mirror of
https://github.com/moses-smt/mosesdecoder.git
synced 2024-09-20 07:42:21 +03:00
refactorings, remove burn-in code
git-svn-id: http://svn.statmt.org/repository/mira@3922 cc96ff50-19ce-11e0-b349-13d7f0bd23df
This commit is contained in:
ehasler
Ondrej Bojar
parent
290e38fb73
commit
f658840f1a
260
mira/Main.cpp
260
mira/Main.cpp
@ -97,9 +97,6 @@ int main(int argc, char** argv) {
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bool print_feature_values;
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bool historyOf1best;
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bool historyOfOracles;
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bool burnIn;
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string burnInInputFile;
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vector<string> burnInReferenceFiles;
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bool sentenceLevelBleu;
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float bleuScoreWeight;
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float margin_slack;
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@ -118,9 +115,6 @@ int main(int argc, char** argv) {
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("base-of-log", po::value<size_t>(&baseOfLog)->default_value(10), "Base for log-ing feature values")
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("batch-size,b", po::value<size_t>(&batchSize)->default_value(1), "Size of batch that is send to optimiser for weight adjustments")
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("bleu-score-weight", po::value<float>(&bleuScoreWeight)->default_value(1.0), "Bleu score weight used in the decoder objective function (on top of the bleu objective weight)")
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("burn-in", po::value<bool>(&burnIn)->default_value(false), "Do a burn-in of the BLEU history before training")
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("burn-in-input-file", po::value<string>(&burnInInputFile), "Input file for burn-in phase of BLEU history")
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("burn-in-reference-files", po::value<vector<string> >(&burnInReferenceFiles), "Reference file for burn-in phase of BLEU history")
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("config,f", po::value<string>(&mosesConfigFile), "Moses ini file")
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("core-weights", po::value<string>(&coreWeightFile), "Weight file containing the core weights (already tuned, have to be non-zero)")
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("decoder-settings", po::value<string>(&decoder_settings)->default_value(""), "Decoder settings for tuning runs")
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@ -307,110 +301,8 @@ int main(int argc, char** argv) {
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historyOf1best = true;
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}
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}
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if (burnIn && sentenceLevelBleu) {
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burnIn = false;
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cerr << "Info: Burn-in not needed when using sentence-level BLEU, deactivating burn-in." << endl;
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}
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if (burnIn) {
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// load burn-in input and references
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vector<string> burnInInputSentences;
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if (!loadSentences(burnInInputFile, burnInInputSentences)) {
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cerr << "Error: Failed to load burn-in input sentences from " << burnInInputFile << endl;
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return 1;
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}
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vector<vector<string> > burnInReferenceSentences(burnInReferenceFiles.size());
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for (size_t i = 0; i < burnInReferenceFiles.size(); ++i) {
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if (!loadSentences(burnInReferenceFiles[i], burnInReferenceSentences[i])) {
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cerr << "Error: Failed to load burn-in reference sentences from "
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<< burnInReferenceFiles[i] << endl;
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return 1;
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}
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if (burnInReferenceSentences[i].size() != burnInInputSentences.size()) {
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cerr << "Error: Burn-in input file length (" << burnInInputSentences.size() << ") != ("
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<< burnInReferenceSentences[i].size() << ") length of burn-in reference file " << i
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<< endl;
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return 1;
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}
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}
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decoder->loadReferenceSentences(burnInReferenceSentences);
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vector<size_t> inputLengths;
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vector<size_t> ref_ids;
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vector<vector<const Word*> > oracles;
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vector<vector<const Word*> > oneBests;
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vector<vector<ScoreComponentCollection> > featureValues;
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vector<vector<float> > bleuScores;
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vector<ScoreComponentCollection> newFeatureValues;
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vector<float> newBleuScores;
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featureValues.push_back(newFeatureValues);
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bleuScores.push_back(newBleuScores);
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vector<size_t> order;
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for (size_t i = 0; i < burnInInputSentences.size(); ++i) {
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order.push_back(i);
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}
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VERBOSE(1, "Rank " << rank << ", starting burn-in phase for approx. BLEU history.." << endl);
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if (historyOf1best) {
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// get 1best translations for the burn-in sentences
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vector<size_t>::const_iterator sid = order.begin();
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while (sid != order.end()) {
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string& input = burnInInputSentences[*sid];
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vector<const Word*> bestModel = decoder->getNBest(input, *sid, 1, 0.0, bleuScoreWeight,
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featureValues[0], bleuScores[0], true,
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distinctNbest, rank, -1);
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inputLengths.push_back(decoder->getCurrentInputLength());
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ref_ids.push_back(*sid);
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decoder->cleanup();
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oneBests.push_back(bestModel);
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++sid;
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}
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// update history
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decoder->updateHistory(oneBests, inputLengths, ref_ids, rank, 0);
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// clean up 1best translations after updating history
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for (size_t i = 0; i < oracles.size(); ++i) {
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for (size_t j = 0; j < oracles[i].size(); ++j) {
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delete oracles[i][j];
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}
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}
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}
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else {
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// get oracle translations for the burn-in sentences
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vector<size_t>::const_iterator sid = order.begin();
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while (sid != order.end()) {
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string& input = burnInInputSentences[*sid];
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vector<const Word*> oracle = decoder->getNBest(input, *sid, 1, 1.0, bleuScoreWeight,
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featureValues[0], bleuScores[0], true, distinctNbest, rank, -1);
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inputLengths.push_back(decoder->getCurrentInputLength());
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ref_ids.push_back(*sid);
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decoder->cleanup();
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oracles.push_back(oracle);
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++sid;
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}
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// update history
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decoder->updateHistory(oracles, inputLengths, ref_ids, rank, 0);
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// clean up oracle translations after updating history
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for (size_t i = 0; i < oracles.size(); ++i) {
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for (size_t j = 0; j < oracles[i].size(); ++j) {
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delete oracles[i][j];
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}
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}
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}
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VERBOSE(1, "Bleu feature history after burn-in: " << endl);
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decoder->printBleuFeatureHistory(cerr);
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decoder->loadReferenceSentences(referenceSentences);
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}
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else {
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decoder->loadReferenceSentences(referenceSentences);
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}
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decoder->loadReferenceSentences(referenceSentences);
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#ifdef MPI_ENABLE
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mpi::broadcast(world, order, 0);
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@ -458,16 +350,12 @@ int main(int argc, char** argv) {
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bool stop = false;
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int sumStillViolatedConstraints;
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int sumStillViolatedConstraints_lastEpoch = 0;
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int sumConstraintChangeAbs;
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int sumConstraintChangeAbs_lastEpoch = 0;
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float *sendbuf, *recvbuf;
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sendbuf = (float *) malloc(sizeof(float));
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recvbuf = (float *) malloc(sizeof(float));
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for (size_t epoch = 0; epoch < epochs && !stop; ++epoch) {
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// sum of violated constraints
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// sum of violated constraints in an epoch
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sumStillViolatedConstraints = 0;
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sumConstraintChangeAbs = 0;
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numberOfUpdatesThisEpoch = 0;
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// Sum up weights over one epoch, final average uses weights from last epoch
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@ -517,10 +405,6 @@ int main(int argc, char** argv) {
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const vector<string>& refs = referenceSentences[*sid];
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cerr << "\nRank " << rank << ", epoch " << epoch << ", input sentence " << *sid << ": \"" << input << "\"" << " (batch pos " << batchPosition << ")" << endl;
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cerr << "model-hope-fear: " << model_hope_fear << endl;
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cerr << "hope-fear: " << hope_fear << endl;
