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| author | Kai Arulkumaran <Kaixhin@users.noreply.github.com> | 2017-12-18 07:43:08 +0000 |
|---|---|---|
| committer | Soumith Chintala <soumith@gmail.com> | 2017-12-18 02:43:08 -0500 |
| commit | e9ef20eab5e5cf361bdc7a425c7f8b873baad9d3 (patch) | |
| tree | 8e4564b78f2e5880d40d21db453b59415808233f /docs | |
| parent | 847c56aeb5857fc4d3f5df88b9e8f937939bb8cc (diff) | |
| download | pytorch-e9ef20eab5e5cf361bdc7a425c7f8b873baad9d3.tar.gz pytorch-e9ef20eab5e5cf361bdc7a425c7f8b873baad9d3.tar.bz2 pytorch-e9ef20eab5e5cf361bdc7a425c7f8b873baad9d3.zip | |
Add Cosine Annealing LR Scheduler (#3311)
* Add Cosine Annealing LR Scheduler
* Update eta_min in tests to prevent numerical mistakes
* Use non-zero min_eta in test_cos_anneal_lr
Diffstat (limited to 'docs')
| -rw-r--r-- | docs/source/optim.rst | 4 |
1 files changed, 3 insertions, 1 deletions
diff --git a/docs/source/optim.rst b/docs/source/optim.rst index 2125d043d1..f44f51a8b8 100644 --- a/docs/source/optim.rst +++ b/docs/source/optim.rst @@ -130,7 +130,7 @@ How to adjust Learning Rate --------------------------- :mod:`torch.optim.lr_scheduler` provides several methods to adjust the learning -rate based on the number of epoches. :class:`torch.optim.lr_scheduler.ReduceLROnPlateau` +rate based on the number of epochs. :class:`torch.optim.lr_scheduler.ReduceLROnPlateau` allows dynamic learning rate reducing based on some validation measurements. .. autoclass:: torch.optim.lr_scheduler.LambdaLR @@ -141,5 +141,7 @@ allows dynamic learning rate reducing based on some validation measurements. :members: .. autoclass:: torch.optim.lr_scheduler.ExponentialLR :members: +.. autoclass:: torch.optim.lr_scheduler.CosineAnnealingLR + :members: .. autoclass:: torch.optim.lr_scheduler.ReduceLROnPlateau :members: |