Class/Object

com.intel.analytics.bigdl.nn

MultiMarginCriterion

Related Docs: object MultiMarginCriterion | package nn

Permalink

class MultiMarginCriterion[T] extends TensorCriterion[T]

Creates a criterion that optimizes a multi-class classification hinge loss (margin-based loss) between input x and output y (which is a target class index).

Annotations
@SerialVersionUID()
Linear Supertypes
TensorCriterion[T], AbstractCriterion[Tensor[T], Tensor[T], T], Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. MultiMarginCriterion
  2. TensorCriterion
  3. AbstractCriterion
  4. Serializable
  5. Serializable
  6. AnyRef
  7. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new MultiMarginCriterion(p: Int = 1, weights: Tensor[T] = null, margin: Double = 1.0, sizeAverage: Boolean = true)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

    Permalink

Value Members

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

    Permalink
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

    Permalink
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0

    Permalink
    Definition Classes
    Any
  5. def backward(input: Tensor[T], target: Tensor[T]): Tensor[T]

    Permalink

    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
  6. def canEqual(other: Any): Boolean

    Permalink
  7. def clone(): AnyRef

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

    Permalink

    Deep copy this criterion

    Deep copy this criterion

    returns

    a deep copied criterion

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

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

    Permalink
    Definition Classes
    MultiMarginCriterionAbstractCriterion → AnyRef → Any
  11. def finalize(): Unit

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

    Permalink

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

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

    Permalink
    Definition Classes
    AbstractCriterion
  15. def hashCode(): Int

    Permalink
    Definition Classes
    MultiMarginCriterionAbstractCriterion → AnyRef → Any
  16. final def isInstanceOf[T0]: Boolean

    Permalink
    Definition Classes
    Any
  17. final def ne(arg0: AnyRef): Boolean

    Permalink
    Definition Classes
    AnyRef
  18. final def notify(): Unit

    Permalink
    Definition Classes
    AnyRef
  19. final def notifyAll(): Unit

    Permalink
    Definition Classes
    AnyRef
  20. var output: T

    Permalink
    Definition Classes
    AbstractCriterion
  21. val p: Int

    Permalink
  22. val sizeAverage: Boolean

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

    Permalink
    Definition Classes
    AnyRef
  24. def toString(): String

    Permalink
    Definition Classes
    MultiMarginCriterion → AnyRef → Any
  25. def updateGradInput(input: Tensor[T], target: Tensor[T]): Tensor[T]

    Permalink

    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
    MultiMarginCriterionAbstractCriterion
  26. def updateOutput(input: Tensor[T], target: Tensor[T]): T

    Permalink

    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
    MultiMarginCriterionAbstractCriterion
  27. final def wait(): Unit

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

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

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  30. val weights: Tensor[T]

    Permalink

Inherited from TensorCriterion[T]

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

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped