# L2Regularization

Include L2 regularization (Gaussian with given scalar as the spherical covariance) in the gradient and value.

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### Abstract Value Members

1. #### abstract 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
2. #### abstract 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

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3. #### abstract def isConverged: Boolean

Whether the optimizer has converged yet.

Whether the optimizer has converged yet.

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4. #### abstract def reset(): Unit

Reset the optimizers internal state (such as Hessian approximation, etc.

Reset the optimizers internal state (such as Hessian approximation, etc.)

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### Concrete Value Members

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

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9. #### def equals(arg0: Any): Boolean

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10. #### def finalize(): Unit

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15. #### final def notify(): Unit

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16. #### final def notifyAll(): Unit

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17. #### def step(weights: WeightsSet, gradient: WeightsMap, value: Double): Unit

weights

The weights

value

The value

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

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19. #### def toString(): String

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21. #### final def wait(): Unit

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

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23. #### final def wait(arg0: Long): Unit

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