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83 lines
2.8 KiB
C++
83 lines
2.8 KiB
C++
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#ifndef MLPP_WGAN_H
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#define MLPP_WGAN_H
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//
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// WGAN.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/containers/vector.h"
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#include "core/math/math_defs.h"
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#include "core/string/ustring.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 MLPPWGAN : public Reference {
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GDCLASS(MLPPWGAN, Reference);
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public:
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Ref<MLPPMatrix> get_output_set();
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void set_output_set(const Ref<MLPPMatrix> &val);
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int get_k() const;
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void set_k(const int val);
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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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MLPPWGAN(real_t k, const Ref<MLPPMatrix> &output_set);
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MLPPWGAN();
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~MLPPWGAN();
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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 WGAN.
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Ref<MLPPVector> model_set_test_discriminator(const Ref<MLPPMatrix> &X); // Evaluator for the discriminator of the WGAN.
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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(Vector<Ref<MLPPMatrix>> hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate);
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void update_generator_parameters(Vector<Ref<MLPPMatrix>> hidden_layer_updations, real_t learning_rate);
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struct DiscriminatorGradientResult {
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Vector<Ref<MLPPMatrix>> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads.
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Ref<MLPPVector> output_w_grad;
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};
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DiscriminatorGradientResult compute_discriminator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);
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Vector<Ref<MLPPMatrix>> compute_generator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);
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void handle_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 /* WGAN_hpp */ |