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Buzz1982

problem in training 2-input boolean functions using feedforward backprpogation

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Hello, I m trying to develop a Feed Forward Network with Backpropogation. Initially i developed it for training simple cases like all possible boolean functions of 2 inputs. For that i developed a feedforward network with 2-inputs, 1 hidden layer with 2 neurons and an output layer with single neuron. I used backpropogation learning algorithm. The problem i m having is that i dont know what value of the 'learning rate' to use. I tried many values form 0.1 to 1.0 but still my network is unable to correctly classify some of the very simple linearly separable cases. Out of 16 possible cases i was able to correctly classify about 9 input patterns in the training set( all 9 were linearly separable cases ). Is this normal to get such results on such simple training data. Or may be i m doing some mistake in my program. I havent used techniques like momentum and weight elimination yet but my question is that,is it possible to not get 100% correct results on training data as simple as this when not using momentum or weight elimination. Actually i want to test my network on boolean functions before training it from other more complex data set. Any tip will be appreciated. Thanks Tariq

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Yes im using sigmoid function at every neuron as my activation function,

1/(1+e^(-x))

I also tried using many values of training rates including values smaller than 0.1 but still its not working. Initially i assigned random weights and biases to all connections b/w 0 and 1. does initial values of weights and biases have any effect on the way traning proceeds?

Thanks

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never mind i found a mistake in my program. I was using wrong weights for calculating Delta. Its corrected and now my network is performing 100% correctly on boolean functions of 2-input.

Thanks for ur reply
Bye

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