Autoencoder

Value Members

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

   Definition Classes

   AnyRef

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

   Definition Classes

   Any

3. final def ##(): Int

   Definition Classes

   AnyRef → Any

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

   Definition Classes

   AnyRef

5. final def ==(arg0: Any): Boolean

   Definition Classes

   Any

6. def activities(input: Mat): List[Mat]

   Definition Classes

   NeuralNetLike

7. def andThen[A](g: (Mat) ⇒ A): (Mat) ⇒ A

   Definition Classes

   Function1

   Annotations

   @unspecialized()

8. def apply(input: Mat): Mat

   Propagates the input from the visible layer up to the top layer

   Propagates the input from the visible layer up to the top layer

   Definition Classes

   NeuralNetLike → Function1

9. final def asInstanceOf[T0]: T0

   Definition Classes

   Any

10. def build(layers: List[Layer]): Autoencoder

    Builds a neural net of the right type and of the right shape out of specified layers.

    Builds a neural net of the right type and of the right shape out of specified layers.

    Note that this method depends on instance, not just a class: fore example the Autoencoder has to know what it's 'central' Layer is.

    Definition Classes

    Autoencoder → NeuralNetLike

11. def clone(): AnyRef

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

12. def compose[A](g: (A) ⇒ Mat): (A) ⇒ Mat

    Definition Classes

    Function1

    Annotations

    @unspecialized()

13. def compress(uncompressed: Mat): Mat

14. def compressionDimension: Int

15. val compressor: NeuralNet

16. var dataSample: Option[Mat]

    Definition Classes

    NeuralNetLike

17. def decompress(compressed: Mat): Mat

18. val decompressor: NeuralNet

19. final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

20. def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

21. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes

    AnyRef → Any

23. def hashCode(): Int

    Definition Classes

    AnyRef → Any

24. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

25. val layers: List[Layer]

    Enumerates layers of this (linear) neural net.

    Enumerates layers of this (linear) neural net.

    TODO: generalize it to arbitrary directed acyclic graphs, what's so special about lists?...

    Definition Classes

    Autoencoder → NeuralNetLike

26. final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

27. final def notify(): Unit

    Definition Classes

    AnyRef

28. final def notifyAll(): Unit

    Definition Classes

    AnyRef

29. def optimize(input: Mat, output: Mat, errorFunctionFactory: DifferentiableErrorFunctionFactory[Mat] = SquareErrorFunctionFactory, relativeValidationSetSize: Double, maxEvals: Int, trainingObservers: List[TrainingObserver]): Autoencoder

    Performs optimization of all parameters of the neural network using the specified input and output, the specified method to define an error function (defaults to SquareErrorFunctionFactory).

    Performs optimization of all parameters of the neural network using the specified input and output, the specified method to define an error function (defaults to SquareErrorFunctionFactory).

    Standard feed-forward algorithm is used for evaluation of the function, backpropagation is used for calculation of the gradient.

    Definition Classes

    NeuralNetLike

30. def prependAffineLinearTransformation(factor: Mat, offset: Mat): Autoencoder

    Assumes that this is a "usual" neural net with alternating unit and connection layers and prepends an affine linear transformation to it.

    Assumes that this is a "usual" neural net with alternating unit and connection layers and prepends an affine linear transformation to it.

    Why the heck did I implement biased layers at all, why didn't I stuff all this cruft into something like "AffineLinearTransform" or so... Damn

    Definition Classes

    NeuralNetLike

31. def reverse(output: Mat): Mat

    Propagates the output from top layer down to the visible layer

    Propagates the output from top layer down to the visible layer

    Definition Classes

    NeuralNetLike

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

    Definition Classes

    AnyRef

33. def toImage: BufferedImage

    Definition Classes

    NeuralNetLike → Visualizable

34. def toImage(w: Int, h: Int): BufferedImage

    Definition Classes

    Visualizable

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

    Definition Classes

    Visualizable

36. def toString(): String

    Definition Classes

    Function1 → AnyRef → Any

37. def unfoldCentralLayer(newCentralLayerType: Int, newCentralLayerDim: Int, rbmTrainingStrategy: RbmTrainingStrategy, data: Mat, errorFunctionFactory: DifferentiableErrorFunctionFactory[Mat], fineTuneInnerLayers: Boolean, trainingObservers: List[TrainingObserver]): Autoencoder

    creates a new autoencoder with an additional central layer

38. final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )