Can someone please tell me how I am supposed to build batch method ina neural network using the batch method?
I
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 athe difference, that being one only needs to sum the errors for all samples in thethe training set and then take the average of it and then use that for updating the weights just like heone does in the online method, ( thethe difference is just that average) sth 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?
- 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?