Relintai/pmlpp
1
0
Fork 0
mirror of https://github.com/Relintai/pmlpp.git synced 2024-11-08 13:12:09 +01:00
Code Issues Packages Projects Releases Wiki Activity

Fixed MLPPANN.

Browse Source
This commit is contained in:
Relintai 2023-12-27 22:38:29 +01:00
parent ce35dfb42a
commit 67f8d49bf5
1 changed files with 7 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

11
mlpp/ann/ann.cpp
View File

@ -889,18 +889,21 @@ MLPPANN::ComputeGradientsResult MLPPANN::compute_gradients(const Ref<MLPPVector>
if (!_network.empty()) {
Ref<MLPPHiddenLayer> layer = _network[_network.size() - 1];
layer->set_delta(_output_layer->get_delta()->outer_product(_output_layer->get_weights())->hadamard_productn(avn.run_activation_deriv_vector(layer->get_activation(), layer->get_z())));
layer->set_delta(_output_layer->get_delta()->outer_product(_output_layer->get_weights())->hadamard_productn(avn.run_activation_deriv_matrix(layer->get_activation(), layer->get_z())));
Ref<MLPPMatrix> hidden_layer_w_grad = layer->get_input()->transposen()->multn(layer->get_delta());
res.cumulative_hidden_layer_w_grad->z_slice_add_mlpp_matrix(hidden_layer_w_grad->addn(regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg()))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
// TODO Are these always uniform in size?
res.cumulative_hidden_layer_w_grad->resize(Size3i(hidden_layer_w_grad->size().x, hidden_layer_w_grad->size().y, 0));
// Adding to our cumulative hidden layer grads. Maintain reg terms as well.
res.cumulative_hidden_layer_w_grad->z_slice_add_mlpp_matrix(hidden_layer_w_grad->addn(regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg())));
for (int i = _network.size() - 2; i >= 0; i--) {
layer = _network[i];
Ref<MLPPHiddenLayer> next_layer = _network[i + 1];
layer->set_delta(next_layer->get_delta()->multn(next_layer->get_weights()->transposen())->hadamard_productn(avn.run_activation_deriv_vector(layer->get_activation(), layer->get_z())));
layer->set_delta(next_layer->get_delta()->multn(next_layer->get_weights()->transposen())->hadamard_productn(avn.run_activation_deriv_matrix(layer->get_activation(), layer->get_z())));
hidden_layer_w_grad = layer->get_input()->transposen()->multn(layer->get_delta());
res.cumulative_hidden_layer_w_grad->z_slice_add_mlpp_matrix(hidden_layer_w_grad->addn(regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg()))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
}