Ftrl

Instance Constructors

new Ftrl(learningRate: Double = 0.001, learningRatePower: Double = -0.5, initialAccumulatorValue: Double = 0.1, l1RegularizationStrength: Double = 0.0, l2RegularizationStrength: Double = 0.0, l2ShrinkageRegularizationStrength: Double = 0.0)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

learningRate
learning rate
learningRatePower
double, must be less or equal to zero. Default is -0.5.
initialAccumulatorValue
double, the starting value for accumulators, require zero or positive values. Default is 0.1.
l1RegularizationStrength
double, must be greater or equal to zero. Default is zero.
l2RegularizationStrength
double, must be greater or equal to zero. Default is zero.
l2ShrinkageRegularizationStrength
double, must be greater or equal to zero. Default is zero. This differs from l2RegularizationStrength above. L2 above is a stabilization penalty, whereas this one is a magnitude penalty.

Value Members

final def !=(arg0: AnyRef): Boolean

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

Definition Classes
Any
final def ##(): Int

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

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

Definition Classes
Any
var accumNew: Tensor[T]
final def asInstanceOf[T0]: T0

Definition Classes
Any
var buffer: Tensor[T]
def checkParam(learningRate: Double, learningRatePower: Double, initialAccumulatorValue: Double, l1RegularizationStrength: Double, l2RegularizationStrength: Double, l2ShrinkageRegularizationStrength: Double): Unit

Attributes
protected
def clearHistory(): Unit

Clear the history information in the OptimMethod state
Clear the history information in the OptimMethod state
returns

Definition Classes
Ftrl → OptimMethod
def clone(): OptimMethod[T]

clone OptimMethod
clone OptimMethod
returns

Definition Classes
OptimMethod → AnyRef
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 getHyperParameter(): String

Get hyper parameter from config table.
Get hyper parameter from config table.

Definition Classes
OptimMethod
def getLearningRate(): Double

get learning rate
get learning rate
returns

Definition Classes
Ftrl → OptimMethod
var gradWithStrinkage: Tensor[T]
def hashCode(): Int

Definition Classes
AnyRef → Any
var initialAccumulatorValue: Double

double, the starting value for accumulators, require zero or positive values.
double, the starting value for accumulators, require zero or positive values. Default is 0.1.
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
var l1RegularizationStrength: Double

double, must be greater or equal to zero.
double, must be greater or equal to zero. Default is zero.
var l2RegularizationStrength: Double

double, must be greater or equal to zero.
double, must be greater or equal to zero. Default is zero.
var l2ShrinkageRegularizationStrength: Double

double, must be greater or equal to zero.
double, must be greater or equal to zero. Default is zero. This differs from l2RegularizationStrength above. L2 above is a stabilization penalty, whereas this one is a magnitude penalty.
var learningRate: Double

learning rate
var learningRatePower: Double

double, must be less or equal to zero.
double, must be less or equal to zero. Default is -0.5.
def loadFromTable(config: Table): Ftrl.this.type

load optimMethod parameters from Table
load optimMethod parameters from Table
config
returns

Definition Classes
Ftrl → OptimMethod
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def optimize(feval: (Tensor[T]) ⇒ (T, Tensor[T]), parameter: Tensor[T]): (Tensor[T], Array[T])

Optimize the model parameter
Optimize the model parameter
feval
a function that takes a single input (X), the point of a evaluation, and returns f(X) and df/dX
parameter
the initial point
returns
the new x vector and the function list, evaluated before the update

Definition Classes
Ftrl → OptimMethod
var quadratic: Tensor[T]
def save(path: String, overWrite: Boolean = false): Ftrl.this.type

save OptimMethod
save OptimMethod
path
path
overWrite
whether to overwrite
returns

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

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def updateHyperParameter(): Unit

Update hyper parameter.
Update hyper parameter. We have updated hyper parameter in method optimize(). But in DistriOptimizer, the method optimize() is only called on the executor side, the driver's hyper parameter is unchanged. So this method is using to update hyper parameter on the driver side.
returns
A string.

Definition Classes
OptimMethod
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( ... )

Deprecated Value Members

def clearHistory(state: Table): Table

Clear the history information in the state
Clear the history information in the state
state
returns

Definition Classes
OptimMethod
Annotations
@deprecated
Deprecated
(Since version 0.2.0) Please use clearHistory() instead
def getHyperParameter(config: Table): String

Get hyper parameter from config table.
Get hyper parameter from config table.
config
a table contains the hyper parameter.

Definition Classes
OptimMethod
Annotations
@deprecated
Deprecated
(Since version 0.2.0) Please use getHyperParameter() instead
def optimize(feval: (Tensor[T]) ⇒ (T, Tensor[T]), parameter: Tensor[T], config: Table, state: Table = null): (Tensor[T], Array[T])

Optimize the model parameter
Optimize the model parameter
feval
a function that takes a single input (X), the point of a evaluation, and returns f(X) and df/dX
parameter
the initial point
config
a table with configuration parameters for the optimizer
state
a table describing the state of the optimizer; after each call the state is modified
returns
the new x vector and the function list, evaluated before the update

Definition Classes
OptimMethod
Annotations
@deprecated
Deprecated
(Since version 0.2.0) Please initialize OptimMethod with parameters when creating it instead of importing table
def updateHyperParameter(config: Table, state: Table): Unit

Update hyper parameter.
Update hyper parameter. We have updated hyper parameter in method optimize(). But in DistriOptimizer, the method optimize() is only called on the executor side, the driver's hyper parameter is unchanged. So this method is using to update hyper parameter on the driver side.
config
config table.
state
state Table.
returns
A string.

Definition Classes
OptimMethod
Annotations
@deprecated
Deprecated
(Since version 0.2.0) Please use updateHyperParameter() instead

class Ftrl[T] extends OptimMethod[T]

Instance Constructors

new Ftrl(learningRate: Double = 0.001, learningRatePower: Double = -0.5, initialAccumulatorValue: Double = 0.1, l1RegularizationStrength: Double = 0.0, l2RegularizationStrength: Double = 0.0, l2ShrinkageRegularizationStrength: Double = 0.0)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

var accumNew: Tensor[T]

final def asInstanceOf[T0]: T0

var buffer: Tensor[T]

def checkParam(learningRate: Double, learningRatePower: Double, initialAccumulatorValue: Double, l1RegularizationStrength: Double, l2RegularizationStrength: Double, l2ShrinkageRegularizationStrength: Double): Unit

def clearHistory(): Unit

def clone(): OptimMethod[T]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def getHyperParameter(): String

def getLearningRate(): Double

var gradWithStrinkage: Tensor[T]

def hashCode(): Int

var initialAccumulatorValue: Double

final def isInstanceOf[T0]: Boolean

var l1RegularizationStrength: Double

var l2RegularizationStrength: Double

var l2ShrinkageRegularizationStrength: Double

var learningRate: Double

var learningRatePower: Double

def loadFromTable(config: Table): Ftrl.this.type

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def optimize(feval: (Tensor[T]) ⇒ (T, Tensor[T]), parameter: Tensor[T]): (Tensor[T], Array[T])

var quadratic: Tensor[T]

def save(path: String, overWrite: Boolean = false): Ftrl.this.type

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

def toString(): String

def updateHyperParameter(): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Deprecated Value Members

def clearHistory(state: Table): Table

def getHyperParameter(config: Table): String

def optimize(feval: (Tensor[T]) ⇒ (T, Tensor[T]), parameter: Tensor[T], config: Table, state: Table = null): (Tensor[T], Array[T])

def updateHyperParameter(config: Table, state: Table): Unit

Inherited from OptimMethod[T]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped