Trainer Base<TOptions,TTransformer,TModel> Class
Base class for L-BFGS-based trainers.
public abstract class LbfgsTrainerBase<TOptions,TTransformer,TModel> : Microsoft.ML.Trainers.TrainerEstimatorBase<TTransformer,TModel> where TOptions : LbfgsTrainerBase<TOptions,TTransformer,TModel>.OptionsBasenew() 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)> = class inherit TrainerEstimatorBase<'ransformer, 'Model (requires 'ransformer :> ISingleFeaturePredictionTransformer<'Model> and 'Model : null)>
Public MustInherit Class LbfgsTrainerBase(Of TOptions, TTransformer, TModel) Inherits TrainerEstimatorBase(Of TTransformer, TModel)
The feature column that the trainer expects.(Inherited from TrainerEstimatorBase<TTransformer,TModel>)
The label column that the trainer expects. Can be
The weight column that the trainer expects. Can be
Trains and returns a ITransformer.(Inherited from TrainerEstimatorBase<TTransformer,TModel>)
|GetOutputSchema(SchemaShape)||(Inherited from TrainerEstimatorBase<TTransformer,TModel>)|
Append a 'caching checkpoint' to the estimator chain. This will ensure that the downstream estimators will be trained against cached data. It is helpful to have a caching checkpoint before trainers that take multiple data passes.
Given an estimator, return a wrapping object that will call a delegate once Fit(IDataView) is called. It is often important for an estimator to return information about what was fit, which is why the Fit(IDataView) method returns a specifically typed object, rather than just a general ITransformer. However, at the same time, IEstimator<TTransformer> are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain<TLastTransformer> where the estimator for which we want to get the transformer is buried somewhere in this chain. For that scenario, we can through this method attach a delegate that will be called once fit is called.