  Effective hyper-parameter tuning is essential to guarantee the performance
that neural networks have come to be known for. In this work, a principled
approach to choosing the learning rate is proposed for shallow feedforward
neural networks. We associate the learning rate with the gradient Lipschitz
constant of the objective to be minimized while training. An upper bound on the
mentioned constant is derived and a search algorithm, which always results in
non-divergent traces, is proposed to exploit the derived bound. It is shown
through simulations that the proposed search method significantly outperforms
the existing tuning methods such as Tree Parzen Estimators (TPE). The proposed
method is applied to three different existing applications: a) channel
estimation in OFDM systems, b) prediction of the exchange currency rates and c)
offset estimation in OFDM receivers, and it is shown to pick better learning
rates than the existing methods using the same or lesser compute power.