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cerr << "perceptron: " << perceptron_update << endl;
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vector<ScoreComponentCollection> newFeatureValues;
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vector<float> newBleuScores;
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if (model_hope_fear) {
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@ -634,58 +518,39 @@ int main(int argc, char** argv) {
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// set weight for bleu feature to 0
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mosesWeights.Assign(featureFunctions.back(), 0);
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// take logs of feature values
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if (logFeatureValues) {
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for (size_t i = 0; i < featureValues.size(); ++i) {
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if (hope_fear) {
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for (size_t j = 0; j < featureValuesHope[i].size(); ++j) {
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featureValuesHope[i][j].ApplyLog(baseOfLog);
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}
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for (size_t j = 0; j < featureValuesFear[i].size(); ++j) {
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featureValuesFear[i][j].ApplyLog(baseOfLog);
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}
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}
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else {
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for (size_t j = 0; j < featureValues[i].size(); ++j) {
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featureValues[i][j].ApplyLog(baseOfLog);
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}
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oracleFeatureValues[i].ApplyLog(baseOfLog);
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}
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takeLogs(featureValuesHope, baseOfLog);
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takeLogs(featureValuesFear, baseOfLog);
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takeLogs(featureValues, baseOfLog);
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for (size_t i = 0; i < oracleFeatureValues.size(); ++i) {
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oracleFeatureValues[i].ApplyLog(baseOfLog);
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}
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}
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// optionally print out the feature values
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// print out the feature values
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if (print_feature_values) {
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cerr << "\nRank " << rank << ", epoch " << epoch << ", feature values: " << endl;
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if (model_hope_fear) {
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for (size_t i = 0; i < featureValues.size(); ++i) {
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for (size_t j = 0; j < featureValues[i].size(); ++j) {
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cerr << featureValues[i][j] << endl;
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}
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}
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cerr << endl;
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}
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if (model_hope_fear) printFeatureValues(featureValues);
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else {
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cerr << "hope: " << endl;
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for (size_t i = 0; i < featureValuesHope.size(); ++i) {
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for (size_t j = 0; j < featureValuesHope[i].size(); ++j) {
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cerr << featureValuesHope[i][j] << endl;
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}
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}
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printFeatureValues(featureValuesHope);
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cerr << "fear: " << endl;
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for (size_t i = 0; i < featureValuesFear.size(); ++i) {
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for (size_t j = 0; j < featureValuesFear[i].size(); ++j) {
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cerr << featureValuesFear[i][j] << endl;
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}
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}
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cerr << endl;
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printFeatureValues(featureValuesFear);
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}
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}
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// set core features to 0 to avoid updating the feature weights
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if (coreWeightMap.size() > 0) {
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ignoreCoreFeatures(featureValues, coreWeightMap);
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ignoreCoreFeatures(featureValuesHope, coreWeightMap);
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ignoreCoreFeatures(featureValuesFear, coreWeightMap);
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}
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// Run optimiser on batch:
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VERBOSE(1, "\nRank " << rank << ", epoch " << epoch << ", run optimiser:" << endl);
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ScoreComponentCollection oldWeights(mosesWeights);
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vector<int> update_status;
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size_t update_status;
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if (perceptron_update) {
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vector<vector<float> > dummy1;
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vector<size_t> dummy2;
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@ -693,47 +558,19 @@ int main(int argc, char** argv) {
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featureValuesHope, featureValuesFear, dummy1, dummy1, dummy2,
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learning_rate, rank, epoch);
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}
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else if (hope_fear) {
