TrainStyle

abstract val algorithm: WeightUpdater

Algorithm

abstract def batch(): Unit

Do mini-batch

abstract def fetch(iter: Int): Unit

Fetch weights

abstract def foreachTestSet(n: Int)(fn: (Pair) ⇒ Unit): Unit

Iterate over given number of test instances

abstract val make: ManipulationType[IN, OUT]

Set of input manipulations

abstract val net: Network

Network

abstract val param: TrainingCriteria

Training parameters

abstract def setNegativeTrainingReference(set: RDD[OUT]): Unit

Set negative sampling method.

abstract def setNegativeTrainingReference(set: Seq[OUT]): Unit

Set negative sampling method.

abstract def setPositiveTrainingReference(set: RDD[Pair]): Unit

Set training instances

abstract def setPositiveTrainingReference(set: Seq[Pair]): Unit

Set training instances

abstract def setTestReference(set: RDD[Pair]): Unit

Set testing instances

abstract def setTestReference(set: Seq[Pair]): Unit

Set testing instances

abstract def update(iter: Int): Unit

Send update of weights

abstract def validationError(): Scalar

Calculate validation error

final def !=(arg0: Any): Boolean

final def ##(): Int

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 hashCode(): Int

final def isInstanceOf[T0]: Boolean

def isUpdateFinished: Future[_]

Indicates whether the asynchronous update is finished or not.

val logger: Logger

Logger

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def stopUntilBatchFinished(): Unit

Non-blocking pending, until all assigned batches are finished

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

def toString(): String

var validationEpoch: Int

size of training set

final def wait(): Unit

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

final def wait(arg0: Long): Unit