mirror of
https://github.com/Relintai/pmlpp.git
synced 2024-12-23 15:06:52 +01:00
289 lines
9.0 KiB
C++
289 lines
9.0 KiB
C++
|
//
|
||
|
|
// MLP.cpp
|
||
|
|
//
|
||
|
|
// Created by Marc Melikyan on 11/4/20.
|
||
|
|
//
|
||
|
|
|
||
|
|
#include "mlp_old.h"
|
||
|
|
|
||
|
|
#include "core/log/logger.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>
|
||
|
|
|
||
|
|
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) {
|
||
|
|
inputSet = p_inputSet;
|
||
|
|
outputSet = p_outputSet;
|
||
|
|
n_hidden = p_n_hidden;
|
||
|
|
n = p_inputSet.size();
|
||
|
|
k = p_inputSet[0].size();
|
||
|
|
reg = p_reg;
|
||
|
|
lambda = p_lambda;
|
||
|
|
alpha = p_alpha;
|
||
|
|
|
||
|
|
MLPPActivation avn;
|
||
|
|
y_hat.resize(n);
|
||
|
|
|
||
|
|
weights1 = MLPPUtilities::weightInitialization(k, n_hidden);
|
||
|
|
weights2 = MLPPUtilities::weightInitialization(n_hidden);
|
||
|
|
bias1 = MLPPUtilities::biasInitialization(n_hidden);
|
||
|
|
bias2 = MLPPUtilities::biasInitialization();
|
||
|
|
}
|
||
|
|
|
||
|
|
std::vector<real_t> MLPPMLPOld::modelSetTest(std::vector<std::vector<real_t>> X) {
|
||
|
|
return Evaluate(X);
|
||
|
|
}
|
||
|
|
|
||
|
|
real_t MLPPMLPOld::modelTest(std::vector<real_t> x) {
|
||
|
|
return Evaluate(x);
|
||
|
|
}
|
||
|
|
|
||
|
|
void MLPPMLPOld::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
|
||
|
|
MLPPActivation avn;
|
||
|
|
MLPPLinAlg alg;
|
||
|
|
MLPPReg regularization;
|
||
|
|
real_t cost_prev = 0;
|
||
|
|
int epoch = 1;
|
||
|
|
forwardPass();
|
||
|
|
|
||
|
|
while (true) {
|
||
|
|
cost_prev = Cost(y_hat, outputSet);
|
||
|
|
|
||
|
|
// Calculating the errors
|
||
|
|
std::vector<real_t> error = alg.subtraction(y_hat, outputSet);
|
||
|
|
|
||
|
|
// Calculating the weight/bias gradients for layer 2
|
||
|
|
|
||
|
|
std::vector<real_t> 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<real_t>> D1_1;
|
||
|
|
D1_1.resize(n);
|
||
|
|
|
||
|
|
D1_1 = alg.outerProduct(error, weights2);
|
||
|
|
|
||
|
|
std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true));
|
||
|
|
|
||
|
|
std::vector<std::vector<real_t>> 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) {
|
||
|
|
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
|
||
|
|
std::cout << "Layer 1:" << std::endl;
|
||
|
|
MLPPUtilities::UI(weights1, bias1);
|
||
|
|
std::cout << "Layer 2:" << std::endl;
|
||
|
|
MLPPUtilities::UI(weights2, bias2);
|
||
|
|
}
|
||
|
|
epoch++;
|
||
|
|
|
||
|
|
if (epoch > max_epoch) {
|
||
|
|
break;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
void MLPPMLPOld::SGD(real_t learning_rate, int max_epoch, bool UI) {
|
||
|
|
MLPPActivation avn;
|
||
|
|
MLPPLinAlg alg;
|
||
|
|
MLPPReg regularization;
|
||
|
|
real_t 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);
|
||
|
|
|
||
|
|
real_t y_hat = Evaluate(inputSet[outputIndex]);
|
||
|
|
auto propagate_result = propagate(inputSet[outputIndex]);
|
||
|
|
auto z2 = std::get<0>(propagate_result);
|
||
|
|
auto a2 = std::get<1>(propagate_result);
|
||
|
|
cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });
|
||
|
|
real_t error = y_hat - outputSet[outputIndex];
|
||
|
|
|
||
|
|
// Weight updation for layer 2
|
||
|
|
std::vector<real_t> 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<real_t> D1_1 = alg.scalarMultiply(error, weights2);
|
||
|
|
std::vector<real_t> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true));
|
||
|
|
std::vector<std::vector<real_t>> 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) {
|
||
|
|
MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ y_hat }, { outputSet[outputIndex] }));
|
||
|
|
std::cout << "Layer 1:" << std::endl;
|
||
|
|
MLPPUtilities::UI(weights1, bias1);
|
||
|
|
std::cout << "Layer 2:" << std::endl;
|
||
|
|
MLPPUtilities::UI(weights2, bias2);
|
||
|
|
}
|
||
|
|
epoch++;
|
||
|
|
|
||
|
|
if (epoch > max_epoch) {
|
||
|
|
break;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
forwardPass();
|
||
|
|
}
|
||
|
|
|
||
|
|
void MLPPMLPOld::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
|
||
|
|
MLPPActivation avn;
|
||
|
|
MLPPLinAlg alg;
|
||
|
|
MLPPReg regularization;
|
||
|
|
real_t cost_prev = 0;
|
||
|
|
int epoch = 1;
|
||
|
|
|
||
|
|
// Creating the mini-batches
|
||
|
|
int n_mini_batch = n / mini_batch_size;
|
||
|
|
auto minibatches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
|
||
|
|
auto inputMiniBatches = std::get<0>(minibatches);
|
||
|
|
auto outputMiniBatches = std::get<1>(minibatches);
|
||
|
|
|
||
|
|
while (true) {
|
||
|
|
for (int i = 0; i < n_mini_batch; i++) {
|
||
|
|
std::vector<real_t> y_hat = Evaluate(inputMiniBatches[i]);
|
||
|
|
auto propagate_result = propagate(inputMiniBatches[i]);
|
||
|
|
auto z2 = std::get<0>(propagate_result);
|
||
|
|
auto a2 = std::get<1>(propagate_result);
|
||
|
|
|
||
|
|
cost_prev = Cost(y_hat, outputMiniBatches[i]);
|
||
|
|
|
||
|
|
// Calculating the errors
|
||
|
|
std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
|
||
|
|
|
||
|
|
// Calculating the weight/bias gradients for layer 2
|
||
|
|
|
||
|
|
std::vector<real_t> 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
|
||
|
|
//real_t 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<real_t>> D1_1 = alg.outerProduct(error, weights2);
|
||
|
|
|
||
|
|
std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true));
|
||
|
|
|
||
|
|
std::vector<std::vector<real_t>> 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) {
|
||
|
|
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
|
||
|
|
std::cout << "Layer 1:" << std::endl;
|
||
|
|
MLPPUtilities::UI(weights1, bias1);
|
||
|
|
std::cout << "Layer 2:" << std::endl;
|
||
|
|
MLPPUtilities::UI(weights2, bias2);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
epoch++;
|
||
|
|
if (epoch > max_epoch) {
|
||
|
|
break;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
forwardPass();
|
||
|
|
}
|
||
|
|
|
||
|
|
real_t MLPPMLPOld::score() {
|
||
|
|
MLPPUtilities util;
|
||
|
|
return util.performance(y_hat, outputSet);
|
||
|
|
}
|
||
|
|
|
||
|
|
void MLPPMLPOld::save(std::string fileName) {
|
||
|
|
MLPPUtilities util;
|
||
|
|
util.saveParameters(fileName, weights1, bias1, false, 1);
|
||
|
|
util.saveParameters(fileName, weights2, bias2, true, 2);
|
||
|
|
}
|
||
|
|
|
||
|
|
real_t MLPPMLPOld::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
|
||
|
|
MLPPReg regularization;
|
||
|
|
class MLPPCost cost;
|
||
|
|
return cost.LogLoss(y_hat, y) + regularization.regTerm(weights2, lambda, alpha, reg) + regularization.regTerm(weights1, lambda, alpha, reg);
|
||
|
|
}
|
||
|
|
|
||
|
|
std::vector<real_t> MLPPMLPOld::Evaluate(std::vector<std::vector<real_t>> X) {
|
||
|
|
MLPPLinAlg alg;
|
||
|
|
MLPPActivation avn;
|
||
|
|
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.sigmoid(alg.scalarAdd(bias2, alg.mat_vec_mult(a2, weights2)));
|
||
|
|
}
|
||
|
|
|
||
|
|
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPMLPOld::propagate(std::vector<std::vector<real_t>> X) {
|
||
|
|
MLPPLinAlg alg;
|
||
|
|
MLPPActivation avn;
|
||
|
|
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 { z2, a2 };
|
||
|
|
}
|
||
|
|
|
||
|
|
real_t MLPPMLPOld::Evaluate(std::vector<real_t> x) {
|
||
|
|
MLPPLinAlg alg;
|
||
|
|
MLPPActivation avn;
|
||
|
|
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.sigmoid(alg.dot(weights2, a2) + bias2);
|
||
|
|
}
|
||
|
|
|
||
|
|
std::tuple<std::vector<real_t>, std::vector<real_t>> MLPPMLPOld::propagate(std::vector<real_t> x) {
|
||
|
|
MLPPLinAlg alg;
|
||
|
|
MLPPActivation avn;
|
||
|
|
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 { z2, a2 };
|
||
|
|
}
|
||
|
|
|
||
|
|
void MLPPMLPOld::forwardPass() {
|
||
|
|
MLPPLinAlg 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)));
|
||
|
|
}
|