CustomLoss

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

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

abstract val loss: AbstractCriterion[Tensor[T], Tensor[T], T]

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

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

def canEqual(other: Any): Boolean

def clone(): AnyRef

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

final def eq(arg0: AnyRef): Boolean

def equals(other: Any): Boolean

def finalize(): Unit

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

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

final def getClass(): Class[_]

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

var gradInput: Tensor[T]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

var output: T

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

def toString(): String

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

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

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

Computes the loss using input and objective function.

final def wait(): Unit

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

final def wait(arg0: Long): Unit