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.

As mentioned in above discussion with @Galen:
Conjecture This might be achieved with custom recurrent layer. We could provide a box plot values as an output, i.e., Five number summary as a monotonic output. Though, internals of recurrent layer is a design choice, see Define custom LSTM Cell in Keras?. Here, output of LSTM cell will be our monotonic summary function. This approach guarantees the inequality.
