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288 lines
9.1 KiB
C++
288 lines
9.1 KiB
C++
//
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// MLP.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 "mlp_old.h"
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#include "core/log/logger.h"
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#include "../activation/activation_old.h"
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#include "../cost/cost.h"
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#include "../lin_alg/lin_alg.h"
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#include "../regularization/reg.h"
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#include "../utilities/utilities.h"
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#include <iostream>
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#include <random>
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MLPPMLPOld::MLPPMLPOld(std::vector<std::vector<real_t>> p_inputSet, std::vector<real_t> p_outputSet, int p_n_hidden, std::string p_reg, real_t p_lambda, real_t p_alpha) {
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inputSet = p_inputSet;
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outputSet = p_outputSet;
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n_hidden = p_n_hidden;
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n = p_inputSet.size();
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k = p_inputSet[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);
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bias1 = MLPPUtilities::biasInitialization(n_hidden);
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bias2 = MLPPUtilities::biasInitialization();
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}
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std::vector<real_t> MLPPMLPOld::modelSetTest(std::vector<std::vector<real_t>> X) {
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return Evaluate(X);
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}
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real_t MLPPMLPOld::modelTest(std::vector<real_t> x) {
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return Evaluate(x);
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}
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void MLPPMLPOld::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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MLPPActivationOld avn;
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MLPPLinAlg alg;
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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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forwardPass();
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while (true) {
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cost_prev = Cost(y_hat, outputSet);
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// Calculating the errors
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std::vector<real_t> error = alg.subtraction(y_hat, outputSet);
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// Calculating the weight/bias gradients for layer 2
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std::vector<real_t> D2_1 = alg.mat_vec_mult(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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weights2 = regularization.regWeights(weights2, lambda, alpha, reg);
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bias2 -= learning_rate * alg.sum_elements(error) / n;
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// Calculating the weight/bias for layer 1
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std::vector<std::vector<real_t>> D1_1;
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D1_1.resize(n);
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D1_1 = alg.outerProduct(error, 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(inputSet), 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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weights1 = regularization.regWeights(weights1, lambda, alpha, reg);
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bias1 = alg.subtractMatrixRows(bias1, alg.scalarMultiply(learning_rate / n, D1_2));
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forwardPass();
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// UI PORTION
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
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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 MLPPMLPOld::SGD(real_t learning_rate, int max_epoch, bool UI) {
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MLPPActivationOld avn;
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MLPPLinAlg alg;
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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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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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real_t y_hat = Evaluate(inputSet[outputIndex]);
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auto propagate_result = propagate(inputSet[outputIndex]);
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auto z2 = std::get<0>(propagate_result);
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auto a2 = std::get<1>(propagate_result);
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cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });
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real_t error = y_hat - outputSet[outputIndex];
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// Weight updation for layer 2
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std::vector<real_t> D2_1 = alg.scalarMultiply(error, a2);
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weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate, D2_1));
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weights2 = regularization.regWeights(weights2, lambda, alpha, reg);
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// Bias updation for layer 2
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bias2 -= learning_rate * error;
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// Weight updation for layer 1
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std::vector<real_t> D1_1 = alg.scalarMultiply(error, weights2);
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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.outerProduct(inputSet[outputIndex], D1_2);
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weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate, D1_3));
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weights1 = regularization.regWeights(weights1, lambda, alpha, reg);
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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 = Evaluate(inputSet[outputIndex]);
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ y_hat }, { outputSet[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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forwardPass();
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}
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void MLPPMLPOld::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
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MLPPActivationOld avn;
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MLPPLinAlg alg;
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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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// Creating the mini-batches
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int n_mini_batch = n / mini_batch_size;
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auto minibatches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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auto inputMiniBatches = std::get<0>(minibatches);
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auto outputMiniBatches = std::get<1>(minibatches);
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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<real_t> y_hat = Evaluate(inputMiniBatches[i]);
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auto propagate_result = propagate(inputMiniBatches[i]);
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auto z2 = std::get<0>(propagate_result);
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auto a2 = std::get<1>(propagate_result);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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// Calculating the errors
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std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
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// Calculating the weight/bias gradients for layer 2
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std::vector<real_t> D2_1 = alg.mat_vec_mult(alg.transpose(a2), error);
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// weights and bias updation for layser 2
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weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate / outputMiniBatches[i].size(), D2_1));
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weights2 = regularization.regWeights(weights2, lambda, alpha, reg);
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// Calculating the bias gradients for layer 2
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//real_t b_gradient = alg.sum_elements(error);
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// Bias Updation for layer 2
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bias2 -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size();
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//Calculating the weight/bias for layer 1
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std::vector<std::vector<real_t>> D1_1 = alg.outerProduct(error, 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 / outputMiniBatches[i].size(), D1_3));
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weights1 = regularization.regWeights(weights1, lambda, alpha, reg);
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bias1 = alg.subtractMatrixRows(bias1, alg.scalarMultiply(learning_rate / outputMiniBatches[i].size(), D1_2));
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y_hat = Evaluate(inputMiniBatches[i]);
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[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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forwardPass();
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}
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real_t MLPPMLPOld::score() {
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MLPPUtilities util;
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return util.performance(y_hat, outputSet);
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}
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void MLPPMLPOld::save(std::string fileName) {
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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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real_t MLPPMLPOld::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
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MLPPReg regularization;
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class MLPPCost cost;
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return cost.LogLoss(y_hat, y) + regularization.regTerm(weights2, lambda, alpha, reg) + regularization.regTerm(weights1, lambda, alpha, reg);
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}
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std::vector<real_t> MLPPMLPOld::Evaluate(std::vector<std::vector<real_t>> X) {
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MLPPLinAlg alg;
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MLPPActivationOld 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 avn.sigmoid(alg.scalarAdd(bias2, alg.mat_vec_mult(a2, weights2)));
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}
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std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPMLPOld::propagate(std::vector<std::vector<real_t>> X) {
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MLPPLinAlg alg;
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MLPPActivationOld 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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real_t MLPPMLPOld::Evaluate(std::vector<real_t> x) {
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MLPPLinAlg alg;
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MLPPActivationOld 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 avn.sigmoid(alg.dot(weights2, a2) + bias2);
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}
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std::tuple<std::vector<real_t>, std::vector<real_t>> MLPPMLPOld::propagate(std::vector<real_t> x) {
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MLPPLinAlg alg;
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MLPPActivationOld 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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void MLPPMLPOld::forwardPass() {
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MLPPLinAlg alg;
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MLPPActivationOld avn;
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z2 = alg.mat_vec_add(alg.matmult(inputSet, weights1), bias1);
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a2 = avn.sigmoid(z2);
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y_hat = avn.sigmoid(alg.scalarAdd(bias2, alg.mat_vec_mult(a2, weights2)));
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}
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