Skip to main content

Feed-forward neural network solution for python

Project description

“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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

ffnet-0.7.1.zip (62.0 kB view hashes)

Uploaded source

ffnet-0.7.1.tar.gz (58.6 kB view hashes)

Uploaded source

Built Distributions

ffnet-0.7.1.win-amd64-py2.7.exe (457.8 kB view hashes)

Uploaded 2 7

ffnet-0.7.1.win32-py2.7.exe (385.7 kB view hashes)

Uploaded 2 7

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page