Beispiel #1
0
void
M2MFstAligner::write_model(string _model_file)
{
    VectorFst<LogArc> model;
    model.AddState();
    model.SetStart(0);
    model.SetFinal(0, LogWeight::One());
    map<LogArc::Label,LogWeight>::iterator it;
    for (it = alignment_model.begin(); it != alignment_model.end(); it++)
        model.AddArc(0, LogArc((*it).first, (*it).first, (*it).second, 0));
    model.SetInputSymbols(isyms);
    model.Write(_model_file);
    return;
}
/*
 * This computes the function value at a given weight vector.
 * This should be parallelized by dividing the training examples
 * into subsets and doing each on a different thread.
 *
 * @param weights - the value of the weights
 * @param the value of the function
 *
 */
double WFST_Trainer_Local::value2(const column_vector &weights) {
  
  cout << "LIKELIHOOD\n";
  double likelihood = 0.0;
  update_arc_weights(weights);

  // ``filler'' FSTs to replace
  // the results from the composition
  // declared outside for loop for efficiency
  VectorFst<LogArc> medial;
  VectorFst<LogArc> final;
  
  medial.SetInputSymbols(fst->InputSymbols());
  medial.SetOutputSymbols(fst->OutputSymbols());
  final.SetInputSymbols(fst->InputSymbols());
/**
 * Create an FST based on an RNN
 */
void FlatBOFstBuilder::convertRNN(CRnnLM & rnnlm, VectorFst<LogArc> &fst) {
	queue<NeuronFstHistory> q;
	VectorFst<LogArc> new_fst;
	
	NeuronFstHistory fsth(rnnlm.getHiddenLayerSize(),getNumBins());
	FstIndex id = 0;
	
	NeuronFstHistory new_fsth(rnnlm.getHiddenLayerSize(),getNumBins());
	FstIndex new_id;

	NeuronFstHistory min_backoff(rnnlm.getHiddenLayerSize(),getNumBins());
	set<NeuronFstHistory>set_min_backoff;
	
	NeuronFstHistory bo_fsth(rnnlm.getHiddenLayerSize(),getNumBins());
	bool backoff = false;
	vector<FstIndex> deleted;


	real p = 0.00;
	real p_joint = 0.00;
	real entropy = 0.0;
	real delta = 0.0;
	vector<real> all_prob(rnnlm.getVocabSize());
 	vector<real> posterior(10);
	
	map< FstIndex,set<FstIndex> > pred;
	vector<bool> non_bo_pred(rnnlm.getVocabSize());
	vector<int> to_be_added;
	vector<int> to_be_removed;
	for (int i = 0; i < rnnlm.getVocabSize(); i++) {
		to_be_removed.push_back(i);
	}
	vector<real> to_be_added_prob;


 	FstIndex n_added = 0;
 	FstIndex n_processed = 0;
 	FstIndex next_n_added = 0;
 	FstIndex next_n_processed = 0;
 	FstIndex n_backoff = 0;
 	FstIndex n_only_backoff = 0;
 	
	int v = rnnlm.getVocabSize();
	int w = 0;


	// Initialize
	rnnlm.copyHiddenLayerToInput();
//	printNeurons(rnnlm.getInputLayer(),0,10);

	// Initial state ( 0 | hidden layer after </s>)
	printNeurons(rnnlm.getHiddenLayer(),0,10);
	fsth.setFstHistory(rnnlm, *dzer);
	fsth.setLastWord(0);
	q.push(fsth);
	addFstState(id, new NeuronFstHistory(fsth), fst);
	fst.SetStart(INIT_STATE);
	
	// Final state (don't care about the associated discrete representation)
	fst.AddState();
	fst.SetFinal(FINAL_STATE, LogWeight::One());
	
 	/*posterior.at(INIT_STATE) = MY_LOG_ONE;*/
	min_backoff.setLastWord(-1);
	computeEntropyAndConditionals(entropy, all_prob, rnnlm, min_backoff);
	min_backoff = getBackoff(rnnlm, min_backoff, set_min_backoff, all_prob, to_be_removed);
	cout << "MIN BACKOFF " << min_backoff.toString() << endl;
	set_min_backoff.insert(min_backoff);
	
//	addFstState(id, min_backoff, fst);
//	q.push(min_backoff);
	

	
	// Estimate number of backoff loop to bound the backoff path length
// 	float ratioa = 0.0;
// 	float ratiob = 0.0;
	float ratio = 0.0;
// 	for (int i=0; i < min_backoff.getNumDims(); i++) {
// 		if (min_backoff.getDim(i) == 1) {
// 			ratioa++;
// 		}
// 		if (fsth.getDim(i) == 1) {
// 			ratiob++;
// 		}
// 	}
// 	ratioa /= min_backoff.getNumDims();
// 	ratiob /= min_backoff.getNumDims();
// 	ratio = (ratioa*(1.0-ratiob))+(ratiob*(1.0-ratioa));
	ratio=1.0;

