2023-01-23 21:13:26 +01:00
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//
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// SoftmaxNet.cpp
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//
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// Created by Marc Melikyan on 10/2/20.
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//
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2023-01-24 18:12:23 +01:00
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#include "softmax_net.h"
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2023-01-24 19:00:54 +01:00
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#include "../activation/activation.h"
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#include "../cost/cost.h"
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2023-01-24 18:12:23 +01:00
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#include "../data/data.h"
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#include "../lin_alg/lin_alg.h"
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2023-01-24 18:12:23 +01:00
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#include "../regularization/reg.h"
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#include "../utilities/utilities.h"
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2023-01-23 21:13:26 +01:00
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#include <iostream>
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#include <random>
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2023-02-11 09:17:02 +01:00
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/*
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Ref<MLPPMatrix> MLPPSoftmaxNet::get_input_set() {
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return _input_set;
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2023-01-24 19:00:54 +01:00
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}
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2023-02-11 09:17:02 +01:00
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void MLPPSoftmaxNet::set_input_set(const Ref<MLPPMatrix> &val) {
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_input_set = val;
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2023-01-24 19:00:54 +01:00
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2023-02-11 09:17:02 +01:00
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_initialized = false;
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2023-01-24 19:00:54 +01:00
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}
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2023-02-11 09:17:02 +01:00
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Ref<MLPPMatrix> MLPPSoftmaxNet::get_output_set() {
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return _output_set;
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2023-01-24 19:00:54 +01:00
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}
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2023-02-11 09:17:02 +01:00
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void MLPPSoftmaxNet::set_output_set(const Ref<MLPPMatrix> &val) {
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_output_set = val;
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2023-01-24 19:00:54 +01:00
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2023-02-11 09:17:02 +01:00
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_initialized = false;
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}
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MLPPReg::RegularizationType MLPPSoftmaxNet::get_reg() {
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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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_initialized = false;
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}
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real_t MLPPSoftmaxNet::get_lambda() {
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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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_initialized = false;
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}
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real_t MLPPSoftmaxNet::get_alpha() {
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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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_initialized = false;
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}
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*/
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std::vector<real_t> MLPPSoftmaxNet::model_test(std::vector<real_t> x) {
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return evaluatev(x);
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}
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std::vector<std::vector<real_t>> MLPPSoftmaxNet::model_set_test(std::vector<std::vector<real_t>> X) {
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return evaluatem(X);
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}
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void MLPPSoftmaxNet::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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MLPPActivation avn;
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2023-01-25 00:29:02 +01:00
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MLPPLinAlg alg;
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2023-01-25 00:54:50 +01:00
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MLPPReg regularization;
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2023-02-11 09:17:02 +01:00
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2023-01-27 13:01:16 +01:00
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real_t cost_prev = 0;
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int epoch = 1;
