public static void testLearningOneLayer()
{
Dimension2D inputDimension = new Dimension2D(5, 5);
Dimension2D convolutionKernel = new Dimension2D(3, 3);
ConvolutionalNetwork convolutionNet = (new ConvolutionalNetwork.Builder()).withInputLayer(5, 5, 1).withConvolutionLayer(3, 3, 2).withFullConnectedLayer(2).build();
// CREATE DATA SET
DataSet dataSet = new DataSet(25, 2);
dataSet.addRow(new double[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, new double[] { 1, 0 });
dataSet.addRow(new double[] { 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0 }, new double[] { 0, 1 });
// TRAIN NETWORK
convolutionNet.LearningRule.MaxError = 0.00001;
convolutionNet.learn(dataSet);
Console.WriteLine("Done training!");
FeatureMapLayer featureMap1 = ((FeatureMapsLayer)convolutionNet.getLayerAt(1)).getFeatureMap(0);
FeatureMapLayer featureMap2 = ((FeatureMapsLayer)convolutionNet.getLayerAt(1)).getFeatureMap(1);
// WeightVisualiser visualiser1 = new WeightVisualiser(featureMap1, convolutionKernel);
// visualiser1.displayWeights();
//
// WeightVisualiser visualiser2 = new WeightVisualiser(featureMap2, convolutionKernel);
// visualiser2.displayWeights();
// CREATE TEST SET
DataSet testSet = new DataSet(25, 2);
testSet.addRow(new double[] { 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, new double[] { 1, 0 });
testSet.addRow(new double[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0 }, new double[] { 1, 0 });
testSet.addRow(new double[] { 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0 }, new double[] { 0, 1 });
}
/// <param name="args"> Command line parameters used to initialize parameters of convolutional network
/// [0] - maximal number of epochs during learning
/// [1] - learning error stop condition
/// [2] - learning rate used during learning process
/// [3] - number of feature maps in 1st convolutional layer
/// [4] - number of feature maps in 2nd convolutional layer
/// [5] - number of feature maps in 3rd convolutional layer </param>
public static void Main(string[] args)
{
try
{
int maxIter = 10000; // Integer.parseInt(args[0]);
double maxError = 0.01; //Double.parseDouble(args[1]);
double learningRate = 0.2; // Double.parseDouble(args[2]);
int layer1 = Convert.ToInt32(args[3]);
int layer2 = Convert.ToInt32(args[4]);
int layer3 = Convert.ToInt32(args[5]);
LOG.info("{}-{}-{}", layer1, layer2, layer3);
DataSet trainSet = MNISTDataSet.createFromFile(MNISTDataSet.TRAIN_LABEL_NAME, MNISTDataSet.TRAIN_IMAGE_NAME, 100);
DataSet testSet = MNISTDataSet.createFromFile(MNISTDataSet.TEST_LABEL_NAME, MNISTDataSet.TEST_IMAGE_NAME, 10000);
Dimension2D inputDimension = new Dimension2D(32, 32);
Dimension2D convolutionKernel = new Dimension2D(5, 5);
Dimension2D poolingKernel = new Dimension2D(2, 2);
ConvolutionalNetwork convolutionNetwork = (new ConvolutionalNetwork.Builder()).withInputLayer(32, 32, 1).withConvolutionLayer(5, 5, layer1).withPoolingLayer(2, 2).withConvolutionLayer(5, 5, layer2).withPoolingLayer(2, 2).withConvolutionLayer(5, 5, layer3).withFullConnectedLayer(10).build();
ConvolutionalBackpropagation backPropagation = new ConvolutionalBackpropagation();
backPropagation.LearningRate = learningRate;
backPropagation.MaxError = maxError;
backPropagation.MaxIterations = maxIter;
backPropagation.addListener(new LearningListener(convolutionNetwork, testSet));
backPropagation.ErrorFunction = new MeanSquaredError();
convolutionNetwork.LearningRule = backPropagation;
convolutionNetwork.learn(trainSet);
Evaluation.runFullEvaluation(convolutionNetwork, testSet);
}
catch (IOException e)
{
Console.WriteLine(e.ToString());
Console.Write(e.StackTrace);
}
}
