# L2RegularizedConstantRate

Simple efficient l2-regularized SGD with a constant learning rate

Note that we must have |rate * l2 / numExamples| < 1.0 or the weights will oscillate.

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### Instance Constructors

1. #### new L2RegularizedConstantRate(l2: Double = 0.1, rate: Double = 0.1, numExamples: Int = 1)

l2

The l2 regularization parameter

rate

The learning rate

numExamples

The number of examples for online training, used to scale regularizer

### Value Members

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

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

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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

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14. #### 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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16. #### def isConverged: Boolean

Whether the optimizer has converged yet.

Whether the optimizer has converged yet.

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

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

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

weights

The weights

value

The value

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