I think distributing the weights will give you guaranteed reproducibility. If your users intend on retraining the network, another idea would be to set performa

I think distributing the weights will give you guaranteed reproducibility. If your users intend on retraining the network, another idea would be to set performance expectations on a validation set within ballpark. So you could tell them, "we got 90% accuracy on this dataset, if you re-train and get below 80 you've probably made a mistake somewhere." The scary thing to me would be the very small test cases where maybe different trained neural nets end up having a lot of variance because there is barely more than noise to learn.