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lamp.data

IOLoops

object IOLoops

Contains a training loops and helpers around it

The two training loops implemented here are:

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  1. case class TrainingLoopContext(epoch: Int, lastValidationLoss: Option[Double], minValidationLoss: Option[Double]) extends Product with Serializable

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  1. final def !=(arg0: Any): Boolean
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  6. def epochs[I, M <: GenericModule[I, Variable], LRState, BatchStreamState, BatchStreamBuffers](model: SupervisedModel[I, M], optimizerFactory: (Seq[(STen, PTag)]) => Optimizer, trainBatchesOverEpoch: (TrainingLoopContext) => BatchStream[I, BatchStreamState, BatchStreamBuffers], validationBatchesOverEpoch: Option[(TrainingLoopContext) => BatchStream[I, BatchStreamState, BatchStreamBuffers]], epochs: Int, trainingCallback: TrainingCallback = TrainingCallback.noop, validationCallback: ValidationCallback = ValidationCallback.noop, checkpointState: Option[(SimpleLoopState, LRState) => IO[Unit]] = None, validationFrequency: Int = 1, logger: Option[Logger] = None, returnMinValidationLossModel: Seq[Int] = Nil, learningRateSchedule: LearningRateSchedule[LRState] = LearningRateSchedule.noop, prefetch: Boolean = false, dataParallelModels: Seq[SupervisedModel[I, M]] = Nil, initState: Option[SimpleLoopState] = None, accumulateGradientOverNBatches: Int = 1, learningRateScheduleInitState: Option[LRState] = None, printOptimizerAllocations: Boolean = false, validationLossExponentialSmoothingFactor: Double = 1.0)(implicit arg0: Load[M]): IO[(Int, SupervisedModel[I, M], List[(Int, Double, Option[(Double, Double)])], LRState, SimpleLoopState)]
  7. final def eq(arg0: AnyRef): Boolean
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  8. def equals(arg0: AnyRef): Boolean
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  9. def forwardAndDiscardBatchStream[I, M <: GenericModule[I, Variable], S, C](batchStream: BatchStream[I, S, C], buffers: (Device) => Resource[IO, C], model: M with GenericModule[I, Variable]): IO[Unit]
  10. final def getClass(): Class[_ <: AnyRef]
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  14. final def notify(): Unit
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  15. final def notifyAll(): Unit
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  16. def oneEpoch[I, M <: GenericModule[I, Variable], S, C](epochCount: Long, trainingCallback: TrainingCallback, model: ModelWithOptimizer[I, M], trainBatches: BatchStream[I, S, C], logger: Option[Logger], learningRateScheduleFactor: Double, prefetch: Boolean, accumulateGradientOverNBatches: Int): IO[Double]
  17. def parallelRunBatchStream[I, O, M <: GenericModule[I, O], S, O2, C](batchStream: BatchStream[I, S, C], bufferPerModel: Resource[IO, List[(Device, C)]], models: Seq[M with GenericModule[I, O]])(tx: ((I, STen), O) => O2)(implicit arg0: Movable[O2], scope: Scope): IO[Vector[O2]]
  18. def runBatchStream[I, M <: GenericModule[I, Variable], S, C](batchStream: BatchStream[I, S, C], buffers: Resource[IO, C], model: M with GenericModule[I, Variable])(implicit scope: Scope): IO[List[STen]]
  19. final def synchronized[T0](arg0: => T0): T0
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  20. def toString(): String
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  21. def validationOneEpoch[I, M <: GenericModule[I, Variable], S, C](model: SupervisedModel[I, M], validationBatches: BatchStream[I, S, C], validationCallback: ValidationCallback, logger: Option[Logger], epochCount: Long): IO[Double]
  22. final def wait(arg0: Long, arg1: Int): Unit
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    @throws(classOf[java.lang.InterruptedException])
  23. final def wait(arg0: Long): Unit
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  24. final def wait(): Unit
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  25. def withSWA[I, M <: GenericModule[I, Variable], LRState, LRStateSWA, BatchStreamState, BatchStreamBuffers](model: SupervisedModel[I, M], optimizerFactory: (Seq[(STen, PTag)]) => Optimizer, trainBatchesOverEpoch: (TrainingLoopContext) => BatchStream[I, BatchStreamState, BatchStreamBuffers], warmupEpochs: Int, swaEpochs: Int, validationBatchesOverEpoch: Option[(TrainingLoopContext) => BatchStream[I, BatchStreamState, BatchStreamBuffers]] = None, trainingCallback: TrainingCallback = TrainingCallback.noop, validationCallback: ValidationCallback = ValidationCallback.noop, checkpointState: Option[(SimpleThenSWALoopState, Either[LRState, LRStateSWA]) => IO[Unit]] = None, logger: Option[Logger] = None, returnMinValidationLossModel: Seq[Int] = Nil, learningRateSchedule: LearningRateSchedule[LRState] = LearningRateSchedule.decrement(20, 0.5), swaLearningRateSchedule: SWALearningRateSchedule[LRStateSWA] = SWA.SWALearningRateSchedule.cyclic( minFactor = 0.01, maxFactor = 1d, cycleLength = 10 ), prefetch: Boolean = false, dataParallelModels: Seq[SupervisedModel[I, M]] = Nil, initState: Option[SimpleThenSWALoopState] = None, accumulateGradientOverNBatches: Int = 1, learningRateScheduleInitState: Option[LRState] = None, swaLearningRateScheduleInitState: Option[LRStateSWA] = None, swaForwardPassAfterTraining: Boolean = true, validationLossExponentialSmoothingFactor: Double = 1.0)(implicit arg0: Load[M]): IO[(Int, SupervisedModel[I, M], List[(Int, Double, Option[(Double, Double)])], SupervisedModel[I, M])]
  26. object TrainingLoopContext extends Serializable

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  1. def finalize(): Unit
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