com.intel.analytics.zoo.pipeline.api.autograd

CustomLossWithVariable

class CustomLossWithVariable[T] extends CustomLoss[T]

Linear Supertypes
CustomLoss[T], TensorLossFunction[T], LossFunction[Tensor[T], Tensor[T], T], AbstractCriterion[Tensor[T], Tensor[T], T], Serializable, Serializable, AnyRef, Any
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Inherited
  1. CustomLossWithVariable
  2. CustomLoss
  3. TensorLossFunction
  4. LossFunction
  5. AbstractCriterion
  6. Serializable
  7. Serializable
  8. AnyRef
  9. Any
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  1. Public
  2. All

Instance Constructors

  1. new CustomLossWithVariable(inputs: Array[Variable[T]], lossVar: Variable[T], sizeAverage: Boolean = true)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

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. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  7. def backward(input: Tensor[T], target: Tensor[T]): Tensor[T]

    Definition Classes
    AbstractCriterion

  8. def canEqual(other: Any): Boolean

    Definition Classes
    AbstractCriterion

  9. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  10. def cloneCriterion(): AbstractCriterion[Tensor[T], Tensor[T], T]

    Definition Classes
    AbstractCriterion

  11. def doGetLoss(inputs: Array[Variable[T]]): AbstractModule[Activity, Activity, T]

    Attributes
    protected
    Definition Classes
    CustomLossWithVariable → CustomLoss

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

    Definition Classes
    AnyRef

  13. def equals(other: Any): Boolean

    Definition Classes
    AbstractCriterion → AnyRef → Any

  14. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  15. def forward(input: Tensor[T], target: Tensor[T]): T

    Definition Classes
    AbstractCriterion

  16. final def generateLossFromVars(inVars: Array[Variable[T]], outVar: Variable[T]): Model[T]

    Definition Classes
    CustomLoss

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

    Definition Classes
    AnyRef → Any

  18. def getInputVars(inputShapes: Array[Shape]): Array[Variable[T]]

    Attributes
    protected
    Definition Classes
    CustomLossWithVariable → CustomLoss

  19. final def getLoss(inputShapes: Array[Shape]): AbstractModule[Activity, Activity, T]

    Definition Classes
    CustomLoss

  20. var gradInput: Tensor[T]

    Definition Classes
    AbstractCriterion

  21. def hashCode(): Int

    Definition Classes
    AbstractCriterion → AnyRef → Any

  22. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  23. val loss: CustomLossWithVariable[T]

    Definition Classes
    CustomLossWithVariable → LossFunction

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

    Definition Classes
    AnyRef

  25. final def notify(): Unit

    Definition Classes
    AnyRef

  26. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  27. var output: T

    Definition Classes
    AbstractCriterion

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

    Definition Classes
    AnyRef

  29. def toString(): String

    Definition Classes
    AnyRef → Any

  30. def updateGradInput(yPred: Tensor[T], yTrue: Tensor[T]): Tensor[T]

    Computing the gradient of the criterion with respect to its own input.

    Computing the gradient of the criterion with respect to its own input. This is returned in gradInput. Also, the gradInput state variable is updated accordingly.

    yPred

    input data

    yTrue

    target data / labels

    returns

    gradient of input

    Definition Classes
    CustomLoss → LossFunction → AbstractCriterion

  31. def updateOutput(yPred: Tensor[T], target: Tensor[T]): T

    Computes the loss using input and objective function.

    Computes the loss using input and objective function. This function returns the result which is stored in the output field.

    yPred

    input of the criterion

    target

    target or labels

    returns

    the loss of the criterion

    Definition Classes
    CustomLoss → LossFunction → AbstractCriterion

  32. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from CustomLoss[T]

Inherited from TensorLossFunction[T]

Inherited from LossFunction[Tensor[T], Tensor[T], T]

Inherited from AbstractCriterion[Tensor[T], Tensor[T], T]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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