dl::Trainer< Model, Dataset, Optimizer > Class Template Reference

Inheritance diagram for dl::Trainer< Model, Dataset, Optimizer >:

Collaboration diagram for dl::Trainer< Model, Dataset, Optimizer >:

Public Member Functions
	Trainer (TrainerConf< Model, Dataset, Optimizer > &&conf)
void	fit (Model &model, auto adapter)
void	validate (Model &model, auto evaluator, auto adapter)
auto	test (Model &model, auto evaluator, auto adapter)

Detailed Description

template<typename Model, typename Dataset, typename Optimizer>
class dl::Trainer< Model, Dataset, Optimizer >

Definition at line 108 of file trainer.hpp.

Constructor & Destructor Documentation

Trainer()

template<typename Model , typename Dataset , typename Optimizer >

dl::Trainer< Model, Dataset, Optimizer >::Trainer ( TrainerConf< Model, Dataset, Optimizer > &&  conf )

inline

Definition at line 124 of file trainer.hpp.

                                                               : conf(std::move(conf)) {
            notify(&TrainerObserver::setSubject, (TrainerSubject&)*this);
        }

Member Function Documentation

fit()

template<typename Model , typename Dataset , typename Optimizer >

void dl::Trainer< Model, Dataset, Optimizer >::fit ( Model &  model, auto  adapter  )

inline

Todo:

log the loss

Todo:

with batching increment by batch size

Definition at line 128 of file trainer.hpp.

                                             {
            setRunning();
            auto& dataset = conf.dataset;
            assert(dataset != nullptr);
            auto dataloader = dataset->trainingData();
            assert(dataloader != nullptr);
            notify(&TrainerObserver::onTrainingBegun, model);
            const auto trainsetSize = std::distance(std::begin(*dataloader), std::end(*dataloader));
            for (size_t epoch = 0; isRunning(); ++epoch) {
                size_t progress = 0;
                for (auto&& [out, in] : *dataloader) {
                    if (!isRunning())
                        break;
                    auto loss = adapter(model, in, out);
                    conf.optimizer->step(loss);
                    notify(&TrainerObserver::progressChanged, epoch, trainsetSize, progress);
                    progress += 1; 
                }
            }
            auto tmp = &TrainerObserver::onTrainingEnded;
            notify(&TrainerObserver::onTrainingEnded, model);
        }

test()

template<typename Model , typename Dataset , typename Optimizer >

auto dl::Trainer< Model, Dataset, Optimizer >::test ( Model &  model, auto  evaluator, auto  adapter  )

inline

Definition at line 152 of file trainer.hpp.

                                                              {
            auto& dataset = conf.dataset;
            assert(dataset != nullptr);
            auto dataloader = dataset->testData();
            assert(dataloader != nullptr);
            for (auto&& [out, in] : *dataloader) {
                evaluator += adapter(model, in, out);
            }
            return evaluator.aggregated();
        }

validate()

template<typename Model , typename Dataset , typename Optimizer >

void dl::Trainer< Model, Dataset, Optimizer >::validate ( Model &  model, auto  evaluator, auto  adapter  )

inline

Definition at line 151 of file trainer.hpp.

{}

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The documentation for this class was generated from the following file:

• dl/learning/trainer.hpp