void BuildNetwork()
{
int[] layers=new int[] {
841,1014,1250,100,10
};
LayerConnector[] map=new LayerConnector[] {
new ConvolutionAuto(5,6,1,3),
new ConvolutionAuto(5,50,6,1),
new FullLayerConnector(),
new FullLayerConnector()
};
ConvolutionalTopology topology=new ConvolutionalTopology(layers,1,map,new HyperbolicTangent());
ConvolutionalGenerator generator=new ConvolutionalGenerator();
Network=generator.Create(topology);
Network.LearnFactor=0.0005;
Network.Reset(-0.1,0.1);
}
private void button3_Click(object sender,EventArgs e)
{
OpenFileDialog dlg = new OpenFileDialog();
dlg.CheckFileExists=true;
dlg.CheckPathExists=true;
dlg.Filter="xml files (*.xml)|*.xml";
dlg.Multiselect=false;
if (dlg.ShowDialog()==DialogResult.OK) {
ConvolutionalXMLSerializer serializer=new ConvolutionalXMLSerializer();
Network=serializer.Deserialize(XDocument.Load(dlg.FileName));
Network.LearnFactor=0.0005;
Loaded=true;
}
}
public XDocument Serialize(ConvolutionalNetwork network)
{
return new XDocument(new XElement("root",
new XElement("layers",network.Structure.Layers.Length.ToString()),
GetElements(network.Structure)));
}
public void TSSetUp()
{
int[] layers=new int[] {
1024, //32x32 input image
4704, //6 @ 28x28 convolution
1176, //6 @ 14x14 subsampling
1600, //16 @ 10x10 convolution
400, //16 @ 5x5 subsampling
120, // 120x1x1 convolution!
84, //full
10 //full
};
LayerConnector[] map=new LayerConnector[] {
new ConvolutionAuto(5,6,1,4),
new SubSampling(6),
new Convolution(5,16,6,4,GetSchema()),
new SubSampling(16),
new ConvolutionAuto(5,120,16,0),
new FullLayerConnector(),
new FullLayerConnector()
};
ConvolutionalTopology topology=new ConvolutionalTopology(layers,1,map);
ConvolutionalGenerator generator=new ConvolutionalGenerator();
Network=generator.Create(topology);
}
public XDocument Serialize(ConvolutionalNetwork network)
{
return(new XDocument(new XElement("root",
new XElement("layers", network.Structure.Layers.Length.ToString()),
GetElements(network.Structure))));
}
