diff options
| -rw-r--r-- | training/dtrain/dtrain.cc | 32 |
1 files changed, 22 insertions, 10 deletions
diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc index 0a27a068..b01cf421 100644 --- a/training/dtrain/dtrain.cc +++ b/training/dtrain/dtrain.cc @@ -44,7 +44,7 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg) ("pclr", po::value<string>()->default_value("no"), "use a (simple|adagrad) per-coordinate learning rate") ("batch", po::value<bool>()->zero_tokens(), "do batch optimization") ("repeat", po::value<unsigned>()->default_value(1), "repeat optimization over kbest list this number of times") - //("test-k-best", po::value<bool>()->zero_tokens(), "check if optimization works (use repeat >= 2)") + ("check", po::value<bool>()->zero_tokens(), "produce list of loss differentials") ("noup", po::value<bool>()->zero_tokens(), "do not update weights"); po::options_description cl("Command Line Options"); cl.add_options() @@ -130,8 +130,8 @@ main(int argc, char** argv) const score_t approx_bleu_d = cfg["approx_bleu_d"].as<score_t>(); const unsigned max_pairs = cfg["max_pairs"].as<unsigned>(); int repeat = cfg["repeat"].as<unsigned>(); - //bool test_k_best = false; - //if (cfg.count("test-k-best")) test_k_best = true; + bool check = false; + if (cfg.count("check")) check = true; weight_t loss_margin = cfg["loss_margin"].as<weight_t>(); bool batch = false; if (cfg.count("batch")) batch = true; @@ -412,27 +412,38 @@ main(int argc, char** argv) int cur_npairs = pairs.size(); npairs += cur_npairs; - score_t kbest_loss_first, kbest_loss_last = 0.0; + score_t kbest_loss_first = 0.0, kbest_loss_last = 0.0; + + if (check) repeat = 2; + vector<float> losses; // for check for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin(); it != pairs.end(); it++) { score_t model_diff = it->first.model - it->second.model; - kbest_loss_first += max(0.0, -1.0 * model_diff); + score_t loss = max(0.0, -1.0 * model_diff); + losses.push_back(loss); + kbest_loss_first += loss; } + score_t kbest_loss = 0.0; for (int ki=0; ki < repeat; ki++) { - score_t kbest_loss = 0.0; // test-k-best SparseVector<weight_t> lambdas_copy; // for l1 regularization SparseVector<weight_t> sum_up; // for pclr if (l1naive||l1clip||l1cumul) lambdas_copy = lambdas; + unsigned pair_idx = 0; // for check for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin(); it != pairs.end(); it++) { score_t model_diff = it->first.model - it->second.model; + score_t loss = max(0.0, -1.0 * model_diff); + + if (check && ki == 1) cout << losses[pair_idx] - loss << endl; + pair_idx++; + if (repeat > 1) { model_diff = lambdas.dot(it->first.f) - lambdas.dot(it->second.f); - kbest_loss += max(0.0, -1.0 * model_diff); + kbest_loss += loss; } bool rank_error = false; score_t margin; @@ -449,7 +460,7 @@ main(int argc, char** argv) if (rank_error || margin < loss_margin) { SparseVector<weight_t> diff_vec = it->first.f - it->second.f; if (batch) { - batch_loss += max(0., -1.0*model_diff); + batch_loss += max(0., -1.0 * model_diff); batch_updates += diff_vec; continue; } @@ -529,9 +540,8 @@ main(int argc, char** argv) if (ki==repeat-1) { // done kbest_loss_last = kbest_loss; if (repeat > 1) { - score_t best_score = -1.; score_t best_model = -std::numeric_limits<score_t>::max(); - unsigned best_idx; + unsigned best_idx = 0; for (unsigned i=0; i < samples->size(); i++) { score_t s = lambdas.dot((*samples)[i].f); if (s > best_model) { @@ -634,6 +644,8 @@ main(int argc, char** argv) Weights::WriteToFile(w_fn, decoder_weights, true); } + if (check) cout << "---" << endl; + } // outer loop if (average) w_average /= (weight_t)T; |