// 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;
}
