LbfgsTrainerBase<TOptions,TTransformer,TModel>.OptionsBase Class

Definition

Namespace:

Microsoft.ML.Trainers

Assembly:

Microsoft.ML.StandardTrainers.dll

Package:

Microsoft.ML v3.0.1

Base options class for trainer estimators deriving fromLbfgsTrainerBase<TOptions,TTransformer,TModel>.

public abstract class LbfgsTrainerBase<TOptions,TTransformer,TModel>.OptionsBase : Microsoft.ML.Trainers.TrainerInputBaseWithWeight where TOptions : LbfgsTrainerBase<TOptions,TTransformer,TModel>.OptionsBase, new() where TTransformer : ISingleFeaturePredictionTransformer<TModel> where TModel : class

type LbfgsTrainerBase<'Options, 'ransformer, 'Model (requires 'Options :> LbfgsTrainerBase<'Options, 'ransformer, 'Model>.OptionsBase and 'Options : (new : unit -> 'Options) and 'ransformer :> ISingleFeaturePredictionTransformer<'Model> and 'Model : null)>.OptionsBase = class
    inherit TrainerInputBaseWithWeight

Public MustInherit Class LbfgsTrainerBase(Of TOptions, TTransformer, TModel).OptionsBase
Inherits TrainerInputBaseWithWeight

Type Parameters

TOptions

TTransformer

TModel

Inheritance

Object

TrainerInputBase

TrainerInputBaseWithLabel

TrainerInputBaseWithWeight

LbfgsTrainerBase<TOptions,TTransformer,TModel>.OptionsBase

Derived

Microsoft.ML.Trainers.LbfgsLogisticRegressionBinaryTrainer.Options

Microsoft.ML.Trainers.LbfgsMaximumEntropyMulticlassTrainer.Options

Microsoft.ML.Trainers.LbfgsPoissonRegressionTrainer.Options

Constructors

LbfgsTrainerBase<TOptions,TTransformer,TModel>.OptionsBase()

Fields

DenseOptimizer	Force densification of the internal optimization vectors. Default is false.
EnforceNonNegativity	Enforce non-negative weights. Default is false.
ExampleWeightColumnName	Column to use for example weight. (Inherited from TrainerInputBaseWithWeight)
FeatureColumnName	Column to use for features. (Inherited from TrainerInputBase)
HistorySize	Number of previous iterations to remember for estimating the Hessian. Lower values mean faster but less accurate estimates.
InitialWeightsDiameter	Initial weights scale.
L1Regularization	L1 regularization weight.
L2Regularization	L2 regularization weight.
LabelColumnName	Column to use for labels. (Inherited from TrainerInputBaseWithLabel)
MaximumNumberOfIterations	Number of iterations.
NumberOfThreads	Number of threads. Null means use the number of processors.
OptimizationTolerance	Tolerance parameter for optimization convergence. (Low = slower, more accurate).
Quiet	Determines whether to produce output during training or not.
StochasticGradientDescentInitilaizationTolerance	Run SGD to initialize LR weights, converging to this tolerance.