I have a multi-label classification task I am implementing. I have done a hyper-parameter tuning to determine the best configuration for my neural network. 

Is it valid to do this (determine the best hyper-parameters) and **then** do a cross validation to get a more accurate test estimation of the dataset? I don't see how this would be invalid as cross validation examples I have seen already have network architectures known a-priori, presumably because this is what they chose or feel is the best way of proceeding.

thank you