DLOPT: Deep Learning Optimization Library

By Andrés Camero, Jamal Toutouh, Enrique Alba. 2018

9 citations recorded by the source corpus.

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 this https URL

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Bayesian Neural Architecture Search using A Training-Free Performance Metric (2020, methodology): “We implemented our approach 1 in Python 3, using DLOPT [6], MIP-EGO [42], Keras [12], and Tensorﬂow [1].”
A Specialized Evolutionary Strategy Using Mean Absolute Error Random Sampling to Design Recurrent Neural Networks (2019, methodology): “We have implemented our proposal in Python1, using DLOPT [40], Keras [41], and Tensorflow [42].”
Waste generation prediction under uncertainty in smart cities through deep neuroevolution (2019, methodology): “We implemented our proposal in Python 3, using the DL optimization library dlopt [44], and the DL frameworks keras [45] and tensorflow [46].”
Random error sampling-based recurrent neural network architecture optimization (2019, methodology): “com/acamero/dlopt), using DLOPT [45], Keras [46], and Tensorflow [47].”