Can I enforce monotonically increasing neural net outputs (min, mean, max)? Hi I'm using a DL model (TensorFlow) to predict daily minimum, mean, and maximum values of a target dataset. I was thinking that the model would have 3 outputs for each day, (min, mean, max).
Is there a clean way to enforce the correct order of these (i.e., min<mean<max)? I can add a penalty to encourage the model to train that way, but that seems like a bit of a work-around.

Unless I have misunderstood the question, here's a simple approach.
For outputs a b and c, you could enforce positive values for the second and third outputs via a suitable activation function, and then train to find values a b and c, such that a is the min, a + b is the mean, and a + b + c is the max value.
