mirror of https://github.com/Relintai/pmlpp.git
92 lines
2.8 KiB
C++
92 lines
2.8 KiB
C++
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#ifndef MLPP_GAN_H
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#define MLPP_GAN_H
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//
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// GAN.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 "../hidden_layer/hidden_layer.h"
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#include "../output_layer/output_layer.h"
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#include "../lin_alg/mlpp_tensor3.h"
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#include "../activation/activation.h"
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#include "../utilities/utilities.h"
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class MLPPGAN : public Reference {
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GDCLASS(MLPPGAN, Reference);
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public:
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/*
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Ref<MLPPMatrix> get_input_set();
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void set_input_set(const Ref<MLPPMatrix> &val);
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Ref<MLPPVector> get_output_set();
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void set_output_set(const Ref<MLPPVector> &val);
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int get_k();
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void set_k(const int val);
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*/
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Ref<MLPPMatrix> generate_example(int n);
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void gradient_descent(real_t learning_rate, int max_epoch, 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 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(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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MLPPGAN(real_t k, const Ref<MLPPMatrix> &output_set);
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MLPPGAN();
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~MLPPGAN();
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protected:
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Ref<MLPPMatrix> model_set_test_generator(const Ref<MLPPMatrix> &X); // Evaluator for the generator of the gan.
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Ref<MLPPVector> model_set_test_discriminator(const Ref<MLPPMatrix> &X); // Evaluator for the discriminator of the gan.
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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_discriminator_parameters(const Ref<MLPPTensor3> &hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate);
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void update_generator_parameters(const Ref<MLPPTensor3> &hidden_layer_updations, real_t learning_rate);
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struct ComputeDiscriminatorGradientsResult {
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Ref<MLPPTensor3> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads.
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Ref<MLPPVector> output_w_grad;
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ComputeDiscriminatorGradientsResult() {
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cumulative_hidden_layer_w_grad.instance();
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output_w_grad.instance();
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}
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};
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ComputeDiscriminatorGradientsResult compute_discriminator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);
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Ref<MLPPTensor3> compute_generator_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> &output_set);
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static void _bind_methods();
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Ref<MLPPMatrix> _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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};
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#endif /* GAN_hpp */ |