Restricting the output of a model didn't improve the loss value of the model evaluation
I suggest you normalise your labels so that it is scaled between 0 and 1, rather than 0 and 80. Once you have a trained model then multiply your output at the end. The network should find it easier to learn values between 0 and 1 (Andrew Ng's coursera course has a good lecture on this). 

Go back to using the standard ReLU: The problem with yours is that it cannot learn if it tries to output a value greater than 80, as there is no gradient with which to update the parameters.

I would also consider putting in a much smaller Dense layer at the end (eg 10 nodes).