integrated updating grammars

2 files changed, 32 insertions, 11 deletions

diff --git a/training/dtrain/dtrain_net.h b/training/dtrain/dtrain_net.h
index ecacf3ee..f6aa08b2 100644
--- a/training/dtrain/dtrain_net.h
+++ b/training/dtrain/dtrain_net.h
@@ -42,7 +42,8 @@ dtrain_net_init(int argc, char** argv, po::variables_map* conf)
     ("decoder_conf,C", po::value<string>(),                      "configuration file for decoder")
     ("k",              po::value<size_t>()->default_value(100),              "size of kbest list")
     ("N",              po::value<size_t>()->default_value(4),          "N for BLEU approximation")
-    ("margin,m",       po::value<weight_t>()->default_value(0.),   "margin for margin perceptron");
+    ("margin,m",       po::value<weight_t>()->default_value(0.),   "margin for margin perceptron")
+    ("output,o",       po::value<string>()->default_value(""),               "final weights file");
   po::options_description cl("Command Line Options");
   cl.add_options()
     ("conf,c", po::value<string>(), "dtrain configuration file")
diff --git a/training/dtrain/dtrain_net_interface.cc b/training/dtrain/dtrain_net_interface.cc
index f484b56b..2719e946 100644
--- a/training/dtrain/dtrain_net_interface.cc
+++ b/training/dtrain/dtrain_net_interface.cc
@@ -20,6 +20,7 @@ main(int argc, char** argv)
   const size_t N              = conf["N"].as<size_t>();
   const weight_t margin       = conf["margin"].as<weight_t>();
   const string master_addr    = conf["addr"].as<string>();
+  const string output_fn      = conf["output"].as<string>();
 
   // setup decoder
   register_feature_functions();
@@ -44,10 +45,13 @@ main(int argc, char** argv)
   cerr << setw(25) << "margin " << margin << endl;
   cerr << setw(25) << "decoder conf " << "'"
        << conf["decoder_conf"].as<string>() << "'" << endl;
+  cerr << setw(25) << "output " << output_fn << endl;
 
-  // socket
+  // setup socket
   nn::socket sock(AF_SP, NN_PAIR);
-  sock.connect(master_addr.c_str());
+  sock.bind(master_addr.c_str());
+  string hello = "hello";
+  sock.send(hello.c_str(), hello.size()+1, 0);
 
   size_t i = 0;
   while(true)
@@ -61,9 +65,11 @@ main(int argc, char** argv)
     if (buf) {
       const string in(buf, buf+sz);
       nn::freemsg(buf);
-      if (in == "shutdown") {
+      cerr << "got input '" << in << "'" << endl;
+      if (in == "shutdown") { // shut down
+        cerr << "got shutdown signal" << endl;
         next = false;
-      } else {
+      } else { // translate
         vector<string> parts;
         boost::algorithm::split_regex(parts, in, boost::regex(" \\|\\|\\| "));
         if (parts[0] == "act:translate") {
@@ -74,11 +80,12 @@ main(int argc, char** argv)
           observer->dont_score = false;
           vector<ScoredHyp>* samples = observer->GetSamples();
           ostringstream os;
+          cerr << "1best features " << (*samples)[0].f << endl;
           PrintWordIDVec((*samples)[0].w, os);
           sock.send(os.str().c_str(), os.str().size()+1, 0);
-          cerr << "done" << endl;
+          cerr << "> done translating, looping" << endl;
           continue;
-        } else {
+        } else { // learn
           cerr << "learning ..." << endl;
           source = parts[0];
           parts.erase(parts.begin());
@@ -103,17 +110,30 @@ main(int argc, char** argv)
     observer->SetReference(refs, rsz);
     decoder.Decode(source, observer);
     vector<ScoredHyp>* samples = observer->GetSamples();
+    cerr << "samples size " << samples->size() << endl;
 
     // get pairs and update
     SparseVector<weight_t> updates;
     CollectUpdates(samples, updates, margin);
-    lambdas.plus_eq_v_times_s(updates, 1.0); // fixme
-    string s = "x";
-    sock.send(s.c_str(), s.size()+1, 0);
+    cerr << "updates size " << updates.size() << endl;
+    cerr << "lambdas before " << lambdas << endl;
+    lambdas.plus_eq_v_times_s(updates, 1.0); // FIXME: learning rate?
+    cerr << "lambdas after " << lambdas << endl;
     i++;
 
-    cerr << "done" << endl;
+    cerr << "> done learning, looping" << endl;
   } // input loop
+  
+  if (output_fn != "") {
+    cerr << "writing final weights to '" << output_fn << "'" << endl;
+    lambdas.init_vector(decoder_weights);
+    Weights::WriteToFile(output_fn, decoder_weights, true);
+  }
+
+  string shutdown = "off";
+  sock.send(shutdown.c_str(), shutdown.size()+1, 0);
+
+  cerr << "shutting down, goodbye" << endl;
 
   return 0;
 }