mirror of https://github.com/Relintai/pmlpp.git
119 lines
5.0 KiB
C++
119 lines
5.0 KiB
C++
#ifndef MLPP_GAN_H
|
|
#define MLPP_GAN_H
|
|
|
|
/*************************************************************************/
|
|
/* gan.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. */
|
|
/*************************************************************************/
|
|
|
|
#ifdef USING_SFW
|
|
#include "sfw.h"
|
|
#else
|
|
#include "core/math/math_defs.h"
|
|
|
|
#include "core/object/reference.h"
|
|
#endif
|
|
|
|
#include "../hidden_layer/hidden_layer.h"
|
|
#include "../output_layer/output_layer.h"
|
|
|
|
#include "../lin_alg/mlpp_tensor3.h"
|
|
|
|
#include "../activation/activation.h"
|
|
#include "../utilities/utilities.h"
|
|
|
|
class MLPPGAN : public Reference {
|
|
GDCLASS(MLPPGAN, Reference);
|
|
|
|
public:
|
|
/*
|
|
Ref<MLPPMatrix> get_input_set();
|
|
void set_input_set(const Ref<MLPPMatrix> &val);
|
|
|
|
Ref<MLPPVector> get_output_set();
|
|
void set_output_set(const Ref<MLPPVector> &val);
|
|
|
|
int get_k();
|
|
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);
|
|
|
|
MLPPGAN(real_t k, const Ref<MLPPMatrix> &output_set);
|
|
|
|
MLPPGAN();
|
|
~MLPPGAN();
|
|
|
|
protected:
|
|
Ref<MLPPMatrix> model_set_test_generator(const Ref<MLPPMatrix> &X); // Evaluator for the generator of the gan.
|
|
Ref<MLPPVector> model_set_test_discriminator(const Ref<MLPPMatrix> &X); // Evaluator for the discriminator of the gan.
|
|
|
|
real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
|
|
void forward_pass();
|
|
|
|
void update_discriminator_parameters(const Ref<MLPPTensor3> &hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate);
|
|
void update_generator_parameters(const Ref<MLPPTensor3> &hidden_layer_updations, real_t learning_rate);
|
|
|
|
struct ComputeDiscriminatorGradientsResult {
|
|
Ref<MLPPTensor3> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads.
|
|
Ref<MLPPVector> output_w_grad;
|
|
|
|
ComputeDiscriminatorGradientsResult() {
|
|
cumulative_hidden_layer_w_grad.instance();
|
|
output_w_grad.instance();
|
|
}
|
|
};
|
|
|
|
ComputeDiscriminatorGradientsResult compute_discriminator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);
|
|
Ref<MLPPTensor3> compute_generator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);
|
|
|
|
void print_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 /* GAN_hpp */ |