mirror of
https://github.com/moses-smt/mosesdecoder.git
synced 2024-11-10 10:59:21 +03:00
remove stop-optimal parameter, default for precision is 1.0, fix flexible slack, inable analytical update
git-svn-id: http://svn.statmt.org/repository/mira@3890 cc96ff50-19ce-11e0-b349-13d7f0bd23df
This commit is contained in:
ehasler
Ondrej Bojar
parent
bc2a2078ea
commit
a58dd23b03
@ -214,6 +214,17 @@ namespace Mira {
|
||||
float scoreWithoutBleu = path.GetTotalScore() - (bleuObjectiveWeight * bleuScoreWeight * bleuScore);
|
||||
bleuAndScore.push_back(bleuScore);
|
||||
bleuAndScore.push_back(scoreWithoutBleu);
|
||||
|
||||
cerr << "1best translation: ";
|
||||
Phrase phrase = path.GetTargetPhrase();
|
||||
for (size_t pos = 0; pos < phrase.GetSize(); ++pos) {
|
||||
const Word &word = phrase.GetWord(pos);
|
||||
Word *newWord = new Word(word);
|
||||
cerr << *newWord;
|
||||
}
|
||||
|
||||
cerr << endl;
|
||||
|
||||
return bleuAndScore;
|
||||
}
|
||||
|
||||
|
||||
513
mira/Main.cpp
513
mira/Main.cpp
@ -156,7 +156,7 @@ int main(int argc, char** argv) {
|
||||
bool adapt_BPfactor;
|
||||
float slack;
|
||||
float slack_step;
|
||||
float slack_max;
|
||||
float slack_min;
|
||||
size_t maxNumberOracles;
|
||||
bool accumulateMostViolatedConstraints;
|
||||
bool averageWeights;
|
||||
@ -176,7 +176,6 @@ int main(int argc, char** argv) {
|
||||
bool print_feature_values;
|
||||
bool stop_dev_bleu;
|
||||
bool stop_approx_dev_bleu;
|
||||
bool stop_optimal;
|
||||
bool train_linear_classifier;
|
||||
int updates_per_epoch;
|
||||
bool multiplyA;
|
||||
@ -187,11 +186,15 @@ int main(int argc, char** argv) {
|
||||
bool sentenceLevelBleu;
|
||||
float bleuScoreWeight;
|
||||
float precision;
|
||||
float min_bleu_change;
|
||||
bool analytical_update;
|
||||
size_t constraints;
|
||||
po::options_description desc("Allowed options");
|
||||
desc.add_options()
|
||||
("accumulate-most-violated-constraints", po::value<bool>(&accumulateMostViolatedConstraints)->default_value(false),"Accumulate most violated constraint per example")
|
||||
("accumulate-weights", po::value<bool>(&accumulateWeights)->default_value(false), "Accumulate and average weights over all epochs")
|
||||
("adapt-BP-factor", po::value<bool>(&adapt_BPfactor)->default_value(0), "Set factor to 1 when optimal translation length in reached")
|
||||
("analytical-update", po::value<bool>(&analytical_update)->default_value(0), "Use one best lists and compute the update analytically")
|
||||
("average-weights", po::value<bool>(&averageWeights)->default_value(false), "Set decoder weights to average weights after each update")
|
||||
("base-of-log", po::value<size_t>(&baseOfLog)->default_value(10), "Base for log-ing feature values")
|
||||
("batch-size,b", po::value<size_t>(&batchSize)->default_value(1), "Size of batch that is send to optimiser for weight adjustments")
|
||||
@ -201,6 +204,7 @@ int main(int argc, char** argv) {
|
||||
("burn-in-input-file", po::value<string>(&burnInInputFile), "Input file for burn-in phase of BLEU history")
|
||||
("burn-in-reference-files", po::value<vector<string> >(&burnInReferenceFiles), "Reference file for burn-in phase of BLEU history")
|
||||
("config,f", po::value<string>(&mosesConfigFile), "Moses ini file")
|
||||
("constraints", po::value<size_t>(&constraints)->default_value(1), "Number of constraints used for analytical update")
|
||||
("control-updates", po::value<bool>(&controlUpdates)->default_value(true), "Ignore updates that increase number of violated constraints AND increase the error")
|
||||
("decoder-settings", po::value<string>(&decoder_settings)->default_value(""), "Decoder settings for tuning runs")
|
||||
("decr-learning-rate", po::value<float>(&decrease_learning_rate)->default_value(0),"Decrease learning rate by the given value after every epoch")
|
||||
@ -218,6 +222,7 @@ int main(int argc, char** argv) {
|
||||
("learning-rate", po::value<float>(&learning_rate)->default_value(1), "Learning rate (fixed or flexible)")
|
||||
("log-feature-values", po::value<bool>(&logFeatureValues)->default_value(false), "Take log of feature values according to the given base.")
