  Liquid argon time projection chamber detector technology provides high
spatial and calorimetric resolutions on the charged particles traversing liquid
argon. As a result, the technology has been used in a number of recent neutrino
experiments, and is the technology of choice for the Deep Underground Neutrino
Experiment (DUNE). In order to perform high precision measurements of neutrinos
in the detector, final state particles need to be effectively identified, and
their energy accurately reconstructed. This article proposes an algorithm based
on a convolutional neural network to perform the classification of energy
deposits and reconstructed particles as track-like or arising from
electromagnetic cascades. Results from testing the algorithm on data from
ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network
identifies track- and shower-like particles, as well as Michel electrons, with
high efficiency. The performance of the algorithm is consistent between data
and simulation.