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2023-02-11 09:17:02 +01:00
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forward_pass();
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while (true) {
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2023-02-11 09:17:02 +01:00
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cost_prev = cost(_y_hat, _output_set);
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2023-01-24 19:00:54 +01:00
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// Calculating the errors
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std::vector<std::vector<real_t>> error = alg.subtraction(_y_hat, _output_set);
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2023-01-24 19:00:54 +01:00
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// Calculating the weight/bias gradients for layer 2
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2023-02-11 09:17:02 +01:00
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std::vector<std::vector<real_t>> D2_1 = alg.matmult(alg.transpose(_a2), error);
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2023-01-24 19:00:54 +01:00
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// weights and bias updation for layer 2
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_weights2 = alg.subtraction(_weights2, alg.scalarMultiply(learning_rate, D2_1));
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//_reg
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_weights2 = regularization.regWeights(_weights2, _lambda, _alpha, "None");
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2023-01-24 19:00:54 +01:00
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2023-02-11 09:17:02 +01:00
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_bias2 = alg.subtractMatrixRows(_bias2, alg.scalarMultiply(learning_rate, error));
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2023-01-24 19:00:54 +01:00
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//Calculating the weight/bias for layer 1
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2023-02-11 09:17:02 +01:00
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std::vector<std::vector<real_t>> D1_1 = alg.matmult(error, alg.transpose(_weights2));
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2023-02-11 09:17:02 +01:00
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std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(_z2, true));
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2023-02-11 09:17:02 +01:00
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std::vector<std::vector<real_t>> D1_3 = alg.matmult(alg.transpose(_input_set), D1_2);
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2023-01-24 19:00:54 +01:00
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// weight an bias updation for layer 1
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_weights1 = alg.subtraction(_weights1, alg.scalarMultiply(learning_rate, D1_3));
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//_reg
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_weights1 = regularization.regWeights(_weights1, _lambda, _alpha, "None");
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2023-01-24 19:00:54 +01:00
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2023-02-11 09:17:02 +01:00
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_bias1 = alg.subtractMatrixRows(_bias1, alg.scalarMultiply(learning_rate, D1_2));
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2023-01-24 19:00:54 +01:00
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2023-02-11 09:17:02 +01:00
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forward_pass();
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2023-01-24 19:00:54 +01:00
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// UI PORTION
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2023-02-11 09:17:02 +01:00
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(_y_hat, _output_set));
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std::cout << "Layer 1:" << std::endl;
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MLPPUtilities::UI(_weights1, _bias1);
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std::cout << "Layer 2:" << std::endl;
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MLPPUtilities::UI(_weights2, _bias2);
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}
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2023-01-24 19:00:54 +01:00
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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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2023-02-11 09:17:02 +01:00
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void MLPPSoftmaxNet::sgd(real_t learning_rate, int max_epoch, bool ui) {
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2023-01-24 19:23:30 +01:00
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MLPPActivation avn;
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2023-01-25 00:29:02 +01:00
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MLPPLinAlg alg;
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2023-01-25 00:54:50 +01:00
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MLPPReg regularization;
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2023-02-11 09:17:02 +01:00
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2023-01-27 13:01:16 +01:00
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real_t cost_prev = 0;
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2023-01-24 19:00:54 +01:00
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int epoch = 1;
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2023-02-11 09:17:02 +01:00
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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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2023-01-24 19:00:54 +01:00
