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-rw-r--r--dtrain/dtrain.cc75
1 files changed, 50 insertions, 25 deletions
diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc
index 0a94f7aa..e96b65aa 100644
--- a/dtrain/dtrain.cc
+++ b/dtrain/dtrain.cc
@@ -20,8 +20,8 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
("stop_after", po::value<unsigned>()->default_value(0), "stop after X input sentences")
("print_weights", po::value<string>(), "weights to print on each iteration")
("hstreaming", po::value<bool>()->zero_tokens(), "run in hadoop streaming mode")
- ("learning_rate", po::value<double>()->default_value(0.0005), "learning rate")
- ("gamma", po::value<double>()->default_value(0), "gamma for SVM (0 for perceptron)")
+ ("learning_rate", po::value<weight_t>()->default_value(0.0005), "learning rate")
+ ("gamma", po::value<weight_t>()->default_value(0), "gamma for SVM (0 for perceptron)")
("tmp", po::value<string>()->default_value("/tmp"), "temp dir to use")
("select_weights", po::value<string>()->default_value("last"), "output 'best' or 'last' weights ('VOID' to throw away)")
("noup", po::value<bool>()->zero_tokens(), "do not update weights");
@@ -134,15 +134,14 @@ main(int argc, char** argv)
observer->SetScorer(scorer);
// init weights
- Weights weights;
- if (cfg.count("input_weights")) weights.InitFromFile(cfg["input_weights"].as<string>());
- SparseVector<double> lambdas;
- weights.InitSparseVector(&lambdas);
- vector<double> dense_weights;
+ vector<weight_t>& dense_weights = decoder.CurrentWeightVector();
+ SparseVector<weight_t> lambdas;
+ if (cfg.count("input_weights")) Weights::InitFromFile(cfg["input_weights"].as<string>(), &dense_weights);
+ Weights::InitSparseVector(dense_weights, &lambdas);
// meta params for perceptron, SVM
- double eta = cfg["learning_rate"].as<double>();
- double gamma = cfg["gamma"].as<double>();
+ weight_t eta = cfg["learning_rate"].as<weight_t>();
+ weight_t gamma = cfg["gamma"].as<weight_t>();
WordID __bias = FD::Convert("__bias");
lambdas.add_value(__bias, 0);
@@ -160,7 +159,7 @@ main(int argc, char** argv)
grammar_buf_out.open(grammar_buf_fn.c_str());
unsigned in_sz = 999999999; // input index, input size
- vector<pair<score_t,score_t> > all_scores;
+ vector<pair<score_t, score_t> > all_scores;
score_t max_score = 0.;
unsigned best_it = 0;
float overall_time = 0.;
@@ -189,6 +188,15 @@ main(int argc, char** argv)
}
+ //LogVal<double> a(2.2);
+ //LogVal<double> b(2.1);
+ //cout << a << endl;
+ //cout << log(a) << endl;
+ //LogVal<double> c = a - b;
+ //cout << log(c) << endl;
+ //exit(0);
+
+
for (unsigned t = 0; t < T; t++) // T epochs
{
@@ -196,7 +204,8 @@ main(int argc, char** argv)
time(&start);
igzstream grammar_buf_in;
if (t > 0) grammar_buf_in.open(grammar_buf_fn.c_str());
- score_t score_sum = 0., model_sum = 0.;
+ score_t score_sum = 0.;
+ score_t model_sum(0);
unsigned ii = 0, nup = 0, npairs = 0;
if (!quiet) cerr << "Iteration #" << t+1 << " of " << T << "." << endl;
@@ -238,10 +247,7 @@ main(int argc, char** argv)
if (next || stop) break;
// weights
- dense_weights.clear();
- weights.InitFromVector(lambdas);
- weights.InitVector(&dense_weights);
- decoder.SetWeights(dense_weights);
+ lambdas.init_vector(&dense_weights);
// getting input
vector<string> in_split; // input: sid\tsrc\tref\tpsg
