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.

Why not applying cumsum layer at the very end of your model?
By doing that, the model will predict the difference between t-1 and t on the layer n-1.
Something like:
tf.math.cumsum(y_features, axis=time_series_axis_index)`