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while (true) {
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int outputIndex = distribution(generator);
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2023-02-11 09:17:02 +01:00
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std::vector<real_t> y_hat = evaluatev(_input_set[outputIndex]);
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2023-02-10 21:26:46 +01:00
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2023-02-11 09:17:02 +01:00
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auto prop_res = propagatev(_input_set[outputIndex]);
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auto z2 = std::get<0>(prop_res);
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auto a2 = std::get<1>(prop_res);
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2023-02-11 09:17:02 +01:00
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cost_prev = cost({ y_hat }, { _output_set[outputIndex] });
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std::vector<real_t> error = alg.subtraction(y_hat, _output_set[outputIndex]);
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2023-01-24 19:00:54 +01:00
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// Weight updation for layer 2
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2023-01-27 13:01:16 +01:00
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std::vector<std::vector<real_t>> D2_1 = alg.outerProduct(error, a2);
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2023-02-11 09:17:02 +01:00
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_weights2 = alg.subtraction(_weights2, alg.scalarMultiply(learning_rate, alg.transpose(D2_1)));
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//_reg
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_weights2 = regularization.regWeights(_weights2, _lambda, _alpha, "None");
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2023-01-24 19:00:54 +01:00
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// Bias updation for layer 2
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_bias2 = alg.subtraction(_bias2, alg.scalarMultiply(learning_rate, error));
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2023-01-24 19:00:54 +01:00
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// Weight updation for layer 1
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2023-02-11 09:17:02 +01:00
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std::vector<real_t> D1_1 = alg.mat_vec_mult(_weights2, error);
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2023-02-10 21:26:46 +01:00
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std::vector<real_t> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true));
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2023-02-11 09:17:02 +01:00
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std::vector<std::vector<real_t>> D1_3 = alg.outerProduct(_input_set[outputIndex], D1_2);
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2023-01-24 19:00:54 +01:00
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2023-02-11 09:17:02 +01:00
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_weights1 = alg.subtraction(_weights1, alg.scalarMultiply(learning_rate, D1_3));
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//_reg
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_weights1 = regularization.regWeights(_weights1, _lambda, _alpha, "None");
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2023-01-24 19:00:54 +01:00
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// Bias updation for layer 1
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2023-02-11 09:17:02 +01:00
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_bias1 = alg.subtraction(_bias1, alg.scalarMultiply(learning_rate, D1_2));
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2023-01-24 19:00:54 +01:00
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2023-02-11 09:17:02 +01:00
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y_hat = evaluatev(_input_set[outputIndex]);
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost({ y_hat }, { _output_set[outputIndex] }));
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2023-01-24 19:00:54 +01:00
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std::cout << "Layer 1:" << std::endl;
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2023-02-11 09:17:02 +01:00
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MLPPUtilities::UI(_weights1, _bias1);
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2023-01-24 19:00:54 +01:00
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std::cout << "Layer 2:" << std::endl;
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2023-02-11 09:17:02 +01:00
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MLPPUtilities::UI(_weights2, _bias2);
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2023-01-24 19:00:54 +01:00
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}
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2023-02-11 09:17:02 +01:00
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2023-01-24 19:00:54 +01:00
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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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2023-02-11 09:17:02 +01:00
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forward_pass();
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2023-01-24 19:00:54 +01:00
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}
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2023-02-11 09:17:02 +01:00
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void MLPPSoftmaxNet::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
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2023-01-24 19:23:30 +01:00
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MLPPActivation avn;
