breeze.optimize.AdaptiveGradientDescent

L2Regularization

trait L2Regularization[T] extends StochasticGradientDescent[T]

Implements the L2 regularization update.

Each step is:

x_{t+1}i = (s_{ti} * x_{ti} - \eta * g_ti) / (eta * regularization + delta + s_ti)

where g_ti is the gradient and s_ti = \sqrt(\sum_t'{t} g_ti2)

Linear Supertypes
StochasticGradientDescent[T], FirstOrderMinimizer[T, StochasticDiffFunction[T]], Logging, Minimizer[T, StochasticDiffFunction[T]], AnyRef, Any
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Inherited
  1. L2Regularization
  2. StochasticGradientDescent
  3. FirstOrderMinimizer
  4. Logging
  5. Minimizer
  6. AnyRef
  7. Any
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Type Members

  1. case class History(sumOfSquaredGradients: T) extends Product with Serializable

    Definition Classes
    L2Regularization → FirstOrderMinimizer

  2. case class State(x: T, value: Double, grad: T, adjustedValue: Double, adjustedGradient: T, iter: Int, initialAdjVal: Double, history: History, fVals: IndexedSeq[Double] = ..., numImprovementFailures: Int = 0, searchFailed: Boolean = false) extends Product with Serializable

    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
    L2Regularization → FirstOrderMinimizer

  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  8. def chooseDescentDirection(state: State): T

    Attributes
    protected
    Definition Classes
    StochasticGradientDescent → FirstOrderMinimizer

  9. def clone(): AnyRef

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

  10. val defaultStepSize: Double

    Definition Classes
    StochasticGradientDescent

  11. val delta: Double

  12. 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
    L2Regularization → StochasticGradientDescent → FirstOrderMinimizer

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

    Definition Classes
    AnyRef

  14. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  15. def finalize(): Unit

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

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

    Definition Classes
    AnyRef → Any

  17. def hashCode(): Int

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    L2Regularization → FirstOrderMinimizer

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

    Attributes
    protected
    Definition Classes
    FirstOrderMinimizer

  20. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

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

    Definition Classes
    FirstOrderMinimizer

  22. val lambda: Double

  23. lazy val logger: Logger

    Attributes
    protected
    Definition Classes
    Logging

  24. val maxIter: Int

    Definition Classes
    StochasticGradientDescent

  25. val minImprovementWindow: Int

    Definition Classes
    FirstOrderMinimizer

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

    Definition Classes
    FirstOrderMinimizer → Minimizer

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

    Definition Classes
    AnyRef

  28. final def notify(): Unit

    Definition Classes
    AnyRef

  29. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  30. def numDepthChargeSteps: Int

    Take this many steps and then reset x to the original x.

    Take this many steps and then reset x to the original x. Mostly for Adagrad, which can behave oddly until it has a good sense of rate of change.

    Attributes
    protected
    Definition Classes
    L2Regularization → FirstOrderMinimizer

  31. val numberOfImprovementFailures: Int

    Definition Classes
    FirstOrderMinimizer

  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
    L2Regularization → StochasticGradientDescent → FirstOrderMinimizer

  34. def toString(): String

    Definition Classes
    AnyRef → Any

  35. def updateFValWindow(oldState: State, newAdjVal: Double): IndexedSeq[Double]

    Attributes
    protected
    Definition Classes
    StochasticGradientDescent → FirstOrderMinimizer

  36. def updateHistory(newX: T, newGrad: T, newValue: Double, oldState: State): History

    Definition Classes
    L2Regularization → FirstOrderMinimizer

  37. implicit val vspace: MutableCoordinateSpace[T, Double]

    Attributes
    protected
    Definition Classes
    StochasticGradientDescent

  38. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws()

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

    Definition Classes
    AnyRef
    Annotations
    @throws()

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

    Definition Classes
    AnyRef
    Annotations
    @throws()

Inherited from StochasticGradientDescent[T]

Inherited from FirstOrderMinimizer[T, StochasticDiffFunction[T]]

Inherited from Logging

Inherited from Minimizer[T, StochasticDiffFunction[T]]

Inherited from AnyRef

Inherited from Any

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