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About Cyclical Learning Rate For Training Neural Networks (Leslie Smith)
This document describes a new method for setting the learning rate when training neural networks called cyclical learning rates (CLR). CLR involves varying the learning rate cyclically between minimum and maximum values rather than monotonically decreasing it. This eliminates the need to tune learning rates through experiments. Testing CLR on image datasets showed it achieved the same accuracy as tuned learning rates in fewer iterations. The paper demonstrates CLR with several network architectures and datasets, showing it is a practical tool for training neural networks without extensive tuning.
Detailed Information
| Author: | ['Nothen'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 10 |
| Language: | English |
| Format: | |
| Price: | FREE |
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