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2023-01-25 00:29:02 +01:00
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MLPPLinAlg alg;
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2023-01-25 00:54:50 +01:00
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MLPPReg regularization;
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2023-01-27 13:01:16 +01:00
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real_t cost_prev = 0;
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2023-01-24 19:00:54 +01:00
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int epoch = 1;
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// Creating the mini-batches
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2023-02-11 09:17:02 +01:00
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int n_mini_batch = _n / mini_batch_size;
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2023-02-10 21:26:46 +01:00
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2023-02-11 09:17:02 +01:00
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auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
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2023-02-10 21:26:46 +01:00
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auto inputMiniBatches = std::get<0>(batches);
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auto outputMiniBatches = std::get<1>(batches);
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2023-01-24 19:00:54 +01:00
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while (true) {
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for (int i = 0; i < n_mini_batch; i++) {
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2023-02-11 09:17:02 +01:00
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std::vector<std::vector<real_t>> y_hat = evaluatem(inputMiniBatches[i]);
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2023-02-10 21:26:46 +01:00
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2023-02-11 09:17:02 +01:00
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auto propagate_res = propagatem(inputMiniBatches[i]);
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2023-02-10 21:26:46 +01:00
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auto z2 = std::get<0>(propagate_res);
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auto a2 = std::get<1>(propagate_res);
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2023-02-11 09:17:02 +01:00
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cost_prev = cost(y_hat, outputMiniBatches[i]);
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2023-01-24 19:00:54 +01:00
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// Calculating the errors
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2023-01-27 13:01:16 +01:00
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std::vector<std::vector<real_t>> error = alg.subtraction(y_hat, outputMiniBatches[i]);
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2023-01-24 19:00:54 +01:00
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// Calculating the weight/bias gradients for layer 2
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2023-01-27 13:01:16 +01:00
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std::vector<std::vector<real_t>> D2_1 = alg.matmult(alg.transpose(a2), error);
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2023-01-24 19:00:54 +01:00
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// weights and bias updation for layser 2
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2023-02-11 09:17:02 +01:00
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_weights2 = alg.subtraction(_weights2, alg.scalarMultiply(learning_rate, D2_1));
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//_reg
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_weights2 = regularization.regWeights(_weights2, _lambda, _alpha, "None");
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2023-01-24 19:00:54 +01:00
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// Bias Updation for layer 2
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2023-02-11 09:17:02 +01:00
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_bias2 = alg.subtractMatrixRows(_bias2, alg.scalarMultiply(learning_rate, error));
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2023-01-24 19:00:54 +01:00
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//Calculating the weight/bias for layer 1
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2023-02-11 09:17:02 +01:00
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std::vector<std::vector<real_t>> D1_1 = alg.matmult(error, alg.transpose(_weights2));
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2023-01-24 19:00:54 +01:00
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2023-02-11 09:17:02 +01:00
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std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true));
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2023-01-24 19:00:54 +01:00
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2023-01-27 13:01:16 +01:00
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std::vector<std::vector<real_t>> D1_3 = alg.matmult(alg.transpose(inputMiniBatches[i]), D1_2);
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2023-01-24 19:00:54 +01:00
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// weight an bias updation for layer 1
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2023-02-11 09:17:02 +01:00
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_weights1 = alg.subtraction(_weights1, alg.scalarMultiply(learning_rate, D1_3));
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//_reg
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_weights1 = regularization.regWeights(_weights1, _lambda, _alpha, "None");
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2023-01-24 19:00:54 +01:00
