiple Rbm training configurations at random, and selects the Rbm which has best reconstruction error on a held out validation set. This strategy differs from the RandomRetryTraininStrategy in that the training of a new random RBM can be interrupted much earlier, if it the progress of the currently trained Rbm does not look very promising compared to the best Rbm seen so far.

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RbmTrainingStrategy, AnyRef, Any
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Instance Constructors

  1. new CompetitiveRetryTrainingStrategy(numRetries: Int, relativeValidationSetSize: Double, acceptableErrorMultiplier: Double)

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  18. def toString(): String

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

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    CompetitiveRetryTrainingStrategy → RbmTrainingStrategy

  20. final def wait(): Unit

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Inherited from RbmTrainingStrategy

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