/*
* constructor to be used for non testing code.
*/
public FeedForwardNeuralNetwork(NNConfig config)
{
int numberOfInputNeurons = config
.getParameterAsint(NUMBER_OF_INPUTS);
int numberOfHiddenNeurons = config
.getParameterAsint(NUMBER_OF_HIDDEN_NEURONS);
int numberOfOutputNeurons = config
.getParameterAsint(NUMBER_OF_OUTPUTS);
double lowerLimitForWeights = config
.getParameterAsDouble(LOWER_LIMIT_WEIGHTS);
double upperLimitForWeights = config
.getParameterAsDouble(UPPER_LIMIT_WEIGHTS);
hiddenLayer = new Layer(numberOfHiddenNeurons, numberOfInputNeurons,
lowerLimitForWeights, upperLimitForWeights,
new LogSigActivationFunction());
outputLayer = new Layer(numberOfOutputNeurons, numberOfHiddenNeurons,
lowerLimitForWeights, upperLimitForWeights,
new PureLinearActivationFunction());
}
/*
* constructor to be used for non testing code.
*/
public FeedForwardNeuralNetwork(NNConfig config) {
int numberOfInputNeurons = config
.getParameterAsint(NUMBER_OF_INPUTS);
int numberOfHiddenNeurons = config
.getParameterAsint(NUMBER_OF_HIDDEN_NEURONS);
int numberOfOutputNeurons = config
.getParameterAsint(NUMBER_OF_OUTPUTS);
double lowerLimitForWeights = config
.getParameterAsDouble(LOWER_LIMIT_WEIGHTS);
double upperLimitForWeights = config
.getParameterAsDouble(UPPER_LIMIT_WEIGHTS);
hiddenLayer = new Layer(numberOfHiddenNeurons, numberOfInputNeurons,
lowerLimitForWeights, upperLimitForWeights,
new LogSigActivationFunction());
outputLayer = new Layer(numberOfOutputNeurons, numberOfHiddenNeurons,
lowerLimitForWeights, upperLimitForWeights,
new PureLinearActivationFunction());
}
public void testDataSetPopulation()
{
DataSet irisDataSet = DataSetFactory.getIrisDataSet();
Numerizer numerizer = new IrisDataSetNumerizer();
NNDataSet innds = new IrisNNDataSet();
innds.createExamplesFromDataSet(irisDataSet, numerizer);
NNConfig config = new NNConfig();
config.setConfig(FeedForwardNeuralNetwork.NUMBER_OF_INPUTS, 4);
config.setConfig(FeedForwardNeuralNetwork.NUMBER_OF_OUTPUTS, 3);
config.setConfig(FeedForwardNeuralNetwork.NUMBER_OF_HIDDEN_NEURONS, 6);
config.setConfig(FeedForwardNeuralNetwork.LOWER_LIMIT_WEIGHTS, -2.0);
config.setConfig(FeedForwardNeuralNetwork.UPPER_LIMIT_WEIGHTS, 2.0);
FeedForwardNeuralNetwork ffnn = new FeedForwardNeuralNetwork(config);
ffnn.setTrainingScheme(new BackPropLearning(0.1, 0.9));
ffnn.trainOn(innds, 10);
innds.refreshDataset();
ffnn.testOnDataSet(innds);
}