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2023-02-11 09:17:02 +01:00
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_bias1 = alg.subtractMatrixRows(_bias1, alg.scalarMultiply(learning_rate, D1_2));
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2023-01-24 19:00:54 +01:00
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2023-02-11 09:17:02 +01:00
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y_hat = evaluatem(inputMiniBatches[i]);
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2023-01-24 19:00:54 +01:00
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2023-02-11 09:17:02 +01:00
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputMiniBatches[i]));
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2023-01-24 19:00:54 +01:00
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std::cout << "Layer 1:" << std::endl;
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2023-02-11 09:17:02 +01:00
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MLPPUtilities::UI(_weights1, _bias1);
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2023-01-24 19:00:54 +01:00
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std::cout << "Layer 2:" << std::endl;
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2023-02-11 09:17:02 +01:00
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MLPPUtilities::UI(_weights2, _bias2);
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2023-01-24 19:00:54 +01:00
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}
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}
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2023-02-11 09:17:02 +01:00
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2023-01-24 19:00:54 +01:00
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epoch++;
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2023-02-11 09:17:02 +01:00
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2023-01-24 19:00:54 +01:00
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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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2023-02-11 09:17:02 +01:00
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forward_pass();
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2023-01-24 19:00:54 +01:00
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}
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2023-01-27 13:01:16 +01:00
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real_t MLPPSoftmaxNet::score() {
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2023-02-10 21:26:46 +01:00
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MLPPUtilities util;
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2023-02-11 09:17:02 +01:00
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return util.performance(_y_hat, _output_set);
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2023-01-24 19:00:54 +01:00
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}
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2023-01-25 00:54:50 +01:00
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void MLPPSoftmaxNet::save(std::string fileName) {
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2023-02-10 21:26:46 +01:00
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MLPPUtilities util;
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2023-02-11 09:17:02 +01:00
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util.saveParameters(fileName, _weights1, _bias1, false, 1);
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util.saveParameters(fileName, _weights2, _bias2, true, 2);
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}
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std::vector<std::vector<real_t>> MLPPSoftmaxNet::get_embeddings() {
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return _weights1;
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}
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bool MLPPSoftmaxNet::is_initialized() {
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|
return _initialized;
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}
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void MLPPSoftmaxNet::initialize() {
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if (_initialized) {
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return;
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}
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//ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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_initialized = true;
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2023-01-24 19:00:54 +01:00
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}
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|
2023-02-11 09:17:02 +01:00
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MLPPSoftmaxNet::MLPPSoftmaxNet(std::vector<std::vector<real_t>> p_input_set, std::vector<std::vector<real_t>> 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 = p_input_set.size();
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_k = p_input_set[0].size();
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_n_hidden = p_n_hidden;
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_n_class = p_output_set[0].size();
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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.resize(_n);
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|
_weights1 = MLPPUtilities::weightInitialization(_k, _n_hidden);
|
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|
_weights2 = MLPPUtilities::weightInitialization(_n_hidden, _n_class);
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|
_bias1 = MLPPUtilities::biasInitialization(_n_hidden);
|
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|
_bias2 = MLPPUtilities::biasInitialization(_n_class);
|
