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authorChris Dyer <cdyer@allegro.clab.cs.cmu.edu>2012-11-18 13:35:42 -0500
committerChris Dyer <cdyer@allegro.clab.cs.cmu.edu>2012-11-18 13:35:42 -0500
commit1b8181bf0d6e9137e6b9ccdbe414aec37377a1a9 (patch)
tree33e5f3aa5abff1f41314cf8f6afbd2c2c40e4bfd /training/utils/optimize.h
parent7c4665949fb93fb3de402e4ce1d19bef67850d05 (diff)
major restructure of the training code
Diffstat (limited to 'training/utils/optimize.h')
-rw-r--r--training/utils/optimize.h92
1 files changed, 92 insertions, 0 deletions
diff --git a/training/utils/optimize.h b/training/utils/optimize.h
new file mode 100644
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+++ b/training/utils/optimize.h
@@ -0,0 +1,92 @@
+#ifndef _OPTIMIZE_H_
+#define _OPTIMIZE_H_
+
+#include <iostream>
+#include <vector>
+#include <string>
+#include <cassert>
+
+#include "lbfgs.h"
+
+// abstract base class for first order optimizers
+// order of invocation: new, Load(), Optimize(), Save(), delete
+class BatchOptimizer {
+ public:
+ 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_; }
+
+ void Optimize(const double& obj,
+ const std::vector<double>& g,
+ std::vector<double>* x) {
+ assert(g.size() == x->size());
+ ++eval_;
+ OptimizeImpl(obj, g, x);
+ scitbx::lbfgs::traditional_convergence_test<double> converged(g.size());
+ has_converged_ = converged(&(*x)[0], &g[0]);
+ }
+
+ void Save(std::ostream* out) const;
+ void Load(std::istream* in);
+ protected:
+ virtual void SaveImpl(std::ostream* out) const;
+ virtual void LoadImpl(std::istream* in);
+ virtual void OptimizeImpl(const double& obj,
+ const std::vector<double>& g,
+ std::vector<double>* x) = 0;
+
+ int eval_;
+ private:
+ bool has_converged_;
+};
+
+class RPropOptimizer : public BatchOptimizer {
+ public:
+ explicit RPropOptimizer(int num_vars,
+ double eta_plus = 1.2,
+ double eta_minus = 0.5,
+ double delta_0 = 0.1,
+ double delta_max = 50.0,
+ double delta_min = 1e-6) :
+ prev_g_(num_vars, 0.0),
+ delta_ij_(num_vars, delta_0),
+ eta_plus_(eta_plus),
+ eta_minus_(eta_minus),
+ delta_max_(delta_max),
+ delta_min_(delta_min) {
+ assert(eta_plus > 1.0);
+ assert(eta_minus > 0.0 && eta_minus < 1.0);
+ assert(delta_max > 0.0);
+ assert(delta_min > 0.0);
+ }
+ std::string Name() const;
+ void OptimizeImpl(const double& obj,
+ const std::vector<double>& g,
+ std::vector<double>* x);
+ void SaveImpl(std::ostream* out) const;
+ void LoadImpl(std::istream* in);
+ private:
+ std::vector<double> prev_g_;
+ std::vector<double> delta_ij_;
+ const double eta_plus_;
+ const double eta_minus_;
+ const double delta_max_;
+ const double delta_min_;
+};
+
+class LBFGSOptimizer : public BatchOptimizer {
+ public:
+ explicit LBFGSOptimizer(int num_vars, int memory_buffers = 10);
+ std::string Name() const;
+ void SaveImpl(std::ostream* out) const;
+ void LoadImpl(std::istream* in);
+ void OptimizeImpl(const double& obj,
+ const std::vector<double>& g,
+ std::vector<double>* x);
+ private:
+ scitbx::lbfgs::minimizer<double> opt_;
+};
+
+#endif