SimpleRNN

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit

val activation: KerasLayer[Tensor[T], Tensor[T], T]

Activation function to use.

def apply(name: String): Option[AbstractModule[Activity, Activity, T]]

final def asInstanceOf[T0]: T0

var bRegularizer: Regularizer[T]

An instance of Regularizer, applied to the bias.

def backward(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T]

var backwardTime: Long

def build(calcInputShape: Shape): Shape

def buildCell(input: Array[Int]): Cell[T]

def canEqual(other: Any): Boolean

final def checkEngineType(): SimpleRNN.this.type

def clearState(): SimpleRNN.this.type

final def clone(deepCopy: Boolean): AbstractModule[Tensor[T], Tensor[T], T]

def clone(): AnyRef

final def cloneModule(): AbstractModule[Tensor[T], Tensor[T], T]

def computeOutputShape(inputShape: Shape): Shape

def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T]

final def eq(arg0: AnyRef): Boolean

def equals(other: Any): Boolean

final def evaluate(): SimpleRNN.this.type

final def evaluate(dataSet: LocalDataSet[MiniBatch[T]], vMethods: Array[_ <: ValidationMethod[T]]): Array[(ValidationResult, ValidationMethod[T])]

final def evaluate(dataset: RDD[Sample[T]], vMethods: Array[_ <: ValidationMethod[T]], batchSize: Option[Int]): Array[(ValidationResult, ValidationMethod[T])]

def finalize(): Unit

def findModules(moduleType: String): ArrayBuffer[AbstractModule[_, _, T]]

final def forward(input: Tensor[T]): Tensor[T]

var forwardTime: Long

def freeze(names: String*): SimpleRNN.this.type

def from[T](vars: Variable[T]*)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

Build graph: some other modules point to current module

final def getClass(): Class[_]

def getExtraParameter(): Array[Tensor[T]]

final def getInputShape(): Shape

final def getName(): String

final def getNumericType(): TensorDataType

final def getOutputShape(): Shape

def getParametersTable(): Table

final def getPrintName(): String

final def getScaleB(): Double

final def getScaleW(): Double

def getTimes(): Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)]

final def getWeightsBias(): Array[Tensor[T]]

val goBackwards: Boolean

Whether the input sequence will be processed backwards.

var gradInput: Tensor[T]

final def hasName: Boolean

def hashCode(): Int

val inputShape: Shape

A Single Shape, does not include the batch dimension.

def inputs(first: (ModuleNode[T], Int), nodesWithIndex: (ModuleNode[T], Int)*): ModuleNode[T]

def inputs(nodes: Array[ModuleNode[T]]): ModuleNode[T]

def inputs(nodes: ModuleNode[T]*): ModuleNode[T]

def isBuilt(): Boolean

def isFrozen[T]()(implicit arg0: ClassTag[T]): Boolean

final def isInstanceOf[T0]: Boolean

def isKerasStyle(): Boolean

final def isTraining(): Boolean

def labor: AbstractModule[Tensor[T], Tensor[T], T]

def labor_=(value: AbstractModule[Tensor[T], Tensor[T], T]): Unit

var line: String

final def loadModelWeights(srcModel: Module[Float], matchAll: Boolean): SimpleRNN.this.type

final def loadWeights(weightPath: String, matchAll: Boolean): SimpleRNN.this.type

val modules: ArrayBuffer[AbstractModule[Activity, Activity, T]]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

var output: Tensor[T]

val outputDim: Int

Hidden unit size.

def parameters(): (Array[Tensor[T]], Array[Tensor[T]])

final def predict(dataset: RDD[Sample[T]], batchSize: Int, shareBuffer: Boolean): RDD[Activity]

final def predictClass(dataset: RDD[Sample[T]], batchSize: Int): RDD[Int]

final def predictImage(imageFrame: ImageFrame, outputLayer: String, shareBuffer: Boolean, batchPerPartition: Int, predictKey: String, featurePaddingParam: Option[PaddingParam[T]]): ImageFrame

def processInputs(first: (ModuleNode[T], Int), nodesWithIndex: (ModuleNode[T], Int)*): ModuleNode[T]

def processInputs(nodes: Seq[ModuleNode[T]]): ModuleNode[T]

final def quantize(): Module[T]

def reset(): Unit

def resetTimes(): Unit

val returnSequences: Boolean

Whether to return the full sequence or only return the last output in the output sequence.

final def saveCaffe(prototxtPath: String, modelPath: String, useV2: Boolean, overwrite: Boolean): SimpleRNN.this.type

final def saveDefinition(path: String, overWrite: Boolean): SimpleRNN.this.type

final def saveModule(path: String, weightPath: String, overWrite: Boolean): SimpleRNN.this.type

final def saveTF(inputs: Seq[(String, Seq[Int])], path: String, byteOrder: ByteOrder, dataFormat: TensorflowDataFormat): SimpleRNN.this.type

final def saveTorch(path: String, overWrite: Boolean): SimpleRNN.this.type

final def saveWeights(path: String, overWrite: Boolean): Unit

var scaleB: Double

var scaleW: Double

final def setExtraParameter(extraParam: Array[Tensor[T]]): SimpleRNN.this.type

final def setLine(line: String): SimpleRNN.this.type

final def setName(name: String): SimpleRNN.this.type

def setScaleB(b: Double): SimpleRNN.this.type

def setScaleW(w: Double): SimpleRNN.this.type

final def setWeightsBias(newWeights: Array[Tensor[T]]): SimpleRNN.this.type

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

def toGraph(startNodes: ModuleNode[T]*): Graph[T]

def toString(): String

var train: Boolean

final def training(): SimpleRNN.this.type

var uRegularizer: Regularizer[T]

An instance of Regularizer, applied the recurrent weights matrices.

def unFreeze(names: String*): SimpleRNN.this.type

def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T]

def updateOutput(input: Tensor[T]): Tensor[T]

var wRegularizer: Regularizer[T]

An instance of Regularizer, (eg.

final def wait(): Unit

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

final def wait(arg0: Long): Unit

final def zeroGradParameters(): Unit

final def save(path: String, overWrite: Boolean): SimpleRNN.this.type