Trainer

implicit abstract val evidence$1: ClassTag[IN]

Class tag of input

implicit abstract val evidence$2: ClassTag[OUT]

Class tag of output

implicit abstract val evidence$3: ClassTag[EXP]

Class tag of network output

abstract def getValidationErr: (Double, Double)

Calculate validation error of evaluation set

abstract val name: String

Name of this trainer

abstract val network: Network[IN, OUT]

Network

abstract val objective: Objective

Objective function

abstract val param: TrainingParam

Training parameter

abstract val testSet: RDD[(IN, EXP)]

Evaluation set

abstract val trainSet: RDD[(IN, EXP)]

Train set

abstract def trainSmallBatch(epoch: Int, patience: Int): Unit

Train single small batch

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

val batchSampler: Iterator[Seq[(IN, EXP)]]

Sampler for mini-batch

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

val file: File

Temporary file path of network

def finalize(): Unit

final def getClass(): Class[_]

final def getMaxEpoch(epoch: Int, patience: Int): Int

Get next maximum epoch.

def getStatus: (Int, Double, Double)

final def getTrainedNetwork: Network[IN, OUT]

Train using given RDD sequence.

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

val logfile: File

val logger: Logger

Logger

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit