MultiThreadTrainStyle

Instance Constructors

new MultiThreadTrainStyle(net: Network, algorithm: WeightUpdater, sc: SparkContext, make: ManipulationType[IN, OUT] = new VectorType(), param: DistBeliefCriteria = DistBeliefCriteria())(implicit arg0: ClassTag[IN], arg1: ClassTag[OUT])

net
Network to be trained
algorithm
Weight update algorithm to be applied
make
Input Operation that supervises how to manipulate input as matrices. This also controls how to compute actual network. (default: VectorType)
param
Training criteria (default: SimpleTrainingCriteria)

Type Members

type Pair = (IN, OUT)

Training Pair Type
Training Pair Type

Definition Classes
TrainStyle
type Sampler = (Int) ⇒ Seq[OUT]

Sampler Type
Sampler Type

Definition Classes
TrainStyle
implicit class WeightOp extends Serializable

Implicit weight operation
Implicit weight operation

Definition Classes
TrainStyle

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
val accCount: Accumulator[Int]

Accumulator variable for counter
Accumulator variable for counter

Attributes
protected
val accNet: Accumulator[IndexedSeq[ScalarMatrix]]

Accumulator variable for networks
Accumulator variable for networks

Attributes
protected
val algorithm: WeightUpdater

Weight update algorithm to be applied
Weight update algorithm to be applied

Definition Classes
MultiThreadTrainStyle → TrainStyle
final def asInstanceOf[T0]: T0

Definition Classes
Any
def batch(): Unit

Do mini-batch
Do mini-batch

Definition Classes
MultiThreadTrainStyle → TrainStyle
def clone(): AnyRef

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

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def fetch(iter: Int): Unit

Fetch weights
Fetch weights
iter
current iteration

Definition Classes
MultiThreadTrainStyle → TrainStyle
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def foreachTestSet(n: Int)(fn: ((IN, OUT)) ⇒ Unit): Unit

Iterate over given number of test instances
Iterate over given number of test instances
n
number of random sampled instances
fn
iteratee function

Definition Classes
MultiThreadTrainStyle → TrainStyle
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def isUpdateFinished: Future[_]

Indicates whether the asynchronous update is finished or not.
Indicates whether the asynchronous update is finished or not.
returns
future object of update

Definition Classes
TrainStyle
val logger: Logger

Logger
Logger

Attributes
protected
Definition Classes
TrainStyle
val make: ManipulationType[IN, OUT]

Input Operation that supervises how to manipulate input as matrices.
Input Operation that supervises how to manipulate input as matrices. This also controls how to compute actual network. (default: VectorType)

Definition Classes
MultiThreadTrainStyle → TrainStyle
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
val net: Network

Network to be trained
Network to be trained

Definition Classes
MultiThreadTrainStyle → TrainStyle
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val param: DistBeliefCriteria

Training criteria (default: SimpleTrainingCriteria)
Training criteria (default: SimpleTrainingCriteria)

Definition Classes
MultiThreadTrainStyle → TrainStyle
val sc: SparkContext
def setPositiveTrainingReference(set: RDD[(IN, OUT)]): Unit

Set training instances
Set training instances
set
RDD of training set

Definition Classes
MultiThreadTrainStyle → TrainStyle
def setPositiveTrainingReference(set: Seq[(IN, OUT)]): Unit

Set training instances
Set training instances
set
Sequence of training set

Definition Classes
MultiThreadTrainStyle → TrainStyle
def setTestReference(set: RDD[(IN, OUT)]): Unit

Set testing instances
Set testing instances
set
RDD of testing set

Definition Classes
MultiThreadTrainStyle → TrainStyle
def setTestReference(set: Seq[(IN, OUT)]): Unit

Set testing instances
Set testing instances
set
Sequence of testing set

Definition Classes
MultiThreadTrainStyle → TrainStyle
def stopUntilBatchFinished(): Unit

Non-blocking pending, until all assigned batches are finished
Non-blocking pending, until all assigned batches are finished

Definition Classes
TrainStyle
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
var testSet: RDD[Pair]

Test Set
Test Set

Attributes
protected
var testSize: Float

Size of test set
Size of test set

Attributes
protected
def toString(): String

Definition Classes
AnyRef → Any
var trainingFraction: Float

Fraction of mini-batch
Fraction of mini-batch

Attributes
protected
var trainingSet: RDD[Pair]

Training set
Training set

Attributes
protected
var trainingSize: Long

Size of training set
Size of training set

Attributes
protected
def unpersist(): Unit

Unpersist all
def update(iter: Int): Unit

Send update of weights
Send update of weights
iter
current iteration

Definition Classes
MultiThreadTrainStyle → TrainStyle
var validationEpoch: Int

size of training set
size of training set

Definition Classes
TrainStyle
def validationError(): Float

Calculate validation error
Calculate validation error
returns
validation error

Definition Classes
MultiThreadTrainStyle → TrainStyle
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

class MultiThreadTrainStyle[IN, OUT] extends TrainStyle[IN, OUT]

Instance Constructors

new MultiThreadTrainStyle(net: Network, algorithm: WeightUpdater, sc: SparkContext, make: ManipulationType[IN, OUT] = new VectorType(), param: DistBeliefCriteria = DistBeliefCriteria())(implicit arg0: ClassTag[IN], arg1: ClassTag[OUT])

Type Members

type Pair = (IN, OUT)

type Sampler = (Int) ⇒ Seq[OUT]

implicit class WeightOp extends Serializable

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

val accCount: Accumulator[Int]

val accNet: Accumulator[IndexedSeq[ScalarMatrix]]

val algorithm: WeightUpdater

final def asInstanceOf[T0]: T0

def batch(): Unit

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def fetch(iter: Int): Unit

def finalize(): Unit

def foreachTestSet(n: Int)(fn: ((IN, OUT)) ⇒ Unit): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def isUpdateFinished: Future[_]

val logger: Logger

val make: ManipulationType[IN, OUT]

final def ne(arg0: AnyRef): Boolean

val net: Network

final def notify(): Unit

final def notifyAll(): Unit

val param: DistBeliefCriteria

val sc: SparkContext

def setPositiveTrainingReference(set: RDD[(IN, OUT)]): Unit

def setPositiveTrainingReference(set: Seq[(IN, OUT)]): Unit

def setTestReference(set: RDD[(IN, OUT)]): Unit

def setTestReference(set: Seq[(IN, OUT)]): Unit

def stopUntilBatchFinished(): Unit

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

var testSet: RDD[Pair]

var testSize: Float

def toString(): String

var trainingFraction: Float

var trainingSet: RDD[Pair]

var trainingSize: Long

def unpersist(): Unit

def update(iter: Int): Unit

var validationEpoch: Int

def validationError(): Float

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from TrainStyle[IN, OUT]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped