  In this paper, we train a Convolutional Neural Network to classify
longitudinally and transversely polarized hadronic $W^\pm$ using the images of
boosted $W^{\pm}$ jets as input. The images capture angular and energy
information from the jet constituents that is faithful to properties of the
original quark/anti-quark $W^{\pm}$ decay products without the need for
invasive substructure cuts. We find that the difference between the
polarizations is too subtle for the network to be used as an event-by-event
tagger. However, given an ensemble of $W^{\pm}$ events with unknown
polarization, the average network output from that ensemble can be used to
extract the longitudinal fraction $f_L$. We test the network on Standard Model
$pp \to W^{\pm}Z$ events and on $pp \to W^{\pm}Z$ in the presence of
dimension-6 operators that perturb the polarization composition.
