pmlpp/mlpp/ann/ann.h
2023-12-30 00:41:59 +01:00

131 lines
6.0 KiB
C++

#ifndef MLPP_ANN_H
#define MLPP_ANN_H
/*************************************************************************/
/* ann.h */
/*************************************************************************/
/* This file is part of: */
/* PMLPP Machine Learning Library */
/* https://github.com/Relintai/pmlpp */
/*************************************************************************/
/* Copyright (c) 2022-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/math/math_defs.h"
#include "core/object/reference.h"
#include "../lin_alg/mlpp_matrix.h"
#include "../lin_alg/mlpp_tensor3.h"
#include "../lin_alg/mlpp_vector.h"
#include "../hidden_layer/hidden_layer.h"
#include "../output_layer/output_layer.h"
#include "../activation/activation.h"
#include "../cost/cost.h"
#include "../regularization/reg.h"
#include "../utilities/utilities.h"
class MLPPANN : public Reference {
GDCLASS(MLPPANN, Reference);
public:
enum SchedulerType {
SCHEDULER_TYPE_NONE = 0,
SCHEDULER_TYPE_TIME,
SCHEDULER_TYPE_EPOCH,
SCHEDULER_TYPE_STEP,
SCHEDULER_TYPE_EXPONENTIAL,
};
public:
Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
real_t 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);
void momentum(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool nag, bool ui = false);
void adagrad(real_t learning_rate, int max_epoch, int mini_batch_size, real_t e, bool ui = false);
void adadelta(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t e, bool ui = false);
void adam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui = false);
void adamax(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui = false);
void nadam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui = false);
void amsgrad(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui = false);
real_t score();
void save(const String &file_name);
void set_learning_rate_scheduler(SchedulerType type, real_t decay_constant);
void set_learning_rate_scheduler_drop(SchedulerType type, real_t decay_constant, real_t drop_rate);
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(MLPPActivation::ActivationFunction activation, MLPPCost::CostTypes loss, 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);
MLPPANN(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set);
MLPPANN();
~MLPPANN();
protected:
real_t apply_learning_rate_scheduler(real_t learning_rate, real_t decay_constant, real_t epoch, real_t drop_rate);
real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
void forward_pass();
void update_parameters(const Vector<Ref<MLPPMatrix>> &hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate);
struct ComputeGradientsResult {
Vector<Ref<MLPPMatrix>> cumulative_hidden_layer_w_grad;
Ref<MLPPVector> output_w_grad;
ComputeGradientsResult() {
output_w_grad.instance();
}
};
ComputeGradientsResult compute_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> &p_output_set);
static void _bind_methods();
Ref<MLPPMatrix> _input_set;
Ref<MLPPVector> _output_set;
Ref<MLPPVector> _y_hat;
Vector<Ref<MLPPHiddenLayer>> _network;
Ref<MLPPOutputLayer> _output_layer;
int _n;
int _k;
SchedulerType _lr_scheduler;
real_t _decay_constant;
real_t _drop_rate;
};
VARIANT_ENUM_CAST(MLPPANN::SchedulerType);
#endif /* ANN_hpp */