  Substantial effort has been devoted to the characterization of transient
phenomena from photometric information. Automated approaches to this problem
have taken advantage of complete phase-coverage of an event, limiting their use
for triggering rapid follow-up of ongoing phenomena. In this work, we introduce
a neural network with a single recurrent layer designed explicitly for early
photometric classification of supernovae. Our algorithm leverages transfer
learning to account for model misspecification, host galaxy photometry to solve
the data scarcity problem soon after discovery, and a custom weighted loss to
prioritize accurate early classification. We first train our algorithm using
state-of-the-art transient and host galaxy simulations, then adapt its weights
and validate it on the spectroscopically-confirmed SNe Ia, SNe II, and SNe Ib/c
from the Zwicky Transient Facility Bright Transient Survey. On observed data,
our method achieves an overall accuracy of $82 \pm 2$% within 3 days of an
event's discovery, and an accuracy of $87 \pm 5$% within 30 days of discovery.
At both early and late phases, our method achieves comparable or superior
results to the leading classification algorithms with a simpler network
architecture. These results help pave the way for rapid photometric and
spectroscopic follow-up of scientifically-valuable transients discovered in
massive synoptic surveys.
