mirror of
https://github.com/Relintai/pmlpp.git
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603 lines
19 KiB
C++
603 lines
19 KiB
C++
/*************************************************************************/
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/* softmax_net.cpp */
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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 "softmax_net.h"
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#include "../activation/activation.h"
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#include "../cost/cost.h"
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#include "../data/data.h"
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#include "../regularization/reg.h"
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#include "../utilities/utilities.h"
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#include "core/log/logger.h"
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#include <random>
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Ref<MLPPMatrix> MLPPSoftmaxNet::get_input_set() const {
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return _input_set;
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}
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void MLPPSoftmaxNet::set_input_set(const Ref<MLPPMatrix> &val) {
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_input_set = val;
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}
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Ref<MLPPMatrix> MLPPSoftmaxNet::get_output_set() const {
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return _output_set;
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}
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void MLPPSoftmaxNet::set_output_set(const Ref<MLPPMatrix> &val) {
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_output_set = val;
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}
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int MLPPSoftmaxNet::get_n_hidden() const {
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return _n_hidden;
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}
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void MLPPSoftmaxNet::set_n_hidden(const int val) {
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_n_hidden = val;
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}
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MLPPReg::RegularizationType MLPPSoftmaxNet::get_reg() const {
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return _reg;
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}
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void MLPPSoftmaxNet::set_reg(const MLPPReg::RegularizationType val) {
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_reg = val;
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}
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real_t MLPPSoftmaxNet::get_lambda() const {
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return _lambda;
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}
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void MLPPSoftmaxNet::set_lambda(const real_t val) {
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_lambda = val;
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}
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real_t MLPPSoftmaxNet::get_alpha() const {
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return _alpha;
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}
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void MLPPSoftmaxNet::set_alpha(const real_t val) {
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_alpha = val;
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}
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Ref<MLPPMatrix> MLPPSoftmaxNet::data_y_hat_get() const {
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return _y_hat;
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}
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void MLPPSoftmaxNet::data_y_hat_set(const Ref<MLPPMatrix> &val) {
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_y_hat = val;
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}
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Ref<MLPPMatrix> MLPPSoftmaxNet::data_weights1_get() const {
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return _weights1;
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}
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void MLPPSoftmaxNet::data_weights1_set(const Ref<MLPPMatrix> &val) {
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_weights1 = val;
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}
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Ref<MLPPMatrix> MLPPSoftmaxNet::data_weights2_get() const {
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return _weights2;
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}
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void MLPPSoftmaxNet::data_weights2_set(const Ref<MLPPMatrix> &val) {
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_weights2 = val;
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}
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Ref<MLPPVector> MLPPSoftmaxNet::data_bias1_get() const {
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return _bias1;
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}
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void MLPPSoftmaxNet::data_bias1_set(const Ref<MLPPVector> &val) {
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_bias1 = val;
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}
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Ref<MLPPVector> MLPPSoftmaxNet::data_bias2_get() const {
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return _bias2;
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}
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void MLPPSoftmaxNet::data_bias2_set(const Ref<MLPPVector> &val) {
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_bias2 = val;
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}
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Ref<MLPPMatrix> MLPPSoftmaxNet::data_z2_get() const {
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return _z2;
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}
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void MLPPSoftmaxNet::data_z2_set(const Ref<MLPPMatrix> &val) {
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_z2 = val;
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}
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Ref<MLPPMatrix> MLPPSoftmaxNet::data_a2_get() const {
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return _a2;
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}
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void MLPPSoftmaxNet::data_a2_set(const Ref<MLPPMatrix> &val) {
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_a2 = val;
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}
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Ref<MLPPVector> MLPPSoftmaxNet::model_test(const Ref<MLPPVector> &x) {
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ERR_FAIL_COND_V(!_input_set.is_valid() || !_output_set.is_valid(), Ref<MLPPVector>());
