ffnet 0.7.1

Feed-forward neural network solution for python

“ffnet” is a fast and easy-to-use feed-forward neural network training solution for python. Many nice features are implemented: arbitrary network connectivity, automatic data normalization, very efficient training tools, support for multicore systems, network export to fortran code…

Basic usage of the package is outlined below:

>>> from ffnet import ffnet, mlgraph, savenet, loadnet, exportnet
>>> conec = mlgraph( (2,2,1) )
>>> net = ffnet(conec)
>>> input = [ [0.,0.], [0.,1.], [1.,0.], [1.,1.] ]
>>> target  = [ [1.], [0.], [0.], [1.] ]
>>> net.train_tnc(input, target, maxfun = 1000)
>>> net.test(input, target, iprint = 2)
>>> savenet(net, "xor.net")
>>> exportnet(net, "xor.f")
>>> net = loadnet("xor.net")
>>> answer = net( [ 0., 0. ] )
>>> partial_derivatives = net.derivative( [ 0., 0. ] )

Release Notes

This release contains mainly documentation improvements and changes in examples. Look also at the new sphinx-based website: http://ffnet.sourceforge.net.