|
||||
("max-number-oracles", po::value<size_t>(&maxNumberOracles)->default_value(1), "Set a maximum number of oracles to use per example")
|
||||
("min-bleu-change", po::value<float>(&min_bleu_change)->default_value(0), "Minimum BLEU change of 1best translations of one epoch")
|
||||
("min-sentence-update", po::value<float>(&min_sentence_update)->default_value(0), "Set a minimum weight update per sentence")
|
||||
("min-learning-rate", po::value<float>(&min_learning_rate)->default_value(0), "Set a minimum learning rate")
|
||||
("max-sentence-update", po::value<float>(&max_sentence_update)->default_value(-1), "Set a maximum weight update per sentence")
|
||||
@ -232,20 +237,19 @@ int main(int argc, char** argv) {
|
||||
("normalise", po::value<bool>(&normaliseWeights)->default_value(false), "Whether to normalise the updated weights before passing them to the decoder")
|
||||
("only-violated-constraints", po::value<bool>(&onlyViolatedConstraints)->default_value(false), "Add only violated constraints to the optimisation problem")
|
||||
("past-and-current-constraints", po::value<bool>(&pastAndCurrentConstraints)->default_value(false), "Accumulate most violated constraint per example and use them along all current constraints")
|
||||
("precision", po::value<float>(&precision)->default_value(0), "Precision when comparing left and right hand side of constraints")
|
||||
("precision", po::value<float>(&precision)->default_value(1.0), "Precision when comparing left and right hand side of constraints")
|
||||
("print-feature-values", po::value<bool>(&print_feature_values)->default_value(false), "Print out feature values")
|
||||
("reference-files,r", po::value<vector<string> >(&referenceFiles), "Reference translation files for training")
|
||||
("scale-by-input-length", po::value<bool>(&scaleByInputLength)->default_value(true), "Scale the BLEU score by a history of the input lengths")
|
||||
("sentence-level-bleu", po::value<bool>(&sentenceLevelBleu)->default_value(false), "Use a sentences level bleu scoring function")
|
||||
("shuffle", po::value<bool>(&shuffle)->default_value(false), "Shuffle input sentences before processing")
|
||||
("slack", po::value<float>(&slack)->default_value(0.01), "Use slack in optimizer")
|
||||
("slack-max", po::value<float>(&slack_max)->default_value(0), "Maximum slack used")
|
||||
("slack-min", po::value<float>(&slack_min)->default_value(0.01), "Minimum slack used")
|
||||
("slack-step", po::value<float>(&slack_step)->default_value(0), "Increase slack from epoch to epoch by the value provided")
|
||||
("stop-dev-bleu", po::value<bool>(&stop_dev_bleu)->default_value(false), "Stop when average Bleu (dev) decreases (or no more increases)")
|
||||
("stop-approx-dev-bleu", po::value<bool>(&stop_approx_dev_bleu)->default_value(false), "Stop when average approx. sentence Bleu (dev) decreases (or no more increases)")
|
||||
("stop-weights", po::value<bool>(&weightConvergence)->default_value(false), "Stop when weights converge")
|
||||
("stop-optimal", po::value<bool>(&stop_optimal)->default_value(true), "Stop when the results of optimization do not improve further")
|
||||
("train-linear-classifier", po::value<bool>(&train_linear_classifier)->default_value(false), "Test algorithm for linear classification")
|
||||
("train-linear-classifier", po::value<bool>(&train_linear_classifier)->default_value(false), "Test algorithm for linear classification")
|
||||
("updates-per-epoch", po::value<int>(&updates_per_epoch)->default_value(-1), "Accumulate updates and apply them to the weight vector the specified number of times per epoch")
|
||||
("use-scaled-reference", po::value<bool>(&useScaledReference)->default_value(true), "Use scaled reference length for comparing target and reference length of phrases")
|
||||
("verbosity,v", po::value<int>(&verbosity)->default_value(0), "Verbosity level")
|
||||
@ -259,311 +263,6 @@ int main(int argc, char** argv) {
|
||||
po::command_line_parser(argc, argv). options(cmdline_options).run(), vm);
|
||||
po::notify(vm);
|
||||
|
||||
if (train_linear_classifier) {
|
||||
FName name_x("x");
|
||||
FName name_y("y");
|
||||
FVector weights;
|
||||
weights.set(name_x, 0);
|
||||
weights.set(name_y, 0);
|
||||
|
||||
vector<FVector> examples;
|
||||
vector<int> outcomes;
|
||||
|
||||
FVector pos1;
|
||||
pos1.set(name_x, 1);
|
||||
pos1.set(name_y, 1);
|
||||
FVector pos2;
|
||||
pos2.set(name_x, 2);
|
||||
pos2.set(name_y, 1);
|
||||
FVector pos3;
|
||||
pos3.set(name_x, 3);
|
||||
pos3.set(name_y, 1);
|
||||
FVector pos4;
|
||||
pos4.set(name_x, 8);
|
||||
pos4.set(name_y, 1);
|
||||
FVector neg1;
|
||||
neg1.set(name_x, 1);
|
||||
neg1.set(name_y, -1);
|
||||
FVector neg2;
|
||||
neg2.set(name_x, 2);
|
||||
neg2.set(name_y, -1);
|
||||
FVector neg3;
|
||||
neg3.set(name_x, 3);
|
||||
neg3.set(name_y, -1);
|
||||
FVector neg4;
|
||||
neg4.set(name_x, 8);
|
||||
neg4.set(name_y, -1);
|
||||
examples.push_back(pos1);
|
||||
examples.push_back(neg1);
|
||||
examples.push_back(pos2);
|
||||
examples.push_back(neg2);
|
||||
examples.push_back(pos3);
|
||||
examples.push_back(neg3);
|
||||
examples.push_back(pos4);
|
||||
examples.push_back(neg4);
|
||||
outcomes.push_back(1);
|
||||
outcomes.push_back(-1);
|
||||
outcomes.push_back(1);
|
||||
outcomes.push_back(-1);
|
||||
outcomes.push_back(1);
|
||||
outcomes.push_back(-1);
|
||||
outcomes.push_back(1);
|
||||
outcomes.push_back(-1);
|
||||
|
||||
// add outlier
|
||||
FVector pos5;
|
||||
pos5.set(name_x, 2.5);
|
||||
pos5.set(name_y, -1.5);
|
||||
examples.push_back(pos5);
|
||||
outcomes.push_back(1);
|
||||
|
||||
FVector neg5;
|
||||
neg5.set(name_x, 0);
|
||||
neg5.set(name_y, -1);
|
||||
examples.push_back(neg5);
|
||||
outcomes.push_back(-1);
|
||||
|
||||
// create order
|
||||
vector<size_t> order;
|
||||
if (rank == 0) {
|
||||
for (size_t i = 0; i < examples.size(); ++i) {
|
||||
order.push_back(i);
|
||||
}
|
||||
}
|
||||
|
||||
cerr << "weights: " << weights << endl;
|
||||
cerr << "slack: " << slack << endl;
|
||||
bool stop = false;
|
||||
FVector prevFinalWeights;
|
||||
FVector prevPrevFinalWeights;
|
||||
// float epsilon = 0.0001;
|
||||
float epsilon = 0.001;
|
||||
for (size_t epoch = 0; !stop && epoch < epochs; ++epoch) {
|
||||
cerr << "\nEpoch " << epoch << endl;
|
||||
size_t updatesThisEpoch = 0;
|
||||
|
||||
// optionally shuffle the input data --> change order
|
||||
if (shuffle && rank == 0) {
|
||||
shuffleInput(order, size, examples.size());
|
||||
}
|
||||
|
||||
// create shard
|
||||
vector<size_t> shard;
|
||||
createShard(order, size, rank, shard);
|
||||
|
||||
cerr << "\nBefore: y * x_i . w >= 1 ?" << endl;
|
||||
size_t numberUnsatisfiedConstraintsBefore = 0;
|
||||
float errorSumBefore = 0;
|
||||
// outcome_i (x_i . w + b) >= +1 (b = 0 in this example)
|
||||
for (size_t i = 0; i < examples.size(); ++i) {
|
||||
float innerProduct = examples[i].inner_product(weights);
|
||||
float leftHandSide = outcomes[i] * innerProduct;
|
||||
cerr << outcomes[i] << " * " << innerProduct << " >= " << 1 << "? " << (leftHandSide >= 1) << " (error: " << 1 - leftHandSide << ")" << endl;
|
||||
float diff = 1 - leftHandSide;
|
||||
if (diff > epsilon) {
|
||||
++numberUnsatisfiedConstraintsBefore;
|
||||
errorSumBefore += 1 - leftHandSide;
|
||||
}
|
||||
}
|
||||
cerr << "unsatisfied constraints before: " << numberUnsatisfiedConstraintsBefore << endl;
|
||||
cerr << "error sum before: " << errorSumBefore << endl;
|
||||
|
||||
// iterate over training data
|
||||
size_t shardPosition = 0;
|
||||
vector<size_t>::const_iterator sid = shard.begin();
|
||||
|
||||
// Use updatedWeights during one epoch
|
||||
FVector updatedWeights(weights);
|
||||
while (sid != shard.end()) {
|
||||
vector<FVector> A;
|
||||
vector<FVector> A_mult;
|
||||
vector<float> b;
|
||||
vector<float> batch_outcomes;
|
||||
|
||||
// collect constraints for batch
|
||||
size_t actualBatchSize = 0;
|
||||
for (size_t batchPosition = 0; batchPosition < batchSize && sid != shard.end(); ++batchPosition) {
|
||||
A.push_back(examples[*sid]);
|
||||
A_mult.push_back(outcomes[*sid]* examples[*sid]);
|
||||
b.push_back(1);
|
||||
batch_outcomes.push_back(outcomes[*sid]);
|
||||
|
||||
// next input sentence
|
||||
++sid;
|
||||
++actualBatchSize;
|
||||
++shardPosition;
|
||||
}
|
||||
|
||||
// check if constraints are already satisfied for batch
|
||||
size_t batch_numberUnsatisfiedConstraintsBefore = 0;
|
||||
float batch_errorSumBefore = 0;
|
||||
for (size_t i = 0; i < A.size(); ++i) {
|
||||
float innerProduct = A[i].inner_product(updatedWeights);
|
||||
float leftHandSide = batch_outcomes[i] * innerProduct;
|
||||
cerr << batch_outcomes[i] << " * " << innerProduct << " >= " << 1 << "? " << (leftHandSide >= 1) << " (error: " << 1 - leftHandSide << ")" << endl;
|
||||
float diff = 1 - leftHandSide;
|
||||
if (diff > epsilon) {
|
||||
++batch_numberUnsatisfiedConstraintsBefore;
|
||||
batch_errorSumBefore += 1 - leftHandSide;
|
||||
}
|
||||
}
|
||||
cerr << "batch: unsatisfied constraints before: " << batch_numberUnsatisfiedConstraintsBefore << endl;
|
||||
cerr << "batch: error sum before: " << batch_errorSumBefore << endl;
|
||||
|
||||
if (batch_numberUnsatisfiedConstraintsBefore != 0) {
|
||||
// pass constraints to optimizer
|
||||
for (size_t j = 0; j < A.size(); ++j){
|
||||
if (multiplyA) {
|
||||
cerr << "A: " << A_mult[j] << endl;
|
||||
}
|
||||
else {
|
||||
cerr << "A: " << A[j] << endl;
|
||||
}
|
||||
}
|
||||
|
||||
vector<float> alphas;
|
||||
if (slack == 0) {
|
||||
if (multiplyA) {
|
||||
alphas = Hildreth::optimise(A_mult, b);
|
||||
}
|
||||
else {
|
||||
alphas = Hildreth::optimise(A, b);
|
||||
}
|
||||
}
|
||||
else {
|
||||
if (multiplyA) {
|
||||
alphas = Hildreth::optimise(A_mult, b, slack);
|
||||
}
|
||||
else{
|
||||
alphas = Hildreth::optimise(A, b, slack);
|
||||
}
|
||||
}
|
||||
|
||||
for (size_t j = 0; j < alphas.size(); ++j) {
|
||||
cerr << "alpha:" << alphas[j] << endl;
|
||||
updatedWeights += batch_outcomes[j] * A[j] * alphas[j];
|
||||
}
|
||||
cerr << "potential new weights: " << updatedWeights << endl;
|
||||
|
||||
// check if constraints are satisfied after processing batch
|
||||
size_t batch_numberUnsatisfiedConstraintsAfter = 0;
|
||||
float batch_errorSumAfter = 0;
|
||||
for (size_t i = 0; i < A.size(); ++i) {
|
||||
float innerProduct = A[i].inner_product(updatedWeights);
|
||||
float leftHandSide = batch_outcomes[i] * innerProduct;
|
||||
cerr << batch_outcomes[i] << " * " << innerProduct << " >= " << 1 << "? " << (leftHandSide >= 1) << " (error: " << 1 - leftHandSide << ")" << endl;
|
||||