//	printf("ratio=%f\t%i BO loops\n", ratio, n_bo_loops);
	
	
	
	//foreach state in the queue
	while (!q.empty()) {
		fsth = q.front();
		q.pop();
		id = h2state[&fsth];
		state2h.push_back(new NeuronFstHistory(fsth));
		if (id == FINAL_STATE) { continue; }


		
		
	dprintf(1,"-- STUDY STATE %li = %s\n", id, fsth.toString().c_str());
	

/*		try { posterior.at(id) = MY_LOG_ONE; }
		catch (exception e) {
			posterior.resize((int) (posterior.size()*1.5)+1);
			posterior.at(id) = MY_LOG_ONE;
		}*/
		
		computeEntropyAndConditionals(entropy, all_prob, rnnlm, fsth);
		
		//compute BO in advance and check if it is a min BO node
		bo_fsth = getBackoff(rnnlm, fsth, set_min_backoff, all_prob, to_be_removed);
		if (bo_fsth == fsth) { bo_fsth = min_backoff; }
			
		//foreach w (ie, foreach word of each class c)
		//test if the edge has to kept or removed
		backoff = false; //no backoff yet since no edge has been removed
		for (w=0; w < rnnlm.getVocabSize(); w++) {
				p = all_prob[w];
				
				/*p_joint = exp(-posterior[id]-p);*/
				p_joint = exp(-p);
				delta = -1.0*p_joint*log2(p_joint);
				
				//accept edge if this leads to a minimum
				//relative gain of the entropy

				dprintf(2,"P = %e \tP_joint = %e \tH = %e \tDelta =%e \tDelta H = %.6f %%\n",exp(-p), p_joint, entropy, delta, 100.0*delta/entropy);

				if (set_min_backoff.find(fsth) != set_min_backoff.end() || (delta > pruning_threshold*entropy)) {
//				if ((fsth == min_backoff) || (delta > pruning_threshold*entropy)) {
					next_n_added++;
					to_be_added.push_back(w);
					to_be_added_prob.push_back(p);
					dprintf(2,"\tACCEPT [%li] -- %i (%s) / %f --> ...\t(%e > %e)\n", id, w, rnnlm.getWordString(w), p, delta, pruning_threshold*entropy);
//					to_be_removed.push_back(w);
 				}
 				//backoff
				else {
//					to_be_removed.push_back(w);
					backoff = true;
					dprintf(2,"\tPRUNE [%li] -- %i / %f --> ...\n", id, w, p);
 				}
 				
 				//print
				if (next_n_processed % 100000 == 0) {
						fprintf(stderr, "\rH=%.5f / N proc'd=%li / N added=%li (%.5f %%) / N bo=%li (%.5f %%) / %li/%li Nodes (%2.1f %%) / N min BO=%i", entropy, n_processed, n_added, ((float) n_added/ (float)n_processed)*100.0, n_backoff, ((float) n_backoff/ (float)n_added)*100.0, id, id+q.size(), 100.0 - (float) (100.0*id/(id+q.size())), (int) set_min_backoff.size());
				}
				next_n_processed++;
 				
//			}
		}


		//Set a part of the new FST history
		new_fsth.setFstHistory(rnnlm, *dzer);

		//if at least one word is backing off
		if (backoff) {
			
			n_backoff++;
			if (to_be_added.size() == 0) {
				n_only_backoff++;
			}
			
			
			if (addFstState(new_id, new NeuronFstHistory(bo_fsth), fst)) {
				q.push(bo_fsth);
				try { non_bo_pred.at(new_id) = false; }
				catch (exception e) {
					non_bo_pred.resize(new_id+(int) (non_bo_pred.size()*0.5)+1);
					non_bo_pred.at(new_id) = false;
				}
				
			}
			dprintf(1,"BACKOFF\t[%li]\t(%s)\n-------\t[%li]\t(%s)\n", id, fsth.toString().c_str(), new_id, bo_fsth.toString().c_str());

			fst.AddArc(id, LogArc(EPSILON, EPSILON, LogWeight::Zero(), new_id));
			
			addPred(pred, new_id, id);
			
		}
		
		
		vector<real>::iterator it_p = to_be_added_prob.begin();
		for (vector<int>::iterator it = to_be_added.begin(); it != to_be_added.end(); ++it) {
			w = *it;
			p = *it_p;

			if (w == 0) {
				fst.AddArc(id, LogArc(FstWord(w),FstWord(w),p,FINAL_STATE));
				dprintf(1,"EDGE [%li] (%s)\n---- %i (%s) / %f -->\n---- [%li] FINAL STATE)\n\n", id, fsth.toString().c_str(), FstWord(w), rnnlm.getWordString(w), p, FINAL_STATE);				
			}
		
