  In the context of deep learning, the costliest phase from a computational
point of view is the full training of the learning algorithm. However, this
process is to be used a significant number of times during the design of a new
artificial neural network, leading therefore to extremely expensive operations.
Here, we propose a low-cost strategy to predict the accuracy of the algorithm,
based only on its initial behaviour. To do so, we train the network of interest
up to convergence several times, modifying its characteristics at each
training. The initial and final accuracies observed during this beforehand
process are stored in a database. We then make use of both curve fitting and
Support Vector Machines techniques, the latter being trained on the created
database, to predict the accuracy of the network, given its accuracy on the
primary iterations of its learning. This approach can be of particular interest
when the space of the characteristics of the network is notably large or when
its full training is highly time-consuming. The results we obtained are
promising and encouraged us to apply this strategy to a topical issue:
hyper-parameter optimisation (HO). In particular, we focused on the HO of a
convolutional neural network for the classification of the databases MNIST and
CIFAR-10. By using our method of prediction, and an algorithm implemented by us
for a probabilistic exploration of the hyper-parameter space, we were able to
find the hyper-parameter settings corresponding to the optimal accuracies
already known in literature, at a quite low-cost.
