  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 where the CNN provides initial
priors on the latter's parameters. Across the test sets of images, the CNN
achieved errors 19 $\pm$ 22 per cent lower than the traditional method's blind
modelling. Using these CNN-predicted parameters as priors for this method to
use in an automated fashion instead achieved 27 $\pm$ 11 per cent lower errors
over the latter. This was reduced further to 37 $\pm$ 11 per cent when also
incorporating the CNN-predicted uncertainties into the priors, helping it to
avoid local minima in parameter space, 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 a factor 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.
