[glial]

Yes, but that's not necessarily bad. You want a model that effectively captures the structure present in your dataset. There are currently only rules-of-thumb in model architecture, and it makes sense to explore the model space to determine which architecture and hyper parameters are suitable to the needs at hand. Two things save this from being a statistical sin: one, the final evaluation set is typically different than the validation set, and evaluation is only performed at the end of the 'fishing expedition', thus providing a reliable measure of the model's ability to generalize. Second, we're doing engineering here, not science, and our goal is to capture the structure of observations and not make a scientific claim about values of latent parameters.