public static double[] Predict(string s, WriteToCMDLine write) { NeuralNetwork net = GetNetwork(write); Alpha a = new Alpha(write); AlphaContext ctxt = new AlphaContext(datatype, write); double[] Results = a.Forward(s, ctxt); Results.WriteArray("Alpha Results : ", write); for (int i = 0; i < net.Layers.Count(); i++) { Results = net.Layers[i].Output(Results); } return(Results); }
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 void Propogate (Sample s, WriteToCMDLine write) { var check = Predict(s); if (s.DesiredOutput.ToList().IndexOf(s.DesiredOutput.Max()) != check.ToList().IndexOf(check.Max())) { Alpha a = new Alpha(write); AlphaContext ctxt1 = new AlphaContext(datatype, write); AlphaContext ctxt2 = new AlphaContext(datatype, write, 1); var Samples = s.ReadSamples(); List <string> lines = new List <string>(); for (int i = 0; i < 5; i++) { NetworkMem ObjMem = new NetworkMem(Network); NetworkMem AlphaMem = new NetworkMem(a.Network); NetworkMem CtxtMem1 = new NetworkMem(ctxt1.Network); NetworkMem CtxtMem2 = new NetworkMem(ctxt2.Network); Parallel.For(0, Samples.Count(), j => { AlphaMem am = new AlphaMem(Samples[j].TextInput.ToCharArray()); Samples[j].TextOutput = a.Forward(Samples[j].TextInput, ctxt1, am); AlphaMem am2 = new AlphaMem(Samples[j].SecondaryText.ToCharArray()); Samples[j].SecondaryTextOutput = a.Forward(Samples[j].SecondaryText, ctxt2, am2); var F = Forward(Samples[j]); lines.AddRange(Samples[j].OutputError(CategoricalCrossEntropy.Forward(F.Last(), Samples[j].DesiredOutput))); var DValues = Backward(Samples[j], F, ObjMem); var DV1 = DValues.ToList().Take(Alpha.DictSize).ToArray(); var DV2 = Enumerable.Reverse(DValues).Take(Alpha.DictSize).Reverse().ToArray(); a.Backward(Samples[j].TextInput, DV1, ctxt1, am, AlphaMem, CtxtMem1); a.Backward(Samples[j].SecondaryText, DV2, ctxt2, am2, AlphaMem, CtxtMem2); }); ObjMem.Update(1, 0.0001, Network); AlphaMem.Update(1, 0.00001, a.Network); CtxtMem1.Update(1, 0.0001, ctxt1.Network); CtxtMem2.Update(1, 0.0001, ctxt2.Network); } lines.ShowErrorOutput(); Network.Save(); a.Network.Save(); ctxt1.Save(); ctxt2.Save(); s.Save(); } }
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 double[] Predict(Sample s) { Alpha a = new Alpha(CMDLibrary.WriteNull); AlphaContext ctxt = new AlphaContext(datatype, CMDLibrary.WriteNull); var input = a.Forward(s.TextInput, ctxt).ToList(); input.AddRange(a.Forward(s.SecondaryText, ctxt)); input.AddRange(s.ValInput); var Results = input.ToArray(); for (int i = 0; i < Network.Layers.Count(); i++) { Results = Network.Layers[i].Output(Results); } return(Results); }
public double[] Forward(string s, AlphaContext context, AlphaMem am) { double[,] loc = new double[s.Length, DictSize]; Parallel.For(0, s.Length, j => { double[] a = s.Locate(j, SearchRange); am.LocationOutputs[j].Add(a); for (int i = 0; i < Network.Layers.Count(); i++) { a = Network.Layers[i].Output(a); am.LocationOutputs[j].Add(a); } loc.SetRank(a, j); am.GlobalContextOutputs[j] = context.Contextualize(s, j, am); }); return(loc.Multiply(Activations.SoftMax(am.GlobalContextOutputs))); }
public void Backward(string s, double[] DValues, AlphaContext context, AlphaMem am, NetworkMem mem, NetworkMem CtxtMem) { var LocDValues = am.DLocation(DValues); DValues = am.DGlobalContext(DValues); DValues = Activations.InverseSoftMax(DValues, am.GlobalContextOutputs); context.Backward(DValues, s.Length, am, CtxtMem); Parallel.For(0, s.Length, j => { var ldv = LocDValues[j]; for (int i = Network.Layers.Count() - 1; i >= 0; i--) { ldv = mem.Layers[i].DActivation(ldv, am.LocationOutputs[j][i + 1]); mem.Layers[i].DBiases(ldv, Network.Layers[i], s.Length); mem.Layers[i].DWeights(ldv, am.LocationOutputs[j][i], Network.Layers[i], s.Length); ldv = mem.Layers[i].DInputs(ldv, Network.Layers[i]); } }); }
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); }