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742 lines
35 KiB
C++
742 lines
35 KiB
C++
//
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// ANN.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 "ANN.hpp"
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#include "Activation/Activation.hpp"
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#include "LinAlg/LinAlg.hpp"
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#include "Regularization/Reg.hpp"
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#include "Utilities/Utilities.hpp"
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#include "Cost/Cost.hpp"
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#include <iostream>
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#include <cmath>
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#include <random>
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namespace MLPP {
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ANN::ANN(std::vector<std::vector<double>> inputSet, std::vector<double> outputSet)
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: inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()), lrScheduler("None"), decayConstant(0), dropRate(0)
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{
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}
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ANN::~ANN(){
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delete outputLayer;
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}
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std::vector<double> ANN::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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double ANN::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 ANN::gradientDescent(double learning_rate, int max_epoch, bool UI){
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class Cost cost;
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LinAlg alg;
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double cost_prev = 0;
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int epoch = 1;
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forwardPass();
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double initial_learning_rate = learning_rate;
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alg.printMatrix(network[network.size() - 1].weights);
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while(true){
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learning_rate = applyLearningRateScheduler(initial_learning_rate, decayConstant, epoch, dropRate);
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cost_prev = Cost(y_hat, outputSet);
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auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputSet);
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cumulativeHiddenLayerWGrad = alg.scalarMultiply(learning_rate/n, cumulativeHiddenLayerWGrad);
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outputWGrad = alg.scalarMultiply(learning_rate/n, outputWGrad);
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updateParameters(cumulativeHiddenLayerWGrad, outputWGrad, learning_rate); // subject to change. may want bias to have this matrix too.
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std::cout << learning_rate << std::endl;
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forwardPass();
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if(UI) { ANN::UI(epoch, cost_prev, y_hat, outputSet); }
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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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void ANN::SGD(double learning_rate, int max_epoch, bool UI){
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class Cost cost;
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LinAlg alg;
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double cost_prev = 0;
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int epoch = 1;
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double initial_learning_rate = learning_rate;
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while(true){
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learning_rate = applyLearningRateScheduler(initial_learning_rate, decayConstant, epoch, dropRate);
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std::random_device rd;
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std::default_random_engine generator(rd());
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std::uniform_int_distribution<int> distribution(0, int(n - 1));
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int outputIndex = distribution(generator);
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std::vector<double> y_hat = modelSetTest({inputSet[outputIndex]});
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cost_prev = Cost({y_hat}, {outputSet[outputIndex]});
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auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, {outputSet[outputIndex]});
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cumulativeHiddenLayerWGrad = alg.scalarMultiply(learning_rate/n, cumulativeHiddenLayerWGrad);
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outputWGrad = alg.scalarMultiply(learning_rate/n, outputWGrad);
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updateParameters(cumulativeHiddenLayerWGrad, outputWGrad, learning_rate); // subject to change. may want bias to have this matrix too.
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y_hat = modelSetTest({inputSet[outputIndex]});
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if(UI) { ANN::UI(epoch, cost_prev, y_hat, {outputSet[outputIndex]}); }
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epoch++;
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if(epoch > max_epoch) { break; }
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}
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forwardPass();
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}
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void ANN::MBGD(double learning_rate, int max_epoch, int mini_batch_size, bool UI){
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class Cost cost;
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LinAlg alg;
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double cost_prev = 0;
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int epoch = 1;
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double initial_learning_rate = learning_rate;
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// Creating the mini-batches
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int n_mini_batch = n/mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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while(true){
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learning_rate = applyLearningRateScheduler(initial_learning_rate, decayConstant, epoch, dropRate);
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for(int i = 0; i < n_mini_batch; i++){
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std::vector<double> y_hat = modelSetTest(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]);
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cumulativeHiddenLayerWGrad = alg.scalarMultiply(learning_rate/n, cumulativeHiddenLayerWGrad);
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outputWGrad = alg.scalarMultiply(learning_rate/n, outputWGrad);
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updateParameters(cumulativeHiddenLayerWGrad, outputWGrad, learning_rate); // subject to change. may want bias to have this matrix too.
