org.kramerlab.autoencoder.neuralnet.rbm

RbmTrainingConfiguration

abstract class RbmTrainingConfiguration extends AnyRef

This class provides settings necessary for rbm training. It stores information about number of epochs, dependence of the momentum and number of steps of the gibbs sampling on the current epoch, as well as functions that determine how big weights will be penalized.

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DefaultRbmTrainingConfiguration
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Instance Constructors

  1. new RbmTrainingConfiguration(epochs: Int, minibatchSize: Int, learningRate: Double, initialBiasScaling: Double, initialWeightScaling: Double, initialMomentum: Double, finalMomentum: Double, initialGibbsSamplingSteps: Int, finalGibbsSamplingSteps: Int, weightPenaltyFactor: Double, sampleVisibleUnitsDeterministically: Boolean)

Abstract Value Members

  1. abstract def gibbsSamplingSteps(epoch: Int): Int

  2. abstract def momentum(epoch: Int): Double

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  8. val epochs: Int

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  10. def equals(arg0: Any): Boolean

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  11. val finalGibbsSamplingSteps: Int

  12. val finalMomentum: Double

  13. def finalize(): Unit

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  16. val initialBiasScaling: Double

  17. val initialGibbsSamplingSteps: Int

  18. val initialMomentum: Double

  19. val initialWeightScaling: Double

  20. final def isInstanceOf[T0]: Boolean

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  21. val learningRate: Double

  22. val minibatchSize: Int

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  24. final def notify(): Unit

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  25. final def notifyAll(): Unit

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  26. val sampleVisibleUnitsDeterministically: Boolean

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

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

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  32. def weightPenalty(weights: Mat): Mat

  33. val weightPenaltyFactor: Double

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