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authorAvneesh Saluja <asaluja@gmail.com>2013-03-28 18:28:16 -0700
committerAvneesh Saluja <asaluja@gmail.com>2013-03-28 18:28:16 -0700
commit3d8d656fa7911524e0e6885647173474524e0784 (patch)
tree81b1ee2fcb67980376d03f0aa48e42e53abff222 /training/utils/optimize_test.cc
parentbe7f57fdd484e063775d7abf083b9fa4c403b610 (diff)
parent96fedabebafe7a38a6d5928be8fff767e411d705 (diff)
fixed conflicts
Diffstat (limited to 'training/utils/optimize_test.cc')
-rw-r--r--training/utils/optimize_test.cc118
1 files changed, 118 insertions, 0 deletions
diff --git a/training/utils/optimize_test.cc b/training/utils/optimize_test.cc
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+#include <cassert>
+#include <iostream>
+#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(BatchOptimizer* opt) {
+ cerr << "TESTING NON-PERSISTENT OPTIMIZER\n";
+
+ // f(x,y) = 4x1^2 + x1*x2 + x2^2 + x3^2 + 6x3 + 5
+ // df/dx1 = 8*x1 + x2
+ // df/dx2 = 2*x2 + x1
+ // df/dx3 = 2*x3 + 6
+ vector<double> x(3);
+ vector<double> g(3);
+ x[0] = 8;
+ x[1] = 8;
+ x[2] = 8;
+ double obj = 0;
+ do {
+ g[0] = 8 * x[0] + x[1];
+ g[1] = 2 * x[1] + x[0];
+ g[2] = 2 * x[2] + 6;
+ obj = 4 * x[0]*x[0] + x[0] * x[1] + x[1]*x[1] + x[2]*x[2] + 6 * x[2] + 5;
+ opt->Optimize(obj, g, &x);
+
+ cerr << x[0] << " " << x[1] << " " << x[2] << endl;
+ cerr << " obj=" << obj << "\td/dx1=" << g[0] << " d/dx2=" << g[1] << " d/dx3=" << g[2] << endl;
+ } while (!opt->HasConverged());
+ return obj;
+}
+
+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
+ // df/dx2 = 2*x2 + x1
+ // df/dx3 = 2*x3 + 6
+ vector<double> x(3);
+ vector<double> g(3);
+ x[0] = 8;
+ x[1] = 8;
+ x[2] = 8;
+ double obj = 0;
+ string state;
+ bool converged = false;
+ while (!converged) {
+ g[0] = 8 * x[0] + x[1];
+ g[1] = 2 * x[1] + x[0];
+ g[2] = 2 * x[2] + 6;
+ obj = 4 * x[0]*x[0] + x[0] * x[1] + x[1]*x[1] + x[2]*x[2] + 6 * x[2] + 5;
+
+ {
+ if (state.size() > 0) {
+ istringstream is(state, ios::binary);
+ opt->Load(&is);
+ }
+ opt->Optimize(obj, g, &x);
+ ostringstream os(ios::binary); opt->Save(&os); state = os.str();
+
+ }
+
+ cerr << x[0] << " " << x[1] << " " << x[2] << endl;
+ cerr << " obj=" << obj << "\td/dx1=" << g[0] << " d/dx2=" << g[1] << " d/dx3=" << g[2] << endl;
+ converged = opt->HasConverged();
+ if (!converged) {
+ // now screw up the state (should be undone by Load)
+ obj += 2.0;
+ g[1] = -g[2];
+ vector<double> x2 = x;
+ try {
+ opt->Optimize(obj, g, &x2);
+ } catch (...) { }
+ }
+ }
+ return obj;
+}
+
+template <class O>
+void TestOptimizerVariants(int num_vars) {
+ O oa(num_vars);
+ cerr << "-------------------------------------------------------------------------\n";
+ cerr << "TESTING: " << oa.Name() << endl;
+ double o1 = TestOptimizer(&oa);
+ O ob(num_vars);
+ double o2 = TestPersistentOptimizer(&ob);
+ if (o1 != o2) {
+ cerr << oa.Name() << " VARIANTS PERFORMED DIFFERENTLY!\n" << o1 << " vs. " << o2 << endl;
+ exit(1);
+ }
+ cerr << oa.Name() << " SUCCESS\n";
+}
+
+using namespace std::tr1;
+
+void TestOnline() {
+ size_t N = 20;
+ double C = 1.0;
+ double eta0 = 0.2;
+ std::tr1::shared_ptr<LearningRateSchedule> r(new ExponentialDecayLearningRate(N, eta0, 0.85));
+ //shared_ptr<LearningRateSchedule> r(new StandardLearningRate(N, eta0));
+ CumulativeL1OnlineOptimizer opt(r, N, C, std::vector<int>());
+ assert(r->eta(10) < r->eta(1));
+}
+
+int main() {
+ int n = 3;
+ TestOptimizerVariants<LBFGSOptimizer>(n);
+ TestOptimizerVariants<RPropOptimizer>(n);
+ TestOnline();
+ return 0;
+}
+