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This commit is contained in:
Tomasz Sobczyk
2020-10-24 13:12:32 +02:00
committed by nodchip
parent c49ae541c4
commit cf3edfed82
5 changed files with 36 additions and 39 deletions
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src/learn/learn.cpp
+21 -22
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@@ -140,15 +140,12 @@ namespace Learner
void print(const std::string& prefix, ostream& s) const void print(const std::string& prefix, ostream& s) const
{ {
s s << "==> " << prefix << "_cross_entropy_eval = " << cross_entropy_eval / count << endl;
<< "--> " s << "==> " << prefix << "_cross_entropy_win = " << cross_entropy_win / count << endl;
<< prefix << "_cross_entropy_eval = " << cross_entropy_eval / count s << "==> " << prefix << "_entropy_eval = " << entropy_eval / count << endl;
<< " , " << prefix << "_cross_entropy_win = " << cross_entropy_win / count s << "==> " << prefix << "_entropy_win = " << entropy_win / count << endl;
<< " , " << prefix << "_entropy_eval = " << entropy_eval / count s << "==> " << prefix << "_cross_entropy = " << cross_entropy / count << endl;
<< " , " << prefix << "_entropy_win = " << entropy_win / count s << "==> " << prefix << "_entropy = " << entropy / count << endl;
<< " , " << prefix << "_cross_entropy = " << cross_entropy / count
<< " , " << prefix << "_entropy = " << entropy / count
<< endl;
} }
}; };
} }
@@ -678,11 +675,13 @@ namespace Learner
TimePoint elapsed = now() - Search::Limits.startTime + 1; TimePoint elapsed = now() - Search::Limits.startTime + 1;
cout << "\n"; cout << "\n";
cout << "PROGRESS (calc_loss): " << now_string() << ", "; cout << "PROGRESS (calc_loss): " << now_string()
cout << total_done << " sfens, "; << ", " << total_done << " sfens"
cout << total_done * 1000 / elapsed << " sfens/second"; << ", " << total_done * 1000 / elapsed << " sfens/second"
cout << ", iteration " << epoch; << ", epoch " << epoch
cout << ", learning rate = " << global_learning_rate << ", "; << endl;
cout << "==> learning rate = " << global_learning_rate << endl;
// For calculation of verification data loss // For calculation of verification data loss
AtomicLoss test_loss_sum{}; AtomicLoss test_loss_sum{};
@@ -699,7 +698,7 @@ namespace Learner
auto& pos = th.rootPos; auto& pos = th.rootPos;
StateInfo si; StateInfo si;
pos.set(StartFEN, false, &si, &th); pos.set(StartFEN, false, &si, &th);
cout << "startpos eval = " << Eval::evaluate(pos) << endl; cout << "==> startpos eval = " << Eval::evaluate(pos) << endl;
}); });
mainThread->wait_for_worker_finished(); mainThread->wait_for_worker_finished();
@@ -722,16 +721,15 @@ namespace Learner
if (psv.size() && test_loss_sum.count > 0.0) if (psv.size() && test_loss_sum.count > 0.0)
{ {
cout << "--> norm = " << sum_norm
<< " , move accuracy = " << (move_accord_count * 100.0 / psv.size()) << "%"
<< endl;
test_loss_sum.print("test", cout); test_loss_sum.print("test", cout);
if (learn_loss_sum.count > 0.0) if (learn_loss_sum.count > 0.0)
{ {
learn_loss_sum.print("learn", cout); learn_loss_sum.print("learn", cout);
} }
cout << "==> norm = " << sum_norm << endl;
cout << "==> move accuracy = " << (move_accord_count * 100.0 / psv.size()) << "%" << endl;
} }
else else
{ {
@@ -847,7 +845,8 @@ namespace Learner
const double latest_loss = latest_loss_sum / latest_loss_count; const double latest_loss = latest_loss_sum / latest_loss_count;
latest_loss_sum = 0.0; latest_loss_sum = 0.0;
latest_loss_count = 0; latest_loss_count = 0;
cout << "loss: " << latest_loss; cout << "INFO (learning_rate):" << endl;
cout << "==> loss = " << latest_loss;
auto tot = total_done; auto tot = total_done;
if (auto_lr_drop) if (auto_lr_drop)
{ {
@@ -877,7 +876,7 @@ namespace Learner
if (--trials > 0 && !is_final) if (--trials > 0 && !is_final)
{ {
cout cout
<< "reducing learning rate from " << global_learning_rate << "==> reducing learning rate from " << global_learning_rate
<< " to " << (global_learning_rate * newbob_decay) << " to " << (global_learning_rate * newbob_decay)
<< " (" << trials << " more trials)" << endl; << " (" << trials << " more trials)" << endl;
@@ -887,7 +886,7 @@ namespace Learner
if (trials == 0) if (trials == 0)
{ {
cout << "converged" << endl; cout << "==> converged" << endl;
return true; return true;
} }
} }
src/learn/sfen_reader.h
+6 -6
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@@ -83,7 +83,7 @@ namespace Learner{
PackedSfenValue ps; PackedSfenValue ps;
if (!read_to_thread_buffer(0, ps)) if (!read_to_thread_buffer(0, ps))
{ {
std::cout << "Error! read packed sfen , failed." << std::endl; std::cout << "ERROR (sfen_reader): Reading failed." << std::endl;
return sfen_for_mse; return sfen_for_mse;
} }
@@ -211,16 +211,16 @@ namespace Learner{
if (sfen_input_stream == nullptr) if (sfen_input_stream == nullptr)
{ {
std::cout << "File does not exist: " << currentFilename << '\n'; std::cout << "INFO (sfen_reader): File does not exist: " << currentFilename << '\n';
} }
else else
{ {
std::cout << "Opened file for reading: " << currentFilename << '\n'; std::cout << "INFO (sfen_reader): Opened file for reading: " << currentFilename << '\n';
// in case the file is empty or was deleted. // in case the file is empty or was deleted.
