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); }