From a13e0e344b907f62b3977fc449e1c58db988f3c0 Mon Sep 17 00:00:00 2001 From: novak_99 Date: Sun, 30 Jan 2022 01:04:23 -0800 Subject: [PATCH] Added learning rate schedulers and decay for neural nets. --- MLPP/ANN/ANN.cpp | 33 ++++++++++++++++++++++++++++++--- MLPP/ANN/ANN.hpp | 6 ++++++ MLPP/GAN/GAN.hpp | 1 - README.md | 4 ++++ a.out | Bin 2192472 -> 2192960 bytes main.cpp | 45 +++++++++++++++++++++++---------------------- 6 files changed, 63 insertions(+), 26 deletions(-) diff --git a/MLPP/ANN/ANN.cpp b/MLPP/ANN/ANN.cpp index 4f41244..c23fac2 100644 --- a/MLPP/ANN/ANN.cpp +++ b/MLPP/ANN/ANN.cpp @@ -16,7 +16,7 @@ namespace MLPP { ANN::ANN(std::vector> inputSet, std::vector outputSet) - : inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()) + : inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()), lrScheduler("None"), decayConstant(0) { } @@ -66,6 +66,7 @@ namespace MLPP { alg.printMatrix(network[network.size() - 1].weights); while(true){ + learning_rate = applyLearningRateScheduler(learning_rate, decayConstant, epoch); cost_prev = Cost(y_hat, outputSet); auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputSet); @@ -96,6 +97,7 @@ namespace MLPP { // always do forward pass only ONCE at end. auto [inputMiniBatches, outputMiniBatches] = Utilities::createMiniBatches(inputSet, outputSet, n_mini_batch); while(true){ + learning_rate = applyLearningRateScheduler(learning_rate, decayConstant, epoch); for(int i = 0; i < n_mini_batch; i++){ std::vector y_hat = modelSetTest(inputMiniBatches[i]); cost_prev = Cost(y_hat, outputMiniBatches[i]); @@ -133,6 +135,7 @@ namespace MLPP { std::vector v_output; while(true){ + learning_rate = applyLearningRateScheduler(learning_rate, decayConstant, epoch); for(int i = 0; i < n_mini_batch; i++){ std::vector y_hat = modelSetTest(inputMiniBatches[i]); cost_prev = Cost(y_hat, outputMiniBatches[i]); @@ -184,6 +187,7 @@ namespace MLPP { std::vector v_output; while(true){ + learning_rate = applyLearningRateScheduler(learning_rate, decayConstant, epoch); for(int i = 0; i < n_mini_batch; i++){ std::vector y_hat = modelSetTest(inputMiniBatches[i]); cost_prev = Cost(y_hat, outputMiniBatches[i]); @@ -234,6 +238,7 @@ namespace MLPP { std::vector v_output; while(true){ + learning_rate = applyLearningRateScheduler(learning_rate, decayConstant, epoch); for(int i = 0; i < n_mini_batch; i++){ std::vector y_hat = modelSetTest(inputMiniBatches[i]); cost_prev = Cost(y_hat, outputMiniBatches[i]); @@ -286,6 +291,7 @@ void ANN::Adam(double learning_rate, int max_epoch, int mini_batch_size, double std::vector m_output; std::vector v_output; while(true){ + learning_rate = applyLearningRateScheduler(learning_rate, decayConstant, epoch); for(int i = 0; i < n_mini_batch; i++){ std::vector y_hat = modelSetTest(inputMiniBatches[i]); cost_prev = Cost(y_hat, outputMiniBatches[i]); @@ -348,6 +354,7 @@ void ANN::Adam(double learning_rate, int max_epoch, int mini_batch_size, double std::vector m_output; std::vector u_output; while(true){ + learning_rate = applyLearningRateScheduler(learning_rate, decayConstant, epoch); for(int i = 0; i < n_mini_batch; i++){ std::vector y_hat = modelSetTest(inputMiniBatches[i]); cost_prev = Cost(y_hat, outputMiniBatches[i]); @@ -409,6 +416,7 @@ void ANN::Adam(double learning_rate, int max_epoch, int mini_batch_size, double std::vector m_output; std::vector v_output; while(true){ + learning_rate = applyLearningRateScheduler(learning_rate, decayConstant, epoch); for(int i = 0; i < n_mini_batch; i++){ std::vector y_hat = modelSetTest(inputMiniBatches[i]); cost_prev = Cost(y_hat, outputMiniBatches[i]); @@ -478,6 +486,7 @@ void ANN::Adam(double learning_rate, int max_epoch, int mini_batch_size, double std::vector v_output_hat; while(true){ + learning_rate = applyLearningRateScheduler(learning_rate, decayConstant, epoch); for(int i = 0; i < n_mini_batch; i++){ std::vector y_hat = modelSetTest(inputMiniBatches[i]); cost_prev = Cost(y_hat, outputMiniBatches[i]); @@ -540,6 +549,25 @@ void ANN::Adam(double learning_rate, int max_epoch, int mini_batch_size, double } } + void ANN::setLearningRateScheduler(std::string type, double decayConstant){ + lrScheduler = type; + ANN::decayConstant = decayConstant; + } + + // 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){ + if(lrScheduler == "Time"){ + return learningRate / (1 + decayConstant * epoch); + } + else if(lrScheduler == "Exponential"){ + return learningRate * std::exp(-decayConstant * epoch); + } + else if(lrScheduler == "Epoch"){ + return learningRate * (decayConstant / std::sqrt(epoch)); + } + } + 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)); @@ -612,8 +640,7 @@ void ANN::Adam(double learning_rate, int max_epoch, int mini_batch_size, double } std::tuple>>, std::vector> ANN::computeGradients(std::vector y_hat, std::vector outputSet){ - std::cout << "BEGIN" << std::endl; - std::cout << k << std::endl; + // std::cout << "BEGIN" << std::endl; class Cost cost; Activation avn; LinAlg alg; diff --git a/MLPP/ANN/ANN.hpp b/MLPP/ANN/ANN.hpp index db21cd8..1d65a55 100644 --- a/MLPP/ANN/ANN.hpp +++ b/MLPP/ANN/ANN.hpp @@ -34,6 +34,9 @@ class ANN{ double score(); void save(std::string fileName); + void setLearningRateScheduler(std::string type, double k); + double applyLearningRateScheduler(double learningRate, double decayConstant, double epoch); + void addLayer(int n_hidden, std::string activation, std::string weightInit = "Default", std::string reg = "None", double lambda = 0.5, double alpha = 0.5); void addOutputLayer(std::string activation, std::string loss, std::string weightInit = "Default", std::string reg = "None", double lambda = 0.5, double alpha = 0.5); @@ -56,6 +59,9 @@ class ANN{ int n; int k; + + std::string lrScheduler; + double decayConstant; }; } diff --git a/MLPP/GAN/GAN.hpp b/MLPP/GAN/GAN.hpp index 77f6a7c..3e1066e 100644 --- a/MLPP/GAN/GAN.hpp +++ b/MLPP/GAN/GAN.hpp @@ -21,7 +21,6 @@ class GAN{ GAN(double k, std::vector> outputSet); ~GAN(); std::vector> generateExample(int n); - double modelTest(std::vector x); void gradientDescent(double learning_rate, int max_epoch, bool UI = 1); double score(); void save(std::string fileName); diff --git a/README.md b/README.md index 2830d8e..d979ceb 100644 --- a/README.md +++ b/README.md @@ -131,6 +131,10 @@ The result will be the model's predictions for the entire dataset. - He Uniform - LeCun Normal - LeCun Uniform + 6. Possible Learning Rate Schedulers + - Time Based + - Exponential + - Epoch Based 3. ***Prebuilt Neural Networks*** 1. Multilayer Peceptron 2. 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{0,1,0,1}}; - // std::vector outputSet = {0,1,1,0}; - // ANN ann(alg.transpose(inputSet), outputSet); - // //ann.addLayer(10, "Sigmoid"); - // ann.addLayer(10, "Sigmoid"); - // ann.addOutputLayer("Sigmoid", "LogLoss"); - // //ann.AMSGrad(0.1, 10000, 1, 0.9, 0.999, 0.000001, 1); - // //ann.Adadelta(1, 1000, 2, 0.9, 0.000001, 1); - // ann.Momentum(0.1, 8000, 2, 0.9, true, 1); - // //ann.MBGD(0.1, 1000, 2, 1); - // alg.printVector(ann.modelSetTest(alg.transpose(inputSet))); - // std::cout << "ACCURACY: " << 100 * ann.score() << "%" << std::endl; + std::vector> inputSet = {{0,0,1,1}, {0,1,0,1}}; + std::vector outputSet = {0,1,1,0}; + ANN ann(alg.transpose(inputSet), outputSet); + ann.addLayer(2, "Sigmoid"); + ann.addLayer(2, "Sigmoid"); + ann.addOutputLayer("Sigmoid", "LogLoss"); + //ann.AMSGrad(0.1, 10000, 1, 0.9, 0.999, 0.000001, 1); + //ann.Adadelta(1, 1000, 2, 0.9, 0.000001, 1); + //ann.Momentum(0.1, 8000, 2, 0.9, true, 1); + ann.setLearningRateScheduler("Time", 0.000000000001); + ann.gradientDescent(0.1, 20000, 1); + alg.printVector(ann.modelSetTest(alg.transpose(inputSet))); + std::cout << "ACCURACY: " << 100 * ann.score() << "%" << std::endl; - std::vector> outputSet = {{1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20}, - {2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40}}; + //std::vector> outputSet = {{1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20}, + // {2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40}}; //Vector outputSet = {0,1,1,0}; - GAN gan(2, alg.transpose(outputSet)); - gan.addLayer(5, "Sigmoid"); - gan.addLayer(2, "RELU"); - gan.addLayer(5, "Sigmoid"); - gan.addOutputLayer("Sigmoid", "LogLoss"); - gan.gradientDescent(0.1, 25000, 0); - std::cout << "GENERATED INPUT: (Gaussian-sampled noise):" << std::endl; - alg.printMatrix(gan.generateExample(5)); + // GAN gan(2, alg.transpose(outputSet)); + // gan.addLayer(5, "Sigmoid"); + // gan.addLayer(2, "RELU"); + // gan.addLayer(5, "Sigmoid"); + // gan.addOutputLayer("Sigmoid", "LogLoss"); + // gan.gradientDescent(0.1, 25000, 0); + // std::cout << "GENERATED INPUT: (Gaussian-sampled noise):" << std::endl; + // alg.printMatrix(gan.generateExample(100)); // typedef std::vector> Matrix;