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normalize samples for SVM classifier (#2460)

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Adrià Arrufat 2021-11-17 14:14:39 +01:00 committed by GitHub
parent 44621a6124
commit e1ac0b43e4
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1 changed files with 7 additions and 1 deletions
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8
examples/dnn_self_supervised_learning_ex.cpp
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@ -295,6 +295,10 @@ try
std::vector<matrix<float, 0, 1>> features;
cout << "Extracting features for linear classifier..." << endl;
features = fnet(training_images, 4 * batch_size);
vector_normalizer<matrix<float, 0, 1>> normalizer;
normalizer.train(features);
for (auto& feature : features)
feature = normalizer(feature);
// Find the most appropriate C setting using find_max_global.
auto cross_validation_score = [&](const double c)
@ -309,7 +313,7 @@ try
cout << "confusion matrix:\n " << cm << endl;
return accuracy;
};
const auto result = find_max_global(cross_validation_score, 1e-4, 10000, max_function_calls(50));
const auto result = find_max_global(cross_validation_score, 1e-3, 1000, max_function_calls(50));
cout << "Best C: " << result.x(0) << endl;
// Proceed to train the SVM classifier with the best C.
@ -346,6 +350,8 @@ try
compute_accuracy(features, training_labels);
cout << "\ntesting accuracy" << endl;
features = fnet(testing_images, 4 * batch_size);
for (auto& feature : features)
feature = normalizer(feature);
compute_accuracy(features, testing_labels);
return EXIT_SUCCESS;
}