typename trainer_type::trained_function_type train2 (
const trainer_type& trainer,
const std::vector<ranking_pair<typename trainer_type::sample_type> >& samples
)
{
pyassert(is_ranking_problem(samples), "Invalid inputs");
return trainer.train(samples);
}
typename trainer_type::trained_function_type train1 (
const trainer_type& trainer,
const ranking_pair<typename trainer_type::sample_type>& sample
)
{
typedef ranking_pair<typename trainer_type::sample_type> st;
pyassert(is_ranking_problem(std::vector<st>(1, sample)), "Invalid inputs");
return trainer.train(sample);
}
typename trainer_type::trained_function_type train (
const trainer_type& trainer,
const std::vector<typename trainer_type::sample_type>& samples,
const std::vector<double>& labels
)
{
pyassert(is_binary_classification_problem(samples,labels), "Invalid inputs");
return trainer.train(samples, labels);
}
void train_batch(IIT input_first, IIT input_last, EIT expected_first, EIT expected_last, trainer_type& trainer, rbm_training_context& context, rbm_t& rbm) {
++batches;
auto input_batch = make_batch(input_first, input_last);
auto expected_batch = make_batch(expected_first, expected_last);
trainer->train_batch(input_batch, expected_batch, context);
context.reconstruction_error += context.batch_error;
context.sparsity += context.batch_sparsity;
cpp::static_if<EnableWatcher && layer_traits<rbm_t>::free_energy()>([&](auto f) {
for (auto& v : input_batch) {
context.free_energy += f(rbm).free_energy(v);
}
});
if (EnableWatcher && layer_traits<rbm_t>::is_verbose()) {
watcher.batch_end(rbm, context, batches, total_batches);
}
}
double get_c (const trainer_type& trainer)
{
return trainer.get_c();
}
void set_c ( trainer_type& trainer, double C)
{
pyassert(C > 0, "C must be > 0");
trainer.set_c(C);
}
double get_epsilon ( const trainer_type& trainer) { return trainer.get_epsilon(); }
void set_epsilon ( trainer_type& trainer, double eps)
{
pyassert(eps > 0, "epsilon must be > 0");
trainer.set_epsilon(eps);
}
double get_c_class2 ( const trainer_type& trainer) { return trainer.get_c_class2(); }
long get_cache_size ( const trainer_type& trainer) { return trainer.get_cache_size(); }
void set_cache_size ( trainer_type& trainer, long cache_size)
{
pyassert(cache_size > 0, "cache size must be > 0");
trainer.set_cache_size(cache_size);
}
