class BigDLSessionImpl[T] extends Session[T]

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Instance Constructors

  1. new BigDLSessionImpl(graph: Seq[NodeDef], context: Context[T], byteOrder: ByteOrder = 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: Any): Boolean
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  2. final def ##(): Int
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  6. final def eq(arg0: AnyRef): Boolean
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  7. def equals(arg0: Any): Boolean
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  8. def finalize(): Unit
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  9. final def getClass(): Class[_]
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  10. 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.

  11. def hashCode(): Int
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  12. final def isInstanceOf[T0]: Boolean
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  13. final def ne(arg0: AnyRef): Boolean
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  14. final def notify(): Unit
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  15. final def notifyAll(): Unit
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  16. 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

    isDataBatch

    if the model input is the batch

    batchSize

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

    Definition Classes
    BigDLSessionImpl → Session
  17. def saveParameters(binFile: String): BigDLSessionImpl.this.type

    Dump varaible contents to a file

    Dump varaible contents to a file

    Definition Classes
    BigDLSessionImpl → Session
  18. final def synchronized[T0](arg0: ⇒ T0): T0
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  19. def toString(): String
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  20. 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

    isDataBatch

    if the model input is the batch

    batchSize

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

    Definition Classes
    BigDLSessionImpl → Session
  21. 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

    Definition Classes
    BigDLSessionImpl → Session
  22. final def wait(): Unit
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  23. final def wait(arg0: Long, arg1: Int): Unit
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  24. final def wait(arg0: Long): Unit
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