float diff = 1 - leftHandSide;
|
||||
if (diff > epsilon) {
|
||||
++batch_numberUnsatisfiedConstraintsAfter;
|
||||
batch_errorSumAfter += 1 - leftHandSide;
|
||||
}
|
||||
}
|
||||
cerr << "batch: unsatisfied constraints after: " << batch_numberUnsatisfiedConstraintsAfter << endl;
|
||||
cerr << "batch: error sum after: " << batch_errorSumAfter << endl << endl;
|
||||
}
|
||||
else {
|
||||
cerr << "batch: all constraints satisfied." << endl;
|
||||
}
|
||||
} // end of epoch */
|
||||
|
||||
cerr << "After: y * x_i . w >= 1 ?" << endl;
|
||||
size_t numberUnsatisfiedConstraintsAfter = 0;
|
||||
|
||||
float errorSumAfter = 0;
|
||||
for (size_t i = 0; i < examples.size(); ++i) {
|
||||
float innerProduct = examples[i].inner_product(updatedWeights);
|
||||
float leftHandSide = outcomes[i] * innerProduct;
|
||||
cerr << outcomes[i] << " * " << innerProduct << " >= " << 1 << "? " << (leftHandSide >= 1) << " (error: " << 1 - leftHandSide << ")" << endl;
|
||||
float diff = 1 - leftHandSide;
|
||||
if (diff > epsilon) {
|
||||
++numberUnsatisfiedConstraintsAfter;
|
||||
errorSumAfter += 1 - leftHandSide;
|
||||
}
|
||||
}
|
||||
cerr << "unsatisfied constraints after: " << numberUnsatisfiedConstraintsAfter << endl;
|
||||
cerr << "error sum after: " << errorSumAfter << endl;
|
||||
|
||||
float epsilon = 0.0001;
|
||||
float diff = errorSumAfter - errorSumBefore;
|
||||
if (numberUnsatisfiedConstraintsAfter == 0) {
|
||||
weights = updatedWeights;
|
||||
cerr << "All constraints satisfied during this epoch, stop." << endl;
|
||||
cerr << "new weights: " << weights << endl;
|
||||
++updatesThisEpoch;
|
||||
stop = true;
|
||||
}
|
||||
else if (numberUnsatisfiedConstraintsAfter < numberUnsatisfiedConstraintsBefore) {
|
||||
cerr << "Constraints improved during this epoch." << endl;
|
||||
weights = updatedWeights;
|
||||
cerr << "new weights: " << weights << endl;
|
||||
++updatesThisEpoch;
|
||||
}
|
||||
else if (numberUnsatisfiedConstraintsAfter == numberUnsatisfiedConstraintsBefore && errorSumAfter < errorSumBefore) {
|
||||
cerr << "Error improved during this epoch." << endl;
|
||||
weights = updatedWeights;
|
||||
cerr << "new weights: " << weights << endl;
|
||||
++updatesThisEpoch;
|
||||
}
|
||||
else if(numberUnsatisfiedConstraintsAfter == numberUnsatisfiedConstraintsBefore && (diff < epsilon && diff > epsilon * -1)) {
|
||||
cerr << "No changes to constraints or error during this epoch." << endl;
|
||||
}
|
||||
else {
|
||||
cerr << "Constraints/error got worse during this epoch." << endl;
|
||||
}
|
||||
|
||||
if (updatesThisEpoch == 0) {
|
||||
stop = true;
|
||||
cerr << "No more updates, stop." << endl;
|
||||
}
|
||||
else if(prevFinalWeights == weights) {
|
||||
stop = true;
|
||||
cerr << "Final weights not changing anymore, stop." << endl;
|
||||
}
|
||||
else if(prevPrevFinalWeights == weights) {
|
||||
stop = true;
|
||||
cerr << "Final weights changing back to previous final weights, take average and stop." << endl;
|
||||
weights = prevFinalWeights;
|
||||
weights += prevPrevFinalWeights;
|
||||
weights /= 2;
|
||||
}
|
||||
|
||||
prevPrevFinalWeights = prevFinalWeights;
|
||||
prevFinalWeights = weights;
|
||||
}
|
||||
|
||||
cerr << "\nFinal: " << endl;
|
||||
cerr << weights << endl;
|
||||
|
||||
// classify new examples
|
||||
cerr << "\nTest examples:" << endl;
|
||||
FVector test_pos1;
|
||||
test_pos1.set(name_x, 7);
|
||||
test_pos1.set(name_y, 1);
|
||||
cerr << "pos1 (7, 1): " << test_pos1.inner_product(weights) << endl;
|
||||
FVector test_pos2;
|
||||
test_pos2.set(name_x, 6);
|
||||
test_pos2.set(name_y, 2);
|
||||
cerr << "pos2 (2, 2): " << test_pos2.inner_product(weights) << endl;
|
||||
/* FVector test_pos3;
|
||||
test_pos3.set(name_x, 1);
|
||||
test_pos3.set(name_y, 0.5);
|
||||
cerr << "pos3 (1, 0.5): " << test_pos3.inner_product(weights) << endl;*/
|
||||
FVector test_neg1;
|
||||
test_neg1.set(name_x, 7);
|
||||
test_neg1.set(name_y, -1);
|
||||
cerr << "neg1 (7, -1): " << test_neg1.inner_product(weights) << endl;
|
||||
FVector test_neg2;
|
||||
test_neg2.set(name_x, 6);
|
||||
test_neg2.set(name_y, -2);
|
||||
cerr << "neg2 (2, -2): " << test_neg2.inner_product(weights) << endl;
|
||||
/* FVector test_neg3;
|
||||
test_neg3.set(name_x, 1);
|
||||
test_neg3.set(name_y, -0.5);
|
||||
cerr << "neg3 (1, -0.5): " << test_neg3.inner_product(weights) << endl;*/
|
||||
|
||||
exit(0);
|
||||
}
|
||||
|
||||
if (help) {
|
||||
std::cout << "Usage: " + string(argv[0])
|
||||
+ " -f mosesini-file -i input-file -r reference-file(s) [options]"
|
||||
@ -631,6 +330,12 @@ int main(int argc, char** argv) {
|
||||
|
||||
if (sentenceLevelBleu) {
|
||||
burnIn = false;
|
||||
cerr << "Burn-in not needed when using sentence-level BLEU, deactivating burn-in." << endl;
|
||||
}
|
||||
|
||||
if (analytical_update) {
|
||||
batchSize = 1;
|
||||
cerr << "Setting batch size to 1 for analytical update" << endl;
|
||||
}
|
||||
|
||||
if (burnIn) {
|
||||
@ -674,7 +379,7 @@ int main(int argc, char** argv) {
|
||||
order.push_back(i);
|
||||
}
|
||||
|
||||
cerr << "Start burn-in phase for approx. BLEU history.." << endl;
|
||||
cerr << "Rank " << rank << ", starting burn-in phase for approx. BLEU history.." << endl;
|
||||
if (historyOf1best) {
|
||||
// get 1best translations for the burn-in sentences
|
||||