if (sfen_input_stream->eof()) if (sfen_input_stream->eof())
{ {
std::cout << "File empty, nothing to read.\n"; std::cout << "INFO (sfen_reader): File empty, nothing to read.\n";
} }
else else
{ {
@@ -232,7 +232,7 @@ namespace Learner{
if (sfen_input_stream == nullptr && !open_next_file()) if (sfen_input_stream == nullptr && !open_next_file())
{ {
std::cout << "..end of files." << std::endl; std::cout << "INFO (sfen_reader): End of files." << std::endl;
end_of_files = true; end_of_files = true;
return; return;
} }
@@ -271,7 +271,7 @@ namespace Learner{
if(!open_next_file()) if(!open_next_file())
{ {
// There was no next file. Abort. // There was no next file. Abort.
std::cout << "..end of files." << std::endl; std::cout << "INFO (sfen_reader): End of files." << std::endl;
end_of_files = true; end_of_files = true;
return; return;
} }
src/nnue/evaluate_nnue_learner.cpp
+1 -3
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@@ -245,7 +245,7 @@ namespace Eval::NNUE {
// save merit function parameters to a file // save merit function parameters to a file
void save_eval(std::string dir_name) { void save_eval(std::string dir_name) {
auto eval_dir = Path::combine(Options["EvalSaveDir"], dir_name); auto eval_dir = Path::combine(Options["EvalSaveDir"], dir_name);
std::cout << "save_eval() start. folder = " << eval_dir << std::endl; std::cout << "INFO (save_eval): Saving current evaluation file in " << eval_dir << std::endl;
// mkdir() will fail if this folder already exists, but // mkdir() will fail if this folder already exists, but
// Apart from that. If not, I just want you to make it. // Apart from that. If not, I just want you to make it.
@@ -261,7 +261,5 @@ namespace Eval::NNUE {
#ifndef NDEBUG #ifndef NDEBUG
assert(result); assert(result);
#endif #endif
std::cout << "save_eval() finished. folder = " << eval_dir << std::endl;
} }
} // namespace Eval::NNUE } // namespace Eval::NNUE
src/nnue/trainer/trainer_clipped_relu.h
+3 -3
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@@ -100,11 +100,11 @@ namespace Eval::NNUE {
std::begin(max_activations_), std::end(max_activations_)); std::begin(max_activations_), std::end(max_activations_));
std::cout << "INFO (check_health):" std::cout << "INFO (check_health):"
<< " layer = " << LayerType::kLayerIndex << " layer " << LayerType::kLayerIndex
<< " , name = " << LayerType::get_name() << " - " << LayerType::get_name()
<< std::endl; << std::endl;
std::cout << "--> largest min activation = " << largest_min_activation std::cout << "==> largest min activation = " << largest_min_activation
<< " , smallest max activation = " << smallest_max_activation << " , smallest max activation = " << smallest_max_activation
<< std::endl; << std::endl;
src/nnue/trainer/trainer_feature_transformer.h
+5 -5
View File
@@ -331,11 +331,11 @@ namespace Eval::NNUE {
// Check if there are any problems with learning // Check if there are any problems with learning
void check_health() { void check_health() {
std::cout << "INFO (check_health):" std::cout << "INFO (check_health):"
<< " layer = " << LayerType::kLayerIndex << " layer " << LayerType::kLayerIndex
<< " , name = " << LayerType::get_name() << " - " << LayerType::get_name()
<< std::endl; << std::endl;
std::cout << "--> observed " << observed_features.count() std::cout << "==> observed " << observed_features.count()
<< " (out of " << kInputDimensions << ") features" << " (out of " << kInputDimensions << ") features"
<< std::endl; << std::endl;
@@ -343,7 +343,7 @@ namespace Eval::NNUE {
std::numeric_limits<typename LayerType::WeightType>::max() / std::numeric_limits<typename LayerType::WeightType>::max() /
kWeightScale; kWeightScale;
std::cout << "--> (min, max) of pre-activations = " std::cout << "==> (min, max) of pre-activations = "
<< min_pre_activation_ << ", " << min_pre_activation_ << ", "
<< max_pre_activation_ << " (limit = " << max_pre_activation_ << " (limit = "
<< kPreActivationLimit << ")" << kPreActivationLimit << ")"
@@ -354,7 +354,7 @@ namespace Eval::NNUE {
const auto smallest_max_activation = *std::min_element( const auto smallest_max_activation = *std::min_element(
std::begin(max_activations_), std::end(max_activations_)); std::begin(max_activations_), std::end(max_activations_));
std::cout << "--> largest min activation = " << largest_min_activation std::cout << "==> largest min activation = " << largest_min_activation
<< " , smallest max activation = " << smallest_max_activation << " , smallest max activation = " << smallest_max_activation
<< std::endl; << std::endl;