mirror of
https://github.com/Relintai/pmlpp.git
synced 2024-11-12 13:47:18 +01:00
129 lines
4.6 KiB
C++
129 lines
4.6 KiB
C++
#ifndef MLPP_MLP_H
|
|
#define MLPP_MLP_H
|
|
|
|
/*************************************************************************/
|
|
/* mlp.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/variant/variant.h"
|
|
|
|
#include "core/object/reference.h"
|
|
|
|
#include "../regularization/reg.h"
|
|
|
|
#include "../lin_alg/mlpp_matrix.h"
|
|
#include "../lin_alg/mlpp_vector.h"
|
|
|
|
#include <map>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
class MLPPMLP : public Reference {
|
|
GDCLASS(MLPPMLP, 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_n_hidden();
|
|
void set_n_hidden(const int val);
|
|
|
|
real_t get_lambda();
|
|
void set_lambda(const real_t val);
|
|
|
|
real_t get_alpha();
|
|
void set_alpha(const real_t val);
|
|
|
|
MLPPReg::RegularizationType get_reg();
|
|
void set_reg(const MLPPReg::RegularizationType val);
|
|
|
|
Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
|
|
real_t model_test(const Ref<MLPPVector> &x);
|
|
|
|
bool is_initialized();
|
|
void initialize();
|
|
|
|
void gradient_descent(real_t learning_rate, int max_epoch, bool UI = false);
|
|
void sgd(real_t learning_rate, int max_epoch, bool UI = false);
|
|
void mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false);
|
|
|
|
real_t score();
|
|
void save(const String &file_name);
|
|
|
|
MLPPMLP(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, int p_n_hidden, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
|
|
|
|
MLPPMLP();
|
|
~MLPPMLP();
|
|
|
|
protected:
|
|
real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
|
|
Ref<MLPPVector> evaluatem(const Ref<MLPPMatrix> &X);
|
|
void propagatem(const Ref<MLPPMatrix> &X, Ref<MLPPMatrix> z2_out, Ref<MLPPMatrix> a2_out);
|
|
|
|
real_t evaluatev(const Ref<MLPPVector> &x);
|
|
void propagatev(const Ref<MLPPVector> &x, Ref<MLPPVector> z2_out, Ref<MLPPVector> a2_out);
|
|
|
|
void forward_pass();
|
|
|
|
static void _bind_methods();
|
|
|
|
Ref<MLPPMatrix> _input_set;
|
|
Ref<MLPPVector> _output_set;
|
|
Ref<MLPPVector> _y_hat;
|
|
|
|
Ref<MLPPMatrix> _weights1;
|
|
Ref<MLPPVector> _weights2;
|
|
|
|
Ref<MLPPVector> _bias1;
|
|
real_t _bias2;
|
|
|
|
Ref<MLPPMatrix> _z2;
|
|
Ref<MLPPMatrix> _a2;
|
|
|
|
int _n;
|
|
int _k;
|
|
int _n_hidden;
|
|
|
|
// Regularization Params
|
|
MLPPReg::RegularizationType _reg;
|
|
real_t _lambda; /* Regularization Parameter */
|
|
real_t _alpha; /* This is the controlling param for Elastic Net*/
|
|
|
|
bool _initialized;
|
|
};
|
|
|
|
#endif /* MLP_hpp */
|