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-rw-r--r--pro-train/mr_pro_reduce.cc82
1 files changed, 47 insertions, 35 deletions
diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc
index aff410a0..98cddba2 100644
--- a/pro-train/mr_pro_reduce.cc
+++ b/pro-train/mr_pro_reduce.cc
@@ -23,11 +23,12 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
opts.add_options()
("weights,w", po::value<string>(), "Weights from previous iteration (used as initialization and interpolation")
- ("interpolation,p",po::value<double>()->default_value(0.9), "Output weights are p*w + (1-p)*w_prev")
+ ("regularize_to_weights,y",po::value<double>()->default_value(0.0), "Differences in learned weights to previous weights are penalized with an l2 penalty with this strength; 0.0 = no effect")
+ ("interpolate_with_weights,p",po::value<double>()->default_value(1.0), "Output weights are p*w + (1-p)*w_prev; 1.0 = no effect")
("memory_buffers,m",po::value<unsigned>()->default_value(200), "Number of memory buffers (LBFGS)")
- ("sigma_squared,s",po::value<double>()->default_value(0.1), "Sigma squared for Gaussian prior")
- ("min_reg,r",po::value<double>()->default_value(1e-8), "When tuning (-T) regularization strength, minimum regularization strenght")
- ("max_reg,R",po::value<double>()->default_value(10.0), "When tuning (-T) regularization strength, maximum regularization strenght")
+ ("regularization_strength,C",po::value<double>()->default_value(1.0), "l2 regularization strength")
+ ("min_reg,r",po::value<double>()->default_value(0.01), "When tuning (-T) regularization strength, minimum regularization strenght")
+ ("max_reg,R",po::value<double>()->default_value(1e6), "When tuning (-T) regularization strength, maximum regularization strenght")
("testset,t",po::value<string>(), "Optional held-out test set")
("tune_regularizer,T", "Use the held out test set (-t) to tune the regularization strength")
("help,h", "Help");
@@ -95,6 +96,27 @@ void GradAdd(const SparseVector<weight_t>& v, const double scale, vector<weight_
}
}
+double ApplyRegularizationTerms(const double C,
+ const double T,
+ const vector<weight_t>& weights,
+ const vector<weight_t>& prev_weights,
+ vector<weight_t>* g) {
+ assert(weights.size() == g->size());
+ double reg = 0;
+ for (size_t i = 0; i < weights.size(); ++i) {
+ const double prev_w_i = (i < prev_weights.size() ? prev_weights[i] : 0.0);
+ const double& w_i = weights[i];
+ double& g_i = (*g)[i];
+ reg += C * w_i * w_i;
+ g_i += 2 * C * w_i;
+
+ const double diff_i = w_i - prev_w_i;
+ reg += T * diff_i * diff_i;
+ g_i += 2 * T * diff_i;
+ }
+ return reg;
+}
+
double TrainingInference(const vector<weight_t>& x,
const vector<pair<bool, SparseVector<weight_t> > >& corpus,
vector<weight_t>* g = NULL) {
@@ -134,8 +156,10 @@ double TrainingInference(const vector<weight_t>& x,
// return held-out log likelihood
double LearnParameters(const vector<pair<bool, SparseVector<weight_t> > >& training,
const vector<pair<bool, SparseVector<weight_t> > >& testing,
- const double sigsq,
+ const double C,
+ const double T,
const unsigned memory_buffers,
+ const vector<weight_t>& prev_x,
vector<weight_t>* px) {
vector<weight_t>& x = *px;
vector<weight_t> vg(FD::NumFeats(), 0.0);
@@ -157,26 +181,12 @@ double LearnParameters(const vector<pair<bool, SparseVector<weight_t> > >& train
}
// handle regularizer
-#if 1
