Class

com.intel.analytics.bigdl.optim

TrainingContext

Related Doc: package optim

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class TrainingContext[T] extends Serializable

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Instance Constructors

  1. new TrainingContext(subModelNumber: Int, numSamples: Int, state: Table)(implicit arg0: ClassTag[T])

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

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  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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

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  8. def equals(arg0: Any): Boolean

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

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  10. def finalize(): Unit

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

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

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  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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

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

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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  19. val numSamples: Int

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  20. val state: Table

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  21. val subModelNumber: Int

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

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  23. def toString(): String

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

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

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  26. final def wait(): Unit

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

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

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