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y_hat = modelSetTest(inputMiniBatches[i]);
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if(UI) { ANN::UI(epoch, cost_prev, y_hat, outputMiniBatches[i]); }
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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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forwardPass();
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}
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void ANN::Momentum(double learning_rate, int max_epoch, int mini_batch_size, double gamma, bool NAG, bool UI){
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class Cost cost;
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LinAlg alg;
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double cost_prev = 0;
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int epoch = 1;
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double initial_learning_rate = learning_rate;
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// Creating the mini-batches
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int n_mini_batch = n/mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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// Initializing necessary components for Adam.
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std::vector<std::vector<std::vector<double>>> v_hidden;
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std::vector<double> v_output;
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while(true){
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learning_rate = applyLearningRateScheduler(initial_learning_rate, decayConstant, epoch, dropRate);
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for(int i = 0; i < n_mini_batch; i++){
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std::vector<double> y_hat = modelSetTest(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]);
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if(!network.empty() && v_hidden.empty()){ // Initing our tensor
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v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad);
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}
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if(v_output.empty()){
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v_output.resize(outputWGrad.size());
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}
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if(NAG){ // "Aposterori" calculation
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updateParameters(v_hidden, v_output, 0); // DON'T update bias.
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}
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v_hidden = alg.addition(alg.scalarMultiply(gamma, v_hidden), alg.scalarMultiply(learning_rate/n, cumulativeHiddenLayerWGrad));
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v_output = alg.addition(alg.scalarMultiply(gamma, v_output), alg.scalarMultiply(learning_rate/n, outputWGrad));
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updateParameters(v_hidden, v_output, learning_rate); // subject to change. may want bias to have this matrix too.
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y_hat = modelSetTest(inputMiniBatches[i]);
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if(UI) { ANN::UI(epoch, cost_prev, y_hat, outputMiniBatches[i]); }
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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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forwardPass();
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}
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void ANN::Adagrad(double learning_rate, int max_epoch, int mini_batch_size, double e, bool UI){
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class Cost cost;
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LinAlg alg;
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double cost_prev = 0;
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int epoch = 1;
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double initial_learning_rate = learning_rate;
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// Creating the mini-batches
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int n_mini_batch = n/mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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// Initializing necessary components for Adam.
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std::vector<std::vector<std::vector<double>>> v_hidden;
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std::vector<double> v_output;
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while(true){
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learning_rate = applyLearningRateScheduler(initial_learning_rate, decayConstant, epoch, dropRate);
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for(int i = 0; i < n_mini_batch; i++){
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std::vector<double> y_hat = modelSetTest(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]);
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if(!network.empty() && v_hidden.empty()){ // Initing our tensor
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v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad);
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}
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if(v_output.empty()){
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v_output.resize(outputWGrad.size());
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}
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v_hidden = alg.addition(v_hidden, alg.exponentiate(cumulativeHiddenLayerWGrad, 2));
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v_output = alg.addition(v_output, alg.exponentiate(outputWGrad, 2));
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std::vector<std::vector<std::vector<double>>> hiddenLayerUpdations = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(cumulativeHiddenLayerWGrad, alg.scalarAdd(e, alg.sqrt(v_hidden))));
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std::vector<double> outputLayerUpdation = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(outputWGrad, alg.scalarAdd(e, alg.sqrt(v_output))));
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updateParameters(hiddenLayerUpdations, outputLayerUpdation, learning_rate); // subject to change. may want bias to have this matrix too.
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y_hat = modelSetTest(inputMiniBatches[i]);
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if(UI) { ANN::UI(epoch, cost_prev, y_hat, outputMiniBatches[i]); }
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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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forwardPass();
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}
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void ANN::Adadelta(double learning_rate, int max_epoch, int mini_batch_size, double b1, double e, bool UI){
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class Cost cost;
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LinAlg alg;
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double cost_prev = 0;
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int epoch = 1;
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double initial_learning_rate = learning_rate;
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// Creating the mini-batches
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int n_mini_batch = n/mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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// Initializing necessary components for Adam.
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std::vector<std::vector<std::vector<double>>> v_hidden;
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std::vector<double> v_output;
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while(true){
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learning_rate = applyLearningRateScheduler(initial_learning_rate, decayConstant, epoch, dropRate);
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for(int i = 0; i < n_mini_batch; i++){
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std::vector<double> y_hat = modelSetTest(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]);
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if(!network.empty() && v_hidden.empty()){ // Initing our tensor
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v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad);
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}
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if(v_output.empty()){
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v_output.resize(outputWGrad.size());
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}
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v_hidden = alg.addition(alg.scalarMultiply(1 - b1, v_hidden), alg.scalarMultiply(b1, alg.exponentiate(cumulativeHiddenLayerWGrad, 2)));
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v_output = alg.addition(v_output, alg.exponentiate(outputWGrad, 2));
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std::vector<std::vector<std::vector<double>>> hiddenLayerUpdations = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(cumulativeHiddenLayerWGrad, alg.scalarAdd(e, alg.sqrt(v_hidden))));
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std::vector<double> outputLayerUpdation = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(outputWGrad, alg.scalarAdd(e, alg.sqrt(v_output))));
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updateParameters(hiddenLayerUpdations, outputLayerUpdation, learning_rate); // subject to change. may want bias to have this matrix too.
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y_hat = modelSetTest(inputMiniBatches[i]);
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if(UI) { ANN::UI(epoch, cost_prev, y_hat, outputMiniBatches[i]); }
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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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forwardPass();
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}
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void ANN::Adam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI){
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class Cost cost;
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LinAlg alg;
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double cost_prev = 0;
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int epoch = 1;
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double initial_learning_rate = learning_rate;
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// Creating the mini-batches
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int n_mini_batch = n/mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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// Initializing necessary components for Adam.
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std::vector<std::vector<std::vector<double>>> m_hidden;
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std::vector<std::vector<std::vector<double>>> v_hidden;
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std::vector<double> m_output;
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std::vector<double> v_output;
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while(true){
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learning_rate = applyLearningRateScheduler(initial_learning_rate, decayConstant, epoch, dropRate);
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for(int i = 0; i < n_mini_batch; i++){
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std::vector<double> y_hat = modelSetTest(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]);
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if(!network.empty() && m_hidden.empty() && v_hidden.empty()){ // Initing our tensor
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m_hidden = alg.resize(m_hidden, cumulativeHiddenLayerWGrad);
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v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad);
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}
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if(m_output.empty() && v_output.empty()){
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m_output.resize(outputWGrad.size());
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v_output.resize(outputWGrad.size());
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}
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m_hidden = alg.addition(alg.scalarMultiply(b1, m_hidden), alg.scalarMultiply(1 - b1, cumulativeHiddenLayerWGrad));
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v_hidden = alg.addition(alg.scalarMultiply(b2, v_hidden), alg.scalarMultiply(1 - b2, alg.exponentiate(cumulativeHiddenLayerWGrad, 2)));
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m_output = alg.addition(alg.scalarMultiply(b1, m_output), alg.scalarMultiply(1 - b1, outputWGrad));
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v_output = alg.addition(alg.scalarMultiply(b2, v_output), alg.scalarMultiply(1 - b2, alg.exponentiate(outputWGrad, 2)));
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std::vector<std::vector<std::vector<double>>> m_hidden_hat = alg.scalarMultiply(1/(1 - std::pow(b1, epoch)), m_hidden);
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std::vector<std::vector<std::vector<double>>> v_hidden_hat = alg.scalarMultiply(1/(1 - std::pow(b2, epoch)), v_hidden);
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std::vector<double> m_output_hat = alg.scalarMultiply(1/(1 - std::pow(b1, epoch)), m_output);
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std::vector<double> v_output_hat = alg.scalarMultiply(1/(1 - std::pow(b2, epoch)), v_output);
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std::vector<std::vector<std::vector<double>>> hiddenLayerUpdations = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(m_hidden_hat, alg.scalarAdd(e, alg.sqrt(v_hidden_hat))));
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std::vector<double> outputLayerUpdation = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(m_output_hat, alg.scalarAdd(e, alg.sqrt(v_output_hat))));
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updateParameters(hiddenLayerUpdations, outputLayerUpdation, learning_rate); // subject to change. may want bias to have this matrix too.
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y_hat = modelSetTest(inputMiniBatches[i]);
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if(UI) { ANN::UI(epoch, cost_prev, y_hat, outputMiniBatches[i]); }
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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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forwardPass();
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}
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void ANN::Adamax(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI){
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class Cost cost;
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LinAlg alg;
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double cost_prev = 0;
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int epoch = 1;
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double initial_learning_rate = learning_rate;
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// Creating the mini-batches
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int n_mini_batch = n/mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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// Initializing necessary components for Adam.
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std::vector<std::vector<std::vector<double>>> m_hidden;
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std::vector<std::vector<std::vector<double>>> u_hidden;
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std::vector<double> m_output;
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std::vector<double> u_output;
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while(true){
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learning_rate = applyLearningRateScheduler(initial_learning_rate, decayConstant, epoch, dropRate);
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for(int i = 0; i < n_mini_batch; i++){
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std::vector<double> y_hat = modelSetTest(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]);
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if(!network.empty() && m_hidden.empty() && u_hidden.empty()){ // Initing our tensor
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m_hidden = alg.resize(m_hidden, cumulativeHiddenLayerWGrad);
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u_hidden = alg.resize(u_hidden, cumulativeHiddenLayerWGrad);
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}
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if(m_output.empty() && u_output.empty()){
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m_output.resize(outputWGrad.size());
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u_output.resize(outputWGrad.size());
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}
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m_hidden = alg.addition(alg.scalarMultiply(b1, m_hidden), alg.scalarMultiply(1 - b1, cumulativeHiddenLayerWGrad));
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u_hidden = alg.max(alg.scalarMultiply(b2, u_hidden), alg.abs(cumulativeHiddenLayerWGrad));
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m_output = alg.addition(alg.scalarMultiply(b1, m_output), alg.scalarMultiply(1 - b1, outputWGrad));
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u_output = alg.max(alg.scalarMultiply(b2, u_output), alg.abs(outputWGrad));
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std::vector<std::vector<std::vector<double>>> m_hidden_hat = alg.scalarMultiply(1/(1 - std::pow(b1, epoch)), m_hidden);
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std::vector<double> m_output_hat = alg.scalarMultiply(1/(1 - std::pow(b1, epoch)), m_output);
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std::vector<std::vector<std::vector<double>>> hiddenLayerUpdations = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(m_hidden_hat, alg.scalarAdd(e, u_hidden)));
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std::vector<double> outputLayerUpdation = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(m_output_hat, alg.scalarAdd(e, u_output)));
|
|
|
|
|
|
updateParameters(hiddenLayerUpdations, outputLayerUpdation, learning_rate); // subject to change. may want bias to have this matrix too.
|
|
y_hat = modelSetTest(inputMiniBatches[i]);
|
|
|
|
if(UI) { ANN::UI(epoch, cost_prev, y_hat, outputMiniBatches[i]); }
|
|
}
|
|
epoch++;
|
|
if(epoch > max_epoch) { break; }
|
|
}
|
|
forwardPass();
|
|
}
|
|
|
|
void ANN::Nadam(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI){
|
|
class Cost cost;
|
|
LinAlg alg;
|
|
|
|
double cost_prev = 0;
|
|
int epoch = 1;
|
|
double initial_learning_rate = learning_rate;
|
|
|
|
// Creating the mini-batches
|
|
int n_mini_batch = n/mini_batch_size;
|
|
// always evaluate the result
|
|
// always do forward pass only ONCE at end.
|
|
auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
|
|
|
|
// Initializing necessary components for Adam.
