void test_dbn() {
float learning_rate = 0.1, lrcoef = 0.95;
float n_epochs =1000;
vector<int> hl;
int nrows = 6;
int ncols = 6;
int outs = 2;
matrix2dPtr train_X = matrix2dPtr ( new matrix2d(nrows, ncols) );
matrix2dPtr train_Y = matrix2dPtr( new matrix2d(nrows, outs) );
(*train_X) <<= 1,1,1, 0, 0, 0,1, 0, 1, 0, 0, 0,1, 1, 1, 0, 0, 0,0, 0, 1, 1, 1, 0,0, 0, 1, 1, 0, 0,0, 0, 1, 1, 1, 0;
(*train_Y ) <<=1,0,1,0,1,0,0,1,0,1,0,1;
hl.push_back(55);
hl.push_back(55);
matrix2dPtr test_X = matrix2dPtr ( new matrix2d(2, ncols) );
(*test_X) <<= 1,1, 0, 0, 0, 0,
0, 0, 0, 1, 1, 0;
DBN dbn = DBN(train_X, train_Y, hl);
dbn.pretrain(learning_rate, lrcoef, 15, n_epochs);
int f_epochs = 200;
float finetuneLR = 0.1 , finetuneCoef = 0.95;
dbn.finetuning( finetuneLR, finetuneCoef, f_epochs );
matrix2dPtr output ;//= matrix2dPtr (new matrix2d (nrows, outs));
output = dbn.predict( test_X );
printMatrix("final output", output);
}
void execute(DBN& dbn, task& task, const std::vector<std::string>& actions) {
print_title("Network");
dbn.display();
using dbn_t = std::decay_t<DBN>;
//Execute all the actions sequentially
for (auto& action : actions) {
if (action == "pretrain") {
print_title("Pretraining");
if (task.pretraining.samples.empty()) {
std::cout << "dllp: error: pretrain is not possible without a pretraining input" << std::endl;
return;
}
std::vector<Container> pt_samples;
//Try to read the samples
if (!read_samples<Three>(task.pretraining.samples, pt_samples)) {
std::cout << "dllp: error: failed to read the pretraining samples" << std::endl;
return;
}
if (task.pt_desc.denoising) {
std::vector<Container> clean_samples;
//Try to read the samples
if (!read_samples<Three>(task.pretraining_clean.samples, clean_samples)) {
std::cout << "dllp: error: failed to read the clean samples" << std::endl;
return;
}
//Pretrain the network
cpp::static_if<dbn_t::layers_t::is_denoising>([&](auto f) {
f(dbn).pretrain_denoising(pt_samples.begin(), pt_samples.end(), clean_samples.begin(), clean_samples.end(), task.pt_desc.epochs);
});
} else {
//Pretrain the network
dbn.pretrain(pt_samples.begin(), pt_samples.end(), task.pt_desc.epochs);
}
} else if (action == "train") {
print_title("Training");
if (task.training.samples.empty() || task.training.labels.empty()) {
std::cout << "dllp: error: train is not possible without samples and labels" << std::endl;
return;
}
std::vector<Container> ft_samples;
std::vector<std::size_t> ft_labels;
//Try to read the samples
if (!read_samples<Three>(task.training.samples, ft_samples)) {
std::cout << "dllp: error: failed to read the training samples" << std::endl;
return;
}
//Try to read the labels
if (!read_labels(task.training.labels, ft_labels)) {
std::cout << "dllp: error: failed to read the training labels" << std::endl;
return;
}
using last_layer = typename dbn_t::template layer_type<dbn_t::layers - 1>;
//Train the network
cpp::static_if<sgd_possible<last_layer>::value>([&](auto f) {
auto ft_error = f(dbn).fine_tune(ft_samples, ft_labels, task.ft_desc.epochs);
std::cout << "Train Classification Error:" << ft_error << std::endl;
});
} else if (action == "test") {
print_title("Testing");
if (task.testing.samples.empty() || task.testing.labels.empty()) {
std::cout << "dllp: error: test is not possible without samples and labels" << std::endl;
return;
}
std::vector<Container> test_samples;
std::vector<std::size_t> test_labels;
//Try to read the samples
if (!read_samples<Three>(task.testing.samples, test_samples)) {
std::cout << "dllp: error: failed to read the test samples" << std::endl;
return;
}
//Try to read the labels
if (!read_labels(task.testing.labels, test_labels)) {
std::cout << "dllp: error: failed to read the test labels" << std::endl;
return;
}
auto classes = dbn_t::output_size();
etl::dyn_matrix<std::size_t, 2> conf(classes, classes, 0.0);
std::size_t n = test_samples.size();
std::size_t tp = 0;
for (std::size_t i = 0; i < test_samples.size(); ++i) {
auto sample = test_samples[i];
auto label = test_labels[i];
auto predicted = dbn.predict(sample);
if (predicted == label) {
++tp;
}
++conf(label, predicted);
}
double test_error = (n - tp) / double(n);
std::cout << "Error rate: " << test_error << std::endl;
std::cout << "Accuracy: " << (1.0 - test_error) << std::endl
<< std::endl;
std::cout << "Results per class" << std::endl;
double overall = 0.0;
std::cout << " | Accuracy | Error rate |" << std::endl;
for (std::size_t l = 0; l < classes; ++l) {
std::size_t total = etl::sum(conf(l));
double acc = (total - conf(l, l)) / double(total);
std::cout << std::setw(3) << l;
std::cout << "|" << std::setw(10) << (1.0 - acc) << "|" << std::setw(12) << acc << "|" << std::endl;
overall += acc;
}
std::cout << std::endl;
std::cout << "Overall Error rate: " << overall / classes << std::endl;
std::cout << "Overall Accuracy: " << 1.0 - (overall / classes) << std::endl
<< std::endl;
std::cout << "Confusion Matrix (%)" << std::endl
<< std::endl;
std::cout << " ";
for (std::size_t l = 0; l < classes; ++l) {
std::cout << std::setw(5) << l << " ";
}
std::cout << std::endl;
for (std::size_t l = 0; l < classes; ++l) {
std::size_t total = etl::sum(conf(l));
std::cout << std::setw(3) << l << "|";
for (std::size_t p = 0; p < classes; ++p) {
std::cout << std::setw(5) << std::setprecision(2) << 100.0 * (conf(l, p) / double(total)) << "|";
}
std::cout << std::endl;
}
std::cout << std::endl;
} else if (action == "save") {
print_title("Save Weights");
dbn.store(task.w_desc.file);
std::cout << "Weights saved" << std::endl;
} else if (action == "load") {
print_title("Load Weights");
dbn.load(task.w_desc.file);
std::cout << "Weights loaded" << std::endl;
} else {
std::cout << "dllp: error: Invalid action: " << action << std::endl;
}
}
//TODO
}