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#include "dtrain_net.h"
#include "sample_net.h"
#include "score.h"
#include "update.h"
#include <nanomsg/nn.h>
#include <nanomsg/pair.h>
#include "nn.hpp"
using namespace dtrain;
int
main(int argc, char** argv)
{
// get configuration
po::variables_map conf;
if (!dtrain_net_init(argc, argv, &conf))
exit(1); // something is wrong
const size_t k = conf["k"].as<size_t>();
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();
SetSilent(true);
ReadFile f(conf["decoder_conf"].as<string>());
Decoder decoder(f.stream());
ScoredKbest* observer = new ScoredKbest(k, new PerSentenceBleuScorer(N));
// weights
vector<weight_t>& decoder_weights = decoder.CurrentWeightVector();
SparseVector<weight_t> lambdas, w_average;
if (conf.count("input_weights")) {
Weights::InitFromFile(conf["input_weights"].as<string>(), &decoder_weights);
Weights::InitSparseVector(decoder_weights, &lambdas);
}
cerr << _p4;
// output configuration
cerr << "dtrain_net" << endl << "Parameters:" << endl;
cerr << setw(25) << "k " << k << endl;
cerr << setw(25) << "N " << N << endl;
cerr << setw(25) << "margin " << margin << endl;
cerr << setw(25) << "decoder conf " << "'"
<< conf["decoder_conf"].as<string>() << "'" << endl;
cerr << setw(25) << "output " << output_fn << endl;
// setup socket
nn::socket sock(AF_SP, NN_PAIR);
sock.bind(master_addr.c_str());
string hello = "hello";
sock.send(hello.c_str(), hello.size()+1, 0);
size_t i = 0;
while(true)
{
char *buf = NULL;
string source;
vector<Ngrams> refs;
vector<size_t> rsz;
bool next = true;
size_t sz = sock.recv(&buf, NN_MSG, 0);
if (buf) {
const string in(buf, buf+sz);
nn::freemsg(buf);
cerr << "got input '" << in << "'" << endl;
if (in == "shutdown") { // shut down
cerr << "got shutdown signal" << endl;
next = false;
} else { // translate
vector<string> parts;
boost::algorithm::split_regex(parts, in, boost::regex(" \\|\\|\\| "));
if (parts[0] == "act:translate") {
cerr << "translating ..." << endl;
lambdas.init_vector(&decoder_weights);
observer->dont_score = true;
decoder.Decode(parts[1], observer);
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 translating, looping" << endl;
continue;
} else { // learn
cerr << "learning ..." << endl;
source = parts[0];
parts.erase(parts.begin());
for (auto s: parts) {
vector<WordID> r;
vector<string> toks;
boost::split(toks, s, boost::is_any_of(" "));
for (auto tok: toks)
r.push_back(TD::Convert(tok));
refs.emplace_back(MakeNgrams(r, N));
rsz.push_back(r.size());
}
}
}
}
if (!next)
break;
// decode
lambdas.init_vector(&decoder_weights);
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);
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 learning, looping" << endl;
string done = "done";
sock.send(done.c_str(), done.size()+1, 0);
} // 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;
}
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