com.intel.analytics.bigdl.utils.tf

BigDLSessionImpl

class BigDLSessionImpl[T] extends Session[T]

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Instance Constructors

  1. new BigDLSessionImpl(graph: Seq[NodeDef], context: Context[T], byteOrder: ByteOrder = java.nio.ByteOrder.LITTLE_ENDIAN)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

Type Members

  1. type DataCache = HashMap[String, Array[Seq[Table]]]

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

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  12. def getRDD(endPoints: Seq[String], sc: SparkContext, hasToBatch: Boolean = true): RDD[Table]

    Get and calculate the data up to the specified endpoints, and return as a RDD[Table]

    Get and calculate the data up to the specified endpoints, and return as a RDD[Table]

    endPoints

    output endpoints

    hasToBatch

    indicate whether the subgraph to be executed already has to batch operation. If yes, the batch operation will be undone at the end of this execution, that is split each tensor by their first dimension.

    returns

  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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

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  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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  18. def predict(endPoints: Seq[String], isDataBatch: Boolean, batchSize: Int, sc: SparkContext): RDD[Activity]

    Predict data with tensorflow graph.

    Predict data with tensorflow graph. The data must hold in a queue

    endPoints
    isDataBatch

    if the model input is the batch

    batchSize

    batch size, which should be original batch size * total core number

    sc
    returns

    Definition Classes
    BigDLSessionImplSession
  19. def saveParameters(binFile: String): BigDLSessionImpl.this.type

    Dump varaible contents to a file

    Dump varaible contents to a file

    binFile
    returns

    Definition Classes
    BigDLSessionImplSession
  20. final def synchronized[T0](arg0: ⇒ T0): T0

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  21. def toString(): String

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  22. def train(endPoints: Seq[String], optMethod: OptimMethod[T], endWhen: Trigger, isDataBatch: Boolean, batchSize: Int, sc: SparkContext, loss: Option[String]): BigDLSessionImpl.this.type

    Train the tensorflow graph.

    Train the tensorflow graph. The model must be fed data with a queue

    endPoints
    optMethod
    endWhen
    isDataBatch

    if the model input is the batch

    batchSize

    batch size, which should be original batch size * total core number

    sc
    loss
    returns

    Definition Classes
    BigDLSessionImplSession
  23. def train(outputs: Seq[String], dataSet: DistributedDataSet[MiniBatch[T]], optMethod: OptimMethod[T], criterion: Criterion[T], endWhen: Trigger): Graph[T]

    Train the tensorflow graph

    Train the tensorflow graph

    outputs
    dataSet
    optMethod
    criterion
    endWhen
    returns

    Definition Classes
    BigDLSessionImplSession
  24. final def wait(): Unit

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

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

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Inherited from Session[T]

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