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update_status = optimiser->updateWeightsHopeFear(mosesWeights,
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featureValuesHope, featureValuesFear, bleuScoresHope, bleuScoresFear, ref_ids,
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learning_rate, rank, epoch);
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}
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else {
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if (hope_fear) {
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if (coreWeightMap.size() > 0) {
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// set core features to 0 to avoid updating the feature weights
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for (size_t i = 0; i < featureValuesHope.size(); ++i) {
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for (size_t j = 0; j < featureValuesHope[i].size(); ++j) {
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// set all core features to 0
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StrFloatMap::iterator p;
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for(p = coreWeightMap.begin(); p!=coreWeightMap.end(); ++p)
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{
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featureValuesHope[i][j].Assign(p->first, 0);
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}
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}
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}
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for (size_t i = 0; i < featureValuesFear.size(); ++i) {
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for (size_t j = 0; j < featureValuesFear[i].size(); ++j) {
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// set all core features to 0
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StrFloatMap::iterator p;
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for(p = coreWeightMap.begin(); p!=coreWeightMap.end(); ++p)
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{
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featureValuesFear[i][j].Assign(p->first, 0);
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}
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}
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}
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}
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update_status = optimiser->updateWeightsHopeFear(mosesWeights,
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featureValuesHope, featureValuesFear, bleuScoresHope, bleuScoresFear, ref_ids,
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learning_rate, rank, epoch);
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}
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else {
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// model_hope_fear
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update_status = ((MiraOptimiser*) optimiser)->updateWeights(mosesWeights, featureValues,
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losses, bleuScores, oracleFeatureValues, oracleBleuScores, ref_ids,
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learning_rate, rank, epoch);
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}
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// model_hope_fear
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update_status = ((MiraOptimiser*) optimiser)->updateWeights(mosesWeights,
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featureValues, losses, bleuScores, oracleFeatureValues, oracleBleuScores, ref_ids,
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learning_rate, rank, epoch);
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}
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sumConstraintChangeAbs += abs(update_status[0] - update_status[1]);
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sumStillViolatedConstraints += update_status[1];
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sumStillViolatedConstraints += update_status;
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// pass new weights to decoder
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if (normaliseWeights) {
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@ -754,7 +591,7 @@ int main(int argc, char** argv) {
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mosesWeights = averageWeights;
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}
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// set new Moses weights (averaged or not)
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// set new Moses weights
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decoder->setWeights(mosesWeights);
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// compute difference to old weights
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@ -764,9 +601,9 @@ int main(int argc, char** argv) {
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// update history (for approximate document Bleu)
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if (sentenceLevelBleu) {
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for (size_t i = 0; i < oracles.size(); ++i) {
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VERBOSE(1, "Rank " << rank << ", epoch " << epoch << ", oracle length: " << oracles[i].size() << " ");
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if (verbosity > 0) {
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if (verbosity > 0) {
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for (size_t i = 0; i < oracles.size(); ++i) {
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cerr << "Rank " << rank << ", epoch " << epoch << ", oracle length: " << oracles[i].size() << " ";
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decoder->printReferenceLength(ref_ids);
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}
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}
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@ -787,16 +624,8 @@ int main(int argc, char** argv) {
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}
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// clean up oracle and 1best translations after updating history
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for (size_t i = 0; i < oracles.size(); ++i) {
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for (size_t j = 0; j < oracles[i].size(); ++j) {
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delete oracles[i][j];
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}
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}
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for (size_t i = 0; i < oneBests.size(); ++i) {
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for (size_t j = 0; j < oneBests[i].size(); ++j) {
