  For any financial institution it is a necessity to be able to apprehend the
behavior of interest rates. Despite the use of Deep Learning that is growing
very fastly, due to many reasons (expertise, ease of use, ...) classic rates
models such as CIR, or the Gaussian family are still being used widely. We
propose to calibrate the five parameters of the G2++ model using Neural
Networks. To achieve that, we construct synthetic data sets of parameters drawn
uniformly from a reference set of parameters calibrated from the market. From
those parameters, we compute Zero-Coupon and Forward rates and their
covariances and correlations. Our first model is a Fully Connected Neural
network and uses only covariances and correlations. We show that covariances
are more suited to the problem than correlations. The second model is a
Convulutional Neural Network using only Zero-Coupon rates with no
transformation. The methods we propose perform very quickly (less than 0.3
seconds for 2 000 calibrations) and have low errors and good fitting.
