  We developed an operational solar flare prediction model using deep neural
networks, named Deep Flare Net (DeFN). DeFN can issue probabilistic forecasts
of solar flares in two categories, such as >=M-class and <M-class events or
>=C-class and <C-class events, occurring in the next 24 h after observations
and the maximum class of flares occurring in the next 24 h. DeFN is set to run
every 6 h and has been operated since January 2019. The input database of solar
observation images taken by the Solar Dynamic Observatory (SDO) is downloaded
from the data archive operated by the Joint Science Operations Center (JSOC) of
Stanford University. Active regions are automatically detected from
magnetograms, and 79 features are extracted from each region nearly in real
time using multiwavelength observation data. Flare labels are attached to the
feature database, and then, the database is standardized and input into DeFN
for prediction. DeFN was pretrained using the datasets obtained from 2010 to
2015. The model was evaluated with the skill score of the true skill statistics
(TSS) and achieved predictions with TSS = 0.80 for >=M-class flares and TSS =
0.63 for >=C-class flares. For comparison, we evaluated the operationally
forecast results from January 2019 to June 2020. We found that operational DeFN
forecasts achieved TSS = 0.70 (0.84) for >=C-class flares with the probability
threshold of 50 (40)%, although there were very few M-class flares during this
period and we should continue monitoring the results for a longer time. Here,
we adopted a chronological split to divide the database into two for training
and testing. The chronological split appears suitable for evaluating
operational models. Furthermore, we proposed the use of time-series
cross-validation. The procedure achieved TSS = 0.70 for >=M-class flares and
0.59 for >=C-class flares using the datasets obtained from 2010 to 2017.
