lr_monitor

Classes

LearningRateMonitor

Automatically monitor and logs learning rate for learning rate schedulers during training.

Learning Rate Monitor

Monitor and logs learning rate for lr schedulers during training.

class pytorch_lightning.callbacks.lr_monitor.LearningRateMonitor(logging_interval=None, log_momentum=False)

Bases: pytorch_lightning.callbacks.base.Callback

Automatically monitor and logs learning rate for learning rate schedulers during training.

Parameters

• logging_interval (Optional[str]) – set to 'epoch' or 'step' to log lr of all optimizers at the same interval, set to None to log at individual interval according to the interval key of each scheduler. Defaults to None.

• log_momentum (bool) – option to also log the momentum values of the optimizer, if the optimizer has the momentum or betas attribute. Defaults to False.

Raises

MisconfigurationException – If logging_interval is none of "step", "epoch", or None.

Example:

>>> from pytorch_lightning import Trainer
>>> from pytorch_lightning.callbacks import LearningRateMonitor
>>> lr_monitor = LearningRateMonitor(logging_interval='step')
>>> trainer = Trainer(callbacks=[lr_monitor])

Logging names are automatically determined based on optimizer class name. In case of multiple optimizers of same type, they will be named Adam, Adam-1 etc. If a optimizer has multiple parameter groups they will be named Adam/pg1, Adam/pg2 etc. To control naming, pass in a name keyword in the construction of the learning rate schedulers. A name keyword can also be used for parameter groups in the construction of the optimizer.

Example:

def configure_optimizer(self):
    optimizer = torch.optim.Adam(...)
    lr_scheduler = {
        'scheduler': torch.optim.lr_scheduler.LambdaLR(optimizer, ...)
        'name': 'my_logging_name'
    }
    return [optimizer], [lr_scheduler]

Example:

def configure_optimizer(self):
    optimizer = torch.optim.SGD(
        [{
            'params': [p for p in self.parameters()],
            'name': 'my_parameter_group_name'
        }],
        lr=0.1
    )
    lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, ...)
    return [optimizer], [lr_scheduler]

on_train_batch_start(trainer, *args, **kwargs)

Called when the train batch begins.

on_train_epoch_start(trainer, *args, **kwargs)

Called when the train epoch begins.

on