SReLU

Instance Constructors

new SReLU(tLeftInit: InitializationMethod = Zeros, aLeftInit: InitializationMethod = Xavier, tRightInit: InitializationMethod = Xavier, aRightInit: InitializationMethod = Ones, sharedAxes: Array[Int] = null, inputShape: Shape = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

tLeftInit
Initialization function for the left part intercept. Default is Zeros. You can also pass in corresponding string representations such as 'zero' or 'normal', etc. for simple init methods in the factory method.
aLeftInit
Initialization function for the left part slope. Default is Xavier. You can also pass in corresponding string representations such as 'glorot_uniform', etc. for simple init methods in the factory method.
tRightInit
Initialization function for the right part intercept. Default is Xavier. You can also pass in corresponding string representations such as 'glorot_uniform', etc. for simple init methods in the factory method.
aRightInit
Initialization function for the right part slope. Default is Ones. You can also pass in corresponding string representations such as 'one' or 'normal', etc. for simple init methods in the factory method.
sharedAxes
Array of Int. The axes along which to share learnable parameters for the activation function. Default is null. For example, if the incoming feature maps are from a 2D convolution with output shape (batch, height, width, channels), and you wish to share parameters across space so that each filter only has one set of parameters, set 'sharedAxes = Array(1,2)'.
inputShape
A Single Shape, does not include the batch dimension.

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 aLeftInit: InitializationMethod

Initialization function for the left part slope.
Initialization function for the left part slope. Default is Xavier. You can also pass in corresponding string representations such as 'glorot_uniform', etc. for simple init methods in the factory method.

Definition Classes
SReLU → SReLU
val aRightInit: InitializationMethod

Initialization function for the right part slope.
Initialization function for the right part slope. Default is Ones. You can also pass in corresponding string representations such as 'one' or 'normal', etc. for simple init methods in the factory method.

Definition Classes
SReLU → SReLU
def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit

Definition Classes
KerasLayer → AbstractModule
def apply(name: String): Option[AbstractModule[Activity, Activity, T]]

Definition Classes
Container → AbstractModule
final def asInstanceOf[T0]: T0

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

Definition Classes
AbstractModule
var backwardTime: Long

Attributes
protected
Definition Classes
AbstractModule
def build(calcInputShape: Shape): Shape

Definition Classes
KerasLayer → InferShape
def canEqual(other: Any): Boolean

Definition Classes
Container → AbstractModule
final def checkEngineType(): SReLU.this.type

Definition Classes
Container → AbstractModule
def clearState(): SReLU.this.type

Definition Classes
Container → AbstractModule
final def clone(deepCopy: Boolean): AbstractModule[Tensor[T], Tensor[T], T]

Definition Classes
AbstractModule
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final def cloneModule(): AbstractModule[Tensor[T], Tensor[T], T]

Definition Classes
AbstractModule
def computeOutputShape(inputShape: Shape): Shape

Definition Classes
IdentityOutputShape → InferShape
def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T]

Definition Classes
SReLU → KerasLayer
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
Container → AbstractModule → AnyRef → Any
final def evaluate(): SReLU.this.type

Definition Classes
Container → AbstractModule
final def evaluate(dataSet: LocalDataSet[MiniBatch[T]], vMethods: Array[_ <: ValidationMethod[T]]): Array[(ValidationResult, ValidationMethod[T])]

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

Definition Classes
AbstractModule
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def findModules(moduleType: String): ArrayBuffer[AbstractModule[_, _, T]]

Definition Classes
Container
final def forward(input: Tensor[T]): Tensor[T]

Definition Classes
AbstractModule
var forwardTime: Long

Attributes
protected
Definition Classes
AbstractModule
def freeze(names: String*): SReLU.this.type

Definition Classes
Container → AbstractModule
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def getExtraParameter(): Array[Tensor[T]]

Definition Classes
Container → AbstractModule
final def getInputShape(): Shape

Definition Classes
InferShape
final def getName(): String

Definition Classes
AbstractModule
final def getNumericType(): TensorDataType

Definition Classes
AbstractModule
final def getOutputShape(): Shape

Definition Classes
InferShape
def getParametersTable(): Table

Definition Classes
Container → AbstractModule
final def getPrintName(): String

Attributes
protected
Definition Classes
AbstractModule
final def getScaleB(): Double

Definition Classes
AbstractModule
final def getScaleW(): Double

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

Definition Classes
Container → AbstractModule
final def getWeightsBias(): Array[Tensor[T]]

Definition Classes
AbstractModule
var gradInput: Tensor[T]

Definition Classes
AbstractModule
final def hasName: Boolean

Definition Classes
AbstractModule
def hashCode(): Int

Definition Classes
Container → AbstractModule → AnyRef → Any
val inputShape: Shape

A Single Shape, does not include the batch dimension.
A Single Shape, does not include the batch dimension.

