cc.factorie.app.nlp.embeddings

LiteHogwildTrainer

class LiteHogwildTrainer extends Trainer

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Instance Constructors

  1. new LiteHogwildTrainer(weightsSet: WeightsSet, optimizer: GradientOptimizer, nThreads: Int = ..., maxIterations: Int = 3)

Value Members

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

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

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  8. final def eq(arg0: AnyRef): Boolean

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  11. final def getClass(): Class[_]

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  12. def hashCode(): Int

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  13. def isConverged: Boolean

    Would more training help?

    Would more training help?

    Definition Classes
    LiteHogwildTrainerTrainer
  14. final def isInstanceOf[T0]: Boolean

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  15. var iteration: Int

  16. val maxIterations: Int

  17. val nThreads: Int

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

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  19. final def notify(): Unit

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  20. final def notifyAll(): Unit

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  21. val optimizer: GradientOptimizer

  22. def processExample(e: optimize.Example): Unit

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

    Process the examples once.

    Process the examples once.

    examples

    Examples to be processed

    Definition Classes
    LiteHogwildTrainerTrainer
  24. final def synchronized[T0](arg0: ⇒ T0): T0

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  25. def toString(): String

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

    Repeatedly process the examples until training has converged.

    Repeatedly process the examples until training has converged.

    Definition Classes
    Trainer
  27. final def wait(): Unit

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

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

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  30. val weightsSet: WeightsSet

Inherited from Trainer

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