Yep, I've been guilty of that one lately. That and solving problems by simply overfitting a neural net to the data in the problem domain.
I mean, it works, but the result is less interesting than what I should have done. :)
I mean, it works, but the result is less interesting than what I should have done. :)
But if in general, you're talking about trying multiple configurations and parameters and submitting the best one, then no that's not dirty and it is standard practice. Realistically, I'm not sure how else you could do this because that's indistinguishable from having a new architecture anyways. People should put variance on values but this can also be computationally expensive and so I definitely understand why it doesn't happen.