Class

com.tencent.angel.ml.core.graphsubmit

GraphLearner

Related Doc: package graphsubmit

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class GraphLearner extends MLLearner

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MLLearner, AnyRef, Any
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Instance Constructors

  1. new GraphLearner(modelClassName: String, ctx: TaskContext)

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Value Members

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

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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

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    Definition Classes
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  4. val LOG: Log

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  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. val conf: Configuration

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    Definition Classes
    MLLearner

  8. val ctx: TaskContext

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    Definition Classes
    MLLearner

  9. val decayOnBatch: Boolean

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  10. val epochNum: Int

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

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    Definition Classes
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  12. def equals(arg0: Any): Boolean

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  13. def finalize(): Unit

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

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

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    Definition Classes
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  15. val globalMetrics: GlobalMetrics

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    Definition Classes
    MLLearner

  16. val graph: AngelGraph

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  17. def hashCode(): Int

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    Definition Classes
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  18. val indexRange: Long

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  19. final def isInstanceOf[T0]: Boolean

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  20. val lr0: Double

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  21. val model: GraphModel

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  22. val modelSize: Long

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

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    Definition Classes
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  24. final def notify(): Unit

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  25. final def notifyAll(): Unit

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    Definition Classes
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  26. def sparsity(weight: DoubleVector, dim: Int): Double

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  27. val ssScheduler: StepSizeScheduler

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

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  29. def toString(): String

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  30. def train(posTrainData: DataBlock[LabeledData], negTrainData: DataBlock[LabeledData], validationData: DataBlock[LabeledData]): MLModel

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  31. def train(trainData: DataBlock[LabeledData], validationData: DataBlock[LabeledData]): MLModel

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    train LR model iteratively

    train LR model iteratively

    trainData

    : trainning data storage

    validationData

    : validation data storage

    returns

    : a learned model

    Definition Classes
    GraphLearner → MLLearner

  32. def trainOneEpoch(epoch: Int, iter: Iterator[Array[LabeledData]], numBatch: Int): Double

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  33. def validate(epoch: Int, valiData: DataBlock[LabeledData]): Unit

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    validate loss, Auc, Precision or other

    validate loss, Auc, Precision or other

    epoch

    : epoch id

    valiData

    : validata data storage

  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 MLLearner

Inherited from AnyRef

Inherited from Any

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