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ERR_FAIL_COND_V(needs_init(), Ref<MLPPVector>());
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return evaluatev(x);
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}
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Ref<MLPPMatrix> MLPPSoftmaxNet::model_set_test(const Ref<MLPPMatrix> &X) {
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ERR_FAIL_COND_V(!_input_set.is_valid() || !_output_set.is_valid(), Ref<MLPPVector>());
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ERR_FAIL_COND_V(needs_init(), Ref<MLPPVector>());
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return evaluatem(X);
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}
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void MLPPSoftmaxNet::train_gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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ERR_FAIL_COND(needs_init());
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MLPPActivation avn;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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forward_pass();
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while (true) {
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cost_prev = cost(_y_hat, _output_set);
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// Calculating the errors
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Ref<MLPPMatrix> error = _y_hat->subn(_output_set);
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// Calculating the weight/bias gradients for layer 2
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Ref<MLPPMatrix> D2_1 = _a2->transposen()->multn(error);
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// weights and bias updation for layer 2
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_weights2->sub(D2_1->scalar_multiplyn(learning_rate));
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_weights2 = regularization.reg_weightsm(_weights2, _lambda, _alpha, _reg);
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_bias2->subtract_matrix_rows(error->scalar_multiplyn(learning_rate));
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//Calculating the weight/bias for layer 1
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Ref<MLPPMatrix> D1_1 = error->multn(_weights2->transposen());
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Ref<MLPPMatrix> D1_2 = D1_1->hadamard_productn(avn.sigmoid_derivm(_z2));
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Ref<MLPPMatrix> D1_3 = _input_set->transposen()->multn(D1_2);
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// weight an bias updation for layer 1
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_weights1->sub(D1_3->scalar_multiplyn(learning_rate));
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_weights1 = regularization.reg_weightsm(_weights1, _lambda, _alpha, _reg);
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_bias1->subtract_matrix_rows(D1_2->scalar_multiplyn(learning_rate));
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forward_pass();
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// UI PORTION
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if (ui) {
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MLPPUtilities::cost_info(epoch, cost_prev, cost(_y_hat, _output_set));
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PLOG_MSG("Layer 1:");
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MLPPUtilities::print_ui_mb(_weights1, _bias1);
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PLOG_MSG("Layer 2:");
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MLPPUtilities::print_ui_mb(_weights2, _bias2);
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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}
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void MLPPSoftmaxNet::train_sgd(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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ERR_FAIL_COND(needs_init());
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MLPPActivation avn;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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int n = _input_set->size().y;
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std::random_device rd;
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std::default_random_engine generator(rd());
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std::uniform_int_distribution<int> distribution(0, int(n - 1));
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Ref<MLPPVector> input_set_row_tmp;
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input_set_row_tmp.instance();
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input_set_row_tmp->resize(_input_set->size().x);
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Ref<MLPPVector> output_set_row_tmp;
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output_set_row_tmp.instance();
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output_set_row_tmp->resize(_output_set->size().x);
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Ref<MLPPMatrix> y_hat_mat_tmp;
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y_hat_mat_tmp.instance();
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y_hat_mat_tmp->resize(Size2i(_bias1->size(), 1));
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Ref<MLPPMatrix> output_row_mat_tmp;
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output_row_mat_tmp.instance();
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output_row_mat_tmp->resize(Size2i(_output_set->size().x, 1));
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while (true) {
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int output_index = distribution(generator);
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_input_set->row_get_into_mlpp_vector(output_index, input_set_row_tmp);
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_output_set->row_get_into_mlpp_vector(output_index, output_set_row_tmp);
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output_row_mat_tmp->row_set_mlpp_vector(0, output_set_row_tmp);
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Ref<MLPPVector> y_hat = evaluatev(input_set_row_tmp);
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y_hat_mat_tmp->row_set_mlpp_vector(0, y_hat);
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PropagateVResult prop_res = propagatev(input_set_row_tmp);
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cost_prev = cost(y_hat_mat_tmp, output_row_mat_tmp);
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Ref<MLPPVector> error = y_hat->subn(output_set_row_tmp);
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// Weight updation for layer 2
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Ref<MLPPMatrix> D2_1 = error->outer_product(prop_res.a2);
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_weights2->sub(D2_1->transposen()->scalar_multiplyn(learning_rate));
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_weights2 = regularization.reg_weightsm(_weights2, _lambda, _alpha, _reg);
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// Bias updation for layer 2
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_bias2->sub(error->scalar_multiplyn(learning_rate));
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// Weight updation for layer 1
