public void trainAndSave() {
System.out.println("Training XOR network to under 1% error rate.");
BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(2));
network.addLayer(new BasicLayer(2));
network.addLayer(new BasicLayer(1));
network.getStructure().finalizeStructure();
network.reset();
NeuralDataSet trainingSet = new BasicNeuralDataSet(XOR_INPUT, XOR_IDEAL);
// train the neural network
final Train train = new ResilientPropagation(network, trainingSet);
do {
train.iteration();
} while (train.getError() > 0.009);
double e = network.calculateError(trainingSet);
System.out.println("Network traiined to error: " + e);
System.out.println("Saving network");
final EncogPersistedCollection encog = new EncogPersistedCollection(FILENAME);
encog.create();
encog.add("network", network);
}
/**
* * <br>
* Updates the neural networks</br> <br>
* Assumes that the *
*/
private void trainNeuralNets(int id) {
Train train =
new ResilientPropagation(array_of_neural_nets[id], input_datas[id].getTrainingSet());
int epoch = 0;
while (train.getError() > error_allowed && epoch < max_epoch) {
train.iteration();
epoch++;
}
}
public static void main(String args[]) {
int inputNeurons = CHAR_WIDTH * CHAR_HEIGHT;
int outputNeurons = DIGITS.length;
BasicNetwork network = new BasicNetwork();
Layer inputLayer = new BasicLayer(new ActivationLinear(), false, inputNeurons);
Layer outputLayer = new BasicLayer(new ActivationLinear(), true, outputNeurons);
network.addLayer(inputLayer);
network.addLayer(outputLayer);
network.getStructure().finalizeStructure();
(new RangeRandomizer(-0.5, 0.5)).randomize(network);
// train it
NeuralDataSet training = generateTraining();
Train train = new TrainAdaline(network, training, 0.01);
int epoch = 1;
do {
train.iteration();
System.out.println("Epoch #" + epoch + " Error:" + train.getError());
epoch++;
} while (train.getError() > 0.01);
//
System.out.println("Error:" + network.calculateError(training));
// test it
for (int i = 0; i < DIGITS.length; i++) {
int output = network.winner(image2data(DIGITS[i]));
for (int j = 0; j < CHAR_HEIGHT; j++) {
if (j == CHAR_HEIGHT - 1) System.out.println(DIGITS[i][j] + " -> " + output);
else System.out.println(DIGITS[i][j]);
}
System.out.println();
}
}
public static double trainNetwork(
final String what, final BasicNetwork network, final NeuralDataSet trainingSet) {
// train the neural network
CalculateScore score = new TrainingSetScore(trainingSet);
final Train trainAlt = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
final Train trainMain = new Backpropagation(network, trainingSet, 0.00001, 0.0);
final StopTrainingStrategy stop = new StopTrainingStrategy();
trainMain.addStrategy(new Greedy());
trainMain.addStrategy(new HybridStrategy(trainAlt));
trainMain.addStrategy(stop);
int epoch = 0;
while (!stop.shouldStop()) {
trainMain.iteration();
System.out.println(
"Training " + what + ", Epoch #" + epoch + " Error:" + trainMain.getError());
epoch++;
}
return trainMain.getError();
}
