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HiddenResilientGradientLearningT Properties

The HiddenResilientGradientLearningT generic type exposes the following members.

Public propertyCurrentIteration
Gets or sets the number of performed iterations.
Public propertyDecreaseFactor
Gets the decrease parameter, also referred as eta minus. Default is 0.5.
Public propertyFunction (Inherited from BaseHiddenConditionalRandomFieldLearningT.)
Public propertyHasConverged
Gets or sets whether the algorithm has converged.
Public propertyIncreaseFactor
Gets the increase parameter, also referred as eta plus. Default is 1.2.
Public propertyIterations Obsolete.
Please use MaxIterations instead.
Public propertyMaxIterations
Gets or sets the maximum number of iterations performed by the learning algorithm.
Public propertyModel
Gets or sets the model being trained.
(Inherited from BaseHiddenConditionalRandomFieldLearningT.)
Public propertyParallelOptions
Gets or sets the parallelization options for this algorithm.
Public propertyRegularization
Gets or sets the amount of the parameter weights which should be included in the objective function. Default is 0 (do not include regularization).
Public propertyStochastic
Public propertyToken
Gets or sets a cancellation token that can be used to stop the learning algorithm while it is running.
(Inherited from BaseHiddenConditionalRandomFieldLearningT.)
Public propertyTolerance
Gets or sets the maximum change in the average log-likelihood after an iteration of the algorithm used to detect convergence.
Public propertyUpdateLowerBound
Gets or sets the minimum possible update step, also referred as delta max. Default is 1e-6.
Public propertyUpdateUpperBound
Gets or sets the maximum possible update step, also referred as delta min. Default is 50.
See Also