BigDLSessionImpl

Instance Constructors

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

Type Members

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

Value Members

final def !=(arg0: AnyRef): Boolean

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final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: AnyRef): Boolean

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

Attributes
protected[java.lang]
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@throws( ... )
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def finalize(): Unit

Attributes
protected[java.lang]
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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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
def hashCode(): Int

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final def isInstanceOf[T0]: Boolean

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final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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

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def toString(): String

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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
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
final def wait(): Unit

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

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final def wait(arg0: Long): Unit

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class BigDLSessionImpl[T] extends Session[T]

Instance Constructors

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

Type Members

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

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

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

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

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

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

def toString(): String

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

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

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Session[T]

Inherited from AnyRef

Inherited from Any

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