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351 lines
11 KiB
C++
351 lines
11 KiB
C++
//
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// AutoEncoder.cpp
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//
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// Created by Marc Melikyan on 11/4/20.
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//
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#include "auto_encoder.h"
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#include "../activation/activation.h"
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#include "../cost/cost.h"
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#include "../lin_alg/lin_alg.h"
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#include "../utilities/utilities.h"
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#include <random>
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//UDPATE
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Ref<MLPPMatrix> MLPPAutoEncoder::get_input_set() {
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return Ref<MLPPMatrix>();
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//return _input_set;
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}
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void MLPPAutoEncoder::set_input_set(const Ref<MLPPMatrix> &val) {
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//_input_set = val;
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_initialized = false;
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}
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int MLPPAutoEncoder::get_n_hidden() {
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return _n_hidden;
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}
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void MLPPAutoEncoder::set_n_hidden(const int val) {
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_n_hidden = val;
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_initialized = false;
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}
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std::vector<std::vector<real_t>> MLPPAutoEncoder::model_set_test(std::vector<std::vector<real_t>> X) {
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ERR_FAIL_COND_V(!_initialized, std::vector<std::vector<real_t>>());
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return evaluatem(X);
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}
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std::vector<real_t> MLPPAutoEncoder::model_test(std::vector<real_t> x) {
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ERR_FAIL_COND_V(!_initialized, std::vector<real_t>());
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return evaluatev(x);
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}
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void MLPPAutoEncoder::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_initialized);
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MLPPActivation avn;
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MLPPLinAlg alg;
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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, _input_set);
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// Calculating the errors
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std::vector<std::vector<real_t>> error = alg.subtraction(_y_hat, _input_set);
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// Calculating the weight/bias gradients for layer 2
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std::vector<std::vector<real_t>> D2_1 = alg.matmult(alg.transpose(_a2), error);
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// weights and bias updation for layer 2
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_weights2 = alg.subtraction(_weights2, alg.scalarMultiply(learning_rate / _n, D2_1));
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// Calculating the bias gradients for layer 2
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_bias2 = alg.subtractMatrixRows(_bias2, alg.scalarMultiply(learning_rate, error));
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//Calculating the weight/bias for layer 1
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std::vector<std::vector<real_t>> D1_1 = alg.matmult(error, alg.transpose(_weights2));
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std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(_z2, 1));
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std::vector<std::vector<real_t>> D1_3 = alg.matmult(alg.transpose(_input_set), D1_2);
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// weight an bias updation for layer 1
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_weights1 = alg.subtraction(_weights1, alg.scalarMultiply(learning_rate / _n, D1_3));
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_bias1 = alg.subtractMatrixRows(_bias1, alg.scalarMultiply(learning_rate / _n, D1_2));
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forward_pass();
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// UI PORTION
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(_y_hat, _input_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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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 MLPPAutoEncoder::sgd(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_initialized);
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MLPPActivation avn;
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MLPPLinAlg alg;
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real_t cost_prev = 0;
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int epoch = 1;
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while (true) {
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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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int outputIndex = distribution(generator);
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std::vector<real_t> y_hat = evaluatev(_input_set[outputIndex]);
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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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cost_prev = cost({ y_hat }, { _input_set[outputIndex] });
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std::vector<real_t> error = alg.subtraction(y_hat, _input_set[outputIndex]);
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// Weight updation for layer 2
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std::vector<std::vector<real_t>> D2_1 = alg.outerProduct(error, a2);
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_weights2 = alg.subtraction(_weights2, alg.scalarMultiply(learning_rate, alg.transpose(D2_1)));
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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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// Weight updation for layer 1
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std::vector<real_t> D1_1 = alg.mat_vec_mult(_weights2, error);
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std::vector<real_t> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
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std::vector<std::vector<real_t>> D1_3 = alg.outerProduct(_input_set[outputIndex], D1_2);
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_weights1 = alg.subtraction(_weights1, alg.scalarMultiply(learning_rate, D1_3));
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// Bias updation for layer 1
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_bias1 = alg.subtraction(_bias1, alg.scalarMultiply(learning_rate, D1_2));
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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 }, { _input_set[outputIndex] }));
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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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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 MLPPAutoEncoder::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
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ERR_FAIL_COND(!_initialized);
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MLPPActivation avn;
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MLPPLinAlg alg;
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real_t cost_prev = 0;
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int epoch = 1;
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// Creating the mini-batches
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int n_mini_batch = _n / mini_batch_size;
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std::vector<std::vector<std::vector<real_t>>> inputMiniBatches = MLPPUtilities::createMiniBatches(_input_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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std::vector<std::vector<real_t>> y_hat = evaluatem(inputMiniBatches[i]);
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auto prop_res = propagatem(inputMiniBatches[i]);
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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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cost_prev = cost(y_hat, inputMiniBatches[i]);
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// Calculating the errors
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std::vector<std::vector<real_t>> error = alg.subtraction(y_hat, inputMiniBatches[i]);
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// Calculating the weight/bias gradients for layer 2
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std::vector<std::vector<real_t>> D2_1 = alg.matmult(alg.transpose(a2), error);
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// weights and bias updation for layer 2
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_weights2 = alg.subtraction(_weights2, alg.scalarMultiply(learning_rate / inputMiniBatches[i].size(), D2_1));
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// Bias Updation for layer 2
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_bias2 = alg.subtractMatrixRows(_bias2, alg.scalarMultiply(learning_rate, error));
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//Calculating the weight/bias for layer 1
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std::vector<std::vector<real_t>> D1_1 = alg.matmult(error, alg.transpose(_weights2));
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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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std::vector<std::vector<real_t>> D1_3 = alg.matmult(alg.transpose(inputMiniBatches[i]), D1_2);
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// weight an bias updation for layer 1
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_weights1 = alg.subtraction(_weights1, alg.scalarMultiply(learning_rate / inputMiniBatches[i].size(), D1_3));
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_bias1 = alg.subtractMatrixRows(_bias1, alg.scalarMultiply(learning_rate / inputMiniBatches[i].size(), D1_2));
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y_hat = evaluatem(inputMiniBatches[i]);
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, inputMiniBatches[i]));
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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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}
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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 MLPPAutoEncoder::score() {
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ERR_FAIL_COND_V(!_initialized, 0);
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MLPPUtilities util;
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return util.performance(_y_hat, _input_set);
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}
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void MLPPAutoEncoder::save(std::string fileName) {
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ERR_FAIL_COND(!_initialized);
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MLPPUtilities util;
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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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MLPPAutoEncoder::MLPPAutoEncoder(std::vector<std::vector<real_t>> p_input_set, int pn_hidden) {
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_input_set = p_input_set;
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_n_hidden = pn_hidden;
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_n = _input_set.size();
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_k = _input_set[0].size();
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MLPPActivation avn;
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_y_hat.resize(_input_set.size());
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_weights1 = MLPPUtilities::weightInitialization(_k, _n_hidden);
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_weights2 = MLPPUtilities::weightInitialization(_n_hidden, _k);
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_bias1 = MLPPUtilities::biasInitialization(_n_hidden);
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_bias2 = MLPPUtilities::biasInitialization(_k);
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_initialized = true;
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}
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MLPPAutoEncoder::MLPPAutoEncoder() {
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_initialized = false;
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}
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MLPPAutoEncoder::~MLPPAutoEncoder() {
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}
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real_t MLPPAutoEncoder::cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
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class MLPPCost cost;
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return cost.MSE(y_hat, _input_set);
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}
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std::vector<real_t> MLPPAutoEncoder::evaluatev(std::vector<real_t> x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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std::vector<real_t> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(_weights1), x), _bias1);
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std::vector<real_t> a2 = avn.sigmoid(z2);
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return alg.addition(alg.mat_vec_mult(alg.transpose(_weights2), a2), _bias2);
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}
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std::tuple<std::vector<real_t>, std::vector<real_t>> MLPPAutoEncoder::propagatev(std::vector<real_t> x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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std::vector<real_t> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(_weights1), x), _bias1);
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std::vector<real_t> a2 = avn.sigmoid(z2);
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return { z2, a2 };
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}
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std::vector<std::vector<real_t>> MLPPAutoEncoder::evaluatem(std::vector<std::vector<real_t>> X) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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std::vector<std::vector<real_t>> z2 = alg.mat_vec_add(alg.matmult(X, _weights1), _bias1);
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std::vector<std::vector<real_t>> a2 = avn.sigmoid(z2);
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return alg.mat_vec_add(alg.matmult(a2, _weights2), _bias2);
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}
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std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPAutoEncoder::propagatem(std::vector<std::vector<real_t>> X) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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std::vector<std::vector<real_t>> z2 = alg.mat_vec_add(alg.matmult(X, _weights1), _bias1);
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std::vector<std::vector<real_t>> a2 = avn.sigmoid(z2);
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return { z2, a2 };
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}
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void MLPPAutoEncoder::forward_pass() {
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MLPPLinAlg alg;
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MLPPActivation avn;
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_z2 = alg.mat_vec_add(alg.matmult(_input_set, _weights1), _bias1);
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_a2 = avn.sigmoid(_z2);
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_y_hat = alg.mat_vec_add(alg.matmult(_a2, _weights2), _bias2);
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}
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void MLPPAutoEncoder::_bind_methods() {
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ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPAutoEncoder::get_input_set);
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ClassDB::bind_method(D_METHOD("set_input_set", "val"), &MLPPAutoEncoder::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_n_hidden"), &MLPPAutoEncoder::get_n_hidden);
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ClassDB::bind_method(D_METHOD("set_n_hidden", "val"), &MLPPAutoEncoder::set_n_hidden);
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ADD_PROPERTY(PropertyInfo(Variant::INT, "n_hidden"), "set_n_hidden", "get_n_hidden");
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/*
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ClassDB::bind_method(D_METHOD("model_set_test", "X"), &MLPPAutoEncoder::model_set_test);
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ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPAutoEncoder::model_test);
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ClassDB::bind_method(D_METHOD("gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPAutoEncoder::gradient_descent, false);
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ClassDB::bind_method(D_METHOD("sgd", "learning_rate", "max_epoch", "ui"), &MLPPAutoEncoder::sgd, false);
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ClassDB::bind_method(D_METHOD("mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPAutoEncoder::mbgd, false);
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ClassDB::bind_method(D_METHOD("score"), &MLPPAutoEncoder::score);
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ClassDB::bind_method(D_METHOD("save", "file_name"), &MLPPAutoEncoder::save);
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ClassDB::bind_method(D_METHOD("is_initialized"), &MLPPAutoEncoder::is_initialized);
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ClassDB::bind_method(D_METHOD("initialize"), &MLPPAutoEncoder::initialize);
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*/
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}
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