Neural Networks XOR Tutorial
So after a fierce battle with my own neurons,I am ready to release part II of my Processing series: “Neural Network!Huah!What is it good for?(Sing it again,now.)”
This example implements a multi-layered neural network that learns via “back propogation.” It’s specifically trained to solve XOR.In other words,there are two inputs and the desired result is input1 XOR input2.
0, 1 –> 1
1, 0 –> 1
0, 0 –> 0
1, 1 –> 0
The structure looks something like this:
However,I think there might be a flaw in my back propogation learning algorithm.For whatever reason,with the above neural structure,I can only successfully train my network (starting with random connection weights between -1 and 1) approximately 60% of the time. For the other 40%,the network gets stuck and can’t find the proper solution.If I add two more neurons to the hidden layer,like so. . .
. . . it trains flawlessly,finding a reasonable solution space aftera few thousand training iterations 100% of the time (or at least as far as I can reasonably test.) What am I missing?
Source: daniel shiffman
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Book (Institute of Industrial Engineers, Inc. (IIE))
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