mirror of https://github.com/Relintai/pmlpp.git
120 lines
4.0 KiB
C++
120 lines
4.0 KiB
C++
#ifndef MLPP_AUTO_ENCODER_H
|
|
#define MLPP_AUTO_ENCODER_H
|
|
|
|
/*************************************************************************/
|
|
/* auto_encoder.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 "../lin_alg/mlpp_matrix.h"
|
|
#include "../lin_alg/mlpp_vector.h"
|
|
|
|
#include "../regularization/reg.h"
|
|
|
|
#include "../lin_alg/mlpp_matrix.h"
|
|
#include "../lin_alg/mlpp_vector.h"
|
|
|
|
class MLPPAutoEncoder : public Reference {
|
|
GDCLASS(MLPPAutoEncoder, Reference);
|
|
|
|
public:
|
|
Ref<MLPPMatrix> get_input_set();
|
|
void set_input_set(const Ref<MLPPMatrix> &val);
|
|
|
|
int get_n_hidden();
|
|
void set_n_hidden(const int val);
|
|
|
|
Ref<MLPPMatrix> model_set_test(const Ref<MLPPMatrix> &X);
|
|
Ref<MLPPVector> model_test(const Ref<MLPPVector> &x);
|
|
|
|
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);
|
|
|
|
MLPPAutoEncoder(const Ref<MLPPMatrix> &p_input_set, int p_n_hidden);
|
|
|
|
MLPPAutoEncoder();
|
|
~MLPPAutoEncoder();
|
|
|
|
protected:
|
|
real_t cost(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
Ref<MLPPVector> evaluatev(const Ref<MLPPVector> &x);
|
|
|
|
struct PropagateVResult {
|
|
Ref<MLPPVector> z2;
|
|
Ref<MLPPVector> a2;
|
|
};
|
|
|
|
PropagateVResult propagatev(const Ref<MLPPVector> &x);
|
|
|
|
Ref<MLPPMatrix> evaluatem(const Ref<MLPPMatrix> &X);
|
|
|
|
struct PropagateMResult {
|
|
Ref<MLPPMatrix> z2;
|
|
Ref<MLPPMatrix> a2;
|
|
};
|
|
|
|
PropagateMResult propagatem(const Ref<MLPPMatrix> &X);
|
|
|
|
void forward_pass();
|
|
|
|
static void _bind_methods();
|
|
|
|
Ref<MLPPMatrix> _input_set;
|
|
Ref<MLPPMatrix> _y_hat;
|
|
|
|
Ref<MLPPMatrix> _weights1;
|
|
Ref<MLPPMatrix> _weights2;
|
|
|
|
Ref<MLPPVector> _bias1;
|
|
Ref<MLPPVector> _bias2;
|
|
|
|
Ref<MLPPMatrix> _z2;
|
|
Ref<MLPPMatrix> _a2;
|
|
|
|
int _n;
|
|
int _k;
|
|
int _n_hidden;
|
|
|
|
bool _initialized;
|
|
};
|
|
|
|
#endif /* AutoEncoder_hpp */
|