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delete oneBests[i][j];
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}
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}
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deleteTranslations(oracles);
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deleteTranslations(oneBests);
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size_t mixing_base = mixingFrequency == 0 ? 0 : shard.size() / mixingFrequency;
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size_t dumping_base = weightDumpFrequency ==0 ? 0 : shard.size() / weightDumpFrequency;
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@ -886,6 +715,7 @@ int main(int argc, char** argv) {
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cerr << "Bleu feature history after epoch " << epoch << endl;
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decoder->printBleuFeatureHistory(cerr);
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}
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cerr << "Rank " << rank << ", epoch " << epoch << ", sum of violated constraints: " << sumStillViolatedConstraints << endl;
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// Check whether there were any weight updates during this epoch
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size_t sumUpdates;
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@ -910,21 +740,6 @@ int main(int argc, char** argv) {
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#endif
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}
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if (epoch > 0) {
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if ((sumConstraintChangeAbs_lastEpoch == sumConstraintChangeAbs) && (sumStillViolatedConstraints_lastEpoch == sumStillViolatedConstraints)) {
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VERBOSE(2, "Rank " << rank << ", epoch " << epoch << ", sum of violated constraints and constraint changes has stayed the same: " << sumStillViolatedConstraints << ", " << sumConstraintChangeAbs << endl);
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}
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else {
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VERBOSE(2, "Rank " << rank << ", epoch " << epoch << ", sum of violated constraints: " << sumStillViolatedConstraints << ", sum of constraint changes " << sumConstraintChangeAbs << endl);
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}
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}
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else {
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VERBOSE(2, "Rank " << rank << ", epoch " << epoch << ", sum of violated constraints: " << sumStillViolatedConstraints << endl);
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}
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sumConstraintChangeAbs_lastEpoch = sumConstraintChangeAbs;
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sumStillViolatedConstraints_lastEpoch = sumStillViolatedConstraints;
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if (!stop) {
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// Test if weights have converged
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if (weightConvergence) {
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@ -1110,4 +925,3 @@ void deleteTranslations(vector<vector<const Word*> > &translations) {
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}
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}
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}
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@ -7,7 +7,7 @@ using namespace std;
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namespace Mira {
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vector<int> MiraOptimiser::updateWeights(ScoreComponentCollection& currWeights,
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size_t MiraOptimiser::updateWeights(ScoreComponentCollection& currWeights,
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const vector<vector<ScoreComponentCollection> >& featureValues,
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const vector<vector<float> >& losses,
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const vector<vector<float> >& bleuScores,
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@ -115,18 +115,14 @@ vector<int> MiraOptimiser::updateWeights(ScoreComponentCollection& currWeights,
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}
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}
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else {
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cerr << "Rank " << rank << ", epoch " << epoch << ", check, no constraint violated for this batch" << endl;
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vector<int> status(2);
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status[0] = 0;
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status[1] = 0;
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return status;
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cerr << "Rank " << rank << ", epoch " << epoch << ", no constraint violated for this batch" << endl;
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return 0;
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}
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// apply learning rate
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if (learning_rate != 1) {
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VERBOSE(1, "Rank " << rank << ", epoch " << epoch << ", update before applying learning rate: " << summedUpdate << endl);
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summedUpdate.MultiplyEquals(learning_rate);
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VERBOSE(1, "Rank " << rank << ", epoch " << epoch << ", update after applying learning rate: " << summedUpdate << endl);
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}
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// scale update by BLEU of oracle
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@ -135,6 +131,8 @@ vector<int> MiraOptimiser::updateWeights(ScoreComponentCollection& currWeights,
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summedUpdate.MultiplyEquals(log10(oracleBleuScores[0]));
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}
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cerr << "Rank " << rank << ", epoch " << epoch << ", update: " << summedUpdate << endl;
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// apply update to weight vector
|
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VERBOSE(1, "Rank " << rank << ", epoch " << epoch << ", weights before update: " << currWeights << endl);
