public ImageCompress()
{
int[] layersizes = new int[3] {
8, 18, 8
};
ActivationFunction[] activFunctions = new ActivationFunction[3] {
ActivationFunction.None, ActivationFunction.Sigmoid, ActivationFunction.Linear
};
XmlDocument xdoc = new XmlDocument();
xdoc.Load(Server.MapPath("resources/ann.xml"));
ds = new DataSet();
ds.Load((XmlElement)xdoc.DocumentElement.ChildNodes[0]);
bpnetwork = new BackPropNetwork(layersizes, activFunctions);
nt = new NetworkTrainer(bpnetwork, ds);
nt.maxError = 0.00001;
nt.maxiterations = 10000;
nt.nudgewindow = 500;
nt.traininrate = 0.1;
nt.TrainDataset();
// save error
double[] err = nt.geteHistory();
string[] filedata = new string[err.Length];
for (int i = 0; i < err.Length; i++)
{
filedata[i] = i.ToString() + " " + err[i].ToString();
}
}
public CompressText()
{
int[] layersizes = new int[10] {
1, 10, 9, 8, 7, 5, 4, 3, 2, 1
};
ActivationFunction[] activFunctions = new ActivationFunction[10] {
ActivationFunction.None, ActivationFunction.Gaussian, ActivationFunction.Sigmoid, ActivationFunction.Sigmoid, ActivationFunction.Sigmoid, ActivationFunction.Sigmoid, ActivationFunction.Sigmoid, ActivationFunction.Sigmoid, ActivationFunction.Sigmoid,
ActivationFunction.Linear
};
XmlDocument xdoc = new XmlDocument();
xdoc.Load(Path.Combine(HttpRuntime.AppDomainAppPath, "resources/ann.xml"));
ds = new DataSet();
ds.Load((XmlElement)xdoc.DocumentElement.ChildNodes[0]);
bpnetwork = new BackPropNetwork(layersizes, activFunctions);
nt = new NetworkTrainer(bpnetwork, ds);
nt.maxError = 0.1;
nt.maxiterations = 10000;
nt.traininrate = 0.1;
nt.TrainDataset();
// save error
double[] err = nt.geteHistory();
string[] filedata = new string[err.Length];
for (int i = 0; i < err.Length; i++)
{
filedata[i] = i.ToString() + " " + err[i].ToString();
}
}