vector<size_t>::const_iterator sid = order.begin();
|
||||
@ -788,7 +493,7 @@ int main(int argc, char** argv) {
|
||||
cerr << "BP-factor: " << BPfactor << endl;
|
||||
cerr << "slack: " << slack << endl;
|
||||
cerr << "slack-step: " << slack_step << endl;
|
||||
cerr << "slack-max: " << slack_max << endl;
|
||||
cerr << "slack-min: " << slack_min << endl;
|
||||
cerr << "max-number-oracles: " << maxNumberOracles << endl;
|
||||
cerr << "accumulate-most-violated-constraints: "
|
||||
<< accumulateMostViolatedConstraints << endl;
|
||||
@ -804,10 +509,14 @@ int main(int argc, char** argv) {
|
||||
cerr << "print-feature-values: " << print_feature_values << endl;
|
||||
cerr << "stop-dev-bleu: " << stop_dev_bleu << endl;
|
||||
cerr << "stop-approx-dev-bleu: " << stop_approx_dev_bleu << endl;
|
||||
cerr << "stop-optimal: " << stop_optimal << endl;
|
||||
cerr << "stop-weights: " << weightConvergence << endl;
|
||||
cerr << "updates-per-epoch: " << updates_per_epoch << endl;
|
||||
cerr << "use-total-weights-for-pruning: " << averageWeights << endl;
|
||||
cerr << "average-weights: " << averageWeights << endl;
|
||||
cerr << "history-of-1best: " << historyOf1best << endl;
|
||||
cerr << "sentence-level-bleu: " << sentenceLevelBleu << endl;
|
||||
cerr << "bleu-score-weight: " << bleuScoreWeight << endl;
|
||||
cerr << "precision: " << precision << endl;
|
||||
cerr << "min-bleu-change: " << min_bleu_change << endl;
|
||||
|
||||
if (learner == "mira") {
|
||||
cerr << "Optimising using Mira" << endl;
|
||||
@ -842,19 +551,20 @@ int main(int argc, char** argv) {
|
||||
ScoreComponentCollection mixedAverageWeightsPrevious;
|
||||
ScoreComponentCollection mixedAverageWeightsBeforePrevious;
|
||||
|
||||
float averageRatio = 0;
|
||||
/* float averageRatio = 0;
|
||||
float averageBleu = 0;
|
||||
float prevAverageBleu = 0;
|
||||
float beforePrevAverageBleu = 0;
|
||||
float summedApproxBleu = 0;
|
||||
float averageApproxBleu = 0;
|
||||
float prevAverageApproxBleu = 0;
|
||||
float beforePrevAverageApproxBleu = 0;
|
||||
float beforePrevAverageApproxBleu = 0;*/
|
||||
bool stop = false;
|
||||
size_t sumStillViolatedConstraints;
|
||||
size_t sumStillViolatedConstraints_lastEpoch = 0;
|
||||
size_t sumConstraintChangeAbs;
|
||||
size_t sumConstraintChangeAbs_lastEpoch = 0;
|
||||
int sumStillViolatedConstraints;
|
||||
int sumStillViolatedConstraints_lastEpoch = 0;
|
||||
int sumConstraintChangeAbs;
|
||||
int sumConstraintChangeAbs_lastEpoch = 0;
|
||||
size_t sumBleuChangeAbs;
|
||||
float *sendbuf, *recvbuf;
|
||||
sendbuf = (float *) malloc(sizeof(float));
|
||||
recvbuf = (float *) malloc(sizeof(float));
|
||||
@ -865,9 +575,10 @@ int main(int argc, char** argv) {
|
||||
// sum of violated constraints
|
||||
sumStillViolatedConstraints = 0;
|
||||
sumConstraintChangeAbs = 0;
|
||||
sumBleuChangeAbs = 0;
|
||||
|
||||
// sum of approx. sentence bleu scores per epoch
|
||||
summedApproxBleu = 0;
|
||||
// summedApproxBleu = 0;
|
||||
|
||||
numberOfUpdatesThisEpoch = 0;
|
||||
// Sum up weights over one epoch, final average uses weights from last epoch
|
||||
@ -919,52 +630,88 @@ int main(int argc, char** argv) {
|
||||
|
||||
size_t pass_n = (epoch == 0)? nbest_first : n;
|
||||
|
||||
// MODEL
|
||||
cerr << "Rank " << rank << ", run decoder to get " << pass_n << "best wrt model score" << endl;
|
||||
vector<const Word*> bestModel = decoder->getNBest(input, *sid, pass_n, 0.0, bleuScoreWeight,
|
||||
if (analytical_update) {
|
||||
if (constraints == 1) {
|
||||
// MODEL
|
||||
cerr << "Rank " << rank << ", run decoder to get " << 1 << "best wrt model score" << endl;
|
||||
vector<const Word*> bestModel = decoder->getNBest(input, *sid, 1, 0.0, bleuScoreWeight,
|
||||
featureValues[batchPosition], bleuScores[batchPosition], true,
|
||||
distinctNbest, rank);
|
||||
inputLengths.push_back(decoder->getCurrentInputLength());
|
||||
ref_ids.push_back(*sid);
|
||||
all_ref_ids.push_back(*sid);
|
||||
allBestModelScore.push_back(bestModel);
|
||||
decoder->cleanup();
|
||||
oneBests.push_back(bestModel);
|
||||
cerr << "Rank " << rank << ", model length: " << bestModel.size() << " Bleu: " << bleuScores[batchPosition][0] << endl;
|
||||
|
||||
// HOPE
|
||||
cerr << "Rank " << rank << ", run decoder to get " << 1 << "best hope translations" << endl;
|
||||
size_t oraclePos = featureValues[batchPosition].size();
|
||||
oraclePositions.push_back(oraclePos);
|
||||
vector<const Word*> oracle = decoder->getNBest(input, *sid, 1, 1.0, bleuScoreWeight,
|
||||
featureValues[batchPosition], bleuScores[batchPosition], true,
|
||||
distinctNbest, rank);
|
||||
decoder->cleanup();
|
||||
oracles.push_back(oracle);
|
||||
cerr << "Rank " << rank << ", oracle length: " << oracle.size() << " Bleu: " << bleuScores[batchPosition][oraclePos] << endl;
|
||||
|
||||
oracleFeatureValues.push_back(featureValues[batchPosition][oraclePos]);
|
||||
float oracleBleuScore = bleuScores[batchPosition][oraclePos];
|
||||
oracleBleuScores.push_back(oracleBleuScore);
|
||||
}
|
||||
else {
|
||||
// TODO:
|
||||
}
|
||||
}
|
||||
else {
|
||||
// MODEL
|
||||
cerr << "Rank " << rank << ", run decoder to get " << pass_n << "best wrt model score" << endl;
|
||||
vector<const Word*> bestModel = decoder->getNBest(input, *sid, pass_n, 0.0, bleuScoreWeight,
|
||||