Definition Classes
SReLU → SReLU
def inputs(first: (ModuleNode[T], Int), nodesWithIndex: (ModuleNode[T], Int)*): ModuleNode[T]

Definition Classes
KerasLayer → AbstractModule
def inputs(nodes: Array[ModuleNode[T]]): ModuleNode[T]

Definition Classes
KerasLayer → AbstractModule
def inputs(nodes: ModuleNode[T]*): ModuleNode[T]

Definition Classes
KerasLayer → AbstractModule
def isBuilt(): Boolean

Definition Classes
KerasLayer → InferShape
def isFrozen[T]()(implicit arg0: ClassTag[T]): Boolean

Definition Classes
Net
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def isKerasStyle(): Boolean

Definition Classes
KerasLayer → InferShape
final def isTraining(): Boolean

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

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

Definition Classes
KerasLayer
var line: String

Attributes
protected
Definition Classes
AbstractModule
final def loadModelWeights(srcModel: Module[Float], matchAll: Boolean): SReLU.this.type

Definition Classes
AbstractModule
final def loadWeights(weightPath: String, matchAll: Boolean): SReLU.this.type

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

Definition Classes
Container
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
var output: Tensor[T]

Definition Classes
AbstractModule
def parameters(): (Array[Tensor[T]], Array[Tensor[T]])

Definition Classes
Container → AbstractModule
final def predict(dataset: RDD[Sample[T]], batchSize: Int, shareBuffer: Boolean): RDD[Activity]

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

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

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

Attributes
protected
Definition Classes
AbstractModule
def processInputs(nodes: Seq[ModuleNode[T]]): ModuleNode[T]

Attributes
protected
Definition Classes
AbstractModule
final def quantize(): Module[T]

Definition Classes
AbstractModule
def reset(): Unit

Definition Classes
Container → AbstractModule
def resetTimes(): Unit

Definition Classes
Container → AbstractModule
final def saveCaffe(prototxtPath: String, modelPath: String, useV2: Boolean, overwrite: Boolean): SReLU.this.type

Definition Classes
AbstractModule
final def saveDefinition(path: String, overWrite: Boolean): SReLU.this.type

Definition Classes
AbstractModule
final def saveModule(path: String, weightPath: String, overWrite: Boolean): SReLU.this.type

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

Definition Classes
AbstractModule
final def saveTorch(path: String, overWrite: Boolean): SReLU.this.type

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

Definition Classes
AbstractModule
var scaleB: Double

Attributes
protected
Definition Classes
AbstractModule
var scaleW: Double

Attributes
protected
Definition Classes
AbstractModule
final def setExtraParameter(extraParam: Array[Tensor[T]]): SReLU.this.type

Definition Classes
AbstractModule
final def setLine(line: String): SReLU.this.type

Definition Classes
AbstractModule
final def setName(name: String): SReLU.this.type

Definition Classes
AbstractModule
def setScaleB(b: Double): SReLU.this.type

Definition Classes
Container → AbstractModule
def setScaleW(w: Double): SReLU.this.type

Definition Classes
Container → AbstractModule
final def setWeightsBias(newWeights: Array[Tensor[T]]): SReLU.this.type

Definition Classes
AbstractModule
val sharedAxes: Array[Int]

Array of Int.
Array of Int. The axes along which to share learnable parameters for the activation function. Default is null. For example, if the incoming feature maps are from a 2D convolution with output shape (batch, height, width, channels), and you wish to share parameters across space so that each filter only has one set of parameters, set 'sharedAxes = Array(1,2)'.

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

Definition Classes
AnyRef
val tLeftInit: InitializationMethod

Initialization function for the left part intercept.
Initialization function for the left part intercept. Default is Zeros. You can also pass in corresponding string representations such as 'zero' or 'normal', etc. for simple init methods in the factory method.

Definition Classes
SReLU → SReLU
val tRightInit: InitializationMethod

Initialization function for the right part intercept.
Initialization function for the right part intercept. Default is Xavier. You can also pass in corresponding string representations such as 'glorot_uniform', etc. for simple init methods in the factory method.

Definition Classes
SReLU → SReLU
def toGraph(startNodes: ModuleNode[T]*): Graph[T]

Definition Classes
AbstractModule
def toString(): String

Definition Classes
AbstractModule → AnyRef → Any
var train: Boolean

Attributes
protected
Definition Classes
AbstractModule
final def training(): SReLU.this.type

Definition Classes
Container → AbstractModule
def unFreeze(names: String*): SReLU.this.type

Definition Classes
Container → AbstractModule
def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T]

Definition Classes
KerasLayer → AbstractModule
def updateOutput(input: Tensor[T]): Tensor[T]

Definition Classes
KerasLayer → AbstractModule
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( ... )
final def zeroGradParameters(): Unit

Definition Classes
AbstractModule

Deprecated Value Members

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

Definition Classes
AbstractModule
Annotations
@deprecated
Deprecated
(Since version 0.3.0) please use recommended saveModule(path, overWrite)

Related Docs: object SReLU | package layers

class SReLU[T] extends bigdl.nn.keras.SReLU[T] with Net

Instance Constructors

new SReLU(tLeftInit: InitializationMethod = Zeros, aLeftInit: InitializationMethod = Xavier, tRightInit: InitializationMethod = Xavier, aRightInit: InitializationMethod = Ones, sharedAxes: Array[Int] = null, inputShape: Shape = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

val aLeftInit: InitializationMethod

val aRightInit: InitializationMethod

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

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

final def asInstanceOf[T0]: T0

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

var backwardTime: Long

def build(calcInputShape: Shape): Shape

def canEqual(other: Any): Boolean

final def checkEngineType(): SReLU.this.type

def clearState(): SReLU.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(): SReLU.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*): SReLU.this.type

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]]

var gradInput: Tensor[T]

final def hasName: Boolean

def hashCode(): Int

val inputShape: Shape

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): SReLU.this.type

final def loadWeights(weightPath: String, matchAll: Boolean): SReLU.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]

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

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

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