Is a Neural network with 2 input nodes, 2 hidden nodes and an output supposed to be able to solve the XOR problem provided there is no bias? Or can it get stuck?
+1
A:
If I remember correctly it's not possible to have XOR without a bias.
Georg
2009-12-12 12:53:44
I have a (I hope working) neural network class that seems to be able to solve it with 2 inputs 3 hidden neurons and 1 output, but can't with 2 hidden neurons. Is it enough when calculating net = w1x1 + w2x2 to just add 1 to the equation, net = w1x1 + w2x2 + 1, or that is still not enough? do i have to have the 3rd weight? Thanks
devoured elysium
2009-12-12 12:59:39
I think that in your example with 3 hidden neurons one of the neurons acts like a bias, that's why it works. I can't think of an example that would work with 2 hidden nodes. What's the reason not to use a bias?
Georg
2009-12-12 18:25:19
+1
A:
Leave the bias in. It doesn't see the values of your inputs.
In terms of a one-to-one analogy, I like to think of the bias as the offsetting c-value in the straight line equation: y = mx + c; it adds an independent degree of freedom to your system that is not influenced by the inputs to your network.
Zaid
2009-12-14 06:31:25
A:
Hello,
I have built a neural network without bias and a 2x2x1 architecture solves XOR in 280 epochs. Am new to this, so didn't know either way, but it works, so it is possible.
Regards,
Katya
2010-09-22 23:34:21