@@ -289,7 +295,8 @@ main(int argc, char** argv)
// get (scored) samples
vector<ScoredHyp>* samples = observer->GetSamples();
- if (verbose) {
+ // FIXME
+ /*if (verbose) {
cout << "[ref: '";
if (t > 0) cout << ref_ids_buf[ii];
else cout << ref_ids;
@@ -297,7 +304,15 @@ main(int argc, char** argv)
cout << _p5 << _np << "1best: " << "'" << (*samples)[0].w << "'" << endl;
cout << "SCORE=" << (*samples)[0].score << ",model="<< (*samples)[0].model << endl;
cout << "F{" << (*samples)[0].f << "} ]" << endl << endl;
- }
+ }*/
+ /*cout << lambdas.get(FD::Convert("PhraseModel_0")) << endl;
+ cout << (*samples)[0].model << endl;
+ cout << "1best: ";
+ for (unsigned u = 0; u < (*samples)[0].w.size(); u++) cout << TD::Convert((*samples)[0].w[u]) << " ";
+ cout << endl;
+ cout << (*samples)[0].f << endl;
+ cout << "___" << endl;*/
+
score_sum += (*samples)[0].score;
model_sum += (*samples)[0].model;
@@ -317,21 +332,21 @@ main(int argc, char** argv)
if (!gamma) {
// perceptron
if (it->first.score - it->second.score < 0) { // rank error
- SparseVector<double> dv = it->second.f - it->first.f;
+ SparseVector<weight_t> dv = it->second.f - it->first.f;
dv.add_value(__bias, -1);
lambdas.plus_eq_v_times_s(dv, eta);
nup++;
}
} else {
// SVM
- double rank_error = it->second.score - it->first.score;
+ score_t rank_error = it->second.score - it->first.score;
if (rank_error > 0) {
- SparseVector<double> dv = it->second.f - it->first.f;
+ SparseVector<weight_t> dv = it->second.f - it->first.f;
dv.add_value(__bias, -1);
lambdas.plus_eq_v_times_s(dv, eta);
}
// regularization
- double margin = it->first.model - it->second.model;
+ score_t margin = it->first.model - it->second.model;
if (rank_error || margin < 1) {
lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta); // reg /= #EXAMPLES or #UPDATES ?
nup++;
@@ -339,6 +354,15 @@ main(int argc, char** argv)
}
}
}
+
+
+ vector<weight_t> x;
+ lambdas.init_vector(&x);
+ for (int q = 0; q < x.size(); q++) {
+ if (x[q] < -10 && x[q] != 0)
+ cout << FD::Convert(q) << " " << x[q] << endl;
+ }
+ cout << " --- " << endl;
++ii;
@@ -358,7 +382,8 @@ main(int argc, char** argv)
// print some stats
score_t score_avg = score_sum/(score_t)in_sz;
score_t model_avg = model_sum/(score_t)in_sz;
- score_t score_diff, model_diff;
+ score_t score_diff;
+ score_t model_diff;
if (t > 0) {
score_diff = score_avg - all_scores[t-1].first;
model_diff = model_avg - all_scores[t-1].second;
@@ -402,10 +427,10 @@ main(int argc, char** argv)
// write weights to file
if (select_weights == "best") {
- weights.InitFromVector(lambdas);
string infix = "dtrain-weights-" + boost::lexical_cast<string>(t);
+ lambdas.init_vector(&dense_weights);
string w_fn = gettmpf(tmp_path, infix, "gz");
- weights.WriteToFile(w_fn, true);
+ Weights::WriteToFile(w_fn, dense_weights, true);
weights_files.push_back(w_fn);
}
@@ -420,7 +445,7 @@ main(int argc, char** argv)
ostream& o = *of.stream();
o.precision(17);
o << _np;
- for (SparseVector<double>::const_iterator it = lambdas.begin(); it != lambdas.end(); ++it) {
+ for (SparseVector<weight_t>::const_iterator it = lambdas.begin(); it != lambdas.end(); ++it) {
if (it->second == 0) continue;
o << FD::Convert(it->first) << '\t' << it->second << endl;
}
generated by cgit v1.2.3 (git 2.39.1) at 2024-09-21 12:34:34 +0000