|
||||
currWeights.PlusEquals(summedUpdate);
|
||||
@ -154,14 +152,10 @@ vector<int> MiraOptimiser::updateWeights(ScoreComponentCollection& currWeights,
|
||||
}
|
||||
VERBOSE(1, "Rank " << rank << ", epoch " << epoch << ", violated constraint before: " << violatedConstraintsBefore << ", after: " << violatedConstraintsAfter << ", change: " << violatedConstraintsBefore - violatedConstraintsAfter << endl);
|
||||
VERBOSE(1, "Rank " << rank << ", epoch " << epoch << ", error before: " << oldDistanceFromOptimum << ", after: " << newDistanceFromOptimum << ", change: " << oldDistanceFromOptimum - newDistanceFromOptimum << endl);
|
||||
|
||||
vector<int> status(2);
|
||||
status[0] = violatedConstraintsBefore;
|
||||
status[1] = violatedConstraintsAfter;
|
||||
return status;
|
||||
return violatedConstraintsAfter;
|
||||
}
|
||||
|
||||
vector<int> MiraOptimiser::updateWeightsHopeFear(Moses::ScoreComponentCollection& currWeights,
|
||||
size_t MiraOptimiser::updateWeightsHopeFear(Moses::ScoreComponentCollection& currWeights,
|
||||
const std::vector< std::vector<Moses::ScoreComponentCollection> >& featureValuesHope,
|
||||
const std::vector< std::vector<Moses::ScoreComponentCollection> >& featureValuesFear,
|
||||
const std::vector<std::vector<float> >& bleuScoresHope,
|
||||
@ -282,19 +276,17 @@ vector<int> MiraOptimiser::updateWeightsHopeFear(Moses::ScoreComponentCollection
|
||||
}
|
||||
else {
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", check, no constraint violated for this batch" << endl;
|
||||
vector<int> status(2);
|
||||
status[0] = 0;
|
||||
status[1] = 0;
|
||||
return status;
|
||||
return 0;
|
||||
}
|
||||
|
||||
// apply learning rate
|
||||
if (learning_rate != 1) {
|
||||
VERBOSE(1, "Rank " << rank << ", epoch " << epoch << ", update before applying learning rate: " << summedUpdate << endl);
|
||||
summedUpdate.MultiplyEquals(learning_rate);
|
||||
VERBOSE(1, "Rank " << rank << ", epoch " << epoch << ", update after applying learning rate: " << summedUpdate << endl);
|
||||
}
|
||||
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", update: " << summedUpdate << endl;
|
||||
|
||||
// apply update to weight vector
|
||||
VERBOSE(1, "Rank " << rank << ", epoch " << epoch << ", weights before update: " << currWeights << endl);
|
||||
currWeights.PlusEquals(summedUpdate);
|
||||
@ -314,12 +306,7 @@ vector<int> MiraOptimiser::updateWeightsHopeFear(Moses::ScoreComponentCollection
|
||||
}
|
||||
VERBOSE(1, "Rank " << rank << ", epoch " << epoch << ", check, violated constraint before: " << violatedConstraintsBefore << ", after: " << violatedConstraintsAfter << ", change: " << violatedConstraintsBefore - violatedConstraintsAfter << endl);
|
||||
VERBOSE(1, "Rank " << rank << ", epoch " << epoch << ", check, error before: " << oldDistanceFromOptimum << ", after: " << newDistanceFromOptimum << ", change: " << oldDistanceFromOptimum - newDistanceFromOptimum << endl);
|
||||
|
||||
|
||||
vector<int> statusPlus(2);
|
||||
statusPlus[0] = violatedConstraintsBefore;
|
||||
statusPlus[1] = violatedConstraintsAfter;
|
||||
return statusPlus;
|
||||
return violatedConstraintsAfter;
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
@ -30,7 +30,7 @@ namespace Mira {
|
||||
public:
|
||||
Optimiser() {}
|
||||
|
||||
virtual std::vector<int> updateWeightsHopeFear(Moses::ScoreComponentCollection& currWeights,
|
||||
virtual size_t updateWeightsHopeFear(Moses::ScoreComponentCollection& currWeights,
|
||||
const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesHope,
|
||||
const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesFear,
|
||||
const std::vector<std::vector<float> >& bleuScoresHope,
|
||||
@ -43,7 +43,7 @@ namespace Mira {
|
||||
|
||||
class Perceptron : public Optimiser {
|
||||
public:
|
||||
virtual std::vector<int> updateWeightsHopeFear(Moses::ScoreComponentCollection& currWeights,
|
||||
virtual size_t updateWeightsHopeFear(Moses::ScoreComponentCollection& currWeights,
|
||||
const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesHope,
|
||||
const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesFear,
|
||||
const std::vector<std::vector<float> >& bleuScoresHope,
|
||||
@ -67,7 +67,7 @@ namespace Mira {
|
||||
m_scale_update(scale_update),
|
||||
m_margin_slack(margin_slack) { }
|
||||
|
||||
std::vector<int> updateWeights(Moses::ScoreComponentCollection& currWeights,
|
||||
size_t updateWeights(Moses::ScoreComponentCollection& currWeights,
|
||||
const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValues,
|
||||
const std::vector<std::vector<float> >& losses,
|
||||
const std::vector<std::vector<float> >& bleuScores,
|
||||
@ -77,7 +77,7 @@ namespace Mira {
|
||||
float learning_rate,
|
||||
size_t rank,
|
||||
size_t epoch);
|
||||
virtual std::vector<int> updateWeightsHopeFear(Moses::ScoreComponentCollection& currWeights,
|
||||
virtual size_t updateWeightsHopeFear(Moses::ScoreComponentCollection& currWeights,
|
||||
const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesHope,
|
||||
const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesFear,
|
||||
const std::vector<std::vector<float> >& bleuScoresHope,
|
||||
|
||||
@ -24,7 +24,7 @@ using namespace std;
|
||||
|
||||
namespace Mira {
|
||||
|
||||
vector<int> Perceptron::updateWeightsHopeFear(ScoreComponentCollection& currWeights,
|
||||
size_t Perceptron::updateWeightsHopeFear(ScoreComponentCollection& currWeights,
|
||||
const vector< vector<ScoreComponentCollection> >& featureValuesHope,
|
||||
const vector< vector<ScoreComponentCollection> >& featureValuesFear,
|
||||
const vector< vector<float> >& dummy1,
|
||||
@ -34,18 +34,15 @@ vector<int> Perceptron::updateWeightsHopeFear(ScoreComponentCollection& currWeig
|
||||
size_t rank,
|
||||
size_t epoch)
|
||||
{
|
||||
cerr << "hope: " << featureValuesHope[0][0] << endl;
|
||||
cerr << "fear: " << featureValuesFear[0][0] << endl;
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", hope: " << featureValuesHope[0][0] << endl;
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", fear: " << featureValuesFear[0][0] << endl;
|
||||
ScoreComponentCollection featureValueDiff = featureValuesHope[0][0];
|
||||
featureValueDiff.MinusEquals(featureValuesFear[0][0]);
|
||||
cerr << "hope - fear: " << featureValueDiff << endl;
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", hope - fear: " << featureValueDiff << endl;
|
||||
featureValueDiff.MultiplyEquals(perceptron_learning_rate);
|
||||
currWeights.PlusEquals(featureValueDiff);
|
||||
|
||||
vector<int> update_status;
|
||||
update_status.push_back(0);
|
||||
update_status.push_back(0);
|
||||
return update_status;
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", update: " << featureValueDiff << endl;
|
||||
return 0;
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user