  We perform a Bayesian analysis for small field models of inflation, using the
most recent datasets produced by Planck`18, ACTPol, and BICEP3. We employ
Artificial Neural Networks (ANN) to perform analyses with model coefficients,
instead of their proxy slow-roll parameters. The ANN connects the models with
their projected scalar index $n_s$ and index running $\alpha$, in lieu of the
less accurate Lyth-Riotto expressions. We recover the most likely coefficients
for a sixth degree polynomial inflationary potential, which yields a
tensor-to-scalar ratio $r\lesssim 0.03$. We do so for the case of joint Planck
and ACTPol datasets, and for each dataset alone. The BICEP3 data is included in
all three analyses. We show that these models are likely, with coefficients
that are tuned to about $\Delta\gtrsim 1/60$. Curiously, we also find a
significant tension between ACTPol and Planck datasets, which we try to account
for.
