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-rw-r--r--training/Makefile.am8
-rw-r--r--training/mr_optimize_reduce.cc6
-rw-r--r--training/online_optimizer.cc14
-rw-r--r--training/online_optimizer.h102
-rw-r--r--training/online_train.cc8
-rw-r--r--training/optimize.cc22
-rw-r--r--training/optimize.h23
-rw-r--r--training/optimize_test.cc19
8 files changed, 158 insertions, 44 deletions
diff --git a/training/Makefile.am b/training/Makefile.am
index 48b19932..a947e4a5 100644
--- a/training/Makefile.am
+++ b/training/Makefile.am
@@ -7,12 +7,16 @@ bin_PROGRAMS = \
grammar_convert \
atools \
plftools \
- collapse_weights
+ collapse_weights \
+ online_train
noinst_PROGRAMS = \
lbfgs_test \
optimize_test
+online_train_SOURCES = online_train.cc online_optimizer.cc
+online_train_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
+
atools_SOURCES = atools.cc
atools_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
@@ -22,7 +26,7 @@ model1_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -l
grammar_convert_SOURCES = grammar_convert.cc
grammar_convert_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
-optimize_test_SOURCES = optimize_test.cc optimize.cc
+optimize_test_SOURCES = optimize_test.cc optimize.cc online_optimizer.cc
optimize_test_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
collapse_weights_SOURCES = collapse_weights.cc
diff --git a/training/mr_optimize_reduce.cc b/training/mr_optimize_reduce.cc
index 42727ecb..b931991d 100644
--- a/training/mr_optimize_reduce.cc
+++ b/training/mr_optimize_reduce.cc
@@ -108,11 +108,9 @@ int main(int argc, char** argv) {
}
wm.InitVector(&means);
}
- shared_ptr<Optimizer> o;
+ shared_ptr<BatchOptimizer> o;
const string omethod = conf["optimization_method"].as<string>();
- if (omethod == "sgd")
- o.reset(new SGDOptimizer(conf["eta"].as<double>()));
- else if (omethod == "rprop")
+ if (omethod == "rprop")
o.reset(new RPropOptimizer(num_feats)); // TODO add configuration
else
o.reset(new LBFGSOptimizer(num_feats, conf["correction_buffers"].as<int>()));
diff --git a/training/online_optimizer.cc b/training/online_optimizer.cc
new file mode 100644
index 00000000..db55c95e
--- /dev/null
+++ b/training/online_optimizer.cc
@@ -0,0 +1,14 @@
+#include "online_optimizer.h"
+
+LearningRateSchedule::~LearningRateSchedule() {}
+
+double StandardLearningRate::eta(int k) const {
+ return eta_0_ / (1.0 + k / N_);
+}
+
+double ExponentialDecayLearningRate::eta(int k) const {
+ return eta_0_ * pow(alpha_, k / N_);
+}
+
+OnlineOptimizer::~OnlineOptimizer() {}
+
diff --git a/training/online_optimizer.h b/training/online_optimizer.h
new file mode 100644
index 00000000..0cd748c4
--- /dev/null
+++ b/training/online_optimizer.h
@@ -0,0 +1,102 @@
+#ifndef _ONL_OPTIMIZE_H_
+#define _ONL_OPTIMIZE_H_
+
+#include <tr1/memory>
+#include <string>
+#include <cmath>
+#include "sparse_vector.h"
+
+struct LearningRateSchedule {
+ virtual ~LearningRateSchedule();
+ // returns the learning rate for iteration k
+ virtual double eta(int k) const = 0;
+};
+
+struct StandardLearningRate : public LearningRateSchedule {
+ StandardLearningRate(
+ size_t training_instances,
+ double eta_0 = 0.2) :
+ eta_0_(eta_0),
+ N_(static_cast<double>(training_instances)) {}
+
+ virtual double eta(int k) const;
+
+ private:
+ const double eta_0_;
+ const double N_;
+};
+
+struct ExponentialDecayLearningRate : public LearningRateSchedule {
+ ExponentialDecayLearningRate(
