Mini-batch support

[junhyukoh]

Mini-batch support

[junhyukoh]

I'm sorry that I don't have enough time to work on this project right now, but I will come back to this and finish TODO lists as soon as possible.
I'm not sure that I understand your idea correctly. Does it mean one forward/backward pass with N*M-sized data blob or M forward/backward passes with N-sized data blob?
In the former case, we have to consider the memory limit. For example, 100-sized 20 mini-batches is roughly equivalent to 2000 mini-batches in feedforward networks in terms of memory usage.
In the latter case, we have to compute the sum of gradients over several forward/backward passes. The main problem is that every layer does not preserve gradient(diff) after one backward pass, because one backward pass always involves one weight update in caffe.
I hope we will find a clever way to deal with this issue.
Thank you for sharing your idea!

[nakosung]

Thank you for your detail explanation. My question was about the former approach as you said. Some researches seem to use 4-size(quite small) minibatch for RNN update. If '4' is not so bad for mini batch size, memory requirement doesn't seem to be as a big problem. For the latter one M-fwd and 1-bwd seems to be sufficient to BP. Isn't it? 1) accumulate gradient for each weight during grouped forward passes 2) and back propagate to achieve large sized mini-batch with 'physical' small-batch.

[junhyukoh]

The former approach seems to be a simple and good option.
Thank you for your comment!

[junhyukoh]

This implementation supports mini-batch update now.