private void cmbMapLayers_SelectedIndexChanged(object sender, EventArgs e)
{
var msg = MyAppManager.Instance.CompositionContainer.GetExportedValue <IMessageService>();
_SelectedFeatureMapLayer = cmbMapLayers.ComboBox.SelectedItem as FeatureMapLayer;
if (_SelectedFeatureMapLayer == null)
{
return;
}
try
{
HOBPackage hob = new HOBPackage();
hob.Owner = Package.Owner;
var sites = hob.ExtractFrom(_SelectedFeatureMapLayer.DataSet.DataTable);
if (sites != null)
{
(Package as HOBOutputPackage).Sites = sites;
}
else
{
var _hob = Package.Parent as HOBPackage;
// msg.ShowError(null, "No sites are found in the selected layer. Default sites will be used, but you can not compare simulated values with observed values" );
(Package as HOBOutputPackage).Sites = _hob.Observations;
}
if (_fhd_pck == null)
{
GetFHDPackage();
}
}
catch (Exception ex)
{
msg.ShowError(null, "Failed to extract observation sites from the selected layer. Error message: " + ex.Message);
return;
}
}
private void initWeights(FeatureMapLayer map)
{
List <double?> weights = new List <double?>();
Neuron neuron = map.getNeuronAt(0);
int counter = 0;
foreach (Connection conn in neuron.InputConnections)
{
if (!(conn.FromNeuron is BiasNeuron))
{
if (counter < kernel.Area)
{
weights.Add(conn.Weight.Value);
counter++;
}
else
{
featureDetector.Add(weights);
weights = new List <>();
weights.Add(conn.Weight.Value);
counter = 1;
}
}
}
featureDetector.Add(weights);
}
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 });
}
public WeightVisualiser(FeatureMapLayer map, Kernel kernel)
{
this.kernel = kernel;
this.featureDetector = new List <>();
initWeights(map);
}
