pmlpp/auto_encoder/auto_encoder.h

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#ifndef MLPP_AUTO_ENCODER_H
#define MLPP_AUTO_ENCODER_H
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/*************************************************************************/
/* auto_encoder.h */
/*************************************************************************/
/* This file is part of: */
/* PMLPP Machine Learning Library */
/* https://github.com/Relintai/pmlpp */
/*************************************************************************/
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/* Copyright (c) 2023-present Péter Magyar. */
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/* 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. */
/*************************************************************************/
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#include "core/math/math_defs.h"
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#include "core/object/reference.h"
#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"
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class MLPPAutoEncoder : public Reference {
GDCLASS(MLPPAutoEncoder, Reference);
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public:
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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);
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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);
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real_t score();
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void save(const String &file_name);
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MLPPAutoEncoder(const Ref<MLPPMatrix> &p_input_set, int p_n_hidden);
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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);
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struct PropagateMResult {
Ref<MLPPMatrix> z2;
Ref<MLPPMatrix> a2;
};
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PropagateMResult propagatem(const Ref<MLPPMatrix> &X);
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void forward_pass();
static void _bind_methods();
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Ref<MLPPMatrix> _input_set;
Ref<MLPPMatrix> _y_hat;
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Ref<MLPPMatrix> _weights1;
Ref<MLPPMatrix> _weights2;
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Ref<MLPPVector> _bias1;
Ref<MLPPVector> _bias2;
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Ref<MLPPMatrix> _z2;
Ref<MLPPMatrix> _a2;
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int _n;
int _k;
int _n_hidden;
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bool _initialized;
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};
#endif /* AutoEncoder_hpp */