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|
|
_initialized = true;
|
|
|
|
}
|
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|
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|
|
|
|
MLPPSoftmaxNet::MLPPSoftmaxNet() {
|
|
|
|
_initialized = false;
|
|
|
|
}
|
|
|
|
MLPPSoftmaxNet::~MLPPSoftmaxNet() {
|
2023-01-24 19:00:54 +01:00
|
|
|
}
|
|
|
|
|
2023-02-11 09:17:02 +01:00
|
|
|
real_t MLPPSoftmaxNet::cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
|
2023-01-25 00:54:50 +01:00
|
|
|
MLPPReg regularization;
|
2023-01-25 00:21:31 +01:00
|
|
|
MLPPData data;
|
2023-01-24 19:37:08 +01:00
|
|
|
class MLPPCost cost;
|
2023-02-11 09:17:02 +01:00
|
|
|
|
|
|
|
//_reg
|
|
|
|
return cost.CrossEntropy(y_hat, y) + regularization.regTerm(_weights1, _lambda, _alpha, "None") + regularization.regTerm(_weights2, _lambda, _alpha, "None");
|
2023-01-24 19:00:54 +01:00
|
|
|
}
|
|
|
|
|
2023-02-11 09:17:02 +01:00
|
|
|
std::vector<real_t> MLPPSoftmaxNet::evaluatev(std::vector<real_t> x) {
|
2023-01-25 00:29:02 +01:00
|
|
|
MLPPLinAlg alg;
|
2023-01-24 19:23:30 +01:00
|
|
|
MLPPActivation avn;
|
2023-02-11 09:17:02 +01:00
|
|
|
|
|
|
|
std::vector<real_t> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(_weights1), x), _bias1);
|
|
|
|
std::vector<real_t> a2 = avn.sigmoid(z2);
|
|
|
|
|
|
|
|
return avn.adjSoftmax(alg.addition(alg.mat_vec_mult(alg.transpose(_weights2), a2), _bias2));
|
2023-01-24 19:00:54 +01:00
|
|
|
}
|
|
|
|
|
2023-02-11 09:17:02 +01:00
|
|
|
std::tuple<std::vector<real_t>, std::vector<real_t>> MLPPSoftmaxNet::propagatev(std::vector<real_t> x) {
|
2023-01-25 00:29:02 +01:00
|
|
|
MLPPLinAlg alg;
|
2023-01-24 19:23:30 +01:00
|
|
|
MLPPActivation avn;
|
2023-02-11 09:17:02 +01:00
|
|
|
|
|
|
|
std::vector<real_t> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(_weights1), x), _bias1);
|
|
|
|
std::vector<real_t> a2 = avn.sigmoid(z2);
|
|
|
|
|
2023-01-24 19:00:54 +01:00
|
|
|
return { z2, a2 };
|
|
|
|
}
|
|
|
|
|
2023-02-11 09:17:02 +01:00
|
|
|
std::vector<std::vector<real_t>> MLPPSoftmaxNet::evaluatem(std::vector<std::vector<real_t>> X) {
|
2023-01-25 00:29:02 +01:00
|
|
|
MLPPLinAlg alg;
|
2023-01-24 19:23:30 +01:00
|
|
|
MLPPActivation avn;
|
2023-02-11 09:17:02 +01:00
|
|
|
|
|
|
|
std::vector<std::vector<real_t>> z2 = alg.mat_vec_add(alg.matmult(X, _weights1), _bias1);
|
|
|
|
std::vector<std::vector<real_t>> a2 = avn.sigmoid(z2);
|
|
|
|
|
|
|
|
return avn.adjSoftmax(alg.mat_vec_add(alg.matmult(a2, _weights2), _bias2));
|
2023-01-24 19:00:54 +01:00
|
|
|
}
|
|
|
|
|
2023-02-11 09:17:02 +01:00
|
|
|
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPSoftmaxNet::propagatem(std::vector<std::vector<real_t>> X) {
|
2023-01-25 00:29:02 +01:00
|
|
|
MLPPLinAlg alg;
|
2023-01-24 19:23:30 +01:00
|
|
|
MLPPActivation avn;
|
2023-02-11 09:17:02 +01:00
|
|
|
|
|
|
|
std::vector<std::vector<real_t>> z2 = alg.mat_vec_add(alg.matmult(X, _weights1), _bias1);
|
|
|
|
std::vector<std::vector<real_t>> a2 = avn.sigmoid(z2);
|
|
|
|
|
2023-01-24 19:00:54 +01:00
|
|
|
return { z2, a2 };
|
|
|
|
}
|
|
|
|
|
2023-02-11 09:17:02 +01:00
|
|
|
void MLPPSoftmaxNet::forward_pass() {
|
2023-01-25 00:29:02 +01:00
|
|
|
MLPPLinAlg alg;
|
2023-01-24 19:23:30 +01:00
|
|
|
MLPPActivation avn;
|
2023-02-11 09:17:02 +01:00
|
|
|
|
|
|
|
_z2 = alg.mat_vec_add(alg.matmult(_input_set, _weights1), _bias1);
|
|
|
|
_a2 = avn.sigmoid(_z2);
|
|
|
|
_y_hat = avn.adjSoftmax(alg.mat_vec_add(alg.matmult(_a2, _weights2), _bias2));
|
|
|
|
}
|
|
|
|
|
|
|
|
void MLPPSoftmaxNet::_bind_methods() {
|
|
|
|
/*
|
|
|
|
ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPSoftmaxNet::get_input_set);
|
|
|
|
ClassDB::bind_method(D_METHOD("set_input_set", "val"), &MLPPSoftmaxNet::set_input_set);
|
|
|
|
ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input_set", "get_input_set");
|
|
|
|
|
|
|
|
ClassDB::bind_method(D_METHOD("get_output_set"), &MLPPSoftmaxNet::get_output_set);
|
|
|
|
ClassDB::bind_method(D_METHOD("set_output_set", "val"), &MLPPSoftmaxNet::set_output_set);
|
|
|
|
ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "output_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_output_set", "get_output_set");
|
|
|
|
|
|
|
|
ClassDB::bind_method(D_METHOD("get_reg"), &MLPPSoftmaxNet::get_reg);
|
|
|
|
ClassDB::bind_method(D_METHOD("set_reg", "val"), &MLPPSoftmaxNet::set_reg);
|
|
|
|
ADD_PROPERTY(PropertyInfo(Variant::INT, "reg"), "set_reg", "get_reg");
|
|
|
|
|
|
|
|
ClassDB::bind_method(D_METHOD("get_lambda"), &MLPPSoftmaxNet::get_lambda);
|
|
|
|
ClassDB::bind_method(D_METHOD("set_lambda", "val"), &MLPPSoftmaxNet::set_lambda);
|
|
|
|
ADD_PROPERTY(PropertyInfo(Variant::REAL, "lambda"), "set_lambda", "get_lambda");
|
|
|
|
|
|
|
|
ClassDB::bind_method(D_METHOD("get_alpha"), &MLPPSoftmaxNet::get_alpha);
|
|
|
|
ClassDB::bind_method(D_METHOD("set_alpha", "val"), &MLPPSoftmaxNet::set_alpha);
|
|
|
|
ADD_PROPERTY(PropertyInfo(Variant::REAL, "alpha"), "set_alpha", "get_alpha");
|
|
|
|
|
|
|
|
ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPSoftmaxNet::model_test);
|
|
|
|
ClassDB::bind_method(D_METHOD("model_set_test", "X"), &MLPPSoftmaxNet::model_set_test);
|
|
|
|
|
|
|
|
ClassDB::bind_method(D_METHOD("gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPSoftmaxNet::gradient_descent, false);
|
|
|
|
ClassDB::bind_method(D_METHOD("sgd", "learning_rate", "max_epoch", "ui"), &MLPPSoftmaxNet::sgd, false);
|
|
|
|
ClassDB::bind_method(D_METHOD("mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPSoftmaxNet::mbgd, false);
|
|
|
|
|
|
|
|
ClassDB::bind_method(D_METHOD("score"), &MLPPSoftmaxNet::score);
|
|
|
|
|
|
|
|
ClassDB::bind_method(D_METHOD("save", "file_name"), &MLPPSoftmaxNet::save);
|
|
|
|
|
|
|
|
ClassDB::bind_method(D_METHOD("is_initialized"), &MLPPSoftmaxNet::is_initialized);
|
|
|
|
ClassDB::bind_method(D_METHOD("initialize"), &MLPPSoftmaxNet::initialize);
|
|
|
|
*/
|
2023-01-24 19:00:54 +01:00
|
|
|
}
|