/**
* Construct the backpropagation trainer.
*
* @param theNetwork The network to train.
* @param theTraining The training data to use.
* @param theLearningRate The learning rate. Can be changed as training runs.
* @param theMomentum The momentum. Can be changed as training runs.
*/
public BackPropagation(
BasicNetwork theNetwork,
List<BasicData> theTraining,
double theLearningRate,
double theMomentum) {
this.network = theNetwork;
this.training = theTraining;
this.learningRate = theLearningRate;
this.momentum = theMomentum;
this.gradients = new GradientCalc(this.network, new CrossEntropyErrorFunction(), this);
this.lastDelta = new double[theNetwork.getWeights().length];
}
/** Run the example. */
public void process() {
try {
final InputStream istream = this.getClass().getResourceAsStream("/iris.csv");
if (istream == null) {
System.out.println("Cannot access data set, make sure the resources are available.");
System.exit(1);
}
final DataSet ds = DataSet.load(istream);
// The following ranges are setup for the Iris data set. If you wish to normalize other files
// you will
// need to modify the below function calls other files.
ds.normalizeRange(0, -1, 1);
ds.normalizeRange(1, -1, 1);
ds.normalizeRange(2, -1, 1);
ds.normalizeRange(3, -1, 1);
final Map<String, Integer> species = ds.encodeOneOfN(4); // species is column 4
istream.close();
final List<BasicData> trainingData = ds.extractSupervised(0, 4, 4, 3);
BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(null, true, 4));
network.addLayer(new BasicLayer(new ActivationReLU(), true, 20));
network.addLayer(new BasicLayer(new ActivationSoftMax(), false, 3));
network.finalizeStructure();
network.reset();
final BackPropagation train = new BackPropagation(network, trainingData, 0.001, 0.9);
performIterations(train, 100000, 0.01, true);
queryOneOfN(network, trainingData, species);
} catch (Throwable t) {
t.printStackTrace();
}
}
