Don't reset the objective estimate on the last iteration #417
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I noticed while working with RNNs in mlpack that the estimated objective would be 0 if I was training sequences of different length one iteration at a time... digging into it, I discovered that any time you optimize a separable function with SGD-like optimizers and the number of iterations you use is an exact multiple of the number of functions (e.g. number of sequences given to the RNN), then the returned objective estimate is 0 unless you specify
exactObjective
.This is because the objective estimate gets reset after every epoch, but if it is the last epoch... it still resets it anyway, it doesn't return the estimate.
So I fixed that for all optimizers I could find that had the condition, and then I updated the documentation to be a little clearer about what does get returned for separable functions.
A test program:
Compile with:
or similar.
Output before this PR:
Output after this PR: