optim

Type Members

1. class AccuracyResult extends ValidationResult

   Represent an accuracy result.

2. class Adadelta[T] extends OptimMethod[T]

   Adadelta implementation for SGD: http://arxiv.

3. class Adagrad[T] extends OptimMethod[T]

   An implementation of Adagrad.

4. class Adam[T] extends OptimMethod[T]

   An implementation of Adam http://arxiv.

5. class Adamax[T] extends OptimMethod[T]

   An implementation of Adamax http://arxiv.

6. class DistriOptimizer[T] extends Optimizer[T, MiniBatch[T]]

   The optimizer run on a distributed cluster.

7. class DistriValidator[T] extends Validator[T, MiniBatch[T]]

   Validate model on a distributed cluster.

8. class Evaluator[T] extends Serializable

   model evaluator

9. case class GradientClippingParams(enableConstantClipping: Boolean, minValueClip: Float, maxValueClip: Float, enableL2NormClipping: Boolean, normValueClip: Float) extends Product with Serializable

10. class L1L2Regularizer[T] extends Regularizer[T]

    Apply both L1 and L2 regularization

11. case class L1Regularizer[T](l1: Double)(implicit evidence$3: ClassTag[T], ev: TensorNumeric[T]) extends L1L2Regularizer[T] with Product with Serializable

    Apply L1 regularization

12. case class L2Regularizer[T](l2: Double)(implicit evidence$4: ClassTag[T], ev: TensorNumeric[T]) extends L1L2Regularizer[T] with Product with Serializable

    Apply L2 regularization

13. class LBFGS[T] extends OptimMethod[T]

    This implementation of L-BFGS relies on a user-provided line search function (state.

14. trait LineSearch[T] extends AnyRef

    Line Search strategy

15. class LocalOptimizer[T] extends Optimizer[T, MiniBatch[T]]

    Optimize a model on a single machine

16. class LocalPredictor[T] extends Serializable

17. class LocalValidator[T] extends Validator[T, MiniBatch[T]]

    Validate a model on a single machine Use given dataset with certain validation methods such as Top1Accuracy as an argument of its test method

18. class Loss[T] extends ValidationMethod[T]

    This evaluation method is calculate loss of output with respect to target

19. class LossResult extends ValidationResult

    Use loss as a validation result

20. class MAE[T] extends ValidationMethod[T]

    This evaluation method is calculate mean absolute error of output with respect to target

21. class Metrics extends Serializable

    Performance metrics for the training process.

22. trait OptimMethod[T] extends Serializable

    Similar to torch Optim method, which is used to update the parameter

23. abstract class Optimizer[T, D] extends AnyRef

    Optimizer is an abstract class which is used to train a model automatically with some certain optimization algorithms.

24. class Predictor[T] extends Serializable

25. class RMSprop[T] extends OptimMethod[T]

    An implementation of RMSprop

26. trait Regularizer[T] extends Serializable

    It is a trait for all regularizers.

27. class SGD[T] extends OptimMethod[T]

    A plain implementation of SGD

28. class Top1Accuracy[T] extends ValidationMethod[T]

    Caculate the percentage that output's max probability index equals target

29. class Top5Accuracy[T] extends ValidationMethod[T]

    Caculate the percentage that target in output's top5 probability indexes

30. class TreeNNAccuracy[T] extends ValidationMethod[T]

    This is a metric to measure the accuracy of Tree Neural Network/Recursive Neural Network

31. trait Trigger extends AnyRef

    A trigger specifies a timespot or several timespots during training, and a corresponding action will be taken when the timespot(s) is reached.

32. trait ValidationMethod[T] extends Serializable

    A method defined to evaluate the model.

33. trait ValidationResult extends Serializable

    A result that calculate the numeric value of a validation method.

34. abstract class Validator[T, D] extends AnyRef

    Validator is an abstract class which is used to test a model automatically with some certain validation methods such as Top1Accuracy, as an argument of its test method.