# Demo

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Unlike a linear system, a neural network is not guaranteed to find the global minimum.  A neural network can actually arrive at different solutions for the same data given different values of the initial network weights. The initial network weights define the starting point on the error surface.  As the network transverses the error surface in the direction of the minimum error, it sometimes gets caught in a local minimum.  Thus, in order to develop a statistically sound neural network model, the network must be trained multiple times.  NeuroSolutions for Excel has a built-in training procedure (Train N Times) that makes this multiple training easy.


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This demo will develop a model for diagnosing breast cancer.  Ten features (radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry, and fractal dimension) have been computed from a digitized image of a fine needle aspirate of a breast mass.  The inputs to the neural network model consist of the mean, standard error, and "worst" (mean of the three largest values) for each of these 10 features resulting in 30 total inputs for each image.  This data is contained within the Excel worksheet named "Breast Cancer Data" located behind this slide.  The first 30 columns have been pre-tagged as "Input", the last 2 columns have been pre-tagged as "Desired", and the first 100 rows have been pre-tagged as "Training".  A two hidden layer MLP will be used as the neural network model.


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We will demonstrate the "Train N Times" procedure by running it on the breast cancer data described in the last slide.  Press the "Train N Times" button below to run this process now.  A dialog box will be displayed with the correct options pre-set such that the network will be trained 5 times (50 epochs for each training run).  Examine the settings then click OK.  At the completion of the multiple trainings, a report named "TrainMultiple Report" will automatically be generated summarizing the training results.


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The first graph on the generated report gives the average of the multiple training runs along with the standard deviation boundaries.  Notice the wide spread in the standard deviation.  This shows that each run can take a significantly different path to the solution.  The goal is to try and find a neural network model for which multiple trainings approach the same final MSE.  This is the case for this model. This can be seen by noting that the standard deviation decreases as the final epoch is approached or by viewing the last graph on the report which is a plot of the learning curves for each of the runs.  There are also two tables on the report which show the average of the minimum MSEs, the average of the final MSEs, and information about the best network over all of the runs.


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You have seen that the "Train N Times" training procedure provides a very easy way to assess the statistical validity of a model.  This is a vital tool for building neural network models.  Click "Next" to return to the main demo panel.


