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Added an option to convert teacher signals to winning probabilities.

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nodchip
2020-08-22 12:19:29 +09:00
parent e5e5d7d4ab
commit 87633b876c
1 changed files with 25 additions and 12 deletions
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37
src/learn/learner.cpp
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@@ -129,6 +129,10 @@ double src_score_min_value = 0.0;
double src_score_max_value = 1.0;
double dest_score_min_value = 0.0;
double dest_score_max_value = 1.0;
// Assume teacher signals are the scores of deep searches, and convert them into winning
// probabilities in the trainer. Sometimes we want to use the winning probabilities in the training
// data directly. In those cases, we set false to this variable.
bool convert_teacher_signal_to_winning_probability = true;
// -----------------------------------
// write phase file
@@ -1247,27 +1251,31 @@ double ELMO_LAMBDA = 0.33;
double ELMO_LAMBDA2 = 0.33;
double ELMO_LAMBDA_LIMIT = 32000;
double calc_grad(Value deep, Value shallow , const PackedSfenValue& psv)
double calc_grad(Value teacher_signal, Value shallow , const PackedSfenValue& psv)
{
// elmo (WCSC27) method
// Correct with the actual game wins and losses.
// Training Formula · Issue #71 · nodchip/Stockfish https://github.com/nodchip/Stockfish/issues/71
double scaled_deep = deep;
double scaled_teacher_signal = teacher_signal;
// Normalize to [0.0, 1.0].
scaled_deep = (scaled_deep - src_score_min_value) / (src_score_max_value - src_score_min_value);
scaled_teacher_signal = (scaled_teacher_signal - src_score_min_value) / (src_score_max_value - src_score_min_value);
// Scale to [dest_score_min_value, dest_score_max_value].
scaled_deep = scaled_deep * (dest_score_max_value - dest_score_min_value) + dest_score_min_value;
scaled_teacher_signal = scaled_teacher_signal * (dest_score_max_value - dest_score_min_value) + dest_score_min_value;
const double q = winning_percentage(shallow);
const double p = winning_percentage(scaled_deep);
// Teacher winning probability.
double p = scaled_teacher_signal;
if (convert_teacher_signal_to_winning_probability) {
p = winning_percentage(scaled_teacher_signal);
}
// Use 1 as the correction term if the expected win rate is 1, 0 if you lose, and 0.5 if you draw.
// game_result = 1,0,-1 so add 1 and divide by 2.
const double t = double(psv.game_result + 1) / 2;
// If the evaluation value in deep search exceeds ELMO_LAMBDA_LIMIT, apply ELMO_LAMBDA2 instead of ELMO_LAMBDA.
const double lambda = (abs(deep) >= ELMO_LAMBDA_LIMIT) ? ELMO_LAMBDA2 : ELMO_LAMBDA;
const double lambda = (abs(teacher_signal) >= ELMO_LAMBDA_LIMIT) ? ELMO_LAMBDA2 : ELMO_LAMBDA;
// Use the actual win rate as a correction term.
// This is the idea of ​​elmo (WCSC27), modern O-parts.
@@ -1278,25 +1286,29 @@ double calc_grad(Value deep, Value shallow , const PackedSfenValue& psv)
// Calculate cross entropy during learning
// The individual cross entropy of the win/loss term and win rate term of the elmo expression is returned to the arguments cross_entropy_eval and cross_entropy_win.
void calc_cross_entropy(Value deep, Value shallow, const PackedSfenValue& psv,
void calc_cross_entropy(Value teacher_signal, Value shallow, const PackedSfenValue& psv,
double& cross_entropy_eval, double& cross_entropy_win, double& cross_entropy,
double& entropy_eval, double& entropy_win, double& entropy)
{
// Training Formula · Issue #71 · nodchip/Stockfish https://github.com/nodchip/Stockfish/issues/71
double scaled_deep = deep;
double scaled_teacher_signal = teacher_signal;
// Normalize to [0.0, 1.0].
scaled_deep = (scaled_deep - src_score_min_value) / (src_score_max_value - src_score_min_value);
scaled_teacher_signal = (scaled_teacher_signal - src_score_min_value) / (src_score_max_value - src_score_min_value);
// Scale to [dest_score_min_value, dest_score_max_value].
scaled_deep = scaled_deep * (dest_score_max_value - dest_score_min_value) + dest_score_min_value;
scaled_teacher_signal = scaled_teacher_signal * (dest_score_max_value - dest_score_min_value) + dest_score_min_value;
const double p /* teacher_winrate */ = winning_percentage(scaled_deep);
// Teacher winning probability.
double p = scaled_teacher_signal;
if (convert_teacher_signal_to_winning_probability) {
p = winning_percentage(scaled_teacher_signal);
}
const double q /* eval_winrate */ = winning_percentage(shallow);
const double t = double(psv.game_result + 1) / 2;
constexpr double epsilon = 0.000001;
// If the evaluation value in deep search exceeds ELMO_LAMBDA_LIMIT, apply ELMO_LAMBDA2 instead of ELMO_LAMBDA.
const double lambda = (abs(deep) >= ELMO_LAMBDA_LIMIT) ? ELMO_LAMBDA2 : ELMO_LAMBDA;
const double lambda = (abs(teacher_signal) >= ELMO_LAMBDA_LIMIT) ? ELMO_LAMBDA2 : ELMO_LAMBDA;
const double m = (1.0 - lambda) * t + lambda * p;
@@ -3179,6 +3191,7 @@ void learn(Position&, istringstream& is)
else if (option == "src_score_max_value") is >> src_score_max_value;
else if (option == "dest_score_min_value") is >> dest_score_min_value;
else if (option == "dest_score_max_value") is >> dest_score_max_value;
else if (option == "convert_teacher_signal_to_winning_probability") is >> convert_teacher_signal_to_winning_probability;
// Otherwise, it's a filename.
else