- double norm = 0;
- for (int i = 1; i < x.size(); ++i) {
- const double mean_i = 0.0;
- const double param = (x[i] - mean_i);
- norm += param * param;
- vg[i] += param / sigsq;
- }
- const double reg = norm / (2.0 * sigsq);
-#else
- double reg = 0;
-#endif
+ double reg = ApplyRegularizationTerms(C, T, x, prev_x, &vg);
cll += reg;
- cerr << cll << " (REG=" << reg << ")\tPPL=" << ppl << "\t TEST_PPL=" << tppl << "\t";
+ cerr << cll << " (REG=" << reg << ")\tPPL=" << ppl << "\t TEST_PPL=" << tppl << "\t" << endl;
try {
- vector<weight_t> old_x = x;
- do {
- opt.Optimize(cll, vg, &x);
- converged = opt.HasConverged();
- } while (!converged && x == old_x);
+ opt.Optimize(cll, vg, &x);
+ converged = opt.HasConverged();
} catch (...) {
cerr << "Exception caught, assuming convergence is close enough...\n";
converged = true;
@@ -201,13 +211,14 @@ int main(int argc, char** argv) {
}
const double min_reg = conf["min_reg"].as<double>();
const double max_reg = conf["max_reg"].as<double>();
- double sigsq = conf["sigma_squared"].as<double>(); // will be overridden if parameter is tuned
- assert(sigsq > 0.0);
+ double C = conf["regularization_strength"].as<double>(); // will be overridden if parameter is tuned
+ const double T = conf["regularize_to_weights"].as<double>();
+ assert(C > 0.0);
assert(min_reg > 0.0);
assert(max_reg > 0.0);
assert(max_reg > min_reg);
- const double psi = conf["interpolation"].as<double>();
- if (psi < 0.0 || psi > 1.0) { cerr << "Invalid interpolation weight: " << psi << endl; }
+ const double psi = conf["interpolate_with_weights"].as<double>();
+ if (psi < 0.0 || psi > 1.0) { cerr << "Invalid interpolation weight: " << psi << endl; return 1; }
ReadCorpus(&cin, &training);
if (conf.count("testset")) {
ReadFile rf(conf["testset"].as<string>());
@@ -231,14 +242,15 @@ int main(int argc, char** argv) {
vector<pair<double,double> > sp;
vector<double> smoothed;
if (tune_regularizer) {
- sigsq = min_reg;
+ C = min_reg;
const double steps = 18;
double sweep_factor = exp((log(max_reg) - log(min_reg)) / steps);
cerr << "SWEEP FACTOR: " << sweep_factor << endl;
- while(sigsq < max_reg) {
- tppl = LearnParameters(training, testing, sigsq, conf["memory_buffers"].as<unsigned>(), &x);
- sp.push_back(make_pair(sigsq, tppl));
- sigsq *= sweep_factor;
+ while(C < max_reg) {
+ cerr << "C=" << C << "\tT=" <<T << endl;
+ tppl = LearnParameters(training, testing, C, T, conf["memory_buffers"].as<unsigned>(), prev_x, &x);
+ sp.push_back(make_pair(C, tppl));
+ C *= sweep_factor;
}
smoothed.resize(sp.size(), 0);
smoothed[0] = sp[0].second;
@@ -257,16 +269,16 @@ int main(int argc, char** argv) {
best_i = i;
}
}
- sigsq = sp[best_i].first;
+ C = sp[best_i].first;
} // tune regularizer
- tppl = LearnParameters(training, testing, sigsq, conf["memory_buffers"].as<unsigned>(), &x);
+ tppl = LearnParameters(training, testing, C, T, conf["memory_buffers"].as<unsigned>(), prev_x, &x);
if (conf.count("weights")) {
for (int i = 1; i < x.size(); ++i) {
x[i] = (x[i] * psi) + prev_x[i] * (1.0 - psi);
}
}
cout.precision(15);
- cout << "# sigma^2=" << sigsq << "\theld out perplexity=";
+ cout << "# C=" << C << "\theld out perplexity=";
if (tppl) { cout << tppl << endl; } else { cout << "N/A\n"; }
if (sp.size()) {
cout << "# Parameter sweep:\n";
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-11 23:34:56 +0000