RMSProp

Instance Constructors

new RMSProp(learningRate: Float = 0.002f, rescaleGradient: Float = 1.0f, gamma1: Float = 0.95f, gamma2: Float = 0.9f, wd: Float = 0.0f, lrScheduler: LRScheduler = null, clipGradient: Float = 0f)

learningRate
Float, Step size.
rescaleGradient
Float, rescaling factor of gradient.
gamma1
Float, decay factor of moving average for gradient, gradient^^2.
gamma2
Float, momentum factor of moving average for gradient.
wd
Float, L2 regularization coefficient add to all the weights
lrScheduler
The learning rate scheduler
clipGradient
Float, clip gradient in range [-clip_gradient, clip_gradient]

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
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def createState(index: Int, weight: NDArray): (NDArray, NDArray, NDArray)

Definition Classes
RMSProp → Optimizer
def deserializeState(bytes: Array[Byte]): AnyRef

Definition Classes
RMSProp → Optimizer
def disposeState(state: AnyRef): Unit

Definition Classes
RMSProp → Optimizer
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def getLr(index: Int, lr: Float): Float

Attributes
protected
Definition Classes
Optimizer
def getWd(index: Int, wd: Float): Float

Attributes
protected
Definition Classes
Optimizer
def hashCode(): Int

Definition Classes
AnyRef → Any
var idx2name: Map[Int, String]

Attributes
protected
Definition Classes
Optimizer
val indexUpdateCount: Map[Int, Int]

Attributes
protected
Definition Classes
Optimizer
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val learningRate: Float

Float, Step size.
val lrMult: Map[Either[Int, String], Float]

Attributes
protected
Definition Classes
Optimizer
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
var numUpdate: Int

Attributes
protected
Definition Classes
Optimizer
var rescaleGrad: Float

Attributes
protected
Definition Classes
Optimizer
def serializeState(state: AnyRef): Array[Byte]

Definition Classes
RMSProp → Optimizer
def setArgNames(argNames: Seq[String]): Unit

Definition Classes
Optimizer
def setIdx2Name(paramIdx2Name: Map[Int, String]): Unit

Definition Classes
Optimizer
def setLrMult(argsLrMult: Map[Either[Int, String], Float]): Unit

Sets an individual learning rate multiplier for each parameter.
Sets an individual learning rate multiplier for each parameter. If you specify a learning rate multiplier for a parameter, then the learning rate for the parameter will be set as the product of the global learning rate and its multiplier. note:: The default learning rate multiplier of a Variable can be set with lr_mult argument in the constructor.

Definition Classes
Optimizer
def setRescaleGrad(rescaleGrad: Float): Unit

Definition Classes
Optimizer
def setSymbol(sym: Symbol): Unit

Definition Classes
Optimizer
def setWdMult(argsWdMult: Map[Either[Int, String], Float]): Unit

Sets an individual weight decay multiplier for each parameter.
Sets an individual weight decay multiplier for each parameter.
By default, the weight decay multipler is set as 0 for all parameters whose name don't end with _weight or _gamma, if you call the setIdx2Name method to set idx2name.
note:: The default weight decay multiplier for a Variable can be set with its wd_mult argument in the constructor.

Definition Classes
Optimizer
var specialized: Boolean

Attributes
protected
Definition Classes
Optimizer
var symbol: Symbol

Attributes
protected
Definition Classes
Optimizer
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def update(index: Int, weight: NDArray, grad: NDArray, state: AnyRef): Unit

Update the parameters.
Update the parameters.
index
An unique integer key used to index the parameters
weight
weight ndarray
grad
grad ndarray
state
NDArray or other objects returned by initState The auxiliary state used in optimization.

Definition Classes
RMSProp → Optimizer
def updateCount(index: Int): Unit

update num_update
update num_update
index
The index will be updated

Attributes
protected
Definition Classes
Optimizer
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( ... )
val wdMult: Map[Either[Int, String], Float]

Attributes
protected
Definition Classes
Optimizer
val weightSet: Set[Int]

Attributes
protected
Definition Classes
Optimizer

Deprecated Value Members

def setLrScale(lrScale: Map[Int, Float]): Unit

Definition Classes
Optimizer
Annotations
@deprecated
Deprecated
(Since version 0.10.0) Use setLrMult instead.

Related Doc: package optimizer

class RMSProp extends Optimizer

Instance Constructors

new RMSProp(learningRate: Float = 0.002f, rescaleGradient: Float = 1.0f, gamma1: Float = 0.95f, gamma2: Float = 0.9f, wd: Float = 0.0f, lrScheduler: LRScheduler = null, clipGradient: Float = 0f)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def createState(index: Int, weight: NDArray): (NDArray, NDArray, NDArray)

def deserializeState(bytes: Array[Byte]): AnyRef

def disposeState(state: AnyRef): Unit

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def getLr(index: Int, lr: Float): Float

def getWd(index: Int, wd: Float): Float

def hashCode(): Int

var idx2name: Map[Int, String]

val indexUpdateCount: Map[Int, Int]

final def isInstanceOf[T0]: Boolean

val learningRate: Float

val lrMult: Map[Either[Int, String], Float]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

var numUpdate: Int

var rescaleGrad: Float

def serializeState(state: AnyRef): Array[Byte]

def setArgNames(argNames: Seq[String]): Unit

def setIdx2Name(paramIdx2Name: Map[Int, String]): Unit

def setLrMult(argsLrMult: Map[Either[Int, String], Float]): Unit

def setRescaleGrad(rescaleGrad: Float): Unit

def setSymbol(sym: Symbol): Unit

def setWdMult(argsWdMult: Map[Either[Int, String], Float]): Unit

var specialized: Boolean

var symbol: Symbol

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

def toString(): String

def update(index: Int, weight: NDArray, grad: NDArray, state: AnyRef): Unit

def updateCount(index: Int): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit

val wdMult: Map[Either[Int, String], Float]

val weightSet: Set[Int]

Deprecated Value Members

def setLrScale(lrScale: Map[Int, Float]): Unit

Inherited from Optimizer

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped