mirror of https://github.com/Relintai/pmlpp.git
151 lines
5.3 KiB
C++
151 lines
5.3 KiB
C++
#ifndef MLPP_SOFTMAX_NET_H
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#define MLPP_SOFTMAX_NET_H
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/*************************************************************************/
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/* softmax_net.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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#include "core/math/math_defs.h"
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#include "core/object/resource.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 "../regularization/reg.h"
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class MLPPSoftmaxNet : public Resource {
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GDCLASS(MLPPSoftmaxNet, Resource);
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public:
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Ref<MLPPMatrix> get_input_set() const;
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void set_input_set(const Ref<MLPPMatrix> &val);
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Ref<MLPPMatrix> get_output_set() const;
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void set_output_set(const Ref<MLPPMatrix> &val);
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int get_n_hidden() const;
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void set_n_hidden(const int val);
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MLPPReg::RegularizationType get_reg() const;
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void set_reg(const MLPPReg::RegularizationType val);
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real_t get_lambda() const;
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void set_lambda(const real_t val);
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real_t get_alpha() const;
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void set_alpha(const real_t val);
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Ref<MLPPMatrix> data_y_hat_get() const;
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void data_y_hat_set(const Ref<MLPPMatrix> &val);
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Ref<MLPPMatrix> data_weights1_get() const;
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void data_weights1_set(const Ref<MLPPMatrix> &val);
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Ref<MLPPMatrix> data_weights2_get() const;
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void data_weights2_set(const Ref<MLPPMatrix> &val);
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Ref<MLPPVector> data_bias1_get() const;
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void data_bias1_set(const Ref<MLPPVector> &val);
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Ref<MLPPVector> data_bias2_get() const;
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void data_bias2_set(const Ref<MLPPVector> &val);
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Ref<MLPPMatrix> data_z2_get() const;
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void data_z2_set(const Ref<MLPPMatrix> &val);
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Ref<MLPPMatrix> data_a2_get() const;
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void data_a2_set(const Ref<MLPPMatrix> &val);
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Ref<MLPPVector> model_test(const Ref<MLPPVector> &x);
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Ref<MLPPMatrix> model_set_test(const Ref<MLPPMatrix> &X);
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void train_gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
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void train_sgd(real_t learning_rate, int max_epoch, bool ui = false);
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void train_mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
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real_t score();
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Ref<MLPPMatrix> get_embeddings(); // This class is used (mostly) for word2Vec. This function returns our embeddings.
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bool needs_init() const;
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void initialize();
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MLPPSoftmaxNet(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &p_output_set, int p_n_hidden, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
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MLPPSoftmaxNet();
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~MLPPSoftmaxNet();
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protected:
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real_t cost(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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Ref<MLPPVector> evaluatev(const Ref<MLPPVector> &x);
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struct PropagateVResult {
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Ref<MLPPVector> z2;
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Ref<MLPPVector> a2;
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};
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PropagateVResult propagatev(const Ref<MLPPVector> &x);
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Ref<MLPPMatrix> evaluatem(const Ref<MLPPMatrix> &X);
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struct PropagateMResult {
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Ref<MLPPMatrix> z2;
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Ref<MLPPMatrix> a2;
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};
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PropagateMResult propagatem(const Ref<MLPPMatrix> &X);
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void forward_pass();
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static void _bind_methods();
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Ref<MLPPMatrix> _input_set;
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Ref<MLPPMatrix> _output_set;
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int _n_hidden;
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// Regularization Params
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MLPPReg::RegularizationType _reg;
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real_t _lambda;
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real_t _alpha; /* This is the controlling param for Elastic Net*/
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Ref<MLPPMatrix> _y_hat;
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Ref<MLPPMatrix> _weights1;
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Ref<MLPPMatrix> _weights2;
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Ref<MLPPVector> _bias1;
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Ref<MLPPVector> _bias2;
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Ref<MLPPMatrix> _z2;
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Ref<MLPPMatrix> _a2;
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};
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#endif /* SoftmaxNet_hpp */
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