| Problem 1 |
| Repeat the problem in: Neural Lab > Mapping > Sine and Cosine using probabilistic neural networks (PNN). Create a New Project called SinCosProb. As the training set and validation set were already created, just copy the files: trainSetInput.csv, trainSetTarget.csv, validSetInput.csv and validSetTarget.csv to a new folder for this problem. |
| SinCosProb\Train.lab |
| Matrix trainSetInput; Matrix trainSetTarget; trainSetInput.Load(); trainSetTarget.Load(); ProbNet net; net.TrainConjGrad(trainSetInput, trainSetTarget, 1000, 0.00001); net.Save(); |
| SinCosProb\CheckTraining.lab |
| //_________________________________________ Load the training set Matrix trainSetInput; trainSetInput.Load(); Matrix trainSetTarget; trainSetTarget.Load(); //_________________________________________ Load the ANN ProbNet net; net.Load(); //_________________________________________ Run Matrix output = net.Run(trainSetInput, trainSetTarget, trainSetInput); double mse = ComputeMse(output, trainSetTarget); //_________________________________________ Relative Error Vector error = ComputeRelError(output, trainSetTarget); XyChart checkTraining; checkTraining.SetCaption("case", false, "Relative Error", false); checkTraining.AddGraphY(error, "Relative Error", 2, 1, 0, 255, 0); checkTraining.SetLogScale(false, true); checkTraining.SetLimits(0, trainSetTarget.GetRowCount(), 1.0e-5, 1.0); checkTraining.SetColorMode(2); checkTraining.SaveAndShow(); checkTraining.SavePDF(0.0); checkTraining.SaveEMF(); |
| SinCosProb\Validation.lab |
| //_________________________________________ Load the training set Matrix trainSetInput; trainSetInput.Load(); Matrix trainSetTarget; trainSetTarget.Load(); //_________________________________________ Load the validation set Matrix validSetInput; validSetInput.Load(); Matrix validSetTarget; validSetTarget.Load(); //_________________________________________ Load the ANN ProbNet net; net.Load(); //_________________________________________ Run Matrix output = net.Run(trainSetInput, trainSetTarget, validSetInput); double mse = ComputeMse(output, validSetTarget); //_________________________________________ Relative Error Vector error = ComputeRelError(output, validSetTarget); XyChart validation; validation.SetCaption("case", false, "Relative Error", false); validation.AddGraphY(error, "Relative Error", 2, 1, 0, 255, 0); validation.SetLogScale(false, true); validation.SetLimits(0, validSetTarget.GetRowCount(), 1.0e-5, 1.0); validation.SetColorMode(2); validation.SaveAndShow(); validation.SavePDF(0.0); validation.SaveEMF(); |
| Problem 2 |
| Compare the validation performance of the PNN with the validation performance of the ANN previously trained. (a) Plot the mse for case for the validation set for the ANN in the problem Neural Lab > Mapping > Sine and Cosine . (b) Plot the mse for case for the validation set for the PNN in problem 1. |
