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PascalCase -> snake_case for consistency with the rest of the codebase.

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This commit is contained in:
Tomasz Sobczyk
2020-10-14 22:42:58 +02:00
committed by nodchip
parent 2398d34e87
commit 146a6b056e
37 changed files with 844 additions and 737 deletions
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28
src/learn/learn.cpp
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@@ -964,7 +964,7 @@ namespace Learner
// Lock the evaluation function so that it is not used during updating.
lock_guard<shared_timed_mutex> write_lock(nn_mutex);
Eval::NNUE::UpdateParameters();
Eval::NNUE::update_parameters();
}
++epoch;
@@ -998,7 +998,7 @@ namespace Learner
// loss calculation
calc_loss(thread_id, done);
Eval::NNUE::CheckHealth();
Eval::NNUE::check_health();
// Make a note of how far you have totaled.
sr.last_done = sr.total_done;
@@ -1127,7 +1127,7 @@ namespace Learner
learn_sum_entropy_win += learn_entropy_win;
learn_sum_entropy += learn_entropy;
Eval::NNUE::AddExample(pos, rootColor, ps, 1.0);
Eval::NNUE::add_example(pos, rootColor, ps, 1.0);
// Since the processing is completed, the counter of the processed number is incremented
sr.total_done++;
@@ -1194,7 +1194,7 @@ namespace Learner
{
cout << " < best (" << best_loss << "), accepted" << endl;
best_loss = latest_loss;
best_nn_directory = Path::Combine((std::string)Options["EvalSaveDir"], dir_name);
best_nn_directory = Path::combine((std::string)Options["EvalSaveDir"], dir_name);
trials = newbob_num_trials;
if (tot >= last_lr_drop + auto_lr_drop)
@@ -1207,13 +1207,13 @@ namespace Learner
{
cout << " < best (" << best_loss << "), accepted" << endl;
best_loss = latest_loss;
best_nn_directory = Path::Combine((std::string)Options["EvalSaveDir"], dir_name);
best_nn_directory = Path::combine((std::string)Options["EvalSaveDir"], dir_name);
trials = newbob_num_trials;
}
else
{
cout << " >= best (" << best_loss << "), rejected" << endl;
best_nn_directory = Path::Combine((std::string)Options["EvalSaveDir"], dir_name);
best_nn_directory = Path::combine((std::string)Options["EvalSaveDir"], dir_name);
if (--trials > 0 && !is_final)
{
@@ -1713,14 +1713,14 @@ namespace Learner
// Display learning game file
if (target_dir != "")
{
string kif_base_dir = Path::Combine(base_dir, target_dir);
string kif_base_dir = Path::combine(base_dir, target_dir);
namespace sys = std::filesystem;
sys::path p(kif_base_dir); // Origin of enumeration
std::for_each(sys::directory_iterator(p), sys::directory_iterator(),
[&](const sys::path& path) {
if (sys::is_regular_file(path))
filenames.push_back(Path::Combine(target_dir, path.filename().generic_string()));
filenames.push_back(Path::combine(target_dir, path.filename().generic_string()));
});
}
@@ -1814,7 +1814,7 @@ namespace Learner
// order so I'll reverse it here. I'm sorry.
for (auto it = filenames.rbegin(); it != filenames.rend(); ++it)
{
sr.filenames.push_back(Path::Combine(base_dir, *it));
sr.filenames.push_back(Path::combine(base_dir, *it));
}
}
@@ -1858,9 +1858,9 @@ namespace Learner
set_learning_search_limits();
cout << "init_training.." << endl;
Eval::NNUE::InitializeTraining(seed);
Eval::NNUE::SetBatchSize(nn_batch_size);
Eval::NNUE::SetOptions(nn_options);
Eval::NNUE::initialize_training(seed);
Eval::NNUE::set_batch_size(nn_batch_size);
Eval::NNUE::set_options(nn_options);
if (newbob_decay != 1.0 && !Options["SkipLoadingEval"]) {
// Save the current net to [EvalSaveDir]\original.
Eval::NNUE::save_eval("original");
@@ -1868,7 +1868,7 @@ namespace Learner
// Set the folder above to best_nn_directory so that the trainer can
// resotre the network parameters from the original net file.
learn_think.best_nn_directory =
Path::Combine(Options["EvalSaveDir"], "original");
Path::combine(Options["EvalSaveDir"], "original");
}
cout << "init done." << endl;
@@ -1925,7 +1925,7 @@ namespace Learner
// Start learning.
learn_think.go_think();
Eval::NNUE::FinalizeNet();
Eval::NNUE::finalize_net();
// Save once at the end.
learn_think.save(true);