RESN uses a training-free random-sampling error estimate to guide an evolutionary search over LSTM architectures, matching or beating several baselines at about half the optimization time.
DLOPT: Deep Learning Optimization Library
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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
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Random Error Sampling-based Recurrent Neural Network Architecture Optimization
RESN uses a training-free random-sampling error estimate to guide an evolutionary search over LSTM architectures, matching or beating several baselines at about half the optimization time.