  The vast quantity of strong galaxy-galaxy gravitational lenses expected by
future large-scale surveys necessitates the development of automated methods to
efficiently model their mass profiles. For this purpose, we train an
approximate Bayesian convolutional neural network (CNN) to predict mass profile
parameters and associated uncertainties, and compare its accuracy to that of
conventional parametric modelling for a range of increasingly complex lensing
systems. These include standard smooth parametric density profiles,
hydrodynamical EAGLE galaxies and the inclusion of foreground mass structures,
combined with parametric sources and sources extracted from the Hubble Ultra
Deep Field. In addition, we also present a method for combining the CNN with
traditional parametric density profile fitting in an automated fashion, where
the CNN provides initial priors on the latter's parameters. On average, the CNN
achieved errors 19 $\pm$ 22 per cent lower than the traditional method's blind
modelling. The combination method instead achieved 27 $\pm$ 11 per cent lower
errors over the blind modelling, reduced further to 37 $\pm$ 11 per cent when
the priors also incorporated the CNN-predicted uncertainties, with errors also
17 $\pm$ 21 per cent lower than the CNN by itself. While the CNN is undoubtedly
the fastest modelling method, the combination of the two increases the speed of
conventional fitting alone by factors of 1.73 and 1.19 with and without
CNN-predicted uncertainties, respectively. This, combined with greatly improved
accuracy, highlights the benefits one can obtain through combining neural
networks with conventional techniques in order to achieve an efficient
automated modelling approach.
