org.kramerlab.autoencoder.neuralnet.rbm

RbmStack

class RbmStack extends Visualizable

Encapsulates training procedures for stacks of Rbm's. The Rbm's must be stored in the following way: the innermost Rbm of the future autoencoder (or: the top Rbm of the stack) must be the last element of the list. The Rbm at the bottom, which is clamped to the input, must be the first element. It's assumed that all dimensions of the Rbm's are compatible. Please keep in mind that the signals are stored in row-vectors, which are propagated by multiplication from the right by the weight matrices.

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Instance Constructors

  1. new RbmStack(rbms: List[Rbm])

Value Members

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

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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  10. def finalize(): Unit

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

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  12. def hashCode(): Int

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  13. final def isInstanceOf[T0]: Boolean

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  14. final def ne(arg0: AnyRef): 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 toImage: BufferedImage

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    RbmStackVisualizable
  19. def toImage(w: Int, h: Int): BufferedImage

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  20. def toImage(colormap: (Double) ⇒ Int): BufferedImage

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  21. def toString(): String

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    RbmStack → AnyRef → Any
  22. def train(data: Mat, trainingStrategies: List[RbmTrainingStrategy], trainingObservers: List[TrainingObserver]): RbmStack

    Trains the whole stack of RBM's with Gibb's sampling-like methods

  23. def unfold: Autoencoder

    Unfolds this RbmStack into an Autoencoder.

    Unfolds this RbmStack into an Autoencoder. The autoencoder has to be trained further by standard backpropagation methods

  24. final def wait(): Unit

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

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

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