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Automating the learning rates for a feedforward backpropagation network?

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Hi, I was thinking of making the learning rate for my feedforward backpropagation network automated. So all you have to do is enter the inputs/desired outputs. What automatic methods of determining the learningRate do you suggest? One idea I read on the internet suggests checking if the new error exceeds the old error by more than a predefined ratio, so if it does the new weights and biases are discarded and the learning rate is decreased by multiplying it against a lrDecrease variable. Otherwise you just increase the learningRate by multiplying against a lrIncrease variable. So how would I go and have the predefined ratios, lrDecrease variables, lrIncrease variables automatically set? Would these variables some how be determined by the data I want?, eg as i am using the sigmoid function and get outputs between 1 and -1, say i wanted accuracy up to 3 decimal places, would I set the lrIncrease to something like 0.0001? Thanks [Edited by - johnnyBravo on August 21, 2005 8:18:45 PM]

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