  We explore the effectiveness of deep learning convolutional neural networks
(CNNs) for estimating strong gravitational lens mass model parameters. We have
investigated a number of practicalities faced when modelling real image data,
such as how network performance depends on the inclusion of lens galaxy light,
the addition of colour information and varying signal-to-noise. Our CNN was
trained and tested with strong galaxy-galaxy lens images simulated to match the
imaging characteristics of the Large Synoptic Survey Telescope (LSST) and
Euclid. For images including lens galaxy light, the CNN can recover the lens
model parameters with an acceptable accuracy, although a 34 per cent average
improvement in accuracy is obtained when lens light is removed. However, the
inclusion of colour information can largely compensate for the drop in accuracy
resulting from the presence of lens light. While our findings show similar
accuracies for single epoch Euclid VIS and LSST r-band datasets, we find a 24
per cent increase in accuracy by adding g- and i-band images to the LSST r-band
without lens light and a 20 per cent increase with lens light. The best network
performance is obtained when it is trained and tested on images where lens
light exactly follows the mass, but when orientation and ellipticity of the
light is allowed to differ from those of the mass, the network performs most
consistently when trained with a moderate amount of scatter in the difference
between the mass and light profiles.
