/*!
* \brief Flip the matrix horizontally and vertically, in place.
*/
derived_t& fflip_inplace() {
static_assert(etl_traits<derived_t>::dimensions() <= 2, "Impossible to fflip a matrix of D > 2");
if constexpr (is_2d<derived_t>) {
as_derived().ensure_cpu_up_to_date();
std::reverse(as_derived().begin(), as_derived().end());
}
double train_denoising(NIterator noisy_it, NIterator noisy_end, CIterator clean_it, CIterator clean_end, std::size_t max_epochs, Args... args) {
dll::rbm_trainer<parent_t, EnableWatcher, RW, true> trainer(args...);
return trainer.train(as_derived(),
noisy_it, noisy_end,
clean_it, clean_end,
max_epochs);
}
auto train_forward_one(const Input& input) const {
// Prepare one fully-ready output
auto output = prepare_one_ready_output(as_derived(), input);
// Forward propagation
train_forward_one(output, input);
// Return the forward-propagated result
return output;
}
void load(std::istream& is) {
load(is, as_derived());
}
void load(const std::string& file) {
load(file, as_derived());
}
void store(std::ostream& os) const {
store(os, as_derived());
}
void store(const std::string& file) const {
store(file, as_derived());
}
void display_hidden_unit_samples() const {
display_hidden_unit_samples(as_derived());
}
void print()
{
as_derived().print_impl();
}
double train_denoising(const Samples& noisy, const Samples& clean, std::size_t max_epochs, Args... args) {
dll::rbm_trainer<parent_t, EnableWatcher, RW, true> trainer(args...);
return trainer.train(as_derived(), noisy.begin(), noisy.end(), clean.begin(), clean.end(), max_epochs);
}
void reconstruct(const Sample& items) {
reconstruct(items, as_derived());
}
derived_t& scale_inplace(E&& e) {
as_derived() *= e;
return as_derived();
}
void select_forward_one(Output&& output, const Input& input) const {
if constexpr (Train) {
as_derived().train_forward_batch(batch_reshape(output), batch_reshape(input));
} else {
void train_forward_one(Output&& output, const Input& input) const {
as_derived().train_forward_batch(batch_reshape(output), batch_reshape(input));
}
double train(const Samples& training_data, std::size_t max_epochs, Args... args) {
dll::rbm_trainer<parent_t, EnableWatcher, RW, false> trainer(args...);
return trainer.train(as_derived(), training_data.begin(), training_data.end(), max_epochs);
}
double train(Iterator&& first, Iterator&& last, std::size_t max_epochs, Args... args) {
dll::rbm_trainer<parent_t, EnableWatcher, RW, false> trainer(args...);
return trainer.train(as_derived(), std::forward<Iterator>(first), std::forward<Iterator>(last), max_epochs);
}
void display_visible_unit_activations() const {
display_visible_unit_activations(as_derived());
}
void display_visible_unit_samples() const {
display_visible_unit_samples(as_derived());
}
ComponentAccessNonConstReturnType operator [] (ComponentIndex const &i) { return as_derived().Derived::operator[](i); }
