mirror of https://github.com/Relintai/pmlpp.git
141 lines
4.9 KiB
C++
141 lines
4.9 KiB
C++
#ifndef MLPP_MULTI_OUTPUT_LAYER_H
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#define MLPP_MULTI_OUTPUT_LAYER_H
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/*************************************************************************/
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/* multi_output_layer.h */
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/*************************************************************************/
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/* This file is part of: */
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/* PMLPP Machine Learning Library */
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/* https://github.com/Relintai/pmlpp */
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/*************************************************************************/
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/* Copyright (c) 2023-present Péter Magyar. */
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/* Copyright (c) 2022-2023 Marc Melikyan */
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/* */
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/* Permission is hereby granted, free of charge, to any person obtaining */
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/* a copy of this software and associated documentation files (the */
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/* "Software"), to deal in the Software without restriction, including */
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/* without limitation the rights to use, copy, modify, merge, publish, */
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/* distribute, sublicense, and/or sell copies of the Software, and to */
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/* permit persons to whom the Software is furnished to do so, subject to */
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/* the following conditions: */
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/* */
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/* The above copyright notice and this permission notice shall be */
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/* included in all copies or substantial portions of the Software. */
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/* */
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/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */
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/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */
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/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.*/
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/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */
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/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */
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/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */
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/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */
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/*************************************************************************/
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#ifdef USING_SFW
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#include "sfw.h"
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#else
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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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#endif
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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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#include "../lin_alg/mlpp_matrix.h"
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#include "../lin_alg/mlpp_vector.h"
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class MLPPMultiOutputLayer : public Reference {
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GDCLASS(MLPPMultiOutputLayer, Reference);
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public:
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int get_n_output();
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void set_n_output(const int val);
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int get_n_hidden();
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void set_n_hidden(const int val);
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MLPPActivation::ActivationFunction get_activation();
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void set_activation(const MLPPActivation::ActivationFunction val);
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MLPPCost::CostTypes get_cost();
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void set_cost(const MLPPCost::CostTypes val);
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Ref<MLPPMatrix> get_input();
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void set_input(const Ref<MLPPMatrix> &val);
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Ref<MLPPMatrix> get_weights();
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void set_weights(const Ref<MLPPMatrix> &val);
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Ref<MLPPVector> get_bias();
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void set_bias(const Ref<MLPPVector> &val);
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Ref<MLPPMatrix> get_z();
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void set_z(const Ref<MLPPMatrix> &val);
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Ref<MLPPMatrix> get_a();
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void set_a(const Ref<MLPPMatrix> &val);
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Ref<MLPPVector> get_z_test();
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void set_z_test(const Ref<MLPPVector> &val);
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Ref<MLPPVector> get_a_test();
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void set_a_test(const Ref<MLPPVector> &val);
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Ref<MLPPMatrix> get_delta();
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void set_delta(const Ref<MLPPMatrix> &val);
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MLPPReg::RegularizationType get_reg();
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void set_reg(const MLPPReg::RegularizationType val);
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real_t get_lambda();
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void set_lambda(const real_t val);
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real_t get_alpha();
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void set_alpha(const real_t val);
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MLPPUtilities::WeightDistributionType get_weight_init();
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void set_weight_init(const MLPPUtilities::WeightDistributionType val);
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void forward_pass();
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void test(const Ref<MLPPVector> &x);
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MLPPMultiOutputLayer(int n_output, int p_n_hidden, MLPPActivation::ActivationFunction p_activation, MLPPCost::CostTypes cost, Ref<MLPPMatrix> p_input, MLPPUtilities::WeightDistributionType p_weight_init, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha);
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MLPPMultiOutputLayer();
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~MLPPMultiOutputLayer();
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protected:
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static void _bind_methods();
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int _n_output;
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int _n_hidden;
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MLPPActivation::ActivationFunction _activation;
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MLPPCost::CostTypes _cost;
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Ref<MLPPMatrix> _input;
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Ref<MLPPMatrix> _weights;
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Ref<MLPPVector> _bias;
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Ref<MLPPMatrix> _z;
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Ref<MLPPMatrix> _a;
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Ref<MLPPVector> _z_test;
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Ref<MLPPVector> _a_test;
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Ref<MLPPMatrix> _delta;
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// Regularization Params
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MLPPReg::RegularizationType _reg;
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real_t _lambda; /* Regularization Parameter */
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real_t _alpha; /* This is the controlling param for Elastic Net*/
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MLPPUtilities::WeightDistributionType _weight_init;
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};
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#endif /* MultiOutputLayer_hpp */
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