+ size_t training_instances,
+ double eta_0 = 0.2,
+ double alpha = 0.85 // recommended by Tsuruoka et al. (ACL 2009)
+ ) : eta_0_(eta_0),
+ N_(static_cast<double>(training_instances)),
+ alpha_(alpha) {
+ assert(alpha > 0);
+ assert(alpha < 1.0);
+ }
+
+ virtual double eta(int k) const;
+
+ private:
+ const double eta_0_;
+ const double N_;
+ const double alpha_;
+};
+
+class OnlineOptimizer {
+ public:
+ virtual ~OnlineOptimizer();
+ OnlineOptimizer(const std::tr1::shared_ptr<LearningRateSchedule>& s,
+ size_t training_instances) : schedule_(s), k_(), N_(training_instances) {}
+ void UpdateWeights(const SparseVector<double>& approx_g, SparseVector<double>* weights) {
+ ++k_;
+ const double eta = schedule_->eta(k_);
+ UpdateWeightsImpl(eta, approx_g, weights);
+ }
+
+ protected:
+ virtual void UpdateWeightsImpl(const double& eta, const SparseVector<double>& approx_g, SparseVector<double>* weights) = 0;
+ const size_t N_; // number of training instances
+
+ private:
+ std::tr1::shared_ptr<LearningRateSchedule> schedule_;
+ int k_; // iteration count
+};
+
+class CumulativeL1OnlineOptimizer : public OnlineOptimizer {
+ public:
+ CumulativeL1OnlineOptimizer(const std::tr1::shared_ptr<LearningRateSchedule>& s,
+ size_t training_instances, double C) :
+ OnlineOptimizer(s, training_instances), C_(C), u_() {}
+
+ protected:
+ void UpdateWeightsImpl(const double& eta, const SparseVector<double>& approx_g, SparseVector<double>* weights) {
+ u_ += eta * C_ / N_;
+ (*weights) += eta * approx_g;
+ for (SparseVector<double>::const_iterator it = approx_g.begin(); it != approx_g.end(); ++it)
+ ApplyPenalty(it->first, weights);
+ }
+
+ private:
+ void ApplyPenalty(int i, SparseVector<double>* w) {
+ const double z = w->value(i);
+ double w_i = z;
+ double q_i = q_.value(i);
+ if (w_i > 0)
+ w_i = std::max(0.0, w_i - (u_ + q_i));
+ else
+ w_i = std::max(0.0, w_i + (u_ - q_i));
+ q_i += w_i - z;
+ q_.set_value(i, q_i);
+ w->set_value(i, w_i);
+ }
+
+ const double C_; // reguarlization strength
+ double u_;
+ SparseVector<double> q_;
+};
+
+#endif
diff --git a/training/online_train.cc b/training/online_train.cc
new file mode 100644
index 00000000..2e906913
--- /dev/null
+++ b/training/online_train.cc
@@ -0,0 +1,8 @@
+#include <iostream>
+
+#include "online_optimizer.h"
+
+int main(int argc, char** argv) {
+ return 0;
+}
+
diff --git a/training/optimize.cc b/training/optimize.cc
index 5194752e..1377caa6 100644
--- a/training/optimize.cc
+++ b/training/optimize.cc
@@ -7,9 +7,9 @@
using namespace std;
-Optimizer::~Optimizer() {}
+BatchOptimizer::~BatchOptimizer() {}
-void Optimizer::Save(ostream* out) const {
+void BatchOptimizer::Save(ostream* out) const {
out->write((const char*)&eval_, sizeof(eval_));
out->write((const char*)&has_converged_, sizeof(has_converged_));
SaveImpl(out);
@@ -17,7 +17,7 @@ void Optimizer::Save(ostream* out) const {
out->write((const char*)&magic, sizeof(magic));
}
-void Optimizer::Load(istream* in) {
+void BatchOptimizer::Load(istream* in) {
in->read((char*)&eval_, sizeof(eval_));
++eval_;
in->read((char*)&has_converged_, sizeof(has_converged_));
@@ -28,11 +28,11 @@ void Optimizer::Load(istream* in) {
cerr << Name() << " EVALUATION #" << eval_ << endl;
}
-void Optimizer::SaveImpl(ostream* out) const {
+void BatchOptimizer::SaveImpl(ostream* out) const {
(void)out;
}
-void Optimizer::LoadImpl(istream* in) {
+void BatchOptimizer::LoadImpl(istream* in) {
