class AdaDeltaGradientDescent[T] extends StochasticGradientDescent[T]
Created by jda on 3/17/15.
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- AdaDeltaGradientDescent
- StochasticGradientDescent
- FirstOrderMinimizer
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Type Members
- case class History(avgSqGradient: T, avgSqDelta: T) extends Product with Serializable
- type State = FirstOrderMinimizer.State[T, Info, History]
- Definition Classes
- FirstOrderMinimizer
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
- def adjust(newX: T, newGrad: T, newVal: Double): (Double, T)
- Attributes
- protected
- Definition Classes
- AdaDeltaGradientDescent → FirstOrderMinimizer
- def adjustFunction(f: StochasticDiffFunction[T]): StochasticDiffFunction[T]
- Attributes
- protected
- Definition Classes
- FirstOrderMinimizer
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- def calculateObjective(f: StochasticDiffFunction[T], x: T, history: History): (Double, T)
- Attributes
- protected
- Definition Classes
- FirstOrderMinimizer
- def chooseDescentDirection(state: State, fn: StochasticDiffFunction[T]): T
- Attributes
- protected
- Definition Classes
- StochasticGradientDescent → FirstOrderMinimizer
- def clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @IntrinsicCandidate()
- val convergenceCheck: ConvergenceCheck[T]
- Definition Classes
- FirstOrderMinimizer
- val defaultStepSize: Double
- Definition Classes
- StochasticGradientDescent
- def determineStepSize(state: State, f: StochasticDiffFunction[T], dir: T): Double
Choose a step size scale for this iteration.
Choose a step size scale for this iteration.
Default is eta / math.pow(state.iter + 1,2.0 / 3.0)
- Definition Classes
- AdaDeltaGradientDescent → StochasticGradientDescent → FirstOrderMinimizer
- val epsilon: Double
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def equals(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef → Any
- final def getClass(): Class[_ <: AnyRef]
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- @native() @IntrinsicCandidate()
- def hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native() @IntrinsicCandidate()
- def infiniteIterations(f: StochasticDiffFunction[T], state: State): Iterator[State]
- Definition Classes
- FirstOrderMinimizer
- def initialHistory(f: StochasticDiffFunction[T], init: T): History
- Attributes
- protected
- Definition Classes
- AdaDeltaGradientDescent → FirstOrderMinimizer
- def initialState(f: StochasticDiffFunction[T], init: T): State
- Attributes
- protected
- Definition Classes
- FirstOrderMinimizer
- final def isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- def iterations(f: StochasticDiffFunction[T], init: T): Iterator[State]
- Definition Classes
- FirstOrderMinimizer
- def logger: LazyLogger
- Attributes
- protected
- Definition Classes
- SerializableLogging
- val maxIter: Int
- Definition Classes
- StochasticGradientDescent
- def minimize(f: StochasticDiffFunction[T], init: T): T
- Definition Classes
- FirstOrderMinimizer → Minimizer
- def minimizeAndReturnState(f: StochasticDiffFunction[T], init: T): State
- Definition Classes
- FirstOrderMinimizer
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def notify(): Unit
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- @native() @IntrinsicCandidate()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @IntrinsicCandidate()
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- def takeStep(state: State, dir: T, stepSize: Double): T
Projects the vector x onto whatever ball is needed.
Projects the vector x onto whatever ball is needed. Can also incorporate regularization, or whatever.
Default just takes a step
- Attributes
- protected
- Definition Classes
- AdaDeltaGradientDescent → StochasticGradientDescent → FirstOrderMinimizer
- def toString(): String
- Definition Classes
- AnyRef → Any
- def updateHistory(newX: T, newGrad: T, newVal: Double, f: StochasticDiffFunction[T], oldState: State): History
- Attributes
- protected
- Definition Classes
- AdaDeltaGradientDescent → FirstOrderMinimizer
- implicit val vspace: NormedModule[T, Double]
- Attributes
- protected
- Definition Classes
- StochasticGradientDescent
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])