ml4chem.optim package¶
Submodules¶
ml4chem.optim.LBFGS module¶
ml4chem.optim.handler module¶
-
ml4chem.optim.handler.get_lr(optimizer)[source]¶ Get current learning rate
- Parameters
optimizer (obj) – An optimizer object.
- Returns
Current learning rate.
- Return type
lr
-
ml4chem.optim.handler.get_lr_scheduler(optimizer, lr_scheduler)[source]¶ Get a learning rate scheduler
With a learning rate scheduler it is possible to perform training with an adaptative learning rate.
- Parameters
optimizer (obj) – An optimizer object.
lr_scheduler (tuple) –
Tuple with structure: scheduler’s name and a dictionary with keyword arguments.
>>> scheduler = ('ReduceLROnPlateau', {'mode': 'min', 'patience': 10})
- Returns
scheduler – A learning rate scheduler object that can be used to train models.
- Return type
obj
Notes
For a list of schedulers and respective keyword arguments, please refer to https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html
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ml4chem.optim.handler.get_optimizer(optimizer, params)[source]¶ Get optimizer to train pytorch models
There are several optimizers available in pytorch, and all of them take different parameters. This function takes as arguments an optimizer tuple with the following structure:
>>> optimizer = ('adam', {'lr': 1e-2, 'weight_decay': 1e-6})
and returns an optimizer object.
- Parameters
optimizer (tuple) – Tuple with name of optimizer and keyword arguments of optimizer as shown above.
params (list) – Parameters obtained from model.parameters() method.
- Returns
optimizer – An optimizer object.
- Return type
obj
Notes
For a list of all supported optimizers please check: