  We introduce deep learning technique to perform complete mode decomposition
for few-mode optical fiber for the first time. Our goal is to learn a fast and
accurate mapping from near-field beam profiles to the complete mode
coefficients, including both modal amplitudes and phases. We train the
convolutional neural network with simulated beam patterns, and evaluate the
network on both of the simulated beam data and the real beam data. In simulated
beam data testing, the correlation between the reconstructed and the ideal beam
profiles can achieve 0.9993 and 0.995 for 3-mode case and 5-mode case
respectively. While in the real 3-mode beam data testing, the average
correlation is 0.9912 and the mode decomposition can be potentially performed
at 33 Hz frequency on Graphic Processing Unit, indicating real-time processing
ability. The quantitative evaluations demonstrate the superiority of our deep
learning based approach.
