pmlpp/wgan/wgan.h

115 lines
5.2 KiB
C++

#ifndef MLPP_WGAN_H
#define MLPP_WGAN_H
/*************************************************************************/
/* wgan.h */
/*************************************************************************/
/* This file is part of: */
/* PMLPP Machine Learning Library */
/* https://github.com/Relintai/pmlpp */
/*************************************************************************/
/* Copyright (c) 2023-present Péter Magyar. */
/* Copyright (c) 2022-2023 Marc Melikyan */
/* */
/* Permission is hereby granted, free of charge, to any person obtaining */
/* a copy of this software and associated documentation files (the */
/* "Software"), to deal in the Software without restriction, including */
/* without limitation the rights to use, copy, modify, merge, publish, */
/* distribute, sublicense, and/or sell copies of the Software, and to */
/* permit persons to whom the Software is furnished to do so, subject to */
/* the following conditions: */
/* */
/* The above copyright notice and this permission notice shall be */
/* included in all copies or substantial portions of the Software. */
/* */
/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */
/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */
/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.*/
/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */
/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */
/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */
/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */
/*************************************************************************/
#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_tensor3.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 create_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_layer(Ref<MLPPHiddenLayer> layer);
Ref<MLPPHiddenLayer> get_layer(const int index);
void remove_layer(const int index);
int get_layer_count() const;
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(int 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(const Vector<Ref<MLPPMatrix>> &hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate);
void update_generator_parameters(const 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() {
output_w_grad.instance();
}
};
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;
int _k;
Vector<Ref<MLPPHiddenLayer>> _network;
Ref<MLPPOutputLayer> _output_layer;
Ref<MLPPVector> _y_hat;
};
#endif /* WGAN_hpp */