pmlpp/wgan/wgan.h

#ifndef MLPP_WGAN_H
#define MLPP_WGAN_H

/*************************************************************************/
/*  wgan.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.                */
/*************************************************************************/

#include "core/containers/vector.h"
#include "core/math/math_defs.h"
#include "core/string/ustring.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 MLPPWGAN : public Reference {
	GDCLASS(MLPPWGAN, Reference);

public:
	Ref<MLPPMatrix> get_output_set();
	void set_output_set(const Ref<MLPPMatrix> &val);

	int get_k() const;
	void set_k(const int val);

	Ref<MLPPMatrix> generate_example(int n);
	void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
	real_t score();
	void save(const String &file_name);

	void create_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_layer(Ref<MLPPHiddenLayer> layer);
	Ref<MLPPHiddenLayer> get_layer(const int index);
	void remove_layer(const int index);
	int get_layer_count() const;

	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);

	MLPPWGAN(int k, const Ref<MLPPMatrix> &output_set);

	MLPPWGAN();
	~MLPPWGAN();

protected:
	Ref<MLPPMatrix> model_set_test_generator(const Ref<MLPPMatrix> &X); // Evaluator for the generator of the WGAN.
	Ref<MLPPVector> model_set_test_discriminator(const Ref<MLPPMatrix> &X); // Evaluator for the discriminator of the WGAN.

	real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);

	void forward_pass();
	void update_discriminator_parameters(const Vector<Ref<MLPPMatrix>> &hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate);
	void update_generator_parameters(const Vector<Ref<MLPPMatrix>> &hidden_layer_updations, real_t learning_rate);

	struct DiscriminatorGradientResult {
		Vector<Ref<MLPPMatrix>> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads.
		Ref<MLPPVector> output_w_grad;

		DiscriminatorGradientResult() {
			output_w_grad.instance();
		}
	};

	DiscriminatorGradientResult compute_discriminator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);
	Vector<Ref<MLPPMatrix>> compute_generator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);

	void handle_ui(int epoch, real_t cost_prev, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);

	static void _bind_methods();

	Ref<MLPPMatrix> _output_set;
	int _k;

	Vector<Ref<MLPPHiddenLayer>> _network;
	Ref<MLPPOutputLayer> _output_layer;

	Ref<MLPPVector> _y_hat;
};

#endif /* WGAN_hpp */