com.intel.analytics.bigdl.nn

BCECriterion

class BCECriterion[T] extends TensorCriterion[T]

This loss function measures the Binary Cross Entropy between the target and the output loss(o, t) = - 1/n sum_i (t[i] * log(o[i]) + (1 - t[i]) * log(1 - o[i])) or in the case of the weights argument being specified: loss(o, t) = - 1/n sum_i weights[i] * (t[i] * log(o[i]) + (1 - t[i]) * log(1 - o[i]))

By default, the losses are averaged for each mini-batch over observations as well as over dimensions. However, if the field sizeAverage is set to false, the losses are instead summed.

T

numeric type

Annotations
@SerialVersionUID( 1953992758534446600L )
Linear Supertypes
TensorCriterion[T], AbstractCriterion[Tensor[T], Tensor[T], T], Serializable, Serializable, AnyRef, Any
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  1. BCECriterion
  2. TensorCriterion
  3. AbstractCriterion
  4. Serializable
  5. Serializable
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Instance Constructors

  1. new BCECriterion(weights: Tensor[T] = null, sizeAverage: Boolean = true)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

    weights

    weights over the input dimension

    sizeAverage

    avgerage or not in each mini-batch

    ev

    numeric operator

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. val buffer: Tensor[T]

  9. def canEqual(other: Any): Boolean

    Definition Classes
    AbstractCriterion

  10. def clone(): AnyRef

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

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

    Deep copy this criterion

    Deep copy this criterion

    returns

    a deep copied criterion

    Definition Classes
    AbstractCriterion

  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

    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: Tensor[T]

    Definition Classes
    AbstractCriterion

  18. def hashCode(): Int

    Definition Classes
    AbstractCriterion → AnyRef → Any

  19. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  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. val onesBuffer: Tensor[T]

  24. var output: T

    Definition Classes
    AbstractCriterion

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

    Definition Classes
    AnyRef

  26. def toString(): String

    Definition Classes
    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
    BCECriterion → AbstractCriterion

  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
    BCECriterion → AbstractCriterion

  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( ... )

  32. val weights: Tensor[T]

    weights over the input dimension

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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