(void)in;
}
@@ -78,18 +78,6 @@ void RPropOptimizer::LoadImpl(istream* in) {
in->read((char*)&delta_ij_[0], sizeof(double) * n);
}
-string SGDOptimizer::Name() const {
- return "SGDOptimizer";
-}
-
-void SGDOptimizer::OptimizeImpl(const double& obj,
- const vector<double>& g,
- vector<double>* x) {
- (void)obj;
- for (int i = 0; i < g.size(); ++i)
- (*x)[i] -= g[i] * eta_;
-}
-
string LBFGSOptimizer::Name() const {
return "LBFGSOptimizer";
}
diff --git a/training/optimize.h b/training/optimize.h
index eddceaad..e2620f93 100644
--- a/training/optimize.h
+++ b/training/optimize.h
@@ -10,10 +10,10 @@
// abstract base class for first order optimizers
// order of invocation: new, Load(), Optimize(), Save(), delete
-class Optimizer {
+class BatchOptimizer {
public:
- Optimizer() : eval_(1), has_converged_(false) {}
- virtual ~Optimizer();
+ BatchOptimizer() : eval_(1), has_converged_(false) {}
+ virtual ~BatchOptimizer();
virtual std::string Name() const = 0;
int EvaluationCount() const { return eval_; }
bool HasConverged() const { return has_converged_; }
@@ -41,7 +41,7 @@ class Optimizer {
bool has_converged_;
};
-class RPropOptimizer : public Optimizer {
+class RPropOptimizer : public BatchOptimizer {
public:
explicit RPropOptimizer(int num_vars,
double eta_plus = 1.2,
@@ -75,20 +75,7 @@ class RPropOptimizer : public Optimizer {
const double delta_min_;
};
-class SGDOptimizer : public Optimizer {
- public:
- explicit SGDOptimizer(int num_vars, double eta = 0.1) : eta_(eta) {
- (void) num_vars;
- }
- std::string Name() const;
- void OptimizeImpl(const double& obj,
- const std::vector<double>& g,
- std::vector<double>* x);
- private:
- const double eta_;
-};
-
-class LBFGSOptimizer : public Optimizer {
+class LBFGSOptimizer : public BatchOptimizer {
public:
explicit LBFGSOptimizer(int num_vars, int memory_buffers = 10);
std::string Name() const;
diff --git a/training/optimize_test.cc b/training/optimize_test.cc
index 0ada7cbb..6fa5efd4 100644
--- a/training/optimize_test.cc
+++ b/training/optimize_test.cc
@@ -3,12 +3,13 @@
#include <sstream>
#include <boost/program_options/variables_map.hpp>
#include "optimize.h"
+#include "online_optimizer.h"
#include "sparse_vector.h"
#include "fdict.h"
using namespace std;
-double TestOptimizer(Optimizer* opt) {
+double TestOptimizer(BatchOptimizer* opt) {
cerr << "TESTING NON-PERSISTENT OPTIMIZER\n";
// f(x,y) = 4x1^2 + x1*x2 + x2^2 + x3^2 + 6x3 + 5
@@ -34,7 +35,7 @@ double TestOptimizer(Optimizer* opt) {
return obj;
}
-double TestPersistentOptimizer(Optimizer* opt) {
+double TestPersistentOptimizer(BatchOptimizer* opt) {
cerr << "\nTESTING PERSISTENT OPTIMIZER\n";
// f(x,y) = 4x1^2 + x1*x2 + x2^2 + x3^2 + 6x3 + 5
// df/dx1 = 8*x1 + x2
@@ -95,11 +96,23 @@ void TestOptimizerVariants(int num_vars) {
cerr << oa.Name() << " SUCCESS\n";
}
+using namespace std::tr1;
+
+void TestOnline() {
+ size_t N = 20;
+ double C = 1.0;
+ double eta0 = 0.2;
+ shared_ptr<LearningRateSchedule> r(new ExponentialDecayLearningRate(N, eta0, 0.85));
+ //shared_ptr<LearningRateSchedule> r(new StandardLearningRate(N, eta0));
+ CumulativeL1OnlineOptimizer opt(r, N, C);
+ assert(r->eta(10) < r->eta(1));
+}
+
int main() {
int n = 3;
- TestOptimizerVariants<SGDOptimizer>(n);
TestOptimizerVariants<LBFGSOptimizer>(n);
TestOptimizerVariants<RPropOptimizer>(n);
+ TestOnline();
return 0;
}
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-22 18:34:11 +0000