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Get train loss from update_parameters.

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This commit is contained in:
Tomasz Sobczyk
2020-11-30 22:17:44 +01:00
committed by nodchip
parent 4eb0e77a2a
commit fafb9557a8
4 changed files with 89 additions and 84 deletions
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24
src/nnue/evaluate_nnue_learner.cpp
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@@ -190,7 +190,7 @@ namespace Eval::NNUE {
}
// update the evaluation function parameters
void update_parameters(
Learner::Loss update_parameters(
ThreadPool& thread_pool,
uint64_t epoch,
bool verbose,
@@ -212,9 +212,12 @@ namespace Eval::NNUE {
bool collect_stats = verbose;
Learner::Loss loss_sum{};
std::vector<double> abs_eval_diff_sum_local(thread_pool.size(), 0.0);
std::vector<double> abs_discrete_eval_sum_local(thread_pool.size(), 0.0);
std::vector<double> gradient_norm_local(thread_pool.size(), 0.0);
std::vector<Learner::Loss> loss_sum_local(thread_pool.size());
auto prev_batch_begin = examples.end();
while ((long)(prev_batch_begin - examples.begin()) >= (long)batch_size) {
@@ -237,11 +240,11 @@ namespace Eval::NNUE {
e.sign * network_output[b] * kPonanzaConstant));
const auto discrete = e.sign * e.discrete_nn_eval;
const auto& psv = e.psv;
const auto loss = calc_loss(shallow, (Value)psv.score, psv.game_result, psv.gamePly);
const double gradient = std::clamp(
auto loss = calc_loss(shallow, (Value)psv.score, psv.game_result, psv.gamePly);
loss.grad = std::clamp(
loss.grad * e.sign * kPonanzaConstant * e.weight, -max_grad, max_grad);
gradients[b] = static_cast<LearnFloatType>(gradient);
gradients[b] = static_cast<LearnFloatType>(loss.grad);
loss_sum_local[thread_id] += loss;
// The discrete eval will only be valid before first backpropagation,
// that is only for the first batch.
@@ -250,7 +253,7 @@ namespace Eval::NNUE {
{
abs_eval_diff_sum_local[thread_id] += std::abs(discrete - shallow);
abs_discrete_eval_sum_local[thread_id] += std::abs(discrete);
gradient_norm_local[thread_id] += std::abs(gradient);
gradient_norm_local[thread_id] += std::abs(loss.grad);
}
}
@@ -277,9 +280,7 @@ namespace Eval::NNUE {
abs_eval_diff_sum = std::accumulate(abs_eval_diff_sum_local.begin(), abs_eval_diff_sum_local.end(), 0.0);
abs_discrete_eval_sum = std::accumulate(abs_discrete_eval_sum_local.begin(), abs_discrete_eval_sum_local.end(), 0.0);
gradient_norm = std::accumulate(gradient_norm_local.begin(), gradient_norm_local.end(), 0.0);
}
if (verbose) {
const double avg_abs_eval_diff = abs_eval_diff_sum / batch_size;
const double avg_abs_discrete_eval = abs_discrete_eval_sum / batch_size;
@@ -300,6 +301,13 @@ namespace Eval::NNUE {
}
send_messages({{"quantize_parameters"}});
for(auto& loss : loss_sum_local)
{
loss_sum += loss;
}
return loss_sum;
}
// Check if there are any problems with learning

2
src/nnue/evaluate_nnue_learner.h
View File

@@ -33,7 +33,7 @@ namespace Eval::NNUE {
double weight);
// update the evaluation function parameters
void update_parameters(
Learner::Loss update_parameters(
ThreadPool& thread_pool,
uint64_t epoch,
bool verbose,