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272 lines
10 KiB
C++
272 lines
10 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.h"
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#include "../activation/activation.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 "../cost/cost.h"
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#include <iostream>
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#include <random>
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namespace MLPP {
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MLP::MLP(std::vector<std::vector<double>> inputSet, std::vector<double> outputSet, int n_hidden, std::string reg, double lambda, double alpha)
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: inputSet(inputSet), outputSet(outputSet), n_hidden(n_hidden), n(inputSet.size()), k(inputSet[0].size()), reg(reg), lambda(lambda), alpha(alpha)
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{
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Activation avn;
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y_hat.resize(n);
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weights1 = Utilities::weightInitialization(k, n_hidden);
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weights2 = Utilities::weightInitialization(n_hidden);
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bias1 = Utilities::biasInitialization(n_hidden);
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bias2 = Utilities::biasInitialization();
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}
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std::vector<double> MLP::modelSetTest(std::vector<std::vector<double>> X){
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return Evaluate(X);
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}
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double MLP::modelTest(std::vector<double> x){
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return Evaluate(x);
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}
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void MLP::gradientDescent(double learning_rate, int max_epoch, bool UI){
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Activation avn;
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LinAlg alg;
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Reg regularization;
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double 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<double> error = alg.subtraction(y_hat, outputSet);
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// Calculating the weight/bias gradients for layer 2
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std::vector<double> 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<double>> 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<double>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
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std::vector<std::vector<double>> 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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Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
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std::cout << "Layer 1:" << std::endl;
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Utilities::UI(weights1, bias1);
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std::cout << "Layer 2:" << std::endl;
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Utilities::UI(weights2, bias2);
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}
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epoch++;
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if(epoch > max_epoch) { break; }
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}
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}
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void MLP::SGD(double learning_rate, int max_epoch, bool UI){
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Activation avn;
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LinAlg alg;
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Reg regularization;
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double 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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double y_hat = Evaluate(inputSet[outputIndex]);
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auto [z2, a2] = propagate(inputSet[outputIndex]);
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cost_prev = Cost({y_hat}, {outputSet[outputIndex]});
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double error = y_hat - outputSet[outputIndex];
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// Weight updation for layer 2
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std::vector<double> 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<double> D1_1 = alg.scalarMultiply(error, weights2);
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std::vector<double> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
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std::vector<std::vector<double>> 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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Utilities::CostInfo(epoch, cost_prev, Cost({y_hat}, {outputSet[outputIndex]}));
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std::cout << "Layer 1:" << std::endl;
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Utilities::UI(weights1, bias1);
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std::cout << "Layer 2:" << std::endl;
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Utilities::UI(weights2, bias2);
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}
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epoch++;
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if(epoch > max_epoch) { break; }
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}
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forwardPass();
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}
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void MLP::MBGD(double learning_rate, int max_epoch, int mini_batch_size, bool UI){
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Activation avn;
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LinAlg alg;
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Reg regularization;
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double 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 [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, 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<double> y_hat = Evaluate(inputMiniBatches[i]);
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auto [z2, a2] = propagate(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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// Calculating the errors
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std::vector<double> 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<double> 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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double 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<double>> D1_1 = alg.outerProduct(error, weights2);
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std::vector<std::vector<double>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
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std::vector<std::vector<double>> 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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Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
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std::cout << "Layer 1:" << std::endl;
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Utilities::UI(weights1, bias1);
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std::cout << "Layer 2:" << std::endl;
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Utilities::UI(weights2, bias2);
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}
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}
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epoch++;
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if(epoch > max_epoch) { break; }
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}
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forwardPass();
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}
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double MLP::score(){
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Utilities util;
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return util.performance(y_hat, outputSet);
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}
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void MLP::save(std::string fileName){
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Utilities util;
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util.saveParameters(fileName, weights1, bias1, 0, 1);
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util.saveParameters(fileName, weights2, bias2, 1, 2);
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}
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double MLP::Cost(std::vector <double> y_hat, std::vector<double> y){
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Reg regularization;
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class Cost 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<double> MLP::Evaluate(std::vector<std::vector<double>> X){
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LinAlg alg;
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Activation avn;
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std::vector<std::vector<double>> z2 = alg.mat_vec_add(alg.matmult(X, weights1), bias1);
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std::vector<std::vector<double>> 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<double>>, std::vector<std::vector<double>>> MLP::propagate(std::vector<std::vector<double>> X){
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LinAlg alg;
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Activation avn;
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std::vector<std::vector<double>> z2 = alg.mat_vec_add(alg.matmult(X, weights1), bias1);
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std::vector<std::vector<double>> a2 = avn.sigmoid(z2);
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return {z2, a2};
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}
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double MLP::Evaluate(std::vector<double> x){
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LinAlg alg;
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Activation avn;
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std::vector<double> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(weights1), x), bias1);
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std::vector<double> 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<double>, std::vector<double>> MLP::propagate(std::vector<double> x){
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LinAlg alg;
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Activation avn;
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std::vector<double> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(weights1), x), bias1);
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std::vector<double> a2 = avn.sigmoid(z2);
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return {z2, a2};
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}
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void MLP::forwardPass(){
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LinAlg alg;
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Activation 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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}
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