ConstantLengthStepSize

Value Members

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

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

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

   

   Definition Classes

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4. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

5. val baseRate: Double

   

6. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

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   @throws( ... )

7. 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

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

   

   Definition Classes

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9. def equals(arg0: Any): Boolean

   

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10. def finalize(): Unit

    

    Attributes

    protected[java.lang]

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    @throws( classOf[java.lang.Throwable] )

11. 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

    GradientStep → GradientOptimizer

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

    

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13. def hashCode(): Int

    

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14. def initializeWeights(weights: WeightsSet): Unit

    

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

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

    weights

    The weights

    Definition Classes

    GradientStep → GradientOptimizer

15. def isConverged: Boolean

    

    Online optimizers generally don't converge

    Online optimizers generally don't converge

    returns

    Always false

    Definition Classes

    GradientStep → GradientOptimizer

16. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

17. var it: Int

    

    Definition Classes

    GradientStep

18. 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

    ConstantLengthStepSize → GradientStep

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

    

    Definition Classes

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20. final def notify(): Unit

    

    Definition Classes

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21. final def notifyAll(): Unit

    

    Definition Classes

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22. 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

23. def reset(): Unit

    

    To override if you want to reset internal state.

    To override if you want to reset internal state.

    Definition Classes

    GradientStep → GradientOptimizer

24. 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

    GradientStep → GradientOptimizer

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

    

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26. def toString(): String

    

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27. final def wait(): Unit

    

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    @throws( ... )

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

    

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    @throws( ... )

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

    

    Definition Classes

    AnyRef

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    @throws( ... )