SgdBinaryTrainerBase<TModel>.OptionsBase Class
Definition
Important
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public class SgdBinaryTrainerBase<TModel>.OptionsBase : Microsoft.ML.Trainers.TrainerInputBaseWithWeight where TModel : classtype SgdBinaryTrainerBase<'Model (requires 'Model : null)>.OptionsBase = class
inherit TrainerInputBaseWithWeightPublic Class SgdBinaryTrainerBase(Of TModel).OptionsBase
Inherits TrainerInputBaseWithWeightType Parameters
- TModel
- Inheritance
-
SgdBinaryTrainerBase<TModel>.OptionsBase
- Derived
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. |