pmlpp/mlpp/mlp/mlp.cpp
2023-01-24 19:37:08 +01:00

274 lines
8.5 KiB
C++

//
// MLP.cpp
//
// Created by Marc Melikyan on 11/4/20.
//
#include "mlp.h"
#include "../activation/activation.h"
#include "../cost/cost.h"
#include "../lin_alg/lin_alg.h"
#include "../regularization/reg.h"
#include "../utilities/utilities.h"
#include <iostream>
#include <random>
MLP::MLP(std::vector<std::vector<double>> inputSet, std::vector<double> outputSet, int n_hidden, std::string reg, double lambda, double alpha) :
inputSet(inputSet), outputSet(outputSet), n_hidden(n_hidden), n(inputSet.size()), k(inputSet[0].size()), reg(reg), lambda(lambda), alpha(alpha) {
MLPPActivation avn;
y_hat.resize(n);
weights1 = Utilities::weightInitialization(k, n_hidden);
weights2 = Utilities::weightInitialization(n_hidden);
bias1 = Utilities::biasInitialization(n_hidden);
bias2 = Utilities::biasInitialization();
}
std::vector<double> MLP::modelSetTest(std::vector<std::vector<double>> X) {
return Evaluate(X);
}
double MLP::modelTest(std::vector<double> x) {
return Evaluate(x);
}
void MLP::gradientDescent(double learning_rate, int max_epoch, bool UI) {
MLPPActivation avn;
LinAlg alg;
Reg regularization;
double cost_prev = 0;
int epoch = 1;
forwardPass();
while (true) {
cost_prev = Cost(y_hat, outputSet);
// Calculating the errors
std::vector<double> error = alg.subtraction(y_hat, outputSet);
// Calculating the weight/bias gradients for layer 2
std::vector<double> D2_1 = alg.mat_vec_mult(alg.transpose(a2), error);
// weights and bias updation for layer 2
weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate / n, D2_1));
weights2 = regularization.regWeights(weights2, lambda, alpha, reg);
bias2 -= learning_rate * alg.sum_elements(error) / n;
// Calculating the weight/bias for layer 1
std::vector<std::vector<double>> D1_1;
D1_1.resize(n);
D1_1 = alg.outerProduct(error, weights2);
std::vector<std::vector<double>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
std::vector<std::vector<double>> D1_3 = alg.matmult(alg.transpose(inputSet), D1_2);
// weight an bias updation for layer 1
weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate / n, D1_3));
weights1 = regularization.regWeights(weights1, lambda, alpha, reg);
bias1 = alg.subtractMatrixRows(bias1, alg.scalarMultiply(learning_rate / n, D1_2));
forwardPass();
// UI PORTION
if (UI) {
Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
std::cout << "Layer 1:" << std::endl;
Utilities::UI(weights1, bias1);
std::cout << "Layer 2:" << std::endl;
Utilities::UI(weights2, bias2);
}
epoch++;
if (epoch > max_epoch) {
break;
}
}
}
void MLP::SGD(double learning_rate, int max_epoch, bool UI) {
MLPPActivation avn;
LinAlg alg;
Reg regularization;
double cost_prev = 0;
int epoch = 1;
while (true) {
std::random_device rd;
std::default_random_engine generator(rd());
std::uniform_int_distribution<int> distribution(0, int(n - 1));
int outputIndex = distribution(generator);
double y_hat = Evaluate(inputSet[outputIndex]);
auto [z2, a2] = propagate(inputSet[outputIndex]);
cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });
double error = y_hat - outputSet[outputIndex];
// Weight updation for layer 2
std::vector<double> D2_1 = alg.scalarMultiply(error, a2);
weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate, D2_1));
weights2 = regularization.regWeights(weights2, lambda, alpha, reg);
// Bias updation for layer 2
bias2 -= learning_rate * error;
// Weight updation for layer 1
std::vector<double> D1_1 = alg.scalarMultiply(error, weights2);
std::vector<double> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
std::vector<std::vector<double>> D1_3 = alg.outerProduct(inputSet[outputIndex], D1_2);
weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate, D1_3));
weights1 = regularization.regWeights(weights1, lambda, alpha, reg);
// Bias updation for layer 1
bias1 = alg.subtraction(bias1, alg.scalarMultiply(learning_rate, D1_2));
y_hat = Evaluate(inputSet[outputIndex]);
if (UI) {
Utilities::CostInfo(epoch, cost_prev, Cost({ y_hat }, { outputSet[outputIndex] }));
std::cout << "Layer 1:" << std::endl;
Utilities::UI(weights1, bias1);
std::cout << "Layer 2:" << std::endl;
Utilities::UI(weights2, bias2);
}
epoch++;
if (epoch > max_epoch) {
break;
}
}
forwardPass();
}
void MLP::MBGD(double learning_rate, int max_epoch, int mini_batch_size, bool UI) {
MLPPActivation avn;
LinAlg alg;
Reg regularization;
double cost_prev = 0;
int epoch = 1;
// Creating the mini-batches
int n_mini_batch = n / mini_batch_size;
auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
while (true) {
for (int i = 0; i < n_mini_batch; i++) {
std::vector<double> y_hat = Evaluate(inputMiniBatches[i]);
auto [z2, a2] = propagate(inputMiniBatches[i]);
cost_prev = Cost(y_hat, outputMiniBatches[i]);
// Calculating the errors
std::vector<double> error = alg.subtraction(y_hat, outputMiniBatches[i]);
// Calculating the weight/bias gradients for layer 2
std::vector<double> D2_1 = alg.mat_vec_mult(alg.transpose(a2), error);
// weights and bias updation for layser 2
weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate / outputMiniBatches[i].size(), D2_1));
weights2 = regularization.regWeights(weights2, lambda, alpha, reg);
// Calculating the bias gradients for layer 2
double b_gradient = alg.sum_elements(error);
// Bias Updation for layer 2
bias2 -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size();
//Calculating the weight/bias for layer 1
std::vector<std::vector<double>> D1_1 = alg.outerProduct(error, weights2);
std::vector<std::vector<double>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
std::vector<std::vector<double>> D1_3 = alg.matmult(alg.transpose(inputMiniBatches[i]), D1_2);
// weight an bias updation for layer 1
weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate / outputMiniBatches[i].size(), D1_3));
weights1 = regularization.regWeights(weights1, lambda, alpha, reg);
bias1 = alg.subtractMatrixRows(bias1, alg.scalarMultiply(learning_rate / outputMiniBatches[i].size(), D1_2));
y_hat = Evaluate(inputMiniBatches[i]);
if (UI) {
Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
std::cout << "Layer 1:" << std::endl;
Utilities::UI(weights1, bias1);
std::cout << "Layer 2:" << std::endl;
Utilities::UI(weights2, bias2);
}
}
epoch++;
if (epoch > max_epoch) {
break;
}
}
forwardPass();
}
double MLP::score() {
Utilities util;
return util.performance(y_hat, outputSet);
}
void MLP::save(std::string fileName) {
Utilities util;
util.saveParameters(fileName, weights1, bias1, 0, 1);
util.saveParameters(fileName, weights2, bias2, 1, 2);
}
double MLP::Cost(std::vector<double> y_hat, std::vector<double> y) {
Reg regularization;
class MLPPCost cost;
return cost.LogLoss(y_hat, y) + regularization.regTerm(weights2, lambda, alpha, reg) + regularization.regTerm(weights1, lambda, alpha, reg);
}
std::vector<double> MLP::Evaluate(std::vector<std::vector<double>> X) {
LinAlg alg;
MLPPActivation avn;
std::vector<std::vector<double>> z2 = alg.mat_vec_add(alg.matmult(X, weights1), bias1);
std::vector<std::vector<double>> a2 = avn.sigmoid(z2);
return avn.sigmoid(alg.scalarAdd(bias2, alg.mat_vec_mult(a2, weights2)));
}
std::tuple<std::vector<std::vector<double>>, std::vector<std::vector<double>>> MLP::propagate(std::vector<std::vector<double>> X) {
LinAlg alg;
MLPPActivation avn;
std::vector<std::vector<double>> z2 = alg.mat_vec_add(alg.matmult(X, weights1), bias1);
std::vector<std::vector<double>> a2 = avn.sigmoid(z2);
return { z2, a2 };
}
double MLP::Evaluate(std::vector<double> x) {
LinAlg alg;
MLPPActivation avn;
std::vector<double> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(weights1), x), bias1);
std::vector<double> a2 = avn.sigmoid(z2);
return avn.sigmoid(alg.dot(weights2, a2) + bias2);
}
std::tuple<std::vector<double>, std::vector<double>> MLP::propagate(std::vector<double> x) {
LinAlg alg;
MLPPActivation avn;
std::vector<double> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(weights1), x), bias1);
std::vector<double> a2 = avn.sigmoid(z2);
return { z2, a2 };
}
void MLP::forwardPass() {
LinAlg alg;
MLPPActivation avn;
z2 = alg.mat_vec_add(alg.matmult(inputSet, weights1), bias1);
a2 = avn.sigmoid(z2);
y_hat = avn.sigmoid(alg.scalarAdd(bias2, alg.mat_vec_mult(a2, weights2)));
}