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-rw-r--r--training/dtrain/dtrain.cc32
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;