We demonstrate a convolutional neural network trained to reproduce the
Kohn-Sham kinetic energy of hydrocarbons from electron density. The output of
the network is used as a non-local correction to the conventional local and
semi-local kinetic functionals. We show that this approximation qualitatively
reproduces Kohn-Sham potential energy surfaces when used with conventional
exchange correlation functionals. Numerical noise inherited from the
non-linearity of the neural network is identified as the major challenge for
the model. Finally we examine the features in the density learned by the neural
network to anticipate the prospects of generalizing these models