|
|
std::vector<std::vector<std::vector<double>>> m_hidden;
|
|
std::vector<std::vector<std::vector<double>>> v_hidden;
|
|
std::vector<std::vector<std::vector<double>>> m_hidden_final;
|
|
|
|
std::vector<double> m_output;
|
|
std::vector<double> v_output;
|
|
while(true){
|
|
learning_rate = applyLearningRateScheduler(initial_learning_rate, decayConstant, epoch, dropRate);
|
|
for(int i = 0; i < n_mini_batch; i++){
|
|
std::vector<double> y_hat = modelSetTest(inputMiniBatches[i]);
|
|
cost_prev = Cost(y_hat, outputMiniBatches[i]);
|
|
|
|
auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]);
|
|
if(!network.empty() && m_hidden.empty() && v_hidden.empty()){ // Initing our tensor
|
|
m_hidden = alg.resize(m_hidden, cumulativeHiddenLayerWGrad);
|
|
v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad);
|
|
}
|
|
|
|
if(m_output.empty() && v_output.empty()){
|
|
m_output.resize(outputWGrad.size());
|
|
v_output.resize(outputWGrad.size());
|
|
}
|
|
|
|
m_hidden = alg.addition(alg.scalarMultiply(b1, m_hidden), alg.scalarMultiply(1 - b1, cumulativeHiddenLayerWGrad));
|
|
v_hidden = alg.addition(alg.scalarMultiply(b2, v_hidden), alg.scalarMultiply(1 - b2, alg.exponentiate(cumulativeHiddenLayerWGrad, 2)));
|
|
|
|
|
|
m_output = alg.addition(alg.scalarMultiply(b1, m_output), alg.scalarMultiply(1 - b1, outputWGrad));
|
|
v_output = alg.addition(alg.scalarMultiply(b2, v_output), alg.scalarMultiply(1 - b2, alg.exponentiate(outputWGrad, 2)));
|
|
|
|
std::vector<std::vector<std::vector<double>>> m_hidden_hat = alg.scalarMultiply(1/(1 - std::pow(b1, epoch)), m_hidden);
|
|
std::vector<std::vector<std::vector<double>>> v_hidden_hat = alg.scalarMultiply(1/(1 - std::pow(b2, epoch)), v_hidden);
|
|
std::vector<std::vector<std::vector<double>>> m_hidden_final = alg.addition(alg.scalarMultiply(b1, m_hidden_hat), alg.scalarMultiply((1 - b1)/(1 - std::pow(b1, epoch)), cumulativeHiddenLayerWGrad));
|
|
|
|
std::vector<double> m_output_hat = alg.scalarMultiply(1/(1 - std::pow(b1, epoch)), m_output);
|
|
std::vector<double> v_output_hat = alg.scalarMultiply(1/(1 - std::pow(b2, epoch)), v_output);
|
|
std::vector<double> m_output_final = alg.addition(alg.scalarMultiply(b1, m_output_hat), alg.scalarMultiply((1 - b1)/(1 - std::pow(b1, epoch)), outputWGrad));
|
|
|
|
std::vector<std::vector<std::vector<double>>> hiddenLayerUpdations = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(m_hidden_final, alg.scalarAdd(e, alg.sqrt(v_hidden_hat))));
|
|
std::vector<double> outputLayerUpdation = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(m_output_final, alg.scalarAdd(e, alg.sqrt(v_output_hat))));
|
|
|
|
|
|
updateParameters(hiddenLayerUpdations, outputLayerUpdation, learning_rate); // subject to change. may want bias to have this matrix too.
|
|
y_hat = modelSetTest(inputMiniBatches[i]);
|
|
|
|
if(UI) { ANN::UI(epoch, cost_prev, y_hat, outputMiniBatches[i]); }
|
|
}
|
|
epoch++;
|
|
if(epoch > max_epoch) { break; }
|
|
}
|
|
forwardPass();
|
|
}
|
|
|
|
void ANN::AMSGrad(double learning_rate, int max_epoch, int mini_batch_size, double b1, double b2, double e, bool UI){
|
|
class Cost cost;
|
|
LinAlg alg;
|
|
|
|
double cost_prev = 0;
|
|
int epoch = 1;
|
|
double initial_learning_rate = learning_rate;
|
|
|
|
// Creating the mini-batches
|
|
int n_mini_batch = n/mini_batch_size;
|
|
// always evaluate the result
|
|
// always do forward pass only ONCE at end.
|
|
auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
|
|
|
|
// Initializing necessary components for Adam.
