Sure. First, check out this
https://stackoverflow.com/questions/2276933/good-open-source-neural-network-python-library
This is my general idea, I'm sketching out how I might approach this, none of this is tested
From
http://pybrain.org/docs/tutorial/netmodcon.html#feed-forward-networks
>>> from pybrain.structure import FeedForwardNetwork
>>> n = FeedForwardNetwork()
>>> n.activate((2, 2))
array([-0.1959887])
We build a neural net, train it (not shown) and get the output. You have a test set, right? You use the test set to generate the data for the ROC curve. For a single output neural net, you want to create a threshold for the output values to translate them to yes or no responses that get the best degree of specificity/sensitivity for your task
This is a good tutorial
http://webhome.cs.uvic.ca/~mgbarsky/DM_LABS/LAB_5/Lab5_ROC_weka.pdf
Then you just plot them. Or you can try to find a library that does it for you
I saw this
http://pypi.python.org/pypi/yard
The point is, that generating at ROC curve is not specific to neural nets, so you may not find a library that does it for you. I've provided the above to show it's fairly simple to roll your own
* More detail *
Your neural network is going to have an output that you will have to translate in to a classification (likely yes/no). To calculate the ROC curve, you're going to take a few thresholds for yes/no (in other words, .75> yes, <.75 no). From this threshold, you translate the output of your neural net into classifications. By comparing those classifications to the true classifications, you get a false positive and true positive rate. You are then plotting the false positive rate and true positive rate when you tweak that threshold.
