Adam

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

var beta_1: Double

first moment coefficient

var beta_2: Double

second moment coefficient

def clearHistory(): Unit

def clone(): OptimMethod[T]

var dampening: Double

var decay: Double

learning rate decay

var epsilon: Double

for numerical stability

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def getHyperParameter(config: Table): String

def getHyperParameter(): String

def getLearningRate(): Double

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

var learningRate: Double

var learningRateDecay: Double

var learningRateSchedule: LearningRateSchedule

var learningRates: Tensor[T]

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

var lr: Double

learning rate

var momentum: Double

final def ne(arg0: AnyRef): Boolean

var nesterov: Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

An implementation of Adam http://arxiv.org/pdf/1412.6980.pdf

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

val schedule: LearningRateSchedule

learning rate schedule

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

def toString(): String

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

def updateHyperParameter(): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit

var weightDecay: Double

var weightDecays: Tensor[T]

def clearHistory(state: Table): Table

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