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fix compilation with double features

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Adrià Arrufat 2021-11-02 13:22:54 +01:00
parent 9a50809ebf
commit 76ebab4b91
1 changed files with 11 additions and 4 deletions
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15
examples/dnn_self_supervised_learning_ex.cpp
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@ -292,10 +292,14 @@ try
// Now, we initialize the feature extractor model with the backbone we have just learned.
model::feats fnet(layer<5>(net));
// And we will generate all the features for the training set to train a multiclass SVM
// classifier.
// classifier. It's always a good idea to use double instead of float to improve the
// convergence speed and the precision of the optimizer.
std::vector<matrix<double, 0, 1>> features;
cout << "Extracting features for linear classifier..." << endl;
features = fnet(training_images, 4 * batch_size);
auto temp = fnet(training_images, 4 * batch_size);
for (auto&& f : temp)
features.push_back(matrix_cast<double>(f));
temp.clear();
svm_multiclass_linear_trainer<linear_kernel<matrix<double,0,1>>, unsigned long> trainer;
trainer.set_num_threads(std::thread::hardware_concurrency());
// The most appropriate C setting could be found automatically by using find_max_global(). See the docs for
@ -307,7 +311,7 @@ try
// Finally, we can compute the accuracy of the model on the CIFAR-10 train and test images.
auto compute_accuracy = [&fnet, &df, batch_size](
const std::vector<matrix<float, 0, 1>>& samples,
const std::vector<matrix<double, 0, 1>>& samples,
const std::vector<unsigned long>& labels
)
{
@ -330,7 +334,10 @@ try
cout << "\ntraining accuracy" << endl;
compute_accuracy(features, training_labels);
cout << "\ntesting accuracy" << endl;
features = fnet(testing_images, 4 * batch_size);
features.clear();
temp = fnet(testing_images, 4 * batch_size);
for (auto&& f : temp)
features.push_back(matrix_cast<double>(f));
compute_accuracy(features, testing_labels);
return EXIT_SUCCESS;
}