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Fixed MLPPWGAN's handle_ui.

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Relintai 2023-02-06 15:05:00 +01:00
parent 7e71ea49d9
commit f8710e6432
1 changed files with 8 additions and 3 deletions
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11
mlpp/wgan/wgan.cpp
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@ -5,6 +5,9 @@
// //
#include "wgan.h" #include "wgan.h"
#include "core/log/logger.h"
#include "../activation/activation.h" #include "../activation/activation.h"
#include "../cost/cost.h" #include "../cost/cost.h"
#include "../lin_alg/lin_alg.h" #include "../lin_alg/lin_alg.h"
@ -314,6 +317,7 @@ MLPPWGAN::DiscriminatorGradientResult MLPPWGAN::compute_discriminator_gradients(
Ref<MLPPHiddenLayer> layer = network[network.size() - 1]; Ref<MLPPHiddenLayer> layer = network[network.size() - 1];
layer->set_delta(alg.hadamard_productm(alg.outer_product(output_layer->get_delta(), output_layer->get_weights()), avn.run_activation_deriv_matrix(layer->get_activation(), layer->get_z()))); layer->set_delta(alg.hadamard_productm(alg.outer_product(output_layer->get_delta(), output_layer->get_weights()), avn.run_activation_deriv_matrix(layer->get_activation(), layer->get_z())));
Ref<MLPPMatrix> hidden_layer_w_grad = alg.matmultm(alg.transposem(layer->get_input()), layer->get_delta()); Ref<MLPPMatrix> hidden_layer_w_grad = alg.matmultm(alg.transposem(layer->get_input()), layer->get_delta());
data.cumulative_hidden_layer_w_grad.push_back(alg.additionm(hidden_layer_w_grad, 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. data.cumulative_hidden_layer_w_grad.push_back(alg.additionm(hidden_layer_w_grad, 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.
@ -378,7 +382,8 @@ Vector<Ref<MLPPMatrix>> MLPPWGAN::compute_generator_gradients(const Ref<MLPPVect
void MLPPWGAN::handle_ui(int epoch, real_t cost_prev, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set) { void MLPPWGAN::handle_ui(int epoch, real_t cost_prev, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set) {
MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, output_set)); MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, output_set));
std::cout << "Layer " << network.size() + 1 << ": " << std::endl;
PLOG_MSG("Layer " + itos(network.size() + 1) + ":");
MLPPUtilities::print_ui_vb(output_layer->get_weights(), output_layer->get_bias()); MLPPUtilities::print_ui_vb(output_layer->get_weights(), output_layer->get_bias());
@ -386,9 +391,9 @@ void MLPPWGAN::handle_ui(int epoch, real_t cost_prev, const Ref<MLPPVector> &y_h
for (int i = network.size() - 1; i >= 0; i--) { for (int i = network.size() - 1; i >= 0; i--) {
Ref<MLPPHiddenLayer> layer = network[i]; Ref<MLPPHiddenLayer> layer = network[i];
std::cout << "Layer " << i + 1 << ": " << std::endl; PLOG_MSG("Layer " + itos(i + 1) + ":");
MLPPUtilities::print_ui_vib(layer->get_weights(), layer->get_bias(), 0); MLPPUtilities::print_ui_mb(layer->get_weights(), layer->get_bias());
} }
} }
} }