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Wrong derivative at Linear- and ThresholdActivationStrategy #1
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@Fokko You're right; the derivative for |
Well, at first, you should never use non-differential functions when using the back-propagation of error algorithm. We could state that at the threshold value the derivative is I found this repository by Stackoverflow, as it is given as an example, but it should of course be correct :) |
Right, you don't want to use non-differentiable functions. I think I'll take the |
Actually, it wasn't that. I was using it to build AND and OR neural networks, but I wasn't actually training them. I think this was when I was just trying to figure out how they worked. |
I've added a comment on the file that says that it shouldn't be used with the backpropagation algorithm. The threshold stuff works for perceptron networks, which is what the AND and OR networks in the code are, which is why I had it in there (i.e., when I was learning about NN). |
Good, fine by me. The error wil asymptotically decrease to zero, as long as your network is powerful enough of course. The final rounding of the output value should be done outside the network. For the rest, nice work! |
Thank you! |
The threshold at the LinearActivationStrategy and ThresholdActivationStrategy should be 1 instead of 0. Otherwise the back-propagation of error algorithm will always cancel out the error.
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