com.intel.analytics.bigdl.nn

MarginRankingCriterion

class MarginRankingCriterion[T] extends AbstractCriterion[Table, Table, T]

Creates a criterion that measures the loss given an input x = {x1, x2}, a table of two Tensors of size 1 (they contain only scalars), and a label y (1 or -1). In batch mode, x is a table of two Tensors of size batchsize, and y is a Tensor of size batchsize containing 1 or -1 for each corresponding pair of elements in the input Tensor. If y == 1 then it assumed the first input should be ranked higher (have a larger value) than the second input, and vice-versa for y == -1.

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@SerialVersionUID( 4746239527786180108L )
Linear Supertypes
AbstractCriterion[Table, Table, T], Serializable, Serializable, AnyRef, Any
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  1. MarginRankingCriterion
  2. AbstractCriterion
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Instance Constructors

  1. new MarginRankingCriterion(margin: Double = 1.0, sizeAverage: Boolean = true)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

    margin

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: Table, target: Table): Table

    Performs a back-propagation step through the criterion, with respect to the given input.

    Performs a back-propagation step through the criterion, with respect to the given input.

    input

    input data

    target

    target

    returns

    gradient corresponding to input data

    Definition Classes
    AbstractCriterion
  8. def canEqual(other: Any): Boolean

  9. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  10. def cloneCriterion(): AbstractCriterion[Table, Table, T]

    Deep copy this criterion

    Deep copy this criterion

    returns

    a deep copied criterion

    Definition Classes
    AbstractCriterion
  11. var dist: Tensor[T]

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

    Definition Classes
    AnyRef
  13. def equals(other: Any): Boolean

    Definition Classes
    MarginRankingCriterionAbstractCriterion → AnyRef → Any
  14. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  15. def forward(input: Table, target: Table): T

    Takes an input object, and computes the corresponding loss of the criterion, compared with target.

    Takes an input object, and computes the corresponding loss of the criterion, compared with target.

    input

    input data

    target

    target

    returns

    the loss of criterion

    Definition Classes
    AbstractCriterion
  16. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  17. var gradInput: Table

    Definition Classes
    AbstractCriterion
  18. def hashCode(): Int

    Definition Classes
    MarginRankingCriterionAbstractCriterion → AnyRef → Any
  19. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  20. val margin: Double

  21. var mask: Tensor[T]

  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. var output: T

    Definition Classes
    AbstractCriterion
  26. val sizeAverage: Boolean

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

    Definition Classes
    AnyRef
  28. def toString(): String

    Definition Classes
    MarginRankingCriterion → AnyRef → Any
  29. def updateGradInput(input: Table, y: Table): Table

    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.

    input

    input data

    returns

    gradient of input

    Definition Classes
    MarginRankingCriterionAbstractCriterion
  30. def updateOutput(input: Table, y: Table): 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.

    input

    input of the criterion

    returns

    the loss of the criterion

    Definition Classes
    MarginRankingCriterionAbstractCriterion
  31. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  32. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  33. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from AbstractCriterion[Table, Table, T]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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