cc.factorie.optimize

InvTLengthStepSize

trait InvTLengthStepSize extends GradientStep

Mixin trait for a step size which is normalized by the length of the gradient and looks like 1/T

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  1. InvTLengthStepSize
  2. GradientStep
  3. GradientOptimizer
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  1. final def !=(arg0: AnyRef): Boolean

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

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  3. final def ##(): Int

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

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

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  6. final def asInstanceOf[T0]: T0

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  7. val baseRate: Double

  8. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  9. def doGradStep(weights: WeightsSet, gradient: WeightsMap, rate: Double): Unit

    Actually adds the gradient to the weights.

    Actually adds the gradient to the weights. ParameterAveraging overrides this.

    weights

    The weights

    gradient

    The gradient

    rate

    The learning rate

    Definition Classes
    GradientStep
  10. final def eq(arg0: AnyRef): Boolean

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  13. def finalizeWeights(weights: WeightsSet): Unit

    Once learning is done, the weights should be copied back into normal tensors.

    Once learning is done, the weights should be copied back into normal tensors.

    weights

    The weights

    Definition Classes
    GradientStepGradientOptimizer
  14. final def getClass(): Class[_]

    Definition Classes
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  15. def hashCode(): Int

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  16. def initializeWeights(weights: WeightsSet): Unit

    Some optimizers swap out weights with special purpose tensors for e.

    Some optimizers swap out weights with special purpose tensors for e.g. efficient scoring while learning.

    weights

    The weights

    Definition Classes
    GradientStepGradientOptimizer
  17. def isConverged: Boolean

    Online optimizers generally don't converge

    Online optimizers generally don't converge

    returns

    Always false

    Definition Classes
    GradientStepGradientOptimizer
  18. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  19. var it: Int

    Definition Classes
    GradientStep
  20. def lRate(weights: WeightsSet, gradient: WeightsMap, value: Double): Double

    Override this method to change the learning rate

    Override this method to change the learning rate

    weights

    The weights

    gradient

    The gradient

    value

    The value

    returns

    The learning rate

    Definition Classes
    InvTLengthStepSizeGradientStep
  21. final def ne(arg0: AnyRef): Boolean

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  22. final def notify(): Unit

    Definition Classes
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  23. final def notifyAll(): Unit

    Definition Classes
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  24. def processGradient(weights: WeightsSet, gradient: WeightsMap): Unit

    Override this method do to some transformation to the gradient before going on with optimization

    Override this method do to some transformation to the gradient before going on with optimization

    weights

    The weights

    gradient

    The gradient

    Definition Classes
    GradientStep
  25. def reset(): Unit

    To override if you want to reset internal state.

    To override if you want to reset internal state.

    Definition Classes
    GradientStepGradientOptimizer
  26. final def step(weights: WeightsSet, gradient: WeightsMap, value: Double): Unit

    Should not be overriden.

    Should not be overriden. The main flow of a GradientStep optimizer.

    weights

    The weights

    gradient

    The gradient

    value

    The value

    Definition Classes
    GradientStepGradientOptimizer
  27. final def synchronized[T0](arg0: ⇒ T0): T0

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  28. def toString(): String

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  29. final def wait(): Unit

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    @throws( ... )
  30. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  31. final def wait(arg0: Long): Unit

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    @throws( ... )

Inherited from GradientStep

Inherited from GradientOptimizer

Inherited from AnyRef

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