#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 get_input_set(); void set_input_set(const Ref &val); int get_n_hidden(); void set_n_hidden(const int val); Ref model_set_test(const Ref &X); Ref model_test(const Ref &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 &p_input_set, int p_n_hidden); MLPPAutoEncoder(); ~MLPPAutoEncoder(); protected: real_t cost(const Ref &y_hat, const Ref &y); Ref evaluatev(const Ref &x); struct PropagateVResult { Ref z2; Ref a2; }; PropagateVResult propagatev(const Ref &x); Ref evaluatem(const Ref &X); struct PropagateMResult { Ref z2; Ref a2; }; PropagateMResult propagatem(const Ref &X); void forward_pass(); static void _bind_methods(); Ref _input_set; Ref _y_hat; Ref _weights1; Ref _weights2; Ref _bias1; Ref _bias2; Ref _z2; Ref _a2; int _n; int _k; int _n_hidden; bool _initialized; }; #endif /* AutoEncoder_hpp */