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More streaming for kbmira

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This commit is contained in:
Michael Denkowski 2014-09-30 14:50:10 -04:00
parent 84ad576750
commit 44b22ece69
2 changed files with 7 additions and 2 deletions
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2
mert/MiraWeightVector.cpp
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@ -139,7 +139,7 @@ ostream& operator<<(ostream& o, const MiraWeightVector& e)
for(size_t i=0; i<e.m_weights.size(); i++) {
if(abs(e.m_weights[i])>1e-8) {
if(i>0) o << " ";
cerr << i << ":" << e.m_weights[i];
o << i << ":" << e.m_weights[i];
}
}
return o;

7
mert/kbmira.cpp
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@ -72,6 +72,7 @@ int main(int argc, char** argv)
float decay = 0.999; // Pseudo-corpus decay \gamma
int n_iters = 60; // Max epochs J
bool streaming = false; // Stream all k-best lists?
bool streaming_out = false; // Stream output after each sentence?
bool no_shuffle = false; // Don't shuffle, even for in memory version
bool model_bg = false; // Use model for background corpus
bool verbose = false; // Verbose updates
@ -97,6 +98,7 @@ int main(int argc, char** argv)
("dense-init,d", po::value<string>(&denseInitFile), "Weight file for dense features. This should have 'name= value' on each line, or (legacy) should be the Moses mert 'init.opt' format.")
("sparse-init,s", po::value<string>(&sparseInitFile), "Weight file for sparse features")
("streaming", po::value(&streaming)->zero_tokens()->default_value(false), "Stream n-best lists to save memory, implies --no-shuffle")
("streaming-out", po::value(&streaming_out)->zero_tokens()->default_value(false), "Stream weights to stdout after each sentence")
("no-shuffle", po::value(&no_shuffle)->zero_tokens()->default_value(false), "Don't shuffle hypotheses before each epoch")
("model-bg", po::value(&model_bg)->zero_tokens()->default_value(false), "Use model instead of hope for BLEU background")
("verbose", po::value(&verbose)->zero_tokens()->default_value(false), "Verbose updates")
@ -235,7 +237,8 @@ int main(int argc, char** argv)
}
// Training loop
cerr << "Initial BLEU = " << decoder->Evaluate(wv.avg()) << endl;
if (!streaming_out)
cerr << "Initial BLEU = " << decoder->Evaluate(wv.avg()) << endl;
ValType bestBleu = 0;
for(int j=0; j<n_iters; j++) {
// MIRA train for one epoch
@ -283,6 +286,8 @@ int main(int argc, char** argv)
}
iNumExamples++;
++sentenceIndex;
if (streaming_out)
cout << wv << endl;
}
// Training Epoch summary
cerr << iNumUpdates << "/" << iNumExamples << " updates"