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Ref<MLPPVector> D1_1 = _weights2->mult_vec(error);
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Ref<MLPPVector> D1_2 = D1_1->hadamard_productn(avn.sigmoid_derivv(prop_res.z2));
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Ref<MLPPMatrix> D1_3 = input_set_row_tmp->outer_product(D1_2);
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_weights1->sub(D1_3->scalar_multiplyn(learning_rate));
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_weights1 = regularization.reg_weightsm(_weights1, _lambda, _alpha, _reg);
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// Bias updation for layer 1
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_bias1->sub(D1_2->scalar_multiplyn(learning_rate));
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y_hat = evaluatev(input_set_row_tmp);
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if (ui) {
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MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat_mat_tmp, output_row_mat_tmp));
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PLOG_MSG("Layer 1:");
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MLPPUtilities::print_ui_mb(_weights1, _bias1);
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PLOG_MSG("Layer 2:");
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MLPPUtilities::print_ui_mb(_weights2, _bias2);
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forward_pass();
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}
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void MLPPSoftmaxNet::train_mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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ERR_FAIL_COND(needs_init());
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MLPPActivation avn;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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int n = _input_set->size().y;
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// Creating the mini-batches
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int n_mini_batch = n / mini_batch_size;
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MLPPUtilities::CreateMiniBatchMMBatch batches = MLPPUtilities::create_mini_batchesmm(_input_set, _output_set, n_mini_batch);
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while (true) {
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for (int i = 0; i < n_mini_batch; i++) {
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Ref<MLPPMatrix> current_input_mini_batch = batches.input_sets[i];
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Ref<MLPPMatrix> current_output_mini_batch = batches.output_sets[i];
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Ref<MLPPMatrix> y_hat = evaluatem(current_input_mini_batch);
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PropagateMResult prop_res = propagatem(current_input_mini_batch);
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cost_prev = cost(y_hat, current_output_mini_batch);
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// Calculating the errors
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Ref<MLPPMatrix> error = y_hat->subn(current_output_mini_batch);
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// Calculating the weight/bias gradients for layer 2
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Ref<MLPPMatrix> D2_1 = prop_res.a2->transposen()->multn(error);
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// weights and bias updation for layser 2
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_weights2->sub(D2_1->scalar_multiplyn(learning_rate));
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_weights2 = regularization.reg_weightsm(_weights2, _lambda, _alpha, _reg);
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// Bias Updation for layer 2
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_bias2->sub(error->scalar_multiplyn(learning_rate));
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//Calculating the weight/bias for layer 1
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Ref<MLPPMatrix> D1_1 = error->multn(_weights2->transposen());
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Ref<MLPPMatrix> D1_2 = D1_1->hadamard_productn(avn.sigmoid_derivm(prop_res.z2));
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Ref<MLPPMatrix> D1_3 = current_input_mini_batch->transposen()->multn(D1_2);
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// weight an bias updation for layer 1
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_weights1->sub(D1_3->scalar_multiplyn(learning_rate));
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_weights1 = regularization.reg_weightsm(_weights1, _lambda, _alpha, _reg);
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_bias1->subtract_matrix_rows(D1_2->scalar_multiplyn(learning_rate));
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y_hat = evaluatem(current_input_mini_batch);
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if (ui) {
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MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, current_output_mini_batch));
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PLOG_MSG("Layer 1:");
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MLPPUtilities::print_ui_mb(_weights1, _bias1);
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PLOG_MSG("Layer 2:");
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MLPPUtilities::print_ui_mb(_weights2, _bias2);
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}
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forward_pass();
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}
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real_t MLPPSoftmaxNet::score() {
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ERR_FAIL_COND_V(!_input_set.is_valid() || !_output_set.is_valid(), 0);
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ERR_FAIL_COND_V(needs_init(), 0);
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MLPPUtilities util;
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return util.performance_mat(_y_hat, _output_set);
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}
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Ref<MLPPMatrix> MLPPSoftmaxNet::get_embeddings() {
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return _weights1;
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}
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bool MLPPSoftmaxNet::needs_init() const {
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if (!_input_set.is_valid()) {
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return true;
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}
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if (!_output_set.is_valid()) {
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return true;
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}
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int n = _input_set->size().y;
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int k = _input_set->size().x;
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int n_class = _output_set->size().x;
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if (_y_hat->size().y != n) {
