I don't know if I worded the title correctly.
I have a big dataset (300000 of images after augumentation) and I've splitted it into 10 parts, because I can't convert the images into a numpy and save it, the file would be too large.
Now, I have a neural network (Using keras with tf). My question is, is it better to train each file individually for X epochs (File 1 for 5 epochs, then File 2 for 5 epochs, etc.), or should I do an epoch for each, repeatedly (File 1 for an epoch, File 2 for an epoch, etc., and repeat for 5 times).
I've used the first and I get an accuracy of about 88%. Whould I get an improvement by doing the latter?