AbstractOptimizer

abstract def getModel[T](models: RDD[Cache[T]], parameters: AllReduceParameter[T], trainingModel: Module[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Module[T]

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 checkpoint[T](cacheTrigger: Option[Trigger], cachePath: Option[String], isOverWrite: Boolean, wallClockTime: Long, models: RDD[Cache[T]], state: Table, parameters: AllReduceParameter[T], optimMethods: Map[String, OptimMethod[T]], trainingModel: Module[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Unit

* Create checkpoint.

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def saveSummary[T](trainSummary: TrainSummary, models: RDD[Cache[T]], driverState: Table, parameters: AllReduceParameter[T], trainingModel: Module[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Unit

Save train summaries.

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

def toString(): String

def validate[T](validationTrigger: Option[Trigger], validationDataSet: Option[DataSet[MiniBatch[T]]], validationMethods: Option[Array[ValidationMethod[T]]], coresPerNode: Int, models: RDD[Cache[T]], state: Table, validationSummary: Option[ValidationSummary], header: String, parameters: AllReduceParameter[T] = null): Unit

Validate current model and save the result.

final def wait(): Unit

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

final def wait(arg0: Long): Unit