// optimize the sigmoid using rprop on the negative log-likelihood void SigmoidFitRpropNLL::train(SigmoidModel& model, LabeledData<RealVector, unsigned int> const& dataset) { LinearModel<> trainModel; trainModel.setStructure(1,1,model.hasOffset()); CrossEntropy loss; ErrorFunction modeling_error( dataset, &trainModel, &loss ); IRpropPlus rprop; rprop.init( modeling_error ); for (unsigned int i=0; i<m_iterations; i++) { rprop.step( modeling_error ); } RealVector solution(2,0.0); solution(0) = rprop.solution().point(0); if(model.slopeIsExpEncoded()){ solution(0) = std::log(solution(0)); } if(model.hasOffset()) solution(1) =-rprop.solution().point(1); model.setParameterVector(solution); }
int main(){ //get problem data Problem problem(1.0); LabeledData<RealVector,unsigned int> training = problem.generateDataset(1000); LabeledData<RealVector,unsigned int> test = problem.generateDataset(100); std::size_t inputs=inputDimension(training); std::size_t outputs = numberOfClasses(training); std::size_t hiddens = 10; unsigned numberOfSteps = 1000; //create network and initialize weights random uniform FFNet<LogisticNeuron,LinearNeuron> network; network.setStructure(inputs,hiddens,outputs); initRandomUniform(network,-0.1,0.1); //create error function CrossEntropy loss; ErrorFunction error(training,&network,&loss); // loss for evaluation // The zeroOneLoss for multiclass problems assigns the class to the highest output ZeroOneLoss<unsigned int, RealVector> loss01; // evaluate initial network Data<RealVector> prediction = network(training.inputs()); cout << "classification error before learning:\t" << loss01.eval(training.labels(), prediction) << endl; //initialize Rprop IRpropPlus optimizer; optimizer.init(error); for(unsigned step = 0; step != numberOfSteps; ++step) optimizer.step(error); // evaluate solution found by training network.setParameterVector(optimizer.solution().point); // set weights to weights found by learning prediction = network(training.inputs()); cout << "classification error after learning:\t" << loss01(training.labels(), prediction) << endl; }