TrainingContext

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. def aggregate[T](gradients: Array[Tensor[T]])(implicit arg0: ClassTag[T]): Tensor[T]

7. final def asInstanceOf[T0]: T0

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8. def clone(): AnyRef

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

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10. def equals(arg0: Any): Boolean

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11. def fetchBatch[T](data: Iterator[MiniBatch[T]])(implicit arg0: ClassTag[T]): Array[MiniBatch[T]]

12. def finalize(): Unit

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

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14. def hasCompleteAllSamples(recordsProcessed: Int, model: Module[T]): Boolean

15. def hashCode(): Int

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16. final def isInstanceOf[T0]: Boolean

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17. final def loadState(state: Table): TrainingContext.this.type

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 numSamples: Int

22. val state: Table

23. val subModelNumber: Int

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

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25. def toString(): String

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26. def train[T](data: Array[MiniBatch[T]], models: Array[Module[T]], criterion: Array[Criterion[T]])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Seq[LossWithElapsedTime]

27. def update[T](optimSegments: Map[String, ParamSegments[T]], weight: Tensor[T], gradient: Tensor[T], averageLoss: Double)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Unit

28. final def wait(): Unit

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

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

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