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return true;
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}
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if (_weights1->size() != Size2i(_n_hidden, k)) {
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return true;
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}
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if (_weights2->size() != Size2i(n_class, _n_hidden)) {
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return true;
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}
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if (_bias1->size() != _n_hidden) {
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return true;
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}
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if (_bias2->size() != n_class) {
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return true;
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}
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return false;
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}
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void MLPPSoftmaxNet::initialize() {
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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int n = _input_set->size().y;
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int k = _input_set->size().x;
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int n_class = _output_set->size().x;
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_y_hat->resize(Size2i(0, n));
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MLPPUtilities utils;
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_weights1->resize(Size2i(_n_hidden, k));
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utils.weight_initializationm(_weights1);
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_weights2->resize(Size2i(n_class, _n_hidden));
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utils.weight_initializationm(_weights2);
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_bias1->resize(_n_hidden);
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utils.bias_initializationv(_bias1);
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_bias2->resize(n_class);
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utils.bias_initializationv(_bias2);
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}
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MLPPSoftmaxNet::MLPPSoftmaxNet(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &p_output_set, int p_n_hidden, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha) {
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_input_set = p_input_set;
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_output_set = p_output_set;
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_n_hidden = p_n_hidden;
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_reg = p_reg;
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_lambda = p_lambda;
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_alpha = p_alpha;
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_y_hat.instance();
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_weights1.instance();
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_weights2.instance();
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_bias1.instance();
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_bias2.instance();
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_z2.instance();
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_a2.instance();
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initialize();
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}
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MLPPSoftmaxNet::MLPPSoftmaxNet() {
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_n_hidden = 0;
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_reg = MLPPReg::REGULARIZATION_TYPE_NONE;
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_lambda = 0;
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_alpha = 0;
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_y_hat.instance();
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_weights1.instance();
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_weights2.instance();
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_bias1.instance();
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_bias2.instance();
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_z2.instance();
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_a2.instance();
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}
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MLPPSoftmaxNet::~MLPPSoftmaxNet() {
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}
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real_t MLPPSoftmaxNet::cost(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
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MLPPReg regularization;
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MLPPData data;
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MLPPCost mlpp_cost;
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return mlpp_cost.cross_entropym(y_hat, y) + regularization.reg_termm(_weights1, _lambda, _alpha, _reg) + regularization.reg_termm(_weights2, _lambda, _alpha, _reg);
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}
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Ref<MLPPVector> MLPPSoftmaxNet::evaluatev(const Ref<MLPPVector> &x) {
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MLPPActivation avn;
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Ref<MLPPVector> z2 = _weights1->transposen()->mult_vec(x)->addn(_bias1);
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Ref<MLPPVector> a2 = avn.sigmoid_normv(z2);
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return avn.adj_softmax_normv(_weights2->transposen()->mult_vec(a2)->addn(_bias2));
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}
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MLPPSoftmaxNet::PropagateVResult MLPPSoftmaxNet::propagatev(const Ref<MLPPVector> &x) {
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MLPPActivation avn;
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PropagateVResult res;
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res.z2 = _weights1->transposen()->mult_vec(x)->addn(_bias1);
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res.a2 = avn.sigmoid_normv(res.z2);
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return res;
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}
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Ref<MLPPMatrix> MLPPSoftmaxNet::evaluatem(const Ref<MLPPMatrix> &X) {
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MLPPActivation avn;
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Ref<MLPPMatrix> z2 = X->multn(_weights1)->add_vecn(_bias1);
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Ref<MLPPMatrix> a2 = avn.sigmoid_normm(z2);
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return avn.adj_softmax_normm(a2->multn(_weights2)->add_vecn(_bias2));
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}
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MLPPSoftmaxNet::PropagateMResult MLPPSoftmaxNet::propagatem(const Ref<MLPPMatrix> &X) {
