Caffe MemoryData Layer And Solver Inteface

I am trying to train a network but rather using lmdb or leveldb, I am feeding data to my network on the fly. So I am following procedure as outlined below My data is loaded in Mem

Solution 1:

1. What is the difference between solver.solve and solver.step?

solve does the entire training run, to whatever limits you've set -- usually the iteration limit. step does only the specified number of iterations.

2. How do I get a memory data layer for test network?

If you're not reading from a supported data channel / format, I think you have to write a customer input routine (your data package).

3. Loss stays constant over multiple iterations, what could be the reason?

There are several possibilities, depending on the surrounding effects. If the loss shows only one value ever, then your back propagation is a likely culprit. Perhaps you're not properly connected to the data set, and you're not getting the expected classifications fed in.

If the loss has a temporary stability but then converges decently, don't worry about it; this is likely an effect of training ordering.

If the loss declines decently and then settles at a fixed value, then you're also doing well: the training converged before it ran out of iterations.