// // MANN.cpp // // Created by Marc Melikyan on 11/4/20. // #include "mann.h" #include "../activation/activation.h" #include "../cost/cost.h" #include "../lin_alg/lin_alg.h" #include "../regularization/reg.h" #include "../utilities/utilities.h" #include MLPPMANN::MLPPMANN(std::vector> inputSet, std::vector> outputSet) : inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()), n_output(outputSet[0].size()) { } MLPPMANN::~MLPPMANN() { delete outputLayer; } std::vector> MLPPMANN::modelSetTest(std::vector> X) { if (!network.empty()) { network[0].input = X; 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 = X; } outputLayer->forwardPass(); return outputLayer->a; } std::vector MLPPMANN::modelTest(std::vector x) { if (!network.empty()) { network[0].Test(x); for (int i = 1; i < network.size(); i++) { network[i].Test(network[i - 1].a_test); } outputLayer->Test(network[network.size() - 1].a_test); } else { outputLayer->Test(x); } return outputLayer->a_test; } void MLPPMANN::gradientDescent(real_t learning_rate, int max_epoch, bool UI) { class MLPPCost cost; MLPPActivation avn; MLPPLinAlg alg; MLPPReg regularization; real_t cost_prev = 0; int epoch = 1; forwardPass(); while (true) { cost_prev = Cost(y_hat, outputSet); if (outputLayer->activation == "Softmax") { outputLayer->delta = alg.subtraction(y_hat, outputSet); } else { 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> outputWGrad = alg.matmult(alg.transpose(outputLayer->input), outputLayer->delta); outputLayer->weights = alg.subtraction(outputLayer->weights, alg.scalarMultiply(learning_rate / n, outputWGrad)); outputLayer->weights = regularization.regWeights(outputLayer->weights, outputLayer->lambda, outputLayer->alpha, outputLayer->reg); outputLayer->bias = alg.subtractMatrixRows(outputLayer->bias, alg.scalarMultiply(learning_rate / n, outputLayer->delta)); 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.matmult(outputLayer->delta, alg.transpose(outputLayer->weights)), (avn.*hiddenLayerAvn)(network[network.size() - 1].z, 1)); std::vector> hiddenLayerWGrad = alg.matmult(alg.transpose(network[network.size() - 1].input), network[network.size() - 1].delta); network[network.size() - 1].weights = alg.subtraction(network[network.size() - 1].weights, alg.scalarMultiply(learning_rate / n, hiddenLayerWGrad)); 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); 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--) { auto hiddenLayerAvn = network[i].activation_map[network[i].activation]; network[i].delta = alg.hadamard_product(alg.matmult(network[i + 1].delta, network[i + 1].weights), (avn.*hiddenLayerAvn)(network[i].z, 1)); std::vector> hiddenLayerWGrad = alg.matmult(alg.transpose(network[i].input), network[i].delta); network[i].weights = alg.subtraction(network[i].weights, alg.scalarMultiply(learning_rate / n, hiddenLayerWGrad)); network[i].weights = regularization.regWeights(network[i].weights, network[i].lambda, network[i].alpha, network[i].reg); network[i].bias = alg.subtractMatrixRows(network[i].bias, alg.scalarMultiply(learning_rate / n, network[i].delta)); } } forwardPass(); if (UI) { MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet)); std::cout << "Layer " << network.size() + 1 << ": " << std::endl; MLPPUtilities::UI(outputLayer->weights, outputLayer->bias); if (!network.empty()) { std::cout << "Layer " << network.size() << ": " << std::endl; for (int i = network.size() - 1; i >= 0; i--) { std::cout << "Layer " << i + 1 << ": " << std::endl; MLPPUtilities::UI(network[i].weights, network[i].bias); } } } epoch++; if (epoch > max_epoch) { break; } } } real_t MLPPMANN::score() { MLPPUtilities util; forwardPass(); return util.performance(y_hat, outputSet); } void MLPPMANN::save(std::string fileName) { MLPPUtilities 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 MLPPMANN::addLayer(int n_hidden, std::string activation, std::string weightInit, std::string reg, real_t lambda, real_t alpha) { if (network.empty()) { network.push_back(MLPPOldHiddenLayer(n_hidden, activation, inputSet, weightInit, reg, lambda, alpha)); network[0].forwardPass(); } else { network.push_back(MLPPOldHiddenLayer(n_hidden, activation, network[network.size() - 1].a, weightInit, reg, lambda, alpha)); network[network.size() - 1].forwardPass(); } } void MLPPMANN::addOutputLayer(std::string activation, std::string loss, std::string weightInit, std::string reg, real_t lambda, real_t alpha) { if (!network.empty()) { outputLayer = new MLPPMultiOutputLayer(n_output, network[0].n_hidden, activation, loss, network[network.size() - 1].a, weightInit, reg, lambda, alpha); } else { outputLayer = new MLPPMultiOutputLayer(n_output, k, activation, loss, inputSet, weightInit, reg, lambda, alpha); } } real_t MLPPMANN::Cost(std::vector> y_hat, std::vector> y) { MLPPReg regularization; class MLPPCost cost; real_t 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 MLPPMANN::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; }