			//accept edge
			else {
				new_fsth.setLastWord(w);
	
				//if sw not in the memory
				//then add a new state for sw in the FST and push sw in the queue
				if (addFstState(new_id, new NeuronFstHistory(new_fsth), fst)) {
					q.push(new_fsth);
					try { non_bo_pred.at(new_id) = true; }
					catch (exception e) {
						non_bo_pred.resize(new_id+(int) (non_bo_pred.size()*0.5)+1);
						non_bo_pred.at(new_id) = true;
					}
				}
				else { /* already exists */ }
			
				//add the edge in the FST
				non_bo_pred.at(new_id) = true;
				fst.AddArc(id, LogArc(FstWord(w),FstWord(w),p,new_id));
				dprintf(1,"EDGE [%li] (%s)\n---- %i (%s) / %f -->\n---- [%li] (%s)\n\n", id, fsth.toString().c_str(), FstWord(w), rnnlm.getWordString(w), p, new_id, new_fsth.toString().c_str());				

//				posterior.at(new_id) += posterior[id]*p;

			}
			
			/*if (posterior[id]+p < LogWeight::Zero().Value()) {
				p_joint = exp(-posterior[id]-p);
				entropy -= p_joint*log2(p_joint);
			}*/
			
			++it_p;
		}
		
		n_added = next_n_added;
		n_processed = next_n_processed;
		
		//reset queues
		to_be_added.clear();
		to_be_added_prob.clear();
//		to_be_removed.clear();
		
	}

	cout << endl;
	
	//compute backoff weights
	deleted = compactBackoffNodes(fst, pred, non_bo_pred);
	computeAllBackoff(fst, pred);


	//remove useless nodes
	removeStates(fst, new_fst, deleted);
	fst.DeleteStates();
	fst = new_fst;
	
	//Fill the table of symbols
	SymbolTable dic("dictionnary");
	dic.AddSymbol("*", 0);
	for (int i=0; i<rnnlm.getVocabSize(); i++) {
		dic.AddSymbol(string(rnnlm.getWordString(i)), i+1);
	}
	fst.SetInputSymbols(&dic);
	fst.SetOutputSymbols(&dic);

						//printf("H=%.5f / N proc'd=%li / N added=%li (%.5f %%) %li/%li Nodes (%2.1f %%)\n", entropy, n_processed, n_added, ((float) n_added/ (float)n_processed)*100.0, id, id+q.size(), 100.0 - (float) (100.0*id/(id+q.size())));
	cout << "END" << endl;
	
}
Beispiel #4
0
void
train_model(string eps, string s1s2_sep, string skip, int order,
            string smooth, string prefix, string seq_sep, string prune,
            double theta, string count_pattern)
{
    namespace s = fst::script;
    using fst::script::FstClass;
    using fst::script::MutableFstClass;
    using fst::script::VectorFstClass;
    using fst::script::WeightClass;

    // create symbols file
    cout << "Generating symbols..." << endl;
    NGramInput *ingram =
        new NGramInput(prefix + ".corpus.aligned", prefix + ".corpus.syms",
                       "", eps, unknown_symbol, "", "");
    ingram->ReadInput(0, 1);

    // compile strings into a far archive
    cout << "Compiling symbols into FAR archive..." << endl;
    fst::FarEntryType fet = fst::StringToFarEntryType(entry_type);
    fst::FarTokenType ftt = fst::StringToFarTokenType(token_type);
    fst::FarType fartype = fst::FarTypeFromString(far_type);

    delete ingram;

    vector<string> in_fname;
    in_fname.push_back(prefix + ".corpus.aligned");

    fst::script::FarCompileStrings(in_fname, prefix + ".corpus.far",
                                   arc_type, fst_type, fartype,
                                   generate_keys, fet, ftt,
                                   prefix + ".corpus.syms", unknown_symbol,
                                   keep_symbols, initial_symbols,
                                   allow_negative_labels, file_list_input,
                                   key_prefix, key_suffix);

    //count n-grams
    cout << "Counting n-grams..." << endl;
    NGramCounter<Log64Weight> ngram_counter(order, epsilon_as_backoff);