|
|
std::vector<std::vector<std::vector<double>>> m_hidden;
|
|
std::vector<std::vector<std::vector<double>>> v_hidden;
|
|
|
|
std::vector<std::vector<std::vector<double>>> v_hidden_hat;
|
|
|
|
std::vector<double> m_output;
|
|
std::vector<double> v_output;
|
|
|
|
std::vector<double> v_output_hat;
|
|
while(true){
|
|
learning_rate = applyLearningRateScheduler(initial_learning_rate, decayConstant, epoch, dropRate);
|
|
for(int i = 0; i < n_mini_batch; i++){
|
|
std::vector<double> y_hat = modelSetTest(inputMiniBatches[i]);
|
|
cost_prev = Cost(y_hat, outputMiniBatches[i]);
|
|
|
|
auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]);
|
|
if(!network.empty() && m_hidden.empty() && v_hidden.empty()){ // Initing our tensor
|
|
m_hidden = alg.resize(m_hidden, cumulativeHiddenLayerWGrad);
|
|
v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad);
|
|
v_hidden_hat = alg.resize(v_hidden_hat, cumulativeHiddenLayerWGrad);
|
|
}
|
|
|
|
if(m_output.empty() && v_output.empty()){
|
|
m_output.resize(outputWGrad.size());
|
|
v_output.resize(outputWGrad.size());
|
|
v_output_hat.resize(outputWGrad.size());
|
|
}
|
|
|
|
m_hidden = alg.addition(alg.scalarMultiply(b1, m_hidden), alg.scalarMultiply(1 - b1, cumulativeHiddenLayerWGrad));
|
|
v_hidden = alg.addition(alg.scalarMultiply(b2, v_hidden), alg.scalarMultiply(1 - b2, alg.exponentiate(cumulativeHiddenLayerWGrad, 2)));
|
|
|
|
m_output = alg.addition(alg.scalarMultiply(b1, m_output), alg.scalarMultiply(1 - b1, outputWGrad));
|
|
v_output = alg.addition(alg.scalarMultiply(b2, v_output), alg.scalarMultiply(1 - b2, alg.exponentiate(outputWGrad, 2)));
|
|
|
|
v_hidden_hat = alg.max(v_hidden_hat, v_hidden);
|
|
|
|
v_output_hat = alg.max(v_output_hat, v_output);
|
|
|
|
std::vector<std::vector<std::vector<double>>> hiddenLayerUpdations = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(m_hidden, alg.scalarAdd(e, alg.sqrt(v_hidden_hat))));
|
|
std::vector<double> outputLayerUpdation = alg.scalarMultiply(learning_rate/n, alg.elementWiseDivision(m_output, alg.scalarAdd(e, alg.sqrt(v_output_hat))));
|
|
|
|
|
|
updateParameters(hiddenLayerUpdations, outputLayerUpdation, learning_rate); // subject to change. may want bias to have this matrix too.
|
|
y_hat = modelSetTest(inputMiniBatches[i]);
|
|
|
|
if(UI) { ANN::UI(epoch, cost_prev, y_hat, outputMiniBatches[i]); }
|
|
}
|
|
epoch++;
|
|
if(epoch > max_epoch) { break; }
|
|
}
|
|
forwardPass();
|
|
}
|
|
|
|
double ANN::score(){
|
|
Utilities util;
|
|
forwardPass();
|
|
return util.performance(y_hat, outputSet);
|
|
}
|
|
|
|
void ANN::save(std::string fileName){
|
|
Utilities util;
|
|
if(!network.empty()){
|
|
util.saveParameters(fileName, network[0].weights, network[0].bias, 0, 1);
|
|
for(int i = 1; i < network.size(); i++){
|
|
util.saveParameters(fileName, network[i].weights, network[i].bias, 1, i + 1);
|
|
}
|
|
util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, 1, network.size() + 1);
|
|
}
|
|
else{
|
|
util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, 0, network.size() + 1);
|
|
}
|
|
}
|
|
|
|
void ANN::setLearningRateScheduler(std::string type, double decayConstant){
|
|
lrScheduler = type;
|
|
ANN::decayConstant = decayConstant;
|
|
}
|
|
|
|
void ANN::setLearningRateScheduler(std::string type, double decayConstant, double dropRate){
|
|
lrScheduler = type;
|
|
ANN::decayConstant = decayConstant;
|
|
ANN::dropRate = dropRate;
|
|
}
|
|
|
|
// https://en.wikipedia.org/wiki/Learning_rate
|
|
// Learning Rate Decay (C2W2L09) - Andrew Ng - Deep Learning Specialization
|
|
double ANN::applyLearningRateScheduler(double learningRate, double decayConstant, double epoch, double dropRate){
|
|
if(lrScheduler == "Time"){
|
|
return learningRate / (1 + decayConstant * epoch);
|
|
}
|
|
else if(lrScheduler == "Epoch"){
|
|
return learningRate * (decayConstant / std::sqrt(epoch));
|
|
}
|
|
else if(lrScheduler == "Step"){
|
|
return learningRate * std::pow(decayConstant, int((1 + epoch)/dropRate)); // Utilizing an explicit int conversion implicitly takes the floor.
