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/ _______________________________________________ BangPypers mailing list BangPypers@python.org http://mail.python.org/mailman/listinfo/bangpypers
