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Removed LOSS_FUNCTION_IS_ELMO_METHOD macro.

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nodchip
2020-09-09 00:17:02 +09:00
parent ef1601218d
commit dbad9d96e0
2 changed files with 1 additions and 72 deletions
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54
src/learn/learner.cpp
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@@ -163,8 +163,6 @@ namespace Learner
return ((y2 - y1) / epsilon) / winning_probability_coefficient;
}
#if defined ( LOSS_FUNCTION_IS_ELMO_METHOD )
// A constant used in elmo (WCSC27). Adjustment required.
// Since elmo does not internally divide the expression, the value is different.
// You can set this value with the learn command.
@@ -293,7 +291,6 @@ namespace Learner
(-m * std::log(m + epsilon) - (1.0 - m) * std::log(1.0 - m + epsilon));
}
#endif
// Other objective functions may be considered in the future...
double calc_grad(Value shallow, const PackedSfenValue& psv)
{
@@ -629,14 +626,12 @@ namespace Learner
stop_flag(false),
save_only_once(false)
{
#if defined ( LOSS_FUNCTION_IS_ELMO_METHOD )
learn_sum_cross_entropy_eval = 0.0;
learn_sum_cross_entropy_win = 0.0;
learn_sum_cross_entropy = 0.0;
learn_sum_entropy_eval = 0.0;
learn_sum_entropy_win = 0.0;
learn_sum_entropy = 0.0;
#endif
newbob_scale = 1.0;
newbob_decay = 1.0;
@@ -689,15 +684,13 @@ namespace Learner
// --- loss calculation
#if defined (LOSS_FUNCTION_IS_ELMO_METHOD)
// For calculation of learning data loss
// For calculation of learning data loss
atomic<double> learn_sum_cross_entropy_eval;
atomic<double> learn_sum_cross_entropy_win;
atomic<double> learn_sum_cross_entropy;
atomic<double> learn_sum_entropy_eval;
atomic<double> learn_sum_entropy_win;
atomic<double> learn_sum_entropy;
#endif
shared_timed_mutex nn_mutex;
double newbob_scale;
@@ -759,13 +752,6 @@ namespace Learner
std::cout << ", iteration " << epoch;
std::cout << ", eta = " << Eval::get_eta() << ", ";
#if !defined(LOSS_FUNCTION_IS_ELMO_METHOD)
double sum_error = 0;
double sum_error2 = 0;
double sum_error3 = 0;
#endif
#if defined (LOSS_FUNCTION_IS_ELMO_METHOD)
// For calculation of verification data loss
atomic<double> test_sum_cross_entropy_eval, test_sum_cross_entropy_win, test_sum_cross_entropy;
atomic<double> test_sum_entropy_eval, test_sum_entropy_win, test_sum_entropy;
@@ -779,7 +765,6 @@ namespace Learner
// norm for learning
atomic<double> sum_norm;
sum_norm = 0;
#endif
// The number of times the pv first move of deep
// search matches the pv first move of search(1).
@@ -841,25 +826,11 @@ namespace Learner
// Note) This code does not consider when
// eval_limit is specified in the learn command.
// --- error calculation
#if !defined(LOSS_FUNCTION_IS_ELMO_METHOD)
auto grad = calc_grad(deep_value, shallow_value, ps);
// something like rmse
sum_error += grad * grad;
// Add the absolute value of the gradient
sum_error2 += abs(grad);
// Add the absolute value of the difference between the evaluation values
sum_error3 += abs(shallow_value - deep_value);
#endif
// --- calculation of cross entropy
// For the time being, regarding the win rate and loss terms only in the elmo method
// Calculate and display the cross entropy.
