NeuralNet

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]): NeuralNet

    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

    NeuralNet → 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. var dataSample: Option[Mat]

    Definition Classes

    NeuralNetLike

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

    Definition Classes

    AnyRef

15. def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

16. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

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

    Definition Classes

    AnyRef → Any

18. def hashCode(): Int

    Definition Classes

    AnyRef → Any

19. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

20. 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

    NeuralNet → NeuralNetLike

21. def layersToString(layers: List[Layer]): String

    Attributes

    protected

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

    Definition Classes

    AnyRef

23. final def notify(): Unit

    Definition Classes

    AnyRef

24. final def notifyAll(): Unit

    Definition Classes

    AnyRef

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

    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

26. def prependAffineLinearTransformation(factor: Mat, offset: Mat): NeuralNet

    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

27. 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

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

    Definition Classes

    AnyRef

29. def toImage: BufferedImage

    Definition Classes

    NeuralNetLike → Visualizable

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

    Definition Classes

    Visualizable

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

    Definition Classes

    Visualizable

32. def toString(): String

    Definition Classes

    NeuralNet → Function1 → AnyRef → Any

33. final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )