pmlpp/mlpp/mlp/mlp_old.cpp

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//
// MLP.cpp
//
// Created by Marc Melikyan on 11/4/20.
//
#include "mlp_old.h"
#include "core/log/logger.h"
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#include "../activation/activation_old.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;
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) {
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MLPPActivationOld 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) {
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MLPPActivationOld 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) {
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MLPPActivationOld 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;
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MLPPActivationOld 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;
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MLPPActivationOld 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;
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MLPPActivationOld 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;
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MLPPActivationOld 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;
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MLPPActivationOld 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)));
}