public static double Propogate
(Sample s, WriteToCMDLine write, bool tf = false)
{
double error = 0;
var Pred = Predict(s.TextInput, CMDLibrary.WriteNull);
if (s.DesiredOutput.ToList().IndexOf(s.DesiredOutput.Max()) != Pred.ToList().IndexOf(Pred.Max()) || tf)
{
NeuralNetwork net = GetNetwork(write);
var Samples = s.ReadSamples(24);
Alpha a = new Alpha(write);
AlphaContext ctxt = new AlphaContext(datatype, write);
NetworkMem NetMem = new NetworkMem(net);
NetworkMem AlphaMem = new NetworkMem(a.Network);
NetworkMem CtxtMem = new NetworkMem(ctxt.Network);
Parallel.For(0, Samples.Count(), j =>
{
AlphaMem am = new AlphaMem(Samples[j].TextInput.ToCharArray());
var output = a.Forward(Samples[j].TextInput, ctxt, am);
var F = net.Forward(output, dropout, write);
error += CategoricalCrossEntropy.Forward(F.Last().GetRank(0), Samples[j].DesiredOutput).Max();
var DValues = net.Backward(F, Samples[j].DesiredOutput, NetMem, write);
a.Backward(Samples[j].TextInput, DValues, ctxt, am, AlphaMem, CtxtMem);
});
NetMem.Update(Samples.Count(), 0.00001, net);
AlphaMem.Update(Samples.Count(), 0.00001, a.Network);
CtxtMem.Update(Samples.Count(), 0.00001, ctxt.Network);
write("Pre Training Error : " + error);
net.Save();
a.Network.Save();
ctxt.Network.Save(datatype);
error = 0;
Parallel.For(0, Samples.Count(), j =>
{
AlphaMem am = new AlphaMem(Samples[j].TextInput.ToCharArray());
var output = a.Forward(Samples[j].TextInput, ctxt, am);
var F = net.Forward(output, dropout, write);
error += CategoricalCrossEntropy.Forward(F.Last().GetRank(0), Samples[j].DesiredOutput).Max();
});
write("Post Training Error : " + error);
s.Save();
}
return(error);
}
public static double Propogate
(WriteToCMDLine write)
{
double error = 0;
NeuralNetwork net = GetNetwork(write);
var Samples = ReadVals(24);
Alpha a = new Alpha(write);
AlphaContext ctxt = new AlphaContext(datatype, write);
NetworkMem OLFMem = new NetworkMem(net);
NetworkMem AlphaMem = new NetworkMem(a.Network);
NetworkMem CtxtMem = new NetworkMem(ctxt.Network);
Parallel.For(0, Samples.Count(), j =>
{
AlphaMem am = new AlphaMem(Samples.Keys.ToList()[j].ToCharArray());
var output = a.Forward(Samples.Keys.ToList()[j], ctxt, am);
var F = net.Forward(output, dropout, write);
var desired = new double[Enum.GetNames(typeof(Command)).Length];
desired[Samples.Values.ToList()[j]] = 1;
error += CategoricalCrossEntropy.Forward(F.Last().GetRank(0), desired).Max();
var DValues = net.Backward(F, desired, OLFMem, write);
a.Backward(Samples.Keys.ToList()[j], DValues, ctxt, am, AlphaMem, CtxtMem);
});
OLFMem.Update(Samples.Count(), 0.0001, net);
AlphaMem.Update(Samples.Count(), 0.0001, a.Network);
CtxtMem.Update(Samples.Count(), 0.0001, ctxt.Network);
write("Pre Training Error : " + error);
net.Save();
a.Network.Save();
ctxt.Network.Save(datatype);
error = 0;
Parallel.For(0, Samples.Count(), j =>
{
AlphaMem am = new AlphaMem(Samples.Keys.ToList()[j].ToCharArray());
var output = a.Forward(Samples.Keys.ToList()[j], ctxt, am);
var F = net.Forward(output, dropout, write);
var desired = new double[Enum.GetNames(typeof(Command)).Length];
desired[Samples.Values.ToList()[j]] = 1;
error += CategoricalCrossEntropy.Forward(F.Last().GetRank(0), desired).Max();
});
write("Post Training Error : " + error);
return(error);
}
public static double Propogate
(WriteToCMDLine write, bool tf = false)
{
double error = 0;
NeuralNetwork net = GetNetwork(write);
var Samples = datatype.ReadSamples(24);
Alpha a = new Alpha(write);
AlphaContext ctxt = new AlphaContext(datatype, write);
