Implementing Batch normalisation in Neural network
Give the whole batch as an input, take the activated output of the first layer and pipe it the next until the final. . 

calculate the gradient decent direction (derivative of the cost function) for each row. U will have the error for each row of ur training set. 

Then add the errors and divide the result with number of the training set. Then finally update it weights. 
I know from experience that tracking all these numbers is hard. 

I don't know what language u use, but if it's Python, numpy will help u a a lot. For example, to add the errors, errors.sum(axis=0). X.dot(weight) to multiply ur entire dataset with ur weight.  So my suggestion is use some matrix library. 

One thing I want to mention is batch processing is susceptible to local minima which u don't want...especially in complex functions. SDG is a nice algorithm. I'm not sure why u needed batch update. There are many interesting tweaks in NN, don't waste ur time on batch update, use SDG