    FstReadOptions opts;
    FarReader<StdArc> *far_reader;
    far_reader = FarReader<StdArc>::Open(prefix + ".corpus.far");
    int fstnumber = 1;
    const Fst<StdArc> *ifst = 0, *lfst = 0;
    while (!far_reader->Done()) {
        if (ifst)
            delete ifst;
        ifst = far_reader->GetFst().Copy();

        if (!ifst) {
            E_FATAL("ngramcount: unable to read fst #%d\n", fstnumber);
            //exit(1);
        }

        bool counted = false;
        if (ifst->Properties(kString | kUnweighted, true)) {
            counted = ngram_counter.Count(*ifst);
        }
        else {
            VectorFst<Log64Arc> log_ifst;
            Map(*ifst, &log_ifst, ToLog64Mapper<StdArc> ());
            counted = ngram_counter.Count(&log_ifst);
        }
        if (!counted)
            cout << "ngramcount: fst #" << fstnumber << endl;

        if (ifst->InputSymbols() != 0) {        // retain for symbol table
            if (lfst)
                delete lfst;    // delete previously observed symbol table
            lfst = ifst;
            ifst = 0;
        }
        far_reader->Next();
        ++fstnumber;
    }
    delete far_reader;

    if (!lfst) {
        E_FATAL("None of the input FSTs had a symbol table\n");
        //exit(1);
    }

    VectorFst<StdArc> vfst;
    ngram_counter.GetFst(&vfst);
    ArcSort(&vfst, StdILabelCompare());
    vfst.SetInputSymbols(lfst->InputSymbols());
    vfst.SetOutputSymbols(lfst->InputSymbols());
    vfst.Write(prefix + ".corpus.cnts");
    StdMutableFst *fst =
        StdMutableFst::Read(prefix + ".corpus.cnts", true);
    if (smooth != "no") {
        cout << "Smoothing model..." << endl;

        bool prefix_norm = 0;
        if (smooth == "presmoothed") {  // only for use with randgen counts
            prefix_norm = 1;
            smooth = "unsmoothed";      // normalizes only based on prefix count
        }
        if (smooth == "kneser_ney") {
            NGramKneserNey ngram(fst, backoff, backoff_label,
                                 norm_eps, check_consistency,
                                 discount_D, bins);
            ngram.MakeNGramModel();
            fst = ngram.GetMutableFst();
        }
        else if (smooth == "absolute") {
            NGramAbsolute ngram(fst, backoff, backoff_label,
                                norm_eps, check_consistency,
                                discount_D, bins);
            ngram.MakeNGramModel();
            fst = ngram.GetMutableFst();
        }
        else if (smooth == "katz") {
            NGramKatz ngram(fst, backoff, backoff_label,
                            norm_eps, check_consistency, bins);
            ngram.MakeNGramModel();
            fst = ngram.GetMutableFst();
        }
        else if (smooth == "witten_bell") {
            NGramWittenBell ngram(fst, backoff, backoff_label,
                                  norm_eps, check_consistency,
                                  witten_bell_k);
            ngram.MakeNGramModel();
            fst = ngram.GetMutableFst();
        }
        else if (smooth == "unsmoothed") {
            NGramUnsmoothed ngram(fst, 1, prefix_norm, backoff_label,
                                  norm_eps, check_consistency);
            ngram.MakeNGramModel();
            fst = ngram.GetMutableFst();
        }
        else {
            E_FATAL("Bad smoothing method: %s\n", smooth.c_str());
        }
    }
    if (prune != "no") {
        cout << "Pruning model..." << endl;

        if (prune == "count_prune") {
            NGramCountPrune ngramsh(fst, count_pattern,
                                    shrink_opt, total_unigram_count,
                                    backoff_label, norm_eps,
                                    check_consistency);
            ngramsh.ShrinkNGramModel();
        }
        else if (prune == "relative_entropy") {
            NGramRelEntropy ngramsh(fst, theta, shrink_opt,
                                    total_unigram_count, backoff_label,
                                    norm_eps, check_consistency);
            ngramsh.ShrinkNGramModel();
        }
        else if (prune == "seymore") {
            NGramSeymoreShrink ngramsh(fst, theta, shrink_opt,
                                       total_unigram_count, backoff_label,
                                       norm_eps, check_consistency);
            ngramsh.ShrinkNGramModel();
        }
        else {
            E_FATAL("Bad shrink method:  %s\n", prune.c_str());
        }
    }

    cout << "Minimizing model..." << endl;
    MutableFstClass *minimized = new s::MutableFstClass(*fst);
    Minimize(minimized, 0, fst::kDelta);
    fst = minimized->GetMutableFst<StdArc>();

    cout << "Correcting final model..." << endl;
    StdMutableFst *out = new StdVectorFst();
    relabel(fst, out, prefix, eps, skip, s1s2_sep, seq_sep);

    cout << "Writing binary model to disk..." << endl;
    out->Write(prefix + ".fst");
}