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Added an option to solve the L2-loss version of the SVM objective function.
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Davis King
parent
1af517bcf0
commit
4203da2b16
@ -47,7 +47,8 @@ namespace dlib
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verbose(false),
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have_bias(true),
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last_weight_1(false),
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do_shrinking(true)
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do_shrinking(true),
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do_svm_l2(false)
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{
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}
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@ -61,7 +62,8 @@ namespace dlib
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verbose(false),
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have_bias(true),
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last_weight_1(false),
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do_shrinking(true)
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do_shrinking(true),
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do_svm_l2(false)
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{
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// make sure requires clause is not broken
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DLIB_ASSERT(0 < C_,
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@ -104,6 +106,13 @@ namespace dlib
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bool enabled
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) { do_shrinking = enabled; }
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bool solving_svm_l2_problem (
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) const { return do_svm_l2; }
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void solve_svm_l2_problem (
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bool enabled
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) { do_svm_l2 = enabled; }
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void be_verbose (
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)
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{
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@ -497,11 +506,15 @@ namespace dlib
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std::vector<long>& index = state.index;
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const long dims = state.dims;
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unsigned long active_size = index.size();
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scalar_type PG_max_prev = std::numeric_limits<scalar_type>::infinity();
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scalar_type PG_min_prev = -std::numeric_limits<scalar_type>::infinity();
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const scalar_type Dii_pos = 1/(2*Cpos);
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const scalar_type Dii_neg = 1/(2*Cneg);
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// main loop
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for (unsigned long iter = 0; iter < max_iterations; ++iter)
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{
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@ -521,8 +534,16 @@ namespace dlib
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{
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const long i = index[ii];
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const scalar_type G = y(i)*dot(w, x(i)) - 1;
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scalar_type G = y(i)*dot(w, x(i)) - 1;
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if (do_svm_l2)
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{
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if (y(i) > 0)
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G += Dii_pos*alpha[i];
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else
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G += Dii_neg*alpha[i];
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}
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const scalar_type C = (y(i) > 0) ? Cpos : Cneg;
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const scalar_type U = do_svm_l2 ? std::numeric_limits<scalar_type>::infinity() : C;
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scalar_type PG = 0;
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if (alpha[i] == 0)
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@ -539,7 +560,7 @@ namespace dlib
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if (G < 0)
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PG = G;
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}
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else if (alpha[i] == C)
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else if (alpha[i] == U)
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{
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if (G < PG_min_prev)
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{
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@ -567,7 +588,7 @@ namespace dlib
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if (std::abs(PG) > 1e-12)
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{
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const scalar_type alpha_old = alpha[i];
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alpha[i] = std::min(std::max(alpha[i] - G/state.Q[i], (scalar_type)0.0), C);
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alpha[i] = std::min(std::max(alpha[i] - G/state.Q[i], (scalar_type)0.0), U);
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const scalar_type delta = (alpha[i]-alpha_old)*y(i);
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add_to(w, x(i), delta);
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if (have_bias && !last_weight_1)
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@ -660,6 +681,7 @@ namespace dlib
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bool have_bias; // having a bias means we pretend all x vectors have an extra element which is always -1.
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bool last_weight_1;
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bool do_shrinking;
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bool do_svm_l2;
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}; // end of class svm_c_linear_dcd_trainer
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@ -67,6 +67,7 @@ namespace dlib
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- #forces_last_weight_to_1() == false
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- #includes_bias() == true
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- #shrinking_enabled() == true
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- #solving_svm_l2_problem() == false
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!*/
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explicit svm_c_linear_dcd_trainer (
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@ -86,6 +87,7 @@ namespace dlib
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- #forces_last_weight_to_1() == false
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- #includes_bias() == true
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- #shrinking_enabled() == true
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- #solving_svm_l2_problem() == false
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!*/
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bool includes_bias (
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@ -140,6 +142,23 @@ namespace dlib
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- #shrinking_enabled() == enabled
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!*/
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bool solving_svm_l2_problem (
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) const;
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/*!
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ensures
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- returns true if this solver will solve the L2 version of the SVM
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objective function. That is, if solving_svm_l2_problem()==true then this
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object, rather than using the hinge loss, uses the squared hinge loss.
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!*/
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void solve_svm_l2_problem (
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bool enabled
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);
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/*!
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ensures
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- #solving_svm_l2_problem() == enabled
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!*/
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void be_verbose (
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);
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/*!
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@ -440,6 +440,65 @@ namespace
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// ----------------------------------------------------------------------------------------
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void test_l2_version ()
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{
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typedef std::map<unsigned long,double> sample_type;
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typedef sparse_linear_kernel<sample_type> kernel_type;
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svm_c_linear_dcd_trainer<kernel_type> linear_trainer;
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linear_trainer.set_c(10);
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linear_trainer.set_epsilon(1e-5);
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std::vector<sample_type> samples;
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std::vector<double> labels;
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// make an instance of a sample vector so we can use it below
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sample_type sample;
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// Now let's go into a loop and randomly generate 10000 samples.
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double label = +1;
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for (int i = 0; i < 1000; ++i)
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{
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// flip this flag
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label *= -1;
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sample.clear();
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// now make a random sparse sample with at most 10 non-zero elements
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for (int j = 0; j < 10; ++j)
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{
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int idx = std::rand()%100;
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double value = static_cast<double>(std::rand())/RAND_MAX;
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sample[idx] = label*value;
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}
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// Also save the samples we are generating so we can let the svm_c_linear_trainer
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// learn from them below.
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samples.push_back(sample);
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labels.push_back(label);
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}
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decision_function<kernel_type> df = linear_trainer.train(samples, labels);
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sample.clear();
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sample[4] = 0.3;
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sample[10] = 0.9;
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DLIB_TEST(df(sample) > 0);
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sample.clear();
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sample[83] = -0.3;
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sample[26] = -0.9;
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sample[58] = -0.7;
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DLIB_TEST(df(sample) < 0);
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sample.clear();
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sample[0] = -0.2;
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sample[9] = -0.8;
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DLIB_TEST(df(sample) < 0);
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}
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class tester_svm_c_linear_dcd : public tester
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{
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public:
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@ -474,6 +533,8 @@ namespace
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print_spinner();
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test_sparse_1_sample(-1);
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print_spinner();
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test_l2_version();
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}
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} a;
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