featureValues[batchPosition], bleuScores[batchPosition], true,
|
||||
distinctNbest, rank);
|
||||
inputLengths.push_back(decoder->getCurrentInputLength());
|
||||
ref_ids.push_back(*sid);
|
||||
all_ref_ids.push_back(*sid);
|
||||
allBestModelScore.push_back(bestModel);
|
||||
decoder->cleanup();
|
||||
oneBests.push_back(bestModel);
|
||||
cerr << "Rank " << rank << ", model length: " << bestModel.size() << " Bleu: " << bleuScores[batchPosition][0] << endl;
|
||||
inputLengths.push_back(decoder->getCurrentInputLength());
|
||||
ref_ids.push_back(*sid);
|
||||
all_ref_ids.push_back(*sid);
|
||||
allBestModelScore.push_back(bestModel);
|
||||
decoder->cleanup();
|
||||
oneBests.push_back(bestModel);
|
||||
cerr << "Rank " << rank << ", model length: " << bestModel.size() << " Bleu: " << bleuScores[batchPosition][0] << endl;
|
||||
|
||||
// HOPE
|
||||
cerr << "Rank " << rank << ", run decoder to get " << pass_n << "best hope translations" << endl;
|
||||
size_t oraclePos = featureValues[batchPosition].size();
|
||||
oraclePositions.push_back(oraclePos);
|
||||
vector<const Word*> oracle = decoder->getNBest(input, *sid, pass_n, 1.0, bleuScoreWeight,
|
||||
// HOPE
|
||||
cerr << "Rank " << rank << ", run decoder to get " << pass_n << "best hope translations" << endl;
|
||||
size_t oraclePos = featureValues[batchPosition].size();
|
||||
oraclePositions.push_back(oraclePos);
|
||||
vector<const Word*> oracle = decoder->getNBest(input, *sid, pass_n, 1.0, bleuScoreWeight,
|
||||
featureValues[batchPosition], bleuScores[batchPosition], true,
|
||||
distinctNbest, rank);
|
||||
decoder->cleanup();
|
||||
oracles.push_back(oracle);
|
||||
cerr << "Rank " << rank << ", oracle length: " << oracle.size() << " Bleu: " << bleuScores[batchPosition][oraclePos] << endl;
|
||||
decoder->cleanup();
|
||||
oracles.push_back(oracle);
|
||||
cerr << "Rank " << rank << ", oracle length: " << oracle.size() << " Bleu: " << bleuScores[batchPosition][oraclePos] << endl;
|
||||
|
||||
oracleFeatureValues.push_back(featureValues[batchPosition][oraclePos]);
|
||||
float oracleBleuScore = bleuScores[batchPosition][oraclePos];
|
||||
oracleBleuScores.push_back(oracleBleuScore);
|
||||
oracleFeatureValues.push_back(featureValues[batchPosition][oraclePos]);
|
||||
float oracleBleuScore = bleuScores[batchPosition][oraclePos];
|
||||
oracleBleuScores.push_back(oracleBleuScore);
|
||||
|
||||
// FEAR
|
||||
cerr << "Rank " << rank << ", run decoder to get " << pass_n << "best fear translations" << endl;
|
||||
size_t fearPos = featureValues[batchPosition].size();
|
||||
vector<const Word*> fear = decoder->getNBest(input, *sid, pass_n, -1.0, bleuScoreWeight,
|
||||
// FEAR
|
||||
cerr << "Rank " << rank << ", run decoder to get " << pass_n << "best fear translations" << endl;
|
||||
size_t fearPos = featureValues[batchPosition].size();
|
||||
vector<const Word*> fear = decoder->getNBest(input, *sid, pass_n, -1.0, bleuScoreWeight,
|
||||
featureValues[batchPosition], bleuScores[batchPosition], true,
|
||||
distinctNbest, rank);
|
||||
decoder->cleanup();
|
||||
cerr << "Rank " << rank << ", fear length: " << fear.size() << " Bleu: " << bleuScores[batchPosition][fearPos] << endl;
|
||||
decoder->cleanup();
|
||||
cerr << "Rank " << rank << ", fear length: " << fear.size() << " Bleu: " << bleuScores[batchPosition][fearPos] << endl;
|
||||
|
||||
// for (size_t i = 0; i < bestModel.size(); ++i) {
|
||||
// delete bestModel[i];
|
||||
// }
|
||||
for (size_t i = 0; i < fear.size(); ++i) {
|
||||
delete fear[i];
|
||||
// for (size_t i = 0; i < bestModel.size(); ++i) {
|
||||
// delete bestModel[i];
|
||||
// }
|
||||
for (size_t i = 0; i < fear.size(); ++i) {
|
||||
delete fear[i];
|
||||
}
|
||||
}
|
||||
|
||||
cerr << "Rank " << rank << ", sentence " << *sid << ", best model Bleu (approximate sentence bleu): " << bleuScores[batchPosition][0] << endl;
|
||||
summedApproxBleu += bleuScores[batchPosition][0];
|
||||
// cerr << "Rank " << rank << ", sentence " << *sid << ", best model Bleu (approximate sentence bleu): " << bleuScores[batchPosition][0] << endl;
|
||||
// summedApproxBleu += bleuScores[batchPosition][0];
|
||||
|
||||
// next input sentence
|
||||
++sid;
|
||||
@ -1024,9 +771,16 @@ int main(int argc, char** argv) {
|
||||
cerr << "\nRank " << rank << ", epoch " << epoch << ", run optimiser:" << endl;
|
||||
ScoreComponentCollection oldWeights(mosesWeights);
|
||||
vector<int> update_status;
|
||||
update_status = optimiser->updateWeights(mosesWeights, featureValues,
|
||||
if (analytical_update) {
|
||||
update_status = optimiser->updateWeightsAnalytically(mosesWeights, featureValues[0][0],
|
||||
losses[0][0], oracleFeatureValues[0], oracleBleuScores[0], ref_ids[0],
|
||||
learning_rate, max_sentence_update, rank, epoch, controlUpdates);
|
||||
}
|
||||
else {
|
||||
update_status = optimiser->updateWeights(mosesWeights, featureValues,
|
||||
losses, bleuScores, oracleFeatureValues, oracleBleuScores, ref_ids,
|
||||
learning_rate, max_sentence_update, rank, epoch, updates_per_epoch, controlUpdates);
|
||||
}
|
||||
|
||||
if (update_status[0] == 1) {
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", no update for batch" << endl;
|
||||
@ -1076,14 +830,15 @@ int main(int argc, char** argv) {
|
||||
string& input = inputSentences[*current_sid_start + i];
