void network_trainer_sgd::train_step(
supervised_data_reader& reader,
training_task_state& task)
{
boost::chrono::steady_clock::time_point start = boost::chrono::high_resolution_clock::now();
std::pair<std::vector<std::vector<float> >, std::string> lr_and_comment = prepare_learning_rates(task.get_current_epoch(), task.data);
task.comments.push_back(lr_and_comment.second);
std::pair<testing_result_smart_ptr, training_stat_smart_ptr> train_result = updater->update(
reader,
lr_and_comment.first,
task.data,
batch_size,
weight_decay,
momentum,
layer_to_dropout_rate_map);
boost::chrono::duration<float> sec = (boost::chrono::high_resolution_clock::now() - start);
float flops = updater->get_flops_for_single_entry();
train_result.first->time_to_complete_seconds = sec.count();
train_result.first->flops = static_cast<float>(train_result.first->get_entry_count()) * flops;
task.history.push_back(train_result);
}
void network_trainer_sgd::train_step(
structured_data_bunch_reader& reader,
training_task_state& task)
{
boost::chrono::steady_clock::time_point start = boost::chrono::high_resolution_clock::now();
std::pair<std::map<std::string, std::vector<float> >, std::string> lr_and_comment = prepare_learning_rates(task.get_current_epoch(), task.data);
task.comments.push_back(lr_and_comment.second);
neuron_value_set_data_bunch_writer writer;
backward_propagation::stat training_stat = backprop->run(
reader,
writer,
*task.data,
task.momentum_data,
lr_and_comment.first,
batch_size,
weight_decay,
momentum);
std::map<std::string, std::pair<layer_configuration_specific, nnforge_shared_ptr<std::vector<float> > > > output_data_average_results;
for(std::map<std::string, std::pair<layer_configuration_specific, neuron_value_set::ptr> >::const_iterator it = writer.layer_name_to_config_and_value_set_map.begin(); it != writer.layer_name_to_config_and_value_set_map.end(); ++it)
output_data_average_results.insert(std::make_pair(it->first, std::make_pair(it->second.first, it->second.second->get_average())));
task.history.push_back(std::make_pair(training_stat, output_data_average_results));
}
