  Deep learning hyper-parameter optimization is a tough task. Finding an
appropriate network configuration is a key to success, however most of the
times this labor is roughly done. In this work we introduce a novel library to
tackle this problem, the Deep Learning Optimization Library: DLOPT. We briefly
describe its architecture and present a set of use examples. This is an open
source project developed under the GNU GPL v3 license and it is freely
available at https://github.com/acamero/dlopt
