Caffe: can I avoid loading new data while training?

Question

I want to overfit my model on a mini-batch of data, to see if my model is correct. My dataset is in lmdb format. The data layer automatically update when I perform solver.step(). How can I avoid solver from loading new data in Caffe?

Answer 1

I use this with the Pycaffe interface:

if overfit: lmdb_cursor.first()

I have a flag (overfit) that when set calls this method which resets the cursor back to the beginning of the database for each batch. Hope this helps.