com.intel.analytics.bigdl.nn

MarginCriterion

class MarginCriterion[T] extends TensorCriterion[T]

Creates a criterion that optimizes a two-class classification hinge loss (margin-based loss) between input x (a Tensor of dimension 1) and output y.

Annotations
@SerialVersionUID( 5028892499250398130L )
Linear Supertypes
TensorCriterion[T], AbstractCriterion[Tensor[T], Tensor[T], T], Serializable, Serializable, AnyRef, Any
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Inherited
  1. MarginCriterion
  2. TensorCriterion
  3. AbstractCriterion
  4. Serializable
  5. Serializable
  6. AnyRef
  7. Any
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Visibility
  1. Public
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Instance Constructors

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

    margin

    if unspecified, is by default 1.

    sizeAverage

    whether to average the loss

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]

    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

    Definition Classes
    MarginCriterionAbstractCriterion
  9. def clone(): AnyRef

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

    Deep copy this criterion

    Deep copy this criterion

    returns

    a deep copied criterion

    Definition Classes
    AbstractCriterion
  11. final def eq(arg0: AnyRef): Boolean

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

    Definition Classes
    MarginCriterionAbstractCriterion → AnyRef → Any
  13. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  14. def forward(input: Tensor[T], target: Tensor[T]): 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
  15. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  16. var gradInput: Tensor[T]

    Definition Classes
    AbstractCriterion
  17. def hashCode(): Int

    Definition Classes
    MarginCriterionAbstractCriterion → AnyRef → Any
  18. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  19. val margin: Double

    if unspecified, is by default 1.

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

    Definition Classes
    AnyRef
  21. final def notify(): Unit

    Definition Classes
    AnyRef
  22. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  23. var output: T

    Definition Classes
    AbstractCriterion
  24. val sizeAverage: Boolean

    whether to average the loss

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

    Definition Classes
    AnyRef
  26. def toString(): String

    Definition Classes
    MarginCriterion → AnyRef → Any
  27. def updateGradInput(input: Tensor[T], target: 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.

    input

    input data

    target

    target data / labels

    returns

    gradient of input

    Definition Classes
    MarginCriterionAbstractCriterion
  28. def updateOutput(input: 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.

    input

    input of the criterion

    target

    target or labels

    returns

    the loss of the criterion

    Definition Classes
    MarginCriterionAbstractCriterion
  29. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from TensorCriterion[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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