SigmoidLayerModelWithSquaredError

Value Members

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

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

   AnyRef → Any

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

   

   Definition Classes

   AnyRef → Any

4. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

5. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

6. def computePrevDelta(nextDelta: DenseMatrix[Double], input: DenseMatrix[Double], delta: DenseMatrix[Double]): Unit

   

   Computes the delta for back propagation.

   Computes the delta for back propagation. Delta is allocated based on the size provided by the LayerModel implementation and the stack (batch) size. Developer is responsible for checking the size of prevDelta when writing to it.

   delta

   delta of this layer

   Definition Classes

   FunctionalLayerModel → LayerModel

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

   

   Definition Classes

   AnyRef

8. def equals(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

9. def eval(data: DenseMatrix[Double], output: DenseMatrix[Double]): Unit

   

   Evaluates the data (process the data through the layer).

   Evaluates the data (process the data through the layer). Output is allocated based on the size provided by the LayerModel implementation and the stack (batch) size. Developer is responsible for checking the size of output when writing to it.

   data

   data

   output

   output (modified in place)

   Definition Classes

   FunctionalLayerModel → LayerModel

10. def finalize(): Unit

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

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

    

    Definition Classes

    AnyRef → Any

12. def grad(delta: DenseMatrix[Double], input: DenseMatrix[Double], cumGrad: DenseVector[Double]): Unit

    

    Computes the gradient.

    Computes the gradient. cumGrad is a wrapper on the part of the weight vector. Size of cumGrad is based on weightSize provided by implementation of LayerModel.

    delta

    delta for this layer

    input

    input data

    cumGrad

    cumulative gradient (modified in place)

    Definition Classes

    FunctionalLayerModel → LayerModel

13. def hashCode(): Int

    

    Definition Classes

    AnyRef → Any

14. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

15. val layer: FunctionalLayer

    

    functional layer

    functional layer

    Definition Classes

    FunctionalLayerModel

16. def loss(output: DenseMatrix[Double], target: DenseMatrix[Double], delta: DenseMatrix[Double]): Double

    

    Returns the value of loss function.

    Returns the value of loss function. Computes loss based on target and output. Writes delta (error) to delta in place. Delta is allocated based on the outputSize of model implementation.

    output

    actual output

    target

    target output

    delta

    delta (updated in place)

    returns

    loss

    Definition Classes

    SigmoidLayerModelWithSquaredError → LossFunction

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

    

    Definition Classes

    AnyRef

18. final def notify(): Unit

    

    Definition Classes

    AnyRef

19. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

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

    

    Definition Classes

    AnyRef

21. def toString(): String

    

    Definition Classes

    AnyRef → Any

22. final def wait(): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

25. val weights: DenseVector[Double]

    

    Definition Classes

    FunctionalLayerModel → LayerModel