  We report a neural-network based erbium-doped fiber amplifier (EDFA) gain
model built from experimental measurements. The model shows low gain-prediction
error for both the same device used for training (MSE $\leq$ 0.04 dB$^2$) and
different physical units of the same make (generalization MSE $\leq$ 0.06
dB$^2$).
