  We explore alternative experimental setups for the iterative sampling (flow)
from Restricted Boltzmann Machines (RBM) mapped on the temperature space of
square lattice Ising models by a neural network thermometer. This framework has
been introduced to explore connections between RBM-based deep neural networks
and the Renormalization Group (RG). It has been found that, under certain
conditions, the flow of an RBM trained with Ising spin configurations
approaches in the temperature space a value around the critical one: $ k_B T_c
/ J \approx 2.269$. In this paper we consider datasets with no information
about model topology to argue that a neural network thermometer is not an
accurate way to detect whether the RBM has learned scale invariance or not.
