  We use a convolutional neural network to retrieve the internuclear distance
in the two-dimensional H$_2^{+}$ molecule ionized by a strong few-cycle laser
pulse based on the photoelectron momentum distribution. We show that a neural
network trained on a relatively small dataset consisting of a few thousand of
images can predict the internuclear distance with an absolute error less than
0.1 a.u. We study the effect of focal averaging, and we find that the
convolutional neural network trained using the focal averaged electron momentum
distributions also shows a good performance in reconstructing the internuclear
distance.
