# Oscillate learning rate

1. To get out of saddle points or local minima
2. When model could be sensitive to initial learning rate

```python
scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer, 1e-4, 1e-3, cycle_momentum=False)
```

![](https://1490490238-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-Ma_JYIRuxx8PCv6UaBM%2F-McAJRex-1TKcbfdH_KQ%2F-McAQF0X-tEFiT2hvS-5%2Ffaster.gif?alt=media\&token=675e8e56-c782-419b-8bea-87ad34c70731)

[Link to reading](https://www.pyimagesearch.com/2019/07/29/cyclical-learning-rates-with-keras-and-deep-learning/)
