  Restricted Boltzmann machine (RBM) provide a general framework for modeling
physical systems, but their behavior is dependent on hyperparameters such as
the learning rate, the number of hidden nodes and the form of the threshold
function. This article accordingly examines in detail the influence of these
parameters on Ising spin system calculations. A tradeoff is identified between
the accuracy of statistical quantities such as the specific heat and that of
the joint distribution of energy and magnetization. The optimal structure of
the RBM therefore depends intrinsically on the physical problem to which it is
applied.
