Class

org.deeplearning4j.spark.impl.paramavg

ParameterAveragingTrainingWorker

Related Doc: package paramavg

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class ParameterAveragingTrainingWorker extends BaseTrainingWorker[ParameterAveragingTrainingResult]

Linear Supertypes
BaseTrainingWorker[ParameterAveragingTrainingResult], TrainingWorker[ParameterAveragingTrainingResult], Serializable, AnyRef, Any
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Inherited
  1. ParameterAveragingTrainingWorker
  2. BaseTrainingWorker
  3. TrainingWorker
  4. Serializable
  5. AnyRef
  6. Any
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Visibility
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Instance Constructors

  1. new ParameterAveragingTrainingWorker(broadcast: Broadcast[NetBroadcastTuple], saveUpdater: Boolean, configuration: WorkerConfiguration, trainingHooks: Collection[TrainingHook], listeners: Collection[IterationListener], routerProvider: StatsStorageRouterProvider)

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Value Members

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

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  2. final def ##(): Int

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

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    Definition Classes
    AnyRef → Any

  4. def addHook(trainingHook: TrainingHook): Unit

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    Definition Classes
    ParameterAveragingTrainingWorker → TrainingWorker

  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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    Attributes
    protected[java.lang]
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    @throws( ... )

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

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )

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

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    Definition Classes
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  11. def getDataConfiguration(): WorkerConfiguration

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    Definition Classes
    ParameterAveragingTrainingWorker → TrainingWorker

  12. def getFinalResult(network: ComputationGraph): ParameterAveragingTrainingResult

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    Definition Classes
    ParameterAveragingTrainingWorker → TrainingWorker

  13. def getFinalResult(network: MultiLayerNetwork): ParameterAveragingTrainingResult

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    Definition Classes
    ParameterAveragingTrainingWorker → TrainingWorker

  14. def getFinalResultNoData(): ParameterAveragingTrainingResult

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    Definition Classes
    ParameterAveragingTrainingWorker → TrainingWorker

  15. def getFinalResultNoDataWithStats(): Pair[ParameterAveragingTrainingResult, SparkTrainingStats]

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    Definition Classes
    ParameterAveragingTrainingWorker → TrainingWorker

  16. def getFinalResultWithStats(graph: ComputationGraph): Pair[ParameterAveragingTrainingResult, SparkTrainingStats]

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    Definition Classes
    ParameterAveragingTrainingWorker → TrainingWorker

  17. def getFinalResultWithStats(network: MultiLayerNetwork): Pair[ParameterAveragingTrainingResult, SparkTrainingStats]

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    Definition Classes
    ParameterAveragingTrainingWorker → TrainingWorker

  18. def getInitialModel(): MultiLayerNetwork

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    Definition Classes
    ParameterAveragingTrainingWorker → TrainingWorker

  19. def getInitialModelGraph(): ComputationGraph

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    ParameterAveragingTrainingWorker → TrainingWorker

  20. def hashCode(): Int

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  21. final def isInstanceOf[T0]: Boolean

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

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  23. final def notify(): Unit

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  24. final def notifyAll(): Unit

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  25. def processMinibatch(dataSet: MultiDataSet, graph: ComputationGraph, isLast: Boolean): ParameterAveragingTrainingResult

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    Definition Classes
    ParameterAveragingTrainingWorker → TrainingWorker

  26. def processMinibatch(dataSet: DataSet, graph: ComputationGraph, isLast: Boolean): ParameterAveragingTrainingResult

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    Definition Classes
    ParameterAveragingTrainingWorker → TrainingWorker

  27. def processMinibatch(dataSet: DataSet, network: MultiLayerNetwork, isLast: Boolean): ParameterAveragingTrainingResult

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    Definition Classes
    ParameterAveragingTrainingWorker → TrainingWorker

  28. def processMinibatchWithStats(dataSet: MultiDataSet, graph: ComputationGraph, isLast: Boolean): Pair[ParameterAveragingTrainingResult, SparkTrainingStats]

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    Definition Classes
    ParameterAveragingTrainingWorker → TrainingWorker

  29. def processMinibatchWithStats(dataSet: DataSet, graph: ComputationGraph, isLast: Boolean): Pair[ParameterAveragingTrainingResult, SparkTrainingStats]

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    Definition Classes
    ParameterAveragingTrainingWorker → TrainingWorker

  30. def processMinibatchWithStats(dataSet: DataSet, network: MultiLayerNetwork, isLast: Boolean): Pair[ParameterAveragingTrainingResult, SparkTrainingStats]

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    Definition Classes
    ParameterAveragingTrainingWorker → TrainingWorker

  31. def removeHook(trainingHook: TrainingHook): Unit

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    Definition Classes
    ParameterAveragingTrainingWorker → TrainingWorker

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

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  33. def toString(): String

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  34. final def wait(): Unit

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    @throws( ... )

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

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

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Inherited from BaseTrainingWorker[ParameterAveragingTrainingResult]

Inherited from TrainingWorker[ParameterAveragingTrainingResult]

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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