From 723cbf8d543ba8be2880b497518c87fc1cbf3573 Mon Sep 17 00:00:00 2001 From: Patrick Simianer Date: Wed, 24 Jun 2015 18:51:21 +0200 Subject: dtrain_net_interace: cleanup --- training/dtrain/Makefile.am | 2 +- training/dtrain/dtrain_net_interface.cc | 124 +++++++++++++++++++++++++++----- training/dtrain/dtrain_net_interface.h | 4 +- training/dtrain/sample_net_interface.h | 2 +- training/dtrain/score.h | 6 +- training/dtrain/score_net_interface.h | 2 +- 6 files changed, 113 insertions(+), 27 deletions(-) diff --git a/training/dtrain/Makefile.am b/training/dtrain/Makefile.am index ef256ffe..a0b5545b 100644 --- a/training/dtrain/Makefile.am +++ b/training/dtrain/Makefile.am @@ -9,5 +9,5 @@ dtrain_net_LDADD = ../../decoder/libcdec.a ../../klm/search/libksearch.a ../.. dtrain_net_interface_SOURCES = dtrain_net_interface.cc dtrain_net_interface.h dtrain.h sample_net_interface.h score_net_interface.h update.h dtrain_net_interface_LDADD = ../../decoder/libcdec.a ../../klm/search/libksearch.a ../../mteval/libmteval.a ../../utils/libutils.a ../../klm/lm/libklm.a ../../klm/util/libklm_util.a ../../klm/util/double-conversion/libklm_util_double.a /fast_scratch/simianer/lfpe/nanomsg-0.5-beta/lib/libnanomsg.so -AM_CPPFLAGS = -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval -I/fast_scratch/simianer/lfpe/nanomsg-0.5-beta/include +AM_CPPFLAGS = -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval -I/fast_scratch/simianer/lfpe/nanomsg-0.5-beta/include -I/fast_scratch/simianer/lfpe/cppnanomsg diff --git a/training/dtrain/dtrain_net_interface.cc b/training/dtrain/dtrain_net_interface.cc index 265bc014..e9612def 100644 --- a/training/dtrain/dtrain_net_interface.cc +++ b/training/dtrain/dtrain_net_interface.cc @@ -1,6 +1,6 @@ -#include "dtrain_net.h" -#include "sample_net.h" -#include "score.h" +#include "dtrain_net_interface.h" +#include "sample_net_interface.h" +#include "score_net_interface.h" #include "update.h" #include @@ -18,9 +18,11 @@ main(int argc, char** argv) exit(1); // something is wrong const size_t k = conf["k"].as(); const size_t N = conf["N"].as(); + weight_t eta = conf["learning_rate"].as(); const weight_t margin = conf["margin"].as(); const string master_addr = conf["addr"].as(); const string output_fn = conf["output"].as(); + const string debug_fn = conf["debug_output"].as(); // setup decoder register_feature_functions(); @@ -39,7 +41,7 @@ main(int argc, char** argv) cerr << _p4; // output configuration - cerr << "dtrain_net" << endl << "Parameters:" << endl; + cerr << "dtrain_net_interface" << endl << "Parameters:" << endl; cerr << setw(25) << "k " << k << endl; cerr << setw(25) << "N " << N << endl; cerr << setw(25) << "margin " << margin << endl; @@ -53,9 +55,18 @@ main(int argc, char** argv) string hello = "hello"; sock.send(hello.c_str(), hello.size()+1, 0); + // debug + ostringstream debug_output; + size_t i = 0; while(true) { + // debug -- + debug_output.str(string()); + debug_output.clear(); + debug_output << "{" << endl; + // -- debug + char *buf = NULL; string source; vector refs; @@ -65,29 +76,33 @@ main(int argc, char** argv) if (buf) { const string in(buf, buf+sz); nn::freemsg(buf); - cerr << "got input '" << in << "'" << endl; + cerr << "[dtrain] got input '" << in << "'" << endl; if (in == "shutdown") { // shut down - cerr << "got shutdown signal" << endl; + cerr << "[dtrain] got shutdown signal" << endl; next = false; } else { // translate vector parts; boost::algorithm::split_regex(parts, in, boost::regex(" \\|\\|\\| ")); if (parts[0] == "act:translate") { - cerr << "translating ..." << endl; + cerr << "[dtrain] translating ..." << endl; lambdas.init_vector(&decoder_weights); observer->dont_score = true; decoder.Decode(parts[1], observer); observer->dont_score = false; vector* samples = observer->GetSamples(); ostringstream os; - cerr << "1best features " << (*samples)[0].f << endl; + cerr << "[dtrain] 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; + cerr << "[dtrain] done translating, looping again" << endl; continue; } else { // learn - cerr << "learning ..." << endl; + cerr << "[dtrain] learning ..." << endl; source = parts[0]; + // debug -- + debug_output << "\"source\":\"" << source.substr(source.find_first_of(">")+1, source.find_last_of("<")-3) << "\"," << endl; + debug_output << "\"target\":\"" << parts[1] << "\"," << endl; + // -- debug parts.erase(parts.begin()); for (auto s: parts) { vector r; @@ -110,24 +125,95 @@ main(int argc, char** argv) observer->SetReference(refs, rsz); decoder.Decode(source, observer); vector* samples = observer->GetSamples(); - cerr << "samples size " << samples->size() << endl; + + // debug -- + debug_output << "\"1best\":\""; + PrintWordIDVec((*samples)[0].w, debug_output); + debug_output << "\"," << endl; + debug_output << "\"kbest\":[" << endl; + size_t h = 0; + for (auto s: *samples) { + debug_output << "\"" << s.gold << " ||| " << s.model << " ||| " << s.rank << " ||| "; + debug_output << "EgivenFCoherent=" << s.f[FD::Convert("EgivenFCoherent")] << " "; + debug_output << "SampleCountF=" << s.f[FD::Convert("CountEF")] << " "; + debug_output << "MaxLexFgivenE=" << s.f[FD::Convert("MaxLexFgivenE")] << " "; + debug_output << "MaxLexEgivenF=" << s.f[FD::Convert("MaxLexEgivenF")] << " "; + debug_output << "IsSingletonF=" << s.f[FD::Convert("IsSingletonF")] << " "; + debug_output << "IsSingletonFE=" << s.f[FD::Convert("IsSingletonFE")] << " "; + debug_output << "Glue=:" << s.f[FD::Convert("Glue")] << " "; + debug_output << "WordPenalty=" << s.f[FD::Convert("WordPenalty")] << " "; + debug_output << "PassThrough=" << s.f[FD::Convert("PassThrough")] << " "; + debug_output << "LanguageModel=" << s.f[FD::Convert("LanguageModel_OOV")]; + debug_output << " ||| "; + PrintWordIDVec(s.w, debug_output); + h += 1; + debug_output << "\""; + if (h < samples->size()) { + debug_output << ","; + } + debug_output << endl; + } + debug_output << "]," << endl; + debug_output << "\"samples_size\":" << samples->size() << "," << endl; + debug_output << "\"weights_before\":{" << endl; + debug_output << "\"EgivenFCoherent\":" << lambdas[FD::Convert("EgivenFCoherent")] << "," << endl; + debug_output << "\"SampleCountF\":" << lambdas[FD::Convert("CountEF")] << "," << endl; + debug_output << "\"MaxLexFgivenE\":" << lambdas[FD::Convert("MaxLexFgivenE")] << "," << endl; + debug_output << "\"MaxLexEgivenF\":" << lambdas[FD::Convert("MaxLexEgivenF")] << "," << endl; + debug_output << "\"IsSingletonF\":" << lambdas[FD::Convert("IsSingletonF")] << "," << endl; + debug_output << "\"IsSingletonFE\":" << lambdas[FD::Convert("IsSingletonFE")] << "," << endl; + debug_output << "\"Glue\":" << lambdas[FD::Convert("Glue")] << "," << endl; + debug_output << "\"WordPenalty\":" << lambdas[FD::Convert("WordPenalty")] << "," << endl; + debug_output << "\"PassThrough\":" << lambdas[FD::Convert("PassThrough")] << "," << endl; + debug_output << "\"LanguageModel\":" << lambdas[FD::Convert("LanguageModel_OOV")] << endl; + debug_output << "}," << endl; + // -- debug // get pairs and update SparseVector 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; + size_t num_up = CollectUpdates(samples, updates, margin); + + // debug -- + debug_output << "\"num_up\":" << num_up << "," << endl; + debug_output << "\"updated_features\":" << updates.size() << "," << endl; + debug_output << "\"learning_rate\":" << eta << "," << endl; + debug_output << "\"best_match\":\""; + PrintWordIDVec((*samples)[0].w, debug_output); + debug_output << "\"," << endl; + debug_output << "\"best_match_score\":" << (*samples)[0].gold << "," << endl ; + // -- debug + + lambdas.plus_eq_v_times_s(updates, eta); i++; - cerr << "> done learning, looping" << endl; + // debug -- + debug_output << "\"weights_after\":{" << endl; + debug_output << "\"EgivenFCoherent\":" << lambdas[FD::Convert("EgivenFCoherent")] << "," << endl; + debug_output << "\"SampleCountF\":" << lambdas[FD::Convert("CountEF")] << "," << endl; + debug_output << "\"MaxLexFgivenE\":" << lambdas[FD::Convert("MaxLexFgivenE")] << "," << endl; + debug_output << "\"MaxLexEgivenF\":" << lambdas[FD::Convert("MaxLexEgivenF")] << "," << endl; + debug_output << "\"IsSingletonF\":" << lambdas[FD::Convert("IsSingletonF")] << "," << endl; + debug_output << "\"IsSingletonFE\":" << lambdas[FD::Convert("IsSingletonFE")] << "," << endl; + debug_output << "\"Glue\":" << lambdas[FD::Convert("Glue")] << "," << endl; + debug_output << "\"WordPenalty\":" << lambdas[FD::Convert("WordPenalty")] << "," << endl; + debug_output << "\"PassThrough\":" << lambdas[FD::Convert("PassThrough")] << "," << endl; + debug_output << "\"LanguageModel\":" << lambdas[FD::Convert("LanguageModel_OOV")] << endl; + debug_output << "}" << endl; + debug_output << "}" << endl; + // -- debug + + cerr << "[dtrain] done learning, looping again" << endl; string done = "done"; sock.send(done.c_str(), done.size()+1, 0); + + // debug -- + WriteFile f(debug_fn); + *f << debug_output.str(); + // -- debug } // input loop if (output_fn != "") { - cerr << "writing final weights to '" << output_fn << "'" << endl; + cerr << "[dtrain] writing final weights to '" << output_fn << "'" << endl; lambdas.init_vector(decoder_weights); Weights::WriteToFile(output_fn, decoder_weights, true); } @@ -135,7 +221,7 @@ main(int argc, char** argv) string shutdown = "off"; sock.send(shutdown.c_str(), shutdown.size()+1, 0); - cerr << "shutting down, goodbye" << endl; + cerr << "[dtrain] shutting down, goodbye" << endl; return 0; } diff --git a/training/dtrain/dtrain_net_interface.h b/training/dtrain/dtrain_net_interface.h index 1c724b55..2c539930 100644 --- a/training/dtrain/dtrain_net_interface.h +++ b/training/dtrain/dtrain_net_interface.h @@ -1,5 +1,5 @@ -#ifndef _DTRAIN_NET_H_ -#define _DTRAIN_NET_H_ +#ifndef _DTRAIN_NET_INTERFACE_H_ +#define _DTRAIN_NET_INTERFACE_H_ #include "dtrain.h" diff --git a/training/dtrain/sample_net_interface.h b/training/dtrain/sample_net_interface.h index 497149d9..98b10c82 100644 --- a/training/dtrain/sample_net_interface.h +++ b/training/dtrain/sample_net_interface.h @@ -3,7 +3,7 @@ #include "kbest.h" -#include "score.h" +#include "score_net_interface.h" namespace dtrain { diff --git a/training/dtrain/score.h b/training/dtrain/score.h index d909dbf3..e6e60acb 100644 --- a/training/dtrain/score.h +++ b/training/dtrain/score.h @@ -1,5 +1,5 @@ -#ifndef _DTRAIN_SCORE_NET_INTERFACE_H_ -#define _DTRAIN_SCORE_NET_INTERFACE_H_ +#ifndef _DTRAIN_SCORE_H_ +#define _DTRAIN_SCORE_H_ #include "dtrain.h" @@ -153,7 +153,7 @@ struct PerSentenceBleuScorer size_t best = numeric_limits::max(); for (auto l: ref_ls) { size_t d = abs(hl-l); - if (d < best) { + if (d < best) { best_idx = i; best = d; } diff --git a/training/dtrain/score_net_interface.h b/training/dtrain/score_net_interface.h index 6e359249..58357cf6 100644 --- a/training/dtrain/score_net_interface.h +++ b/training/dtrain/score_net_interface.h @@ -153,7 +153,7 @@ struct PerSentenceBleuScorer size_t best = numeric_limits::max(); for (auto l: ref_ls) { size_t d = abs(hl-l); - if (d < best) { + if (d < best) { best_idx = i; best = d; } -- cgit v1.2.3