|
|
}
|
|
else if(lrScheduler == "Exponential"){
|
|
return learningRate * std::exp(-decayConstant * epoch);
|
|
}
|
|
return learningRate;
|
|
}
|
|
|
|
void ANN::addLayer(int n_hidden, std::string activation, std::string weightInit, std::string reg, double lambda, double alpha){
|
|
if(network.empty()){
|
|
network.push_back(HiddenLayer(n_hidden, activation, inputSet, weightInit, reg, lambda, alpha));
|
|
network[0].forwardPass();
|
|
}
|
|
else{
|
|
network.push_back(HiddenLayer(n_hidden, activation, network[network.size() - 1].a, weightInit, reg, lambda, alpha));
|
|
network[network.size() - 1].forwardPass();
|
|
}
|
|
}
|
|
|
|
void ANN::addOutputLayer(std::string activation, std::string loss, std::string weightInit, std::string reg, double lambda, double alpha){
|
|
LinAlg alg;
|
|
if(!network.empty()){
|
|
outputLayer = new OutputLayer(network[network.size() - 1].n_hidden, activation, loss, network[network.size() - 1].a, weightInit, reg, lambda, alpha);
|
|
}
|
|
else{
|
|
outputLayer = new OutputLayer(k, activation, loss, inputSet, weightInit, reg, lambda, alpha);
|
|
}
|
|
}
|
|
|
|
double ANN::Cost(std::vector<double> y_hat, std::vector<double> y){
|
|
Reg regularization;
|
|
class Cost cost;
|
|
double totalRegTerm = 0;
|
|
|
|
auto cost_function = outputLayer->cost_map[outputLayer->cost];
|
|
if(!network.empty()){
|
|
for(int i = 0; i < network.size() - 1; i++){
|
|
totalRegTerm += regularization.regTerm(network[i].weights, network[i].lambda, network[i].alpha, network[i].reg);
|
|
}
|
|
}
|
|
return (cost.*cost_function)(y_hat, y) + totalRegTerm + regularization.regTerm(outputLayer->weights, outputLayer->lambda, outputLayer->alpha, outputLayer->reg);
|
|
}
|
|
|
|
void ANN::forwardPass(){
|
|
if(!network.empty()){
|
|
network[0].input = inputSet;
|
|
network[0].forwardPass();
|
|
|
|
for(int i = 1; i < network.size(); i++){
|
|
network[i].input = network[i - 1].a;
|
|
network[i].forwardPass();
|
|
}
|
|
outputLayer->input = network[network.size() - 1].a;
|
|
}
|
|
else{
|
|
outputLayer->input = inputSet;
|
|
}
|
|
outputLayer->forwardPass();
|
|
y_hat = outputLayer->a;
|
|
}
|
|
|
|
void ANN::updateParameters(std::vector<std::vector<std::vector<double>>> hiddenLayerUpdations, std::vector<double> outputLayerUpdation, double learning_rate){
|
|
LinAlg alg;
|
|
|
|
outputLayer->weights = alg.subtraction(outputLayer->weights, outputLayerUpdation);
|
|
outputLayer->bias -= learning_rate * alg.sum_elements(outputLayer->delta) / n;
|
|
|
|
if(!network.empty()){
|
|
|
|
network[network.size() - 1].weights = alg.subtraction(network[network.size() - 1].weights, hiddenLayerUpdations[0]);
|
|
network[network.size() - 1].bias = alg.subtractMatrixRows(network[network.size() - 1].bias, alg.scalarMultiply(learning_rate/n, network[network.size() - 1].delta));
|
|
|
|
for(int i = network.size() - 2; i >= 0; i--){
|
|
network[i].weights = alg.subtraction(network[i].weights, hiddenLayerUpdations[(network.size() - 2) - i + 1]);
|
|
network[i].bias = alg.subtractMatrixRows(network[i].bias, alg.scalarMultiply(learning_rate/n, network[i].delta));
|
|
}
|
|
}
|
|
}
|
|
|
|
std::tuple<std::vector<std::vector<std::vector<double>>>, std::vector<double>> ANN::computeGradients(std::vector<double> y_hat, std::vector<double> outputSet){
|
|
// std::cout << "BEGIN" << std::endl;
|
|
class Cost cost;
|
|
Activation avn;
|
|
LinAlg alg;
|
|
Reg regularization;
|
|
|
|
std::vector<std::vector<std::vector<double>>> cumulativeHiddenLayerWGrad; // Tensor containing ALL hidden grads.