NetworkMem OLFMem = new NetworkMem(net);
NetworkMem AlphaMem = new NetworkMem(a.Network);
NetworkMem CtxtMem = new NetworkMem(ctxt.Network);
Parallel.For(0, Samples.Count(), j =>
{
AlphaMem am = new AlphaMem(Samples[j].TextInput.ToCharArray());
var output = a.Forward(Samples[j].TextInput, ctxt, am);
var F = net.Forward(output, dropout, write);
error += CategoricalCrossEntropy.Forward(F.Last().GetRank(0), Samples[j].DesiredOutput).Max();
var DValues = net.Backward(F, Samples[j].DesiredOutput, OLFMem, write);
a.Backward(Samples[j].TextInput, DValues, ctxt, am, AlphaMem, CtxtMem);
});
OLFMem.Update(Samples.Count(), 0.0001, net);
AlphaMem.Update(Samples.Count(), 0.0001, a.Network);
CtxtMem.Update(Samples.Count(), 0.0001, ctxt.Network);
write("Pre Training Error : " + error);
net.Save();
a.Network.Save();
ctxt.Network.Save(Datatype.OccupantLoadFactor);
error = 0;
Parallel.For(0, Samples.Count(), j =>
{
AlphaMem am = new AlphaMem(Samples[j].TextInput.ToCharArray());
var output = a.Forward(Samples[j].TextInput, ctxt, am);
var F = net.Forward(output, dropout, write);
error += CategoricalCrossEntropy.Forward(F.Last().GetRank(0), Samples[j].DesiredOutput).Max();
});
write("Post Training Error : " + error);
return(error);
}
public static double Propogate
(Sample s, WriteToCMDLine write, bool tf = false)
{
double error = 0;
//var Pred = Predict(s.TextInput, new WriteToCMDLine(CMDLibrary.WriteNull));
//if (s.DesiredOutput.ToList().IndexOf(s.DesiredOutput.Max()) != Pred.ToList().IndexOf(Pred.Max()) || tf)
{
NeuralNetwork net = GetNetwork(write);
var Samples = s.ReadSamples(24);
Alpha2 a = datatype.LoadAlpha(write);
var am = a.CreateMemory();
//Alpha a = new Alpha(write);
//AlphaContext ctxt = new AlphaContext(datatype, write);
NetworkMem MFMem = new NetworkMem(net);
//NetworkMem AlphaMem = new NetworkMem(a.Network);
//NetworkMem CtxtMem = new NetworkMem(ctxt.Network);
try
{
Parallel.For(0, Samples.Count(), j =>
{
//AlphaMem am = new AlphaMem(Samples[j].TextInput.ToCharArray());
//var output = a.Forward(Samples[j].TextInput, ctxt, am);
var AMem = a.CreateAlphaMemory(Samples[j].TextInput);
var output = a.Forward(Samples[j].TextInput, AMem, write);
var F = net.Forward(output, dropout, write);
error += CategoricalCrossEntropy.Forward(F.Last().GetRank(0), Samples[j].DesiredOutput).Max();
var DValues = net.Backward(F, Samples[j].DesiredOutput, MFMem, write);
a.Backward(Samples[j].TextInput, DValues, AMem, am, write);
//a.Backward(Samples[j].TextInput, DValues, ctxt, am, AlphaMem, CtxtMem);
});
}
catch (Exception e) { e.OutputError(); }
MFMem.Update(Samples.Count(), 0.00001, net);
a.Update(am, Samples.Count());
//AlphaMem.Update(Samples.Count(), 0.00001, a.Network);
//CtxtMem.Update(Samples.Count(), 0.00001, ctxt.Network);
write("Pre Training Error : " + error);
net.Save();
a.Save();
//a.Network.Save();
//ctxt.Network.Save(Datatype.Masterformat);
error = 0;
Parallel.For(0, Samples.Count(), j =>
{
var AMem = a.CreateAlphaMemory(Samples[j].TextInput);
var output = a.Forward(Samples[j].TextInput, AMem, write);
var F = net.Forward(output, 0, write);
error += CategoricalCrossEntropy.Forward(F.Last().GetRank(0), Samples[j].DesiredOutput).Max();
//AlphaMem am = new AlphaMem(Samples[j].TextInput.ToCharArray());
//var output = a.Forward(Samples[j].TextInput, ctxt, am);
//var F = net.Forward(output, dropout, write);
//error += CategoricalCrossEntropy.Forward(F.Last().GetRank(0), Samples[j].DesiredOutput).Max();
});
write("Post Training Error : " + error);
//s.Save();
}
return(error);
}