|
||||
bestModelNew = decoder->getBleuAndScore(input, *current_sid_start + i, 0.0, bleuScoreWeight, distinctNbest);
|
||||
decoder->cleanup();
|
||||
sumBleuChangeAbs += abs(bestModelOld_batch[i][0] - bestModelNew[0]);
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", 1best model bleu, old: " << bestModelOld_batch[i][0] << ", new: " << bestModelNew[0] << endl;
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", 1best model score, old: " << bestModelOld_batch[i][1] << ", new: " << bestModelNew[1] << endl;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// update history (for approximate document Bleu)
|
||||
if (!sentenceLevelBleu) {
|
||||
// update history (for approximate document Bleu)
|
||||
if (historyOf1best) {
|
||||
for (size_t i = 0; i < oneBests.size(); ++i) {
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", 1best length: " << oneBests[i].size() << " ";
|
||||
@ -1152,6 +907,7 @@ int main(int argc, char** argv) {
|
||||
string& input = inputSentences[*current_sid_start + i];
|
||||
bestModelNew = decoder->getBleuAndScore(input, *current_sid_start + i, 0.0, bleuScoreWeight, distinctNbest);
|
||||
decoder->cleanup();
|
||||
sumBleuChangeAbs += abs(bestModelOld_batch[i][0] - bestModelNew[0]);
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", 1best model bleu, old: " << bestModelOld_batch[i][0] << ", new: " << bestModelNew[0] << endl;
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", 1best model score, old: " << bestModelOld_batch[i][1] << ", new: " << bestModelNew[1] << endl;
|
||||
}
|
||||
@ -1249,6 +1005,7 @@ int main(int argc, char** argv) {
|
||||
cerr << "Bleu feature history after epoch " << epoch << endl;
|
||||
decoder->printBleuFeatureHistory(cerr);
|
||||
|
||||
// Check whether there were any weight updates during this epoch
|
||||
size_t sumUpdates;
|
||||
size_t *sendbuf_uint, *recvbuf_uint;
|
||||
sendbuf_uint = (size_t *) malloc(sizeof(size_t));
|
||||
@ -1271,25 +1028,33 @@ int main(int argc, char** argv) {
|
||||
#endif
|
||||
}
|
||||
|
||||
if (stop_optimal) {
|
||||
if (epoch > 0) {
|
||||
if (sumConstraintChangeAbs_lastEpoch == sumConstraintChangeAbs && sumStillViolatedConstraints_lastEpoch == sumStillViolatedConstraints) {
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", sum of violated constraints and constraint changes has stayed the same: " << sumStillViolatedConstraints << ", " << sumConstraintChangeAbs << endl;
|
||||
}
|
||||
else {
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", sum of violated constraints: " << sumStillViolatedConstraints << ", sum of constraint changes " << sumConstraintChangeAbs << endl;
|
||||
}
|
||||
if (epoch > 0) {
|
||||
if ((sumConstraintChangeAbs_lastEpoch == sumConstraintChangeAbs) && (sumStillViolatedConstraints_lastEpoch == sumStillViolatedConstraints)) {
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", sum of violated constraints and constraint changes has stayed the same: " << sumStillViolatedConstraints << ", " << sumConstraintChangeAbs << endl;
|
||||
}
|
||||
else {
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", sum of violated constraints: " << sumStillViolatedConstraints << endl;
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", sum of violated constraints: " << sumStillViolatedConstraints << ", sum of constraint changes " << sumConstraintChangeAbs << endl;
|
||||
}
|
||||
}
|
||||
else {
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", sum of violated constraints: " << sumStillViolatedConstraints << endl;
|
||||
}
|
||||
|
||||
sumConstraintChangeAbs_lastEpoch = sumConstraintChangeAbs;
|
||||
sumStillViolatedConstraints_lastEpoch = sumStillViolatedConstraints;
|
||||
sumConstraintChangeAbs_lastEpoch = sumConstraintChangeAbs;
|
||||
sumStillViolatedConstraints_lastEpoch = sumStillViolatedConstraints;
|
||||
|
||||
if (min_bleu_change > 0) {
|
||||
if (sumBleuChangeAbs < min_bleu_change) {
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", sum of BLEU score changes was smaller than " << min_bleu_change << " (" << sumBleuChangeAbs << ")." << endl;
|
||||
stop = true;
|
||||
}
|
||||
else {
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", sum of BLEU score changes: " << sumBleuChangeAbs << "." << endl;
|
||||
}
|
||||
}
|
||||
|
||||
if (!stop) {
|
||||
if (devBleu) {
|
||||
/* if (devBleu) {
|
||||
// calculate bleu score of dev set
|
||||
vector<float> bleuAndRatio = decoder->calculateBleuOfCorpus(allBestModelScore, all_ref_ids, epoch, rank);
|
||||
float bleu = bleuAndRatio[0];
|
||||
@ -1388,7 +1153,7 @@ int main(int argc, char** argv) {
|
||||
#ifdef MPI_ENABLE
|
||||
mpi::broadcast(world, stop, 0);
|
||||
#endif
|
||||
} // end if (dev_bleu)
|
||||
} // end if (dev_bleu) */
|
||||
|
||||
// Test if weights have converged
|
||||
if (weightConvergence) {
|
||||
@ -1447,11 +1212,11 @@ int main(int argc, char** argv) {
|
||||
}
|
||||
}
|
||||
|
||||
// if using flexible slack, increase slack for next epoch
|
||||
// if using flexible regularization, decrease regularization parameter for next epoch
|
||||
if (slack_step > 0) {
|
||||
if (slack + slack_step <= slack_max) {
|
||||
if (slack - slack_step >= slack_min) {
|
||||
if (typeid(*optimiser) == typeid(MiraOptimiser)) {
|
||||
slack += slack_step;
|
||||
slack -= slack_step;
|
||||