|
|
|
|
auto costDeriv = outputLayer->costDeriv_map[outputLayer->cost];
|
|
auto outputAvn = outputLayer->activation_map[outputLayer->activation];
|
|
outputLayer->delta = alg.hadamard_product((cost.*costDeriv)(y_hat, outputSet), (avn.*outputAvn)(outputLayer->z, 1));
|
|
std::vector<double> outputWGrad = alg.mat_vec_mult(alg.transpose(outputLayer->input), outputLayer->delta);
|
|
outputWGrad = alg.addition(outputWGrad, regularization.regDerivTerm(outputLayer->weights, outputLayer->lambda, outputLayer->alpha, outputLayer->reg));
|
|
|
|
if(!network.empty()){
|
|
auto hiddenLayerAvn = network[network.size() - 1].activation_map[network[network.size() - 1].activation];
|
|
network[network.size() - 1].delta = alg.hadamard_product(alg.outerProduct(outputLayer->delta, outputLayer->weights), (avn.*hiddenLayerAvn)(network[network.size() - 1].z, 1));
|
|
std::vector<std::vector<double>> hiddenLayerWGrad = alg.matmult(alg.transpose(network[network.size() - 1].input), network[network.size() - 1].delta);
|
|
|
|
cumulativeHiddenLayerWGrad.push_back(alg.addition(hiddenLayerWGrad, regularization.regDerivTerm(network[network.size() - 1].weights, network[network.size() - 1].lambda, network[network.size() - 1].alpha, network[network.size() - 1].reg))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
|
|
|
|
for(int i = network.size() - 2; i >= 0; i--){
|
|
auto hiddenLayerAvn = network[i].activation_map[network[i].activation];
|
|
network[i].delta = alg.hadamard_product(alg.matmult(network[i + 1].delta, alg.transpose(network[i + 1].weights)), (avn.*hiddenLayerAvn)(network[i].z, 1));
|
|
std::vector<std::vector<double>> hiddenLayerWGrad = alg.matmult(alg.transpose(network[i].input), network[i].delta);
|
|
cumulativeHiddenLayerWGrad.push_back(alg.addition(hiddenLayerWGrad, regularization.regDerivTerm(network[i].weights, network[i].lambda, network[i].alpha, network[i].reg))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
|
|
|
|
}
|
|
}
|
|
return {cumulativeHiddenLayerWGrad, outputWGrad};
|
|
}
|
|
|
|
void ANN::UI(int epoch, double cost_prev, std::vector<double> y_hat, std::vector<double> outputSet){
|
|
Utilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
|
|
std::cout << "Layer " << network.size() + 1 << ": " << std::endl;
|
|
Utilities::UI(outputLayer->weights, outputLayer->bias);
|
|
if(!network.empty()){
|
|
for(int i = network.size() - 1; i >= 0; i--){
|
|
std::cout << "Layer " << i + 1 << ": " << std::endl;
|
|
Utilities::UI(network[i].weights, network[i].bias);
|
|
}
|
|
}
|
|
}
|
|
} |