mirror of https://github.com/Relintai/pmlpp.git
83 lines
2.8 KiB
C++
83 lines
2.8 KiB
C++
|
|
#ifndef MLPP_WGAN_H
|
|
#define MLPP_WGAN_H
|
|
|
|
//
|
|
// WGAN.hpp
|
|
//
|
|
// Created by Marc Melikyan on 11/4/20.
|
|
//
|
|
|
|
#include "core/containers/vector.h"
|
|
#include "core/math/math_defs.h"
|
|
#include "core/string/ustring.h"
|
|
|
|
#include "core/object/reference.h"
|
|
|
|
#include "../lin_alg/mlpp_matrix.h"
|
|
#include "../lin_alg/mlpp_vector.h"
|
|
|
|
#include "../hidden_layer/hidden_layer.h"
|
|
#include "../output_layer/output_layer.h"
|
|
|
|
#include "../activation/activation.h"
|
|
#include "../cost/cost.h"
|
|
#include "../regularization/reg.h"
|
|
#include "../utilities/utilities.h"
|
|
|
|
class MLPPWGAN : public Reference {
|
|
GDCLASS(MLPPWGAN, Reference);
|
|
|
|
public:
|
|
Ref<MLPPMatrix> get_output_set();
|
|
void set_output_set(const Ref<MLPPMatrix> &val);
|
|
|
|
int get_k() const;
|
|
void set_k(const int val);
|
|
|
|
Ref<MLPPMatrix> generate_example(int n);
|
|
void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
|
|
real_t score();
|
|
void save(const String &file_name);
|
|
|
|
void add_layer(int n_hidden, MLPPActivation::ActivationFunction activation, MLPPUtilities::WeightDistributionType weight_init = MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_DEFAULT, MLPPReg::RegularizationType reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t lambda = 0.5, real_t alpha = 0.5);
|
|
void add_output_layer(MLPPUtilities::WeightDistributionType weight_init = MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_DEFAULT, MLPPReg::RegularizationType reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t lambda = 0.5, real_t alpha = 0.5);
|
|
|
|
MLPPWGAN(real_t k, const Ref<MLPPMatrix> &output_set);
|
|
|
|
MLPPWGAN();
|
|
~MLPPWGAN();
|
|
|
|
protected:
|
|
Ref<MLPPMatrix> model_set_test_generator(const Ref<MLPPMatrix> &X); // Evaluator for the generator of the WGAN.
|
|
Ref<MLPPVector> model_set_test_discriminator(const Ref<MLPPMatrix> &X); // Evaluator for the discriminator of the WGAN.
|
|
|
|
real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
|
|
void forward_pass();
|
|
void update_discriminator_parameters(Vector<Ref<MLPPMatrix>> hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate);
|
|
void update_generator_parameters(Vector<Ref<MLPPMatrix>> hidden_layer_updations, real_t learning_rate);
|
|
|
|
struct DiscriminatorGradientResult {
|
|
Vector<Ref<MLPPMatrix>> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads.
|
|
Ref<MLPPVector> output_w_grad;
|
|
};
|
|
|
|
DiscriminatorGradientResult compute_discriminator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);
|
|
Vector<Ref<MLPPMatrix>> compute_generator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);
|
|
|
|
void handle_ui(int epoch, real_t cost_prev, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);
|
|
|
|
static void _bind_methods();
|
|
|
|
Ref<MLPPMatrix> _output_set;
|
|
Ref<MLPPVector> _y_hat;
|
|
|
|
Vector<Ref<MLPPHiddenLayer>> _network;
|
|
Ref<MLPPOutputLayer> _output_layer;
|
|
|
|
int _n;
|
|
int _k;
|
|
};
|
|
|
|
#endif /* WGAN_hpp */ |