Can someone please tell me how I am supposed to build a neural network using the batch method?
I have read that, in batch mode, for all samples in the training set, we calculate the error, delta and thus delta weights for each neuron in the network and then instead of immediately updating the weights, we accumulate them, and then before starting the next epoch, we update the weights.
I also read somewhere that, the batch method is like the online method but with the difference being one only needs to sum the errors for all samples in the training set and then take the average of it and then use that for updating the weights just like one does in the online method (the difference is just that average) like this:
for epoch=1 to numberOfEpochs for all i samples in training set calculate the errors in output layer SumOfErrors += (d[i] - y[i]) end errorAvg = SumOfErrors / number of Samples in training set now update the output layer with this error update all other previous layers go to the next epoch end
- Which one of these are truly the correct form of batch method?
- In case of the first one, doesn't accumulating all the delta weights result in a huge number?