SgdBinaryTrainerBase<TModel>.OptionsBase Class

Definition

Namespace:

Microsoft.ML.Trainers

Assembly:

Microsoft.ML.StandardTrainers.dll

Package:

Microsoft.ML v3.0.1

public class SgdBinaryTrainerBase<TModel>.OptionsBase : Microsoft.ML.Trainers.TrainerInputBaseWithWeight where TModel : class

type SgdBinaryTrainerBase<'Model (requires 'Model : null)>.OptionsBase = class
    inherit TrainerInputBaseWithWeight

Public Class SgdBinaryTrainerBase(Of TModel).OptionsBase
Inherits TrainerInputBaseWithWeight

Type Parameters

TModel

Inheritance

Object

TrainerInputBase

TrainerInputBaseWithLabel

TrainerInputBaseWithWeight

SgdBinaryTrainerBase<TModel>.OptionsBase

Derived

Microsoft.ML.Trainers.SgdCalibratedTrainer.Options

Microsoft.ML.Trainers.SgdNonCalibratedTrainer.Options

Constructors

SgdBinaryTrainerBase<TModel>.OptionsBase()

Fields

CheckFrequency	Determines the frequency of checking for convergence in terms of number of iterations.
ConvergenceTolerance	The convergence tolerance. If the exponential moving average of loss reductions falls below this tolerance, the algorithm is deemed to have converged and will stop.
ExampleWeightColumnName	Column to use for example weight. (Inherited from TrainerInputBaseWithWeight)
FeatureColumnName	Column to use for features. (Inherited from TrainerInputBase)
L2Regularization	The L2 weight for regularization.
LabelColumnName	Column to use for labels. (Inherited from TrainerInputBaseWithLabel)
LearningRate	The initial learning rate used by SGD.
NumberOfIterations	The maximum number of passes through the training dataset.
NumberOfThreads	The degree of lock-free parallelism used by SGD.
PositiveInstanceWeight	The weight to be applied to the positive class. This is useful for training with imbalanced data.
Shuffle	Determines whether to shuffle data for each training iteration.