pmlpp/mlpp/auto_encoder/auto_encoder.cpp

254 lines
8.2 KiB
C++

//
// AutoEncoder.cpp
//
// Created by Marc Melikyan on 11/4/20.
//
#include "auto_encoder.h"
#include "../activation/activation.h"
#include "../cost/cost.h"
#include "../lin_alg/lin_alg.h"
#include "../utilities/utilities.h"
#include <iostream>
#include <random>
MLPPAutoEncoder::MLPPAutoEncoder(std::vector<std::vector<real_t>> inputSet, int n_hidden) :
inputSet(inputSet), n_hidden(n_hidden), n(inputSet.size()), k(inputSet[0].size()) {
MLPPActivation avn;
y_hat.resize(inputSet.size());
weights1 = MLPPUtilities::weightInitialization(k, n_hidden);
weights2 = MLPPUtilities::weightInitialization(n_hidden, k);
bias1 = MLPPUtilities::biasInitialization(n_hidden);
bias2 = MLPPUtilities::biasInitialization(k);
}
std::vector<std::vector<real_t>> MLPPAutoEncoder::modelSetTest(std::vector<std::vector<real_t>> X) {
return Evaluate(X);
}
std::vector<real_t> MLPPAutoEncoder::modelTest(std::vector<real_t> x) {
return Evaluate(x);
}
void MLPPAutoEncoder::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
MLPPActivation avn;
MLPPLinAlg alg;
real_t cost_prev = 0;
int epoch = 1;
forwardPass();
while (true) {
cost_prev = Cost(y_hat, inputSet);
// Calculating the errors
std::vector<std::vector<real_t>> error = alg.subtraction(y_hat, inputSet);
// Calculating the weight/bias gradients for layer 2
std::vector<std::vector<real_t>> D2_1 = alg.matmult(alg.transpose(a2), error);
// weights and bias updation for layer 2
weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate / n, D2_1));
// Calculating the bias gradients for layer 2
bias2 = alg.subtractMatrixRows(bias2, alg.scalarMultiply(learning_rate, error));
//Calculating the weight/bias for layer 1
std::vector<std::vector<real_t>> D1_1 = alg.matmult(error, alg.transpose(weights2));
std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
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));
bias1 = alg.subtractMatrixRows(bias1, alg.scalarMultiply(learning_rate / n, D1_2));
forwardPass();
// UI PORTION
if (UI) {
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, inputSet));
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 MLPPAutoEncoder::SGD(real_t learning_rate, int max_epoch, bool UI) {
MLPPActivation avn;
MLPPLinAlg alg;
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);
std::vector<real_t> y_hat = Evaluate(inputSet[outputIndex]);
auto [z2, a2] = propagate(inputSet[outputIndex]);
cost_prev = Cost({ y_hat }, { inputSet[outputIndex] });
std::vector<real_t> error = alg.subtraction(y_hat, inputSet[outputIndex]);
// Weight updation for layer 2
std::vector<std::vector<real_t>> D2_1 = alg.outerProduct(error, a2);
weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate, alg.transpose(D2_1)));
// Bias updation for layer 2
bias2 = alg.subtraction(bias2, alg.scalarMultiply(learning_rate, error));
// Weight updation for layer 1
std::vector<real_t> D1_1 = alg.mat_vec_mult(weights2, error);
std::vector<real_t> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
std::vector<std::vector<real_t>> D1_3 = alg.outerProduct(inputSet[outputIndex], D1_2);
weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate, D1_3));
// 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 }, { inputSet[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 MLPPAutoEncoder::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
MLPPActivation avn;
MLPPLinAlg alg;
real_t cost_prev = 0;
int epoch = 1;
// Creating the mini-batches
int n_mini_batch = n / mini_batch_size;
std::vector<std::vector<std::vector<real_t>>> inputMiniBatches = MLPPUtilities::createMiniBatches(inputSet, n_mini_batch);
while (true) {
for (int i = 0; i < n_mini_batch; i++) {
std::vector<std::vector<real_t>> y_hat = Evaluate(inputMiniBatches[i]);
auto [z2, a2] = propagate(inputMiniBatches[i]);
cost_prev = Cost(y_hat, inputMiniBatches[i]);
// Calculating the errors
std::vector<std::vector<real_t>> error = alg.subtraction(y_hat, inputMiniBatches[i]);
// Calculating the weight/bias gradients for layer 2
std::vector<std::vector<real_t>> D2_1 = alg.matmult(alg.transpose(a2), error);
// weights and bias updation for layer 2
weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate / inputMiniBatches[i].size(), D2_1));
// Bias Updation for layer 2
bias2 = alg.subtractMatrixRows(bias2, alg.scalarMultiply(learning_rate, error));
//Calculating the weight/bias for layer 1
std::vector<std::vector<real_t>> D1_1 = alg.matmult(error, alg.transpose(weights2));
std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
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 / inputMiniBatches[i].size(), D1_3));
bias1 = alg.subtractMatrixRows(bias1, alg.scalarMultiply(learning_rate / inputMiniBatches[i].size(), D1_2));
y_hat = Evaluate(inputMiniBatches[i]);
if (UI) {
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, inputMiniBatches[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 MLPPAutoEncoder::score() {
MLPPUtilities util;
return util.performance(y_hat, inputSet);
}
void MLPPAutoEncoder::save(std::string fileName) {
MLPPUtilities util;
util.saveParameters(fileName, weights1, bias1, 0, 1);
util.saveParameters(fileName, weights2, bias2, 1, 2);
}
real_t MLPPAutoEncoder::Cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
class MLPPCost cost;
return cost.MSE(y_hat, inputSet);
}
std::vector<std::vector<real_t>> MLPPAutoEncoder::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 alg.mat_vec_add(alg.matmult(a2, weights2), bias2);
}
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPAutoEncoder::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 };
}
std::vector<real_t> MLPPAutoEncoder::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 alg.addition(alg.mat_vec_mult(alg.transpose(weights2), a2), bias2);
}
std::tuple<std::vector<real_t>, std::vector<real_t>> MLPPAutoEncoder::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 MLPPAutoEncoder::forwardPass() {
MLPPLinAlg alg;
MLPPActivation avn;
z2 = alg.mat_vec_add(alg.matmult(inputSet, weights1), bias1);
a2 = avn.sigmoid(z2);
y_hat = alg.mat_vec_add(alg.matmult(a2, weights2), bias2);
}