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Add verbose flag to learn. Only print update parameters info when vebose=true

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This commit is contained in:
Tomasz Sobczyk
2020-10-24 11:39:16 +02:00
committed by nodchip
parent ec436d3dfd
commit a351c1d65e
3 changed files with 25 additions and 15 deletions
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29
src/nnue/evaluate_nnue_learner.cpp
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@@ -173,7 +173,7 @@ namespace Eval::NNUE {
}
// update the evaluation function parameters
void update_parameters() {
void update_parameters(uint64_t epoch, bool verbose) {
assert(batch_size > 0);
const auto learning_rate = static_cast<LearnFloatType>(
@@ -186,7 +186,7 @@ namespace Eval::NNUE {
double abs_discrete_eval_sum = 0.0;
double gradient_norm = 0.0;
bool is_first_batch = true;
bool collect_stats = verbose;
while (examples.size() >= batch_size) {
std::vector<Example> batch(examples.end() - batch_size, examples.end());
@@ -207,7 +207,7 @@ namespace Eval::NNUE {
// The discrete eval will only be valid before first backpropagation,
// that is only for the first batch.
// Similarily we want only gradients from one batch.
if (is_first_batch)
if (collect_stats)
{
abs_eval_diff_sum += std::abs(discrete - shallow);
abs_discrete_eval_sum += std::abs(discrete);
@@ -217,19 +217,22 @@ namespace Eval::NNUE {
trainer->backpropagate(gradients.data(), learning_rate);
is_first_batch = false;
collect_stats = false;
}
const double avg_abs_eval_diff = abs_eval_diff_sum / batch_size;
const double avg_abs_discrete_eval = abs_discrete_eval_sum / batch_size;
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;
std::cout << "INFO (update_weights):"
<< " avg_abs(trainer_eval-nnue_eval) = " << avg_abs_eval_diff
<< " , avg_abs(nnue_eval) = " << avg_abs_discrete_eval
<< " , avg_relative_error = " << avg_abs_eval_diff / avg_abs_discrete_eval
<< " , batch_size = " << batch_size
<< " , grad_norm = " << gradient_norm
<< std::endl;
std::cout << "INFO (update_parameters):"
<< " epoch = " << epoch
<< " , avg_abs(trainer_eval-nnue_eval) = " << avg_abs_eval_diff
<< " , avg_abs(nnue_eval) = " << avg_abs_discrete_eval
<< " , avg_relative_error = " << avg_abs_eval_diff / avg_abs_discrete_eval
<< " , batch_size = " << batch_size
<< " , grad_norm = " << gradient_norm
<< std::endl;
}
send_messages({{"quantize_parameters"}});
}