  We introduce an efficient neural network (NN) architecture for classifying
wave functions in terms of their localization. Our approach integrates a
versatile quantum phase space parametrization leading to a custom 'quantum' NN,
with the pattern recognition capabilities of a modified convolutional model.
This design accepts wave functions of any dimension as inputs and makes
accurate predictions at an affordable computational cost. This scalability
becomes crucial to explore the localization rate at the semiclassical limit, a
long standing question in the quantum scattering field. Moreover, the physical
meaning built in the model allows for the interpretation of the learning
process
