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102 lines
3.8 KiB
C++
102 lines
3.8 KiB
C++
#ifndef MLPP_ANN_H
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#define MLPP_ANN_H
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//
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// ANN.hpp
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//
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// Created by Marc Melikyan on 11/4/20.
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//
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#include "core/math/math_defs.h"
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#include "core/object/reference.h"
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#include "../lin_alg/mlpp_matrix.h"
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#include "../lin_alg/mlpp_vector.h"
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#include "../hidden_layer/hidden_layer.h"
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#include "../output_layer/output_layer.h"
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#include "../activation/activation.h"
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#include "../cost/cost.h"
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#include "../regularization/reg.h"
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#include "../utilities/utilities.h"
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class MLPPANN : public Reference {
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GDCLASS(MLPPANN, Reference);
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public:
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enum SchedulerType {
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SCHEDULER_TYPE_NONE = 0,
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SCHEDULER_TYPE_TIME,
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SCHEDULER_TYPE_EPOCH,
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SCHEDULER_TYPE_STEP,
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SCHEDULER_TYPE_EXPONENTIAL,
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};
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public:
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Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
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real_t model_test(const Ref<MLPPVector> &x);
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void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
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void sgd(real_t learning_rate, int max_epoch, bool ui = false);
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void mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
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void momentum(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool nag, bool ui = false);
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void adagrad(real_t learning_rate, int max_epoch, int mini_batch_size, real_t e, bool ui = false);
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void adadelta(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t e, bool ui = false);
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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);
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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);
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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);
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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);
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real_t score();
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void save(const String &file_name);
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void set_learning_rate_scheduler(SchedulerType type, real_t decay_constant);
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void set_learning_rate_scheduler_drop(SchedulerType type, real_t decay_constant, real_t drop_rate);
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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);
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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);
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MLPPANN(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set);
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MLPPANN();
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~MLPPANN();
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protected:
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real_t apply_learning_rate_scheduler(real_t learning_rate, real_t decay_constant, real_t epoch, real_t drop_rate);
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real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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void forward_pass();
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void update_parameters(const Vector<Ref<MLPPMatrix>> &hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate);
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struct ComputeGradientsResult {
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Vector<Ref<MLPPMatrix>> cumulative_hidden_layer_w_grad;
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Ref<MLPPVector> output_w_grad;
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};
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ComputeGradientsResult compute_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &_output_set);
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void print_ui(int epoch, real_t cost_prev, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &p_output_set);
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static void _bind_methods();
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Ref<MLPPMatrix> _input_set;
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Ref<MLPPVector> _output_set;
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Ref<MLPPVector> _y_hat;
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Vector<Ref<MLPPHiddenLayer>> _network;
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Ref<MLPPOutputLayer> _output_layer;
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int _n;
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int _k;
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SchedulerType _lr_scheduler;
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real_t _decay_constant;
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real_t _drop_rate;
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};
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VARIANT_ENUM_CAST(MLPPANN::SchedulerType);
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#endif /* ANN_hpp */ |