cerr << "Change slack to: " << slack << endl;
|
||||
((MiraOptimiser*) optimiser)->setSlack(slack);
|
||||
}
|
||||
|
||||
@ -94,7 +94,7 @@ vector<int> MiraOptimiser::updateWeights(ScoreComponentCollection& currWeights,
|
||||
bool violated = false;
|
||||
bool addConstraint = true;
|
||||
float diff = loss - modelScoreDiff;
|
||||
// cerr << "constraint: " << modelScoreDiff << " >= " << loss << endl;
|
||||
cerr << "constraint: " << modelScoreDiff << " >= " << loss << endl;
|
||||
if (diff > (epsilon + m_precision)) {
|
||||
violated = true;
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", current violation: " << diff << " (loss: " << loss << ")" << endl;
|
||||
@ -308,9 +308,9 @@ vector<int> MiraOptimiser::updateWeights(ScoreComponentCollection& currWeights,
|
||||
}
|
||||
|
||||
vector<int> MiraOptimiser::updateWeightsAnalytically(ScoreComponentCollection& currWeights,
|
||||
const ScoreComponentCollection& featureValues,
|
||||
ScoreComponentCollection& featureValues,
|
||||
float loss,
|
||||
const ScoreComponentCollection& oracleFeatureValues,
|
||||
ScoreComponentCollection& oracleFeatureValues,
|
||||
float oracleBleuScore,
|
||||
size_t sentenceId,
|
||||
float learning_rate,
|
||||
@ -329,11 +329,11 @@ vector<int> MiraOptimiser::updateWeightsAnalytically(ScoreComponentCollection& c
|
||||
float modelScoreDiff = featureValueDiff.InnerProduct(currWeights);
|
||||
float diff = loss - modelScoreDiff;
|
||||
// approximate comparison between floats
|
||||
if (diff > epsilon) {
|
||||
cerr << "constraint: " << modelScoreDiff << " >= " << loss << endl;
|
||||
if (diff > (epsilon + m_precision)) {
|
||||
// constraint violated
|
||||
oldDistanceFromOptimum += (loss - modelScoreDiff);
|
||||
oldDistanceFromOptimum += diff;
|
||||
constraintViolatedBefore = true;
|
||||
float lossMinusModelScoreDiff = loss - modelScoreDiff;
|
||||
|
||||
// compute alpha for given constraint: (loss - model score diff) / || feature value diff ||^2
|
||||
// featureValueDiff.GetL2Norm() * featureValueDiff.GetL2Norm() == featureValueDiff.InnerProduct(featureValueDiff)
|
||||
@ -342,9 +342,14 @@ vector<int> MiraOptimiser::updateWeightsAnalytically(ScoreComponentCollection& c
|
||||
float squaredNorm = featureValueDiff.GetL2Norm() * featureValueDiff.GetL2Norm();
|
||||
|
||||
if (squaredNorm > 0) {
|
||||
float alpha = (lossMinusModelScoreDiff) / squaredNorm;
|
||||
if (m_slack > 0 && alpha > m_slack) {
|
||||
alpha = m_slack;
|
||||
float alpha = diff / squaredNorm;
|
||||
if (m_slack > 0 ) {
|
||||
if (alpha > m_slack) {
|
||||
alpha = m_slack;
|
||||
}
|
||||
else if (alpha < m_slack*(-1)) {
|
||||
alpha = m_slack*(-1);
|
||||
}
|
||||
}
|
||||
|
||||
cerr << "Rank " << rank << ", epoch " << epoch << ", alpha: " << alpha << endl;
|
||||
@ -374,7 +379,6 @@ vector<int> MiraOptimiser::updateWeightsAnalytically(ScoreComponentCollection& c
|
||||
// sanity check: constraint still violated after optimisation?
|
||||
ScoreComponentCollection newWeights(currWeights);
|
||||
newWeights.PlusEquals(weightUpdate);
|
||||
|
||||
bool constraintViolatedAfter = false;
|
||||
float newDistanceFromOptimum = 0;
|
||||
featureValueDiff = oracleFeatureValues;
|
||||
|
||||
@ -30,9 +30,9 @@ namespace Mira {
|
||||
public:
|
||||
Optimiser() {}
|
||||
virtual std::vector<int> updateWeightsAnalytically(Moses::ScoreComponentCollection& weights,
|
||||
const Moses::ScoreComponentCollection& featureValues,
|
||||
Moses::ScoreComponentCollection& featureValues,
|
||||
float loss,
|
||||
const Moses::ScoreComponentCollection& oracleFeatureValues,
|
||||
Moses::ScoreComponentCollection& oracleFeatureValues,
|
||||
float oracleBleuScore,
|
||||
size_t sentenceId,
|
||||
float learning_rate,
|
||||
@ -58,9 +58,9 @@ namespace Mira {
|
||||
class Perceptron : public Optimiser {
|
||||
public:
|
||||
virtual std::vector<int> updateWeightsAnalytically(Moses::ScoreComponentCollection& weights,
|
||||
const Moses::ScoreComponentCollection& featureValues,
|
||||
Moses::ScoreComponentCollection& featureValues,
|
||||
float loss,
|
||||
const Moses::ScoreComponentCollection& oracleFeatureValues,
|
||||
Moses::ScoreComponentCollection& oracleFeatureValues,
|
||||
float oracleBleuScore,
|
||||
size_t sentenceId,
|
||||
float learning_rate,
|
||||
@ -106,9 +106,9 @@ namespace Mira {
|
||||
~MiraOptimiser() {}
|
||||
|
||||
virtual std::vector<int> updateWeightsAnalytically(Moses::ScoreComponentCollection& weights,
|
||||
const Moses::ScoreComponentCollection& featureValues,
|
||||
Moses::ScoreComponentCollection& featureValues,
|
||||
float loss,
|
||||
const Moses::ScoreComponentCollection& oracleFeatureValues,
|
||||
Moses::ScoreComponentCollection& oracleFeatureValues,
|
||||
float oracleBleuScores,
|
||||
size_t sentenceId,
|
||||
float learning_rate,
|
||||
|
||||
@ -25,9 +25,9 @@ using namespace std;
|
||||
namespace Mira {
|
||||
|
||||
vector<int> Perceptron::updateWeightsAnalytically(ScoreComponentCollection& currWeights,
|
||||
const ScoreComponentCollection& featureValues,
|
||||
ScoreComponentCollection& featureValues,
|
||||
float loss,
|
||||
const ScoreComponentCollection& oracleFeatureValues,
|
||||
ScoreComponentCollection& oracleFeatureValues,
|
||||
float oracleBleuScore,
|
||||
size_t sentenceId,
|
||||
float learning_rate,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user