DLOPT: Deep Learning Optimization Library
This provides a tool for researchers and practitioners to streamline hyper-parameter tuning in deep learning, though it appears incremental as it builds on existing optimization concepts.
The authors tackled the challenging problem of deep learning hyper-parameter optimization by introducing DLOPT, a novel library designed to automate network configuration, which is freely available as open-source software.
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