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MLPPActivation avn;
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MLPPSoftmaxNet::PropagateMResult res;
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res.z2 = X->multn(_weights1)->add_vecn(_bias1);
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res.a2 = avn.sigmoid_normm(res.z2);
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return res;
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}
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void MLPPSoftmaxNet::forward_pass() {
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MLPPActivation avn;
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_z2 = _input_set->multn(_weights1)->add_vecn(_bias1);
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_a2 = avn.sigmoid_normm(_z2);
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_y_hat = avn.adj_softmax_normm(_a2->multn(_weights2)->add_vecn(_bias2));
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}
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void MLPPSoftmaxNet::_bind_methods() {
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ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPSoftmaxNet::get_input_set);
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ClassDB::bind_method(D_METHOD("set_input_set", "val"), &MLPPSoftmaxNet::set_input_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input_set", "get_input_set");
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ClassDB::bind_method(D_METHOD("get_output_set"), &MLPPSoftmaxNet::get_output_set);
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ClassDB::bind_method(D_METHOD("set_output_set", "val"), &MLPPSoftmaxNet::set_output_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "output_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_output_set", "get_output_set");
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ClassDB::bind_method(D_METHOD("get_reg"), &MLPPSoftmaxNet::get_reg);
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ClassDB::bind_method(D_METHOD("set_reg", "val"), &MLPPSoftmaxNet::set_reg);
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ADD_PROPERTY(PropertyInfo(Variant::INT, "reg"), "set_reg", "get_reg");
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ClassDB::bind_method(D_METHOD("get_lambda"), &MLPPSoftmaxNet::get_lambda);
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ClassDB::bind_method(D_METHOD("set_lambda", "val"), &MLPPSoftmaxNet::set_lambda);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "lambda"), "set_lambda", "get_lambda");
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ClassDB::bind_method(D_METHOD("get_alpha"), &MLPPSoftmaxNet::get_alpha);
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ClassDB::bind_method(D_METHOD("set_alpha", "val"), &MLPPSoftmaxNet::set_alpha);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "alpha"), "set_alpha", "get_alpha");
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ADD_GROUP("Data", "data");
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ClassDB::bind_method(D_METHOD("data_y_hat_get"), &MLPPSoftmaxNet::data_y_hat_get);
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ClassDB::bind_method(D_METHOD("data_y_hat_set", "val"), &MLPPSoftmaxNet::data_y_hat_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_y_hat", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "data_y_hat_set", "data_y_hat_get");
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ClassDB::bind_method(D_METHOD("data_weights1_get"), &MLPPSoftmaxNet::data_weights1_get);
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ClassDB::bind_method(D_METHOD("data_weights1_set", "val"), &MLPPSoftmaxNet::data_weights1_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_weights1", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "data_weights1_set", "data_weights1_get");
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ClassDB::bind_method(D_METHOD("data_weights2_get"), &MLPPSoftmaxNet::data_weights2_get);
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ClassDB::bind_method(D_METHOD("data_weights2_set", "val"), &MLPPSoftmaxNet::data_weights2_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_weights2", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "data_weights2_set", "data_weights2_get");
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ClassDB::bind_method(D_METHOD("data_bias1_get"), &MLPPSoftmaxNet::data_bias1_get);
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ClassDB::bind_method(D_METHOD("data_bias1_set", "val"), &MLPPSoftmaxNet::data_bias1_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_bias1", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "data_bias1_set", "data_bias1_get");
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ClassDB::bind_method(D_METHOD("data_bias2_get"), &MLPPSoftmaxNet::data_bias2_get);
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ClassDB::bind_method(D_METHOD("data_bias2_set", "val"), &MLPPSoftmaxNet::data_bias2_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_bias2", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "data_bias2_set", "data_bias2_get");
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ClassDB::bind_method(D_METHOD("data_z2_get"), &MLPPSoftmaxNet::data_z2_get);
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ClassDB::bind_method(D_METHOD("data_z2_set", "val"), &MLPPSoftmaxNet::data_z2_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_z2", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "data_z2_set", "data_z2_get");
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ClassDB::bind_method(D_METHOD("data_a2_get"), &MLPPSoftmaxNet::data_a2_get);
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ClassDB::bind_method(D_METHOD("data_a2_set", "val"), &MLPPSoftmaxNet::data_a2_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_a2", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "data_a2_set", "data_a2_get");
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ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPSoftmaxNet::model_test);
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ClassDB::bind_method(D_METHOD("model_set_test", "X"), &MLPPSoftmaxNet::model_set_test);
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ClassDB::bind_method(D_METHOD("train_gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPSoftmaxNet::train_gradient_descent, false);
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ClassDB::bind_method(D_METHOD("train_sgd", "learning_rate", "max_epoch", "ui"), &MLPPSoftmaxNet::train_sgd, false);
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ClassDB::bind_method(D_METHOD("train_mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPSoftmaxNet::train_mbgd, false);
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ClassDB::bind_method(D_METHOD("score"), &MLPPSoftmaxNet::score);
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ClassDB::bind_method(D_METHOD("get_embeddings"), &MLPPSoftmaxNet::get_embeddings);
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ClassDB::bind_method(D_METHOD("needs_init"), &MLPPSoftmaxNet::needs_init);
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ClassDB::bind_method(D_METHOD("initialize"), &MLPPSoftmaxNet::initialize);
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}
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