Comments (1)
You can change your learning rate in any way you like, but the whole point of writing a scheduler is to abstract lr changes into a separate class. It looks like you're trying to incorporate your schedule straight into the main training loop. You can look at this file to get the idea of how it is usually done in PyTorch. Maybe it could also be helpful to walk through some tutorials at PyTorch site to get a feeling of how the training loop is constructed and how schedulers fit in it.
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Related Issues (8)
- StopIteration HOT 7
- Add License HOT 1
- Getting Stop Iteration when running for training
- scheduler.batch_step() AttributeError: 'CosineLRWithRestarts' object has no attribute 'batch_increment' HOT 1
- Hypergradient Descent HOT 5
- Persisting CosineAnnealingLRWithRestarts HOT 2
- Lower/Upper Bound for LR and Upper Bound decay HOT 2
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