cc.factorie.optimize

BatchTrainer

class BatchTrainer extends Trainer with FastLogging

Learns the parameters of a Model by summing the gradients and values of all Examples, and passing them to a GradientOptimizer (such as ConjugateGradient or LBFGS).

Linear Supertypes
FastLogging, Logging, Trainer, AnyRef, Any
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Inherited
  1. BatchTrainer
  2. FastLogging
  3. Logging
  4. Trainer
  5. AnyRef
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Visibility
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Instance Constructors

  1. new BatchTrainer(weightsSet: WeightsSet, optimizer: GradientOptimizer = ...)

    weightsSet

    The parameters to be optimized

    optimizer

    The optimizer

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 clone(): AnyRef

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

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

    Definition Classes
    AnyRef

  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  10. def finalize(): Unit

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

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

    Definition Classes
    AnyRef → Any

  12. val gradientAccumulator: LocalWeightsMapAccumulator

  13. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  14. def isConverged: Boolean

    Would more training help?

    Would more training help?

    Definition Classes
    BatchTrainer → Trainer

  15. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  16. val logger: Logger

    Definition Classes
    FastLogging → Logging

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

    Definition Classes
    AnyRef

  18. final def notify(): Unit

    Definition Classes
    AnyRef

  19. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  20. val optimizer: GradientOptimizer

    The optimizer

  21. def processExamples(examples: Iterable[Example]): Unit

    Process the examples once.

    Process the examples once.

    examples

    Examples to be processed

    Definition Classes
    BatchTrainer → Trainer

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

    Definition Classes
    AnyRef

  23. def toString(): String

    Definition Classes
    AnyRef → Any

  24. def trainFromExamples(examples: Iterable[Example]): Unit

    Repeatedly process the examples until training has converged.

    Repeatedly process the examples until training has converged.

    Definition Classes
    Trainer

  25. val valueAccumulator: LocalDoubleAccumulator

  26. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  28. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  29. val weightsSet: WeightsSet

    The parameters to be optimized

Inherited from FastLogging

Inherited from Logging

Inherited from Trainer

Inherited from AnyRef

Inherited from Any

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