This is first release of ffnet.
ffnet is fast and easy to use feed-forward neural network
training solution for python. Using it you are able to
train/test/save/load and use artificial neural network
with sigmoid activation functions.
Unique features present in ffnet:
1. Any network connectivity without cycles is allowed
(not only layered).
2. Training can be performed with use of several optimization
schemes including genetic alorithm based optimization.
3. There is access to exact partial derivatives of network outputs
vs. its inputs.
4. Normalization of data is handled automatically by ffnet.
The basic usage is outlined below:
from ffnet import ffnet, mlgraph, savenet, loadnet
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)
net = loadnet("xor.net")
answer = net( [ 0., 0. ] )
partial_derivatives = net.derivative( [ 0., 0. ] )
Examples with full description can be found in examples
directory of the source distribution downloadable from:
Visit also home page of the project: