/**
* Performs the network's training iterations. The training continues until it reach the max error
* or the max number of iterations set on the network.
*
* @param network AbstractNeuralNetwork
* @return List of iterations
*/
public static List<String> train(AbstractNeuralNetwork network) {
MLTrain training = network.getTrainStrategy();
List<String> output = new ArrayList<String>();
double error = 0;
int epoch = 1;
do {
training.iteration();
error = training.getError();
output.add(epoch + "\t" + String.format(Locale.US, "%.20f", error));
// System.out.println("Iteration #" + epoch + " Error = " + training.getError());
epoch++;
} while (continueIterations(network, training.getError(), epoch));
network.updateTrainError();
System.out.println("Ended training: Iteration #" + --epoch + " Error = " + training.getError());
training.finishTraining();
return output;
}
/**
* Determines if the training may continue. Checks the network permited max training error and max
* number of iteraitions.
*
* @param network AbstractNeuralNetwork
* @param error Current training error
* @param iteration Current iteration
* @return May continue?
*/
private static boolean continueIterations(
AbstractNeuralNetwork network, double error, int iteration) {
if (network.getMaxIterations() > 0 && iteration > 0)
return network.getMaxIterations() >= iteration && network.getMaxError() < error;
else return network.getMaxError() < error;
}