  The transverse field profile of light is being recognized as a resource for
classical and quantum communications for which reliable methods of sorting or
demultiplexing spatial optical modes are required. Here, we demonstrate,
experimentally, state-of-the-art mode demultiplexing of Laguerre-Gaussian beams
according to both their orbital angular momentum and radial topological numbers
using a flow of two concatenated deep neural networks. The first network serves
as a transfer function from experimentally-generated to ideal
numerically-generated data, while using a unique "Histogram Weighted Loss"
function that solves the problem of images with limited significant
information. The second network acts as a spatial-modes classifier. Our method
uses only the intensity profile of modes or their superposition, making the
phase information redundant.
