  Recently a number of papers have suggested using neural-networks in order to
approximate policy functions in DSGE models, while avoiding the curse of
dimensionality, which for example arises when solving many HANK models, and
while preserving non-linearity. One important step of this method is to
represent the constraints of the economic model in question in the outputs of
the neural-network. I propose, and demonstrate the advantages of, a novel
approach to handling these constraints which involves directly constraining the
neural-network outputs, such that the economic constraints are satisfied by
construction. This is achieved by a combination of re-scaling operations that
are differentiable and therefore compatible with the standard gradient descent
approach used when fitting neural-networks. This has a number of attractive
properties, and is shown to out-perform the penalty-based approach suggested by
the existing literature, which while theoretically sound, can be poorly behaved
practice for a number of reasons that I identify.
