Multibit neural XOR network

I'm trying to train an 8-bit neural network to output XOR of its inputs. I'm using ffnet library (http://ffnet.sourceforge.net/). For low number of input bits (up to 4) backpropagation produces expected results. For 8 bits, NN seems to 'converge', meaning that it outputs the same value for any input. I'm using a multilayer NN: inputs, hidden layer, output, plus bias node.

Am I doing something wrong? Does this NN need to be of certain shape, to be able to learn to XOR?

Edit:

This is the code I'm using:

def experiment(bits, input, solution, iters):
    conec开发者_如何学运维 = mlgraph( (bits, bits, 1) )
    net = ffnet(conec)
    net.randomweights()
    net.train_momentum(input, solution, eta=0.5, momentum=0.0, maxiter=iters)
    net.test(input, solution, iprint=2)

I'm using momentum=0.0 to get pure back-propagation.

This is a part of the results I get:

Testing results for 256 testing cases:
OUTPUT 1 (node nr 17):
Targets vs. outputs:
   1      1.000000      0.041238
   2      1.000000      0.041125
   3      1.000000      0.041124
   4      1.000000      0.041129
   5      1.000000      0.041076
   6      1.000000      0.041198
   7      0.000000      0.041121
   8      1.000000      0.041198

It goes on like this for every vector (256 values)