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@ -298,6 +298,8 @@ try
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features = fnet(training_images, 4 * batch_size);
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svm_multiclass_linear_trainer<linear_kernel<matrix<float,0,1>>, unsigned long> trainer;
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trainer.set_num_threads(std::thread::hardware_concurrency());
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// The most appropriate C setting could be found automatically by using find_max_global(). See the docs for
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// find_max_global() for further information and take particular note of model_selection_ex.cpp.
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trainer.set_c(svm_c);
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cout << "Training Multiclass SVM..." << endl;
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const auto df = trainer.train(features, training_labels);
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