A couple of questions about TMultiLayerPerceptron

I have a couple of questions about the use of TMultiLayerPerceptron within root.

Firstly, I do not understand the output it gives in the form of a plot labled differences (Impact of variables on ANN), especially as this produces a plot with no labels on the axes. Can anybody tell me how to interperate this?

Secondly, I have been trying to use the .cxx file which is produced by training the network, but I have found that I get very strange output values. I give the function the same inputs as I used to train the network to begin with, but the output values are very very large numbers, ~1000, rather than being around 0 to 1 as I would expect. Does anybody know how I might have misunderstood how to use the output code?

Thanks for you help

As stated in the documentation, it draws the distribution (on the test sample) of the impact on the network output of a small variation of each input. Units are arbitrary.
So it’s a tool to improve the network by avoiding both large dependencies (implying systematics) and small dependencies (i.e. useless input variables). A good network should have all distributions in the same range. I must admit that this is difficult to interpret.

Could you send a code showing the way you use the C++ exported class ? This should not happen since the upper limit on the network output is the final weight.