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IOLoops

object IOLoops

Contains a training loops and helpers around it

The two training loops implemented here are:

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  6. def epochs[I, M <: GenericModule[I, Variable]](model: SupervisedModel[I, M], optimizerFactory: (Seq[(STen, PTag)]) ⇒ Optimizer, trainBatchesOverEpoch: () ⇒ BatchStream[I], validationBatchesOverEpoch: Option[() ⇒ BatchStream[I]], epochs: Int, trainingCallback: TrainingCallback = TrainingCallback.noop, validationCallback: ValidationCallback = ValidationCallback.noop, checkpointFile: Option[File] = None, minimumCheckpointFile: Option[File] = None, validationFrequency: Int = 1, logger: Option[Logger] = None, returnMinValidationLossModel: Seq[Int] = Nil, learningRateSchedule: LearningRateSchedule = LearningRateSchedule.noop, prefetch: Boolean = false, dataParallelModels: Seq[SupervisedModel[I, M]] = Nil)(implicit arg0: Load[M]): IO[(Int, SupervisedModel[I, M], List[(Int, Double, Option[Double])])]
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  9. def finalize(): Unit
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  10. def forwardBatchStream[I, M <: GenericModule[I, Variable]](batchStream: BatchStream[I], model: M with GenericModule[I, Variable]): IO[Unit]
  11. final def getClass(): Class[_]
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  16. final def notifyAll(): Unit
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  17. def oneEpoch[I, M <: GenericModule[I, Variable]](epochCount: Long, trainingCallback: TrainingCallback, model: ModelWithOptimizer[I, M], trainBatches: BatchStream[I], logger: Option[Logger], learningRateScheduleFactor: Double, prefetch: Boolean): IO[Double]
  18. def runBatchStream[I, M <: GenericModule[I, Variable]](batchStream: BatchStream[I], model: M with GenericModule[I, Variable])(implicit scope: Scope): IO[List[STen]]
  19. final def synchronized[T0](arg0: ⇒ T0): T0
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  21. def validationOneEpoch[I, M <: GenericModule[I, Variable]](model: SupervisedModel[I, M], validationBatches: BatchStream[I], validationCallback: ValidationCallback, logger: Option[Logger], epochCount: Long, minimumCheckpointFile: Option[File], minimumValidationLossSoFar: Option[Double]): IO[Double]
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  24. final def wait(arg0: Long): Unit
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  25. def withSWA[I, M <: GenericModule[I, Variable]](model: SupervisedModel[I, M], optimizerFactory: (Seq[(STen, PTag)]) ⇒ Optimizer, trainBatchesOverEpoch: () ⇒ BatchStream[I], warmupEpochs: Int, swaEpochs: Int, validationBatchesOverEpoch: Option[() ⇒ BatchStream[I]] = None, trainingCallback: TrainingCallback = TrainingCallback.noop, validationCallback: ValidationCallback = ValidationCallback.noop, checkpointFile: Option[File] = None, minimumCheckpointFile: Option[File] = None, logger: Option[Logger] = None, returnMinValidationLossModel: Seq[Int] = Nil, learningRateSchedule: LearningRateSchedule = ..., swaLearningRateSchedule: SWALearningRateSchedule = ..., prefetch: Boolean = false, dataParallelModels: Seq[SupervisedModel[I, M]] = Nil)(implicit arg0: Load[M]): IO[(Int, SupervisedModel[I, M], List[(Int, Double, Option[Double])])]

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