I have a reasonably large set of images that I want to classify using a neural network. I can't fil them all into memory at once, so I decided to process them in batches of 200. I'm using an cross-entropy cost function with a minimization algorithm from numpy.
My question is: is it correct to pass learned weights between batches and use them as a starting point of the minimization? Would this eventually cause my hypothesis to fit all the data, or will each iteration simply re-fit the weights for itself? What is the general approach to such a problem?