  Recurrent neural networks are a powerful tool, but they are very sensitive to
their hyper-parameter configuration. Moreover, training properly a recurrent
neural network is a tough task, therefore selecting an appropriate
configuration is critical. Varied strategies have been proposed to tackle this
issue. However, most of them are still impractical because of the
time/resources needed. In this study, we propose a low computational cost model
to evaluate the expected performance of a given architecture based on the
distribution of the error of random samples of the weights. We empirically
validate our proposal using three use cases. The results suggest that this is a
promising alternative to reduce the cost of exploration for hyper-parameter
optimization.
