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197 lines
8.4 KiB
C++
197 lines
8.4 KiB
C++
//
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// MANN.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 "mann.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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namespace MLPP {
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MANN::MANN(std::vector<std::vector<double>> inputSet, std::vector<std::vector<double>> outputSet)
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: inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()), n_output(outputSet[0].size())
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{
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}
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MANN::~MANN(){
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delete outputLayer;
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}
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std::vector<std::vector<double>> MANN::modelSetTest(std::vector<std::vector<double>> X){
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if(!network.empty()){
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network[0].input = X;
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network[0].forwardPass();
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for(int i = 1; i < network.size(); i++){
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network[i].input = network[i - 1].a;
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network[i].forwardPass();
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}
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outputLayer->input = network[network.size() - 1].a;
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}
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else {
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outputLayer->input = X;
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}
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outputLayer->forwardPass();
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return outputLayer->a;
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}
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std::vector<double> MANN::modelTest(std::vector<double> x){
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if(!network.empty()){
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network[0].Test(x);
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for(int i = 1; i < network.size(); i++){
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network[i].Test(network[i - 1].a_test);
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}
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outputLayer->Test(network[network.size() - 1].a_test);
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}
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else{
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outputLayer->Test(x);
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}
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return outputLayer->a_test;
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}
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void MANN::gradientDescent(double learning_rate, int max_epoch, bool UI){
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class Cost cost;
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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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if(outputLayer->activation == "Softmax"){
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outputLayer->delta = alg.subtraction(y_hat, outputSet);
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}
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else{
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auto costDeriv = outputLayer->costDeriv_map[outputLayer->cost];
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auto outputAvn = outputLayer->activation_map[outputLayer->activation];
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outputLayer->delta = alg.hadamard_product((cost.*costDeriv)(y_hat, outputSet), (avn.*outputAvn)(outputLayer->z, 1));
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}
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std::vector<std::vector<double>> outputWGrad = alg.matmult(alg.transpose(outputLayer->input), outputLayer->delta);
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outputLayer->weights = alg.subtraction(outputLayer->weights, alg.scalarMultiply(learning_rate/n, outputWGrad));
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outputLayer->weights = regularization.regWeights(outputLayer->weights, outputLayer->lambda, outputLayer->alpha, outputLayer->reg);
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outputLayer->bias = alg.subtractMatrixRows(outputLayer->bias, alg.scalarMultiply(learning_rate/n, outputLayer->delta));
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if(!network.empty()){
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auto hiddenLayerAvn = network[network.size() - 1].activation_map[network[network.size() - 1].activation];
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network[network.size() - 1].delta = alg.hadamard_product(alg.matmult(outputLayer->delta, alg.transpose(outputLayer->weights)), (avn.*hiddenLayerAvn)(network[network.size() - 1].z, 1));
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std::vector<std::vector<double>> hiddenLayerWGrad = alg.matmult(alg.transpose(network[network.size() - 1].input), network[network.size() - 1].delta);
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network[network.size() - 1].weights = alg.subtraction(network[network.size() - 1].weights, alg.scalarMultiply(learning_rate/n, hiddenLayerWGrad));
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network[network.size() - 1].weights = regularization.regWeights(network[network.size() - 1].weights, network[network.size() - 1].lambda, network[network.size() - 1].alpha, network[network.size() - 1].reg);
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network[network.size() - 1].bias = alg.subtractMatrixRows(network[network.size() - 1].bias, alg.scalarMultiply(learning_rate/n, network[network.size() - 1].delta));
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for(int i = network.size() - 2; i >= 0; i--){
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auto hiddenLayerAvn = network[i].activation_map[network[i].activation];
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network[i].delta = alg.hadamard_product(alg.matmult(network[i + 1].delta, network[i + 1].weights), (avn.*hiddenLayerAvn)(network[i].z, 1));
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std::vector<std::vector<double>> hiddenLayerWGrad = alg.matmult(alg.transpose(network[i].input), network[i].delta);
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network[i].weights = alg.subtraction(network[i].weights, alg.scalarMultiply(learning_rate/n, hiddenLayerWGrad));
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network[i].weights = regularization.regWeights(network[i].weights, network[i].lambda, network[i].alpha, network[i].reg);
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network[i].bias = alg.subtractMatrixRows(network[i].bias, alg.scalarMultiply(learning_rate/n, network[i].delta));
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}
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}
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forwardPass();
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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 " << network.size() + 1 << ": " << std::endl;
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Utilities::UI(outputLayer->weights, outputLayer->bias);
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if(!network.empty()){
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std::cout << "Layer " << network.size() << ": " << std::endl;
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for(int i = network.size() - 1; i >= 0; i--){
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std::cout << "Layer " << i + 1 << ": " << std::endl;
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Utilities::UI(network[i].weights, network[i].bias);
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}
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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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}
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double MANN::score(){
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Utilities util;
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forwardPass();
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return util.performance(y_hat, outputSet);
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}
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void MANN::save(std::string fileName){
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Utilities util;
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if(!network.empty()){
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util.saveParameters(fileName, network[0].weights, network[0].bias, 0, 1);
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for(int i = 1; i < network.size(); i++){
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util.saveParameters(fileName, network[i].weights, network[i].bias, 1, i + 1);
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}
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util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, 1, network.size() + 1);
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}
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else{
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util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, 0, network.size() + 1);
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}
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}
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void MANN::addLayer(int n_hidden, std::string activation, std::string weightInit, std::string reg, double lambda, double alpha){
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if(network.empty()){
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network.push_back(HiddenLayer(n_hidden, activation, inputSet, weightInit, reg, lambda, alpha));
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network[0].forwardPass();
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}
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else{
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network.push_back(HiddenLayer(n_hidden, activation, network[network.size() - 1].a, weightInit, reg, lambda, alpha));
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network[network.size() - 1].forwardPass();
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}
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}
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void MANN::addOutputLayer(std::string activation, std::string loss, std::string weightInit, std::string reg, double lambda, double alpha){
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if(!network.empty()){
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outputLayer = new MultiOutputLayer(n_output, network[0].n_hidden, activation, loss, network[network.size() - 1].a, weightInit, reg, lambda, alpha);
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}
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else{
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outputLayer = new MultiOutputLayer(n_output, k, activation, loss, inputSet, weightInit, reg, lambda, alpha);
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}
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}
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double MANN::Cost(std::vector<std::vector<double>> y_hat, std::vector<std::vector<double>> y){
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Reg regularization;
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class Cost cost;
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double totalRegTerm = 0;
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auto cost_function = outputLayer->cost_map[outputLayer->cost];
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if(!network.empty()){
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for(int i = 0; i < network.size() - 1; i++){
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totalRegTerm += regularization.regTerm(network[i].weights, network[i].lambda, network[i].alpha, network[i].reg);
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}
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}
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return (cost.*cost_function)(y_hat, y) + totalRegTerm + regularization.regTerm(outputLayer->weights, outputLayer->lambda, outputLayer->alpha, outputLayer->reg);
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}
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void MANN::forwardPass(){
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if(!network.empty()){
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network[0].input = inputSet;
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network[0].forwardPass();
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for(int i = 1; i < network.size(); i++){
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network[i].input = network[i - 1].a;
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network[i].forwardPass();
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}
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outputLayer->input = network[network.size() - 1].a;
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}
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else{
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outputLayer->input = inputSet;
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}
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outputLayer->forwardPass();
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y_hat = outputLayer->a;
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}
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} |