  Gradient-only and probabilistic line searches have recently reintroduced the
ability to adaptively determine learning rates in dynamic mini-batch
sub-sampled neural network training. However, stochastic line searches are
still in their infancy and thus call for an ongoing investigation. We study the
application of the Gradient-Only Line Search that is Inexact (GOLS-I) to
automatically determine the learning rate schedule for a selection of popular
neural network training algorithms, including NAG, Adagrad, Adadelta, Adam and
LBFGS, with numerous shallow, deep and convolutional neural network
architectures trained on different datasets with various loss functions. We
find that GOLS-I's learning rate schedules are competitive with manually tuned
learning rates, over seven optimization algorithms, three types of neural
network architecture, 23 datasets and two loss functions. We demonstrate that
algorithms, which include dominant momentum characteristics, are not well
suited to be used with GOLS-I. However, we find GOLS-I to be effective in
automatically determining learning rate schedules over 15 orders of magnitude,
for most popular neural network training algorithms, effectively removing the
need to tune the sensitive hyperparameters of learning rate schedules in neural
network training.
