object LarsSGD extends Serializable

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  6. def containsLarsSGD[T](optimMethods: Map[String, OptimMethod[T]]): Option[Double]

    Check if there is LarsSGD in optimMethods.

    Check if there is LarsSGD in optimMethods. If so, return the weight decay of the first found LarsSGD. Else, return None

    returns

    The weight decay of the first found LarsSGD in the optimMethods. Or None if there is not one

  7. def createOptimForModule[T](model: Module[T], trust: Double = 1.0, learningRate: Double = 1e-3, learningRateDecay: Double = 0.01, weightDecay: Double = 0.005, momentum: Double = 0.5, learningRateSchedule: LearningRateSchedule = Default())(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Map[String, OptimMethod[T]]

    Create a Map(String, OptimMethod) for a container.

    Create a Map(String, OptimMethod) for a container. For each submodule in the container, generate (module.getName(), new Lars[T]) pair in the returned map. The resulting map can be used in setOptimMethods. Note: each Lars optim uses the same LearningRateSchedule

    model

    the container to build LARS optim method for

    trust

    the trust on the learning rate scale, should be in 0 to 1

    learningRate

    learning rate

    learningRateDecay

    learning rate decay

    weightDecay

    weight decay

    momentum

    momentum

    learningRateSchedule

    the learning rate scheduler

  8. def createOptimLRSchedulerForModule[A <: Activity, B <: Activity, T](model: Container[A, B, T], lrScheGenerator: (AbstractModule[Activity, Activity, T]) ⇒ (LearningRateSchedule, Boolean), trust: Double = 1.0, learningRate: Double = 1e-3, learningRateDecay: Double = 0.01, weightDecay: Double = 0.005, momentum: Double = 0.5)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Map[String, OptimMethod[T]]

    Create a Map(String, OptimMethod) for a container.

    Create a Map(String, OptimMethod) for a container. For each submodule in the container, generate (module.getName(), new Lars[T]) pair in the returned map. The resulting map can be used in setOptimMethods. This function sets different LearningRateSchedules for different submodules

    model

    the container to build LARS optim method for

    lrScheGenerator

    the learning rate schedule generator for each sub-module. Generator accepts the sub-module that the schedule is linked to. It should return a tuple (learningRateSchedule, isOwner), where isOwner indicates whether the corresponding LARS optim method is responsible for showing the learning rate in getHyperParameter (multiple LARS optim methods may share one learning rate scheduler)

    trust

    the trust on the learning rate scale, should be in 0 to 1

    learningRate

    learning rate

    learningRateDecay

    learning rate decay

    weightDecay

    weight decay

    momentum

    momentum

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