fnn = buildNetwork(trndata.indim, 5, trndata.outdim, outclass=SoftmaxLayer)
trainer = BackpropTrainer(fnn, dataset=trndata, momentum=0.1, verbose=True,
    weightdecay=0.01)
ticks = arange(-3.0, 6.0, 0.2)
X, Y = meshgrid(ticks, ticks)
griddata = ClassificationDataSet(2, 1, nb_classes=n_klass)
for i in range(X.size):
    griddata.addSample([X.ravel()[i], Y.ravel()[i]], [0])
griddata._convertToOneOfMany()
Library in python for neural networks to plot ROC, AUC, DET