[BangPypers] Fw: ffnet-0.7 released
Senthil Kumaran
senthil at uthcode.com
Wed Aug 10 14:36:33 CEST 2011
I am not sure if, if still Neural networks hype is present in
colleges. But it used be high!
http://sourceforge.net/projects/ffnet/
Seems to be nice library to deal with feed forward neural network.
On Tue, Aug 09, 2011 at 02:56:31AM +0200, Marek Wojciechowski wrote:
> ffnet version 0.7 has been released and is available for download at:
>
> http://sourceforge.net/projects/ffnet/
>
> This release contains couple of important changes:
> - neural network can be trained now using the power of multi-processor
> systems (see example mptrain.py)
> - attributes necessary for calculation of network derivatives
> are now generated only on demand;
> - data normalization limits are not changed when re-trainig with new
> data set; net.renormalize=True have to be set first;
> - compatibility with newest versions of numpy, scipy and networkx
> is enhanced;
> - support for *export to java* and *drawing network with drawffnet*
> is dropped.
> Basic API is left almost untouched. Exactly the same trainig scriptsas
> for older versions should work without problems.
>
>
> What is ffnet?
> --------------
> ffnet is a fast and easy-to-use feed-forward neuralnetwork training
> solution for python.
>
> Unique features present in ffnet
> --------------------------------
> 1. Any network connectivity without cycles is allowed.
> 2. Training can be performed with use of several optimization
> schemes including: standard backpropagation with momentum, rprop,
> conjugate gradient, bfgs, tnc (with multiprocessing)
> and genetic alorithm based optimization.
> 3. There is access to exact partial derivatives of network outputs
> vs. its inputs.
> 4. Automatic normalization of data.
>
> Basic assumptions and limitations
> ---------------------------------
> 1. Network has feed-forward architecture.
> 2. Input units have identity activation function, all other units
> have sigmoid activation function.
> 3. Provided data are automatically normalized, both input and
> output, with a linear mapping to the range (0.15, 0.85).
> Each input and output is treated separately (i.e. linear map is
> unique for each input and output).
> 4. Function minimized during training is a sum of squared errors of
> each output for each training pattern.
>
> Performance
> -----------
> Excellent computational performance is achieved implementing core
> functions in fortran 77 and wrapping them with f2py. ffnet outstands
> in performance pure python training packages and is competitive to
> 'compiled language' software. Incorporation of multiprocessing capabilities
> (tnc algorithm so far) makes ffnet ideal for large scale (really!)
> problems. Moreover, a trained network can be exported to fortran
> sources,compiled and called from many programming languages.
>
> Usage
> -----
> Basic usage of the package is outlined below.See description of
> ffnet module and its functions(and especially ffnet class) for
> detailed explanations.
>
> >>>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. ] )
>
> Usage examples with full description can be found inexamples
> directory of the source distribution.
>
> --
> Marek Wojciechowski
> --
> http://mail.python.org/mailman/listinfo/python-announce-list
>
> Support the Python Software Foundation:
> http://www.python.org/psf/donations/
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