Hi,

I am having difficulty in training a neural network using the package "neuralnet". My neural network has 2 input neurons (covariates), 1 hidden layer with 2 hidden neurons  and 2 output neurons (responses).  I am training my neural network with a dataset that has been transformed so that each column is of type "numeric". The difficulty I am facing is that the responses of my trained neural network are always around the mean of the corresponding training set response values. For example, if both the output neurons in the training set assume values between 0 and 1 (say from a normal distribution with mean 0.5 and standard deviation 0.15), then the responses of my trained neural network for both the neurons are around 0.5 (+ or - 0.02).

I don't specify the initial values of the weights (startweight).  I am letting "neuralnet" to pick arbitrary values for the startweight. Is it possible to design a neural network in R using "neuralnet" so that the response is not constrained to the mean of the response neurons? Do I have to use a different neural network library such as "nnet"?  Please advise.

Thanks,
Sai.


Srisairam Achuthan, Ph.D.,
Senior Scientific Programmer,
Translational Research Informatics,
City of Hope, Los Angeles.



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