  Gravitationally lensed sources may have unresolved or blended multiple
images, and for time varying sources the lightcurves from individual images can
overlap. We use convolutional neural nets to both classify the lightcurves as
due to unlensed, double, or quad lensed sources and fit for the time delays.
Focusing on lensed supernova systems with time delays $\Delta t\gtrsim6$ days,
we achieve 100\% precision and recall in identifying the number of images and
then estimating the time delays to $\sigma_{\Delta t}\approx1$ day, with a
$1000\times$ speedup relative to our previous Monte Carlo technique. This also
succeeds for flux noise levels $\sim10\%$. For $\Delta t\in[2,6]$ days we
obtain 94--98\% accuracy, depending on image configuration. We also explore
using partial lightcurves where observations only start near maximum light,
without the rise time data, and quantify the success.
