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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  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 hashCode(): Int

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  13. final def isInstanceOf[T0]: Boolean

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

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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  17. 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
    BigDLSessionImpl → Session

  18. def saveParameters(binFile: String): BigDLSessionImpl.this.type

    Dump varaible contents to a file

    Dump varaible contents to a file

    binFile
    returns

    Definition Classes
    BigDLSessionImpl → Session

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

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  20. def toString(): String

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  21. 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
    BigDLSessionImpl → Session

  22. 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
    BigDLSessionImpl → Session

  23. final def wait(): Unit

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

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

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Inherited from Session[T]

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