Trainer

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3. final def ##(): Int

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6. final def asInstanceOf[T0]: T0

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7. def batchTrain(parameters: WeightsSet, examples: Seq[Example], evaluate: () ⇒ Unit = () => (), useParallelTrainer: Boolean = true, maxIterations: Int = 200, optimizer: GradientOptimizer = new LBFGS with L2Regularization, nThreads: Int = ...)(implicit random: Random): Unit

   A convenient way to call Trainer.

   A convenient way to call Trainer.train() for batch training.

   parameters

   The parameters to be optimized

   examples

   The examples

   evaluate

   The evaluation function

   useParallelTrainer

   Whether to use a parallel trainer

   maxIterations

   The maximum number of iterations

   optimizer

   The optimizer

8. def clone(): AnyRef

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11. def finalize(): Unit

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13. def hashCode(): Int

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15. final def ne(arg0: AnyRef): Boolean

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16. final def notify(): Unit

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17. final def notifyAll(): Unit

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18. def onlineTrain(parameters: WeightsSet, examples: Seq[Example], evaluate: () ⇒ Unit = () => (), useParallelTrainer: Boolean = false, maxIterations: Int = 3, optimizer: GradientOptimizer = new AdaGrad with ParameterAveraging, logEveryN: Int = 1, nThreads: Int = ..., miniBatch: Int = 1)(implicit random: Random): Unit

    A convenient way to call Trainer.

    A convenient way to call Trainer.train() for online trainers.

    parameters

    The parameters to be optimized

    examples

    The examples

    evaluate

    The evaluation function

    useParallelTrainer

    Whether to train in parallel

    maxIterations

    The maximum number of iterations

    optimizer

    The optimizer

    logEveryN

    How often to log

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

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20. def toString(): String

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21. def train(parameters: WeightsSet, examples: Seq[Example], maxIterations: Int, evaluate: () ⇒ Unit, optimizer: GradientOptimizer, useParallelTrainer: Boolean, useOnlineTrainer: Boolean, logEveryN: Int = 1, nThreads: Int = ..., miniBatch: Int)(implicit random: Random): Unit

    Convenient function for training.

    Convenient function for training. Creates a trainer, trains until convergence, and evaluates after every iteration.

    parameters

    The parameters to be optimized

    examples

    The examples to train on

    maxIterations

    The maximum number of iterations for training

    evaluate

    The function for evaluation

    optimizer

    The optimizer

    useParallelTrainer

    Whether to use parallel training

    useOnlineTrainer

    Whether to use online training

    logEveryN

    How often to log, if using online training

22. final def wait(): Unit

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

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24. final def wait(arg0: Long): Unit

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