public void Save(string filename)
{
var file = File.Create(filename);
using (var writer = XmlWriter.Create(file, new XmlWriterSettings {
Indent = true
})) {
writer.WriteStartElement(GetType().Name);
//Constructor arguments
writer.WriteElementString("matsize", _matsize.ToString());
writer.WriteElementString("vecsize", _vecsize.ToString());
writer.WriteElementString("labels", _labels.ToString());
writer.WriteElementString("depth", _depth.ToString());
writer.XmlSerialize(_args);
//Layers
foreach (var conv in ConvolutionalLayers)
{
conv.Serialize(writer);
}
LinearHiddenLayer.Serialize(writer);
OutputLayer.Serialize(writer);
writer.WriteEndElement();
}
file.Close();
}
public Vector <float> Compute(StatePair input, bool training)
{
// Forward propagate.
var img = InputLayer.Compute(input.Spatial);
for (int i = 0; i < ConvolutionalLayers.Length; i++)
{
img = SubSampleLayers[i].Compute(ConvolutionalLayers[i].Compute(img));
}
_vecp.left = FlattenLayer.Compute(img);
_vecp.right = LinearHiddenLayer.Compute(VectorInput.Compute(input.Linear));
IsOutputFromTraining = false;
var res = OutputLayer.Compute(CombinationLayer.Compute(_vecp));
if (ValuesComputed != null)
{
ValuesComputed(res, training);
}
return(res);
}
