breeze.optimize

AdaDeltaGradientDescent

class AdaDeltaGradientDescent[T] extends StochasticGradientDescent[T]

Created by jda on 3/17/15.

Linear Supertypes
StochasticGradientDescent[T], FirstOrderMinimizer[T, StochasticDiffFunction[T]], SerializableLogging, Serializable, Serializable, Minimizer[T, StochasticDiffFunction[T]], AnyRef, Any
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Inherited
  1. AdaDeltaGradientDescent
  2. StochasticGradientDescent
  3. FirstOrderMinimizer
  4. SerializableLogging
  5. Serializable
  6. Serializable
  7. Minimizer
  8. AnyRef
  9. Any
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  1. Public
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Instance Constructors

  1. new AdaDeltaGradientDescent(rho: Double, maxIter: Int, tolerance: Double = 1.0E-5, improvementTolerance: Double = 1.0E-4, minImprovementWindow: Int = 50)(implicit vspace: MutableFiniteCoordinateField[T, _, Double], rand: RandBasis = breeze.stats.distributions.Rand)

Type Members

  1. case class History(avgSqGradient: T, avgSqDelta: T) extends Product with Serializable

    Any history the derived minimization function needs to do its updates.

  2. type State = FirstOrderMinimizer.State[T, Info, History]

    Definition Classes
    FirstOrderMinimizer

Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any

  6. def adjust(newX: T, newGrad: T, newVal: Double): (Double, T)

    Attributes
    protected
    Definition Classes
    AdaDeltaGradientDescent → FirstOrderMinimizer

  7. def adjustFunction(f: StochasticDiffFunction[T]): StochasticDiffFunction[T]

    Attributes
    protected
    Definition Classes
    FirstOrderMinimizer

  8. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  9. def calculateObjective(f: StochasticDiffFunction[T], x: T, history: History): (Double, T)

    Attributes
    protected
    Definition Classes
    FirstOrderMinimizer

  10. def chooseDescentDirection(state: State, fn: StochasticDiffFunction[T]): T

    Attributes
    protected
    Definition Classes
    StochasticGradientDescent → FirstOrderMinimizer

  11. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  12. val convergenceCheck: ConvergenceCheck[T]

    Definition Classes
    FirstOrderMinimizer

  13. val defaultStepSize: Double

    Definition Classes
    StochasticGradientDescent

  14. 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

  15. val epsilon: Double

  16. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  17. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  18. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  19. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any

  20. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  21. def infiniteIterations(f: StochasticDiffFunction[T], state: State): Iterator[State]

    Definition Classes
    FirstOrderMinimizer

  22. def initialHistory(f: StochasticDiffFunction[T], init: T): History

    Attributes
    protected
    Definition Classes
    AdaDeltaGradientDescent → FirstOrderMinimizer

  23. def initialState(f: StochasticDiffFunction[T], init: T): State

    Attributes
    protected
    Definition Classes
    FirstOrderMinimizer

  24. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  25. def iterations(f: StochasticDiffFunction[T], init: T): Iterator[State]

    Definition Classes
    FirstOrderMinimizer

  26. def logger: LazyLogger

    Attributes
    protected
    Definition Classes
    SerializableLogging

  27. def minimize(f: StochasticDiffFunction[T], init: T): T

    Definition Classes
    FirstOrderMinimizer → Minimizer

  28. def minimizeAndReturnState(f: StochasticDiffFunction[T], init: T): State

    Definition Classes
    FirstOrderMinimizer

  29. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  30. final def notify(): Unit

    Definition Classes
    AnyRef

  31. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    AnyRef

  33. 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

  34. def toString(): String

    Definition Classes
    AnyRef → Any

  35. def updateHistory(newX: T, newGrad: T, newVal: Double, f: StochasticDiffFunction[T], oldState: State): History

    Attributes
    protected
    Definition Classes
    AdaDeltaGradientDescent → FirstOrderMinimizer

  36. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  38. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from StochasticGradientDescent[T]

Inherited from FirstOrderMinimizer[T, StochasticDiffFunction[T]]

Inherited from SerializableLogging

Inherited from Serializable

Inherited from Serializable

Inherited from Minimizer[T, StochasticDiffFunction[T]]

Inherited from AnyRef

Inherited from Any

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