  In the current era of stellar spectroscopic surveys, synthetic spectral
libraries are the basis for the derivation of stellar parameters and chemical
abundances. In this paper, we compare the stellar parameters determined using
five popular synthetic spectral grids (INTRIGOSS, FERRE, AMBRE, PHOENIX, and
MPIA/1DNLTE) with our convolutional neural network (CNN, $\texttt{StarNet}$).
The stellar parameters are determined for six physical properties (effective
temperature, surface gravity, metallicity, [$\alpha$/Fe], radial velocity, and
rotational velocity) given the spectral resolution, signal-to-noise, and
wavelength range of optical FLAMES-UVES spectra from the Gaia-ESO Survey. Both
CNN modelling and epistemic uncertainties are incorporated through training an
ensemble of networks. $\texttt{StarNet}$ training was also adapted to mitigate
differences between the synthetic grids and observed spectra by augmenting with
realistic observational signatures (i.e. resolution matching, wavelength
sampling, Gaussian noise, zeroing flux values, rotational and radial
velocities, continuum removal, and masking telluric regions). Using the
FLAMES-UVES spectra for FGK type dwarfs and giants as a test set, we quantify
the accuracy and precision of the stellar label predictions from
$\texttt{StarNet}$. We find excellent results over a wide range of parameters
when $\texttt{StarNet}$ is trained on the MPIA/1DNLTE synthetic grid, and
acceptable results over smaller parameter ranges when trained on the 1DLTE
grids. These tests also show that our CNN pipeline is highly adaptable to
multiple simulation grids.