#if defined (LOSS_FUNCTION_IS_ELMO_METHOD)
double test_cross_entropy_eval, test_cross_entropy_win, test_cross_entropy;
double test_entropy_eval, test_entropy_win, test_entropy;
calc_cross_entropy(
@@ -881,7 +852,6 @@ namespace Learner
test_sum_entropy_win += test_entropy_win;
test_sum_entropy += test_entropy;
sum_norm += (double)abs(shallow_value);
#endif
// Determine if the teacher's move and the score of the shallow search match
{
@@ -905,17 +875,6 @@ namespace Learner
while (task_count)
sleep(1);
#if !defined(LOSS_FUNCTION_IS_ELMO_METHOD)
// rmse = root mean square error: mean square error
// mae = mean absolute error: mean absolute error
auto dsig_rmse = std::sqrt(sum_error / (sfen_for_mse.size() + epsilon));
auto dsig_mae = sum_error2 / (sfen_for_mse.size() + epsilon);
auto eval_mae = sum_error3 / (sfen_for_mse.size() + epsilon);
cout << " , dsig rmse = " << dsig_rmse << " , dsig mae = " << dsig_mae
<< " , eval mae = " << eval_mae;
#endif
#if defined(LOSS_FUNCTION_IS_ELMO_METHOD)
latest_loss_sum += test_sum_cross_entropy - test_sum_entropy;
latest_loss_count += sr.sfen_for_mse.size();
@@ -960,9 +919,6 @@ namespace Learner
learn_sum_entropy_eval = 0.0;
learn_sum_entropy_win = 0.0;
learn_sum_entropy = 0.0;
#else
<< endl;
#endif
}
void LearnerThink::thread_worker(size_t thread_id)
@@ -1144,7 +1100,6 @@ namespace Learner
? Eval::evaluate(pos)
: -Eval::evaluate(pos);
#if defined (LOSS_FUNCTION_IS_ELMO_METHOD)
// Calculate loss for training data
double learn_cross_entropy_eval, learn_cross_entropy_win, learn_cross_entropy;
double learn_entropy_eval, learn_entropy_win, learn_entropy;
@@ -1165,7 +1120,6 @@ namespace Learner
learn_sum_entropy_eval += learn_entropy_eval;
learn_sum_entropy_win += learn_entropy_win;
learn_sum_entropy += learn_entropy;
#endif
const double example_weight =
(discount_rate != 0 && ply != (int)pv.size()) ? discount_rate : 1.0;
@@ -1600,12 +1554,10 @@ namespace Learner
// Turn on if you want to pass a pre-shuffled file.
bool no_shuffle = false;
#if defined (LOSS_FUNCTION_IS_ELMO_METHOD)
// elmo lambda
ELMO_LAMBDA = 0.33;
ELMO_LAMBDA2 = 0.33;
ELMO_LAMBDA_LIMIT = 32000;
#endif
// Discount rate. If this is set to a value other than 0,
// the slope will be added even at other than the PV termination.
@@ -1703,13 +1655,11 @@ namespace Learner
else if (option == "freeze_kkpp") is >> freeze[3];
#endif
#if defined (LOSS_FUNCTION_IS_ELMO_METHOD)
// LAMBDA
else if (option == "lambda") is >> ELMO_LAMBDA;
else if (option == "lambda2") is >> ELMO_LAMBDA2;
else if (option == "lambda_limit") is >> ELMO_LAMBDA_LIMIT;
#endif
else if (option == "reduction_gameply") is >> reduction_gameply;
// shuffle related
@@ -1900,11 +1850,9 @@ namespace Learner
reduction_gameply = max(reduction_gameply, 1);
cout << "reduction_gameply : " << reduction_gameply << endl;
#if defined (LOSS_FUNCTION_IS_ELMO_METHOD)
cout << "LAMBDA : " << ELMO_LAMBDA << endl;
cout << "LAMBDA2 : " << ELMO_LAMBDA2 << endl;
cout << "LAMBDA_LIMIT : " << ELMO_LAMBDA_LIMIT << endl;
#endif
cout << "mirror_percentage : " << mirror_percentage << endl;
cout << "eval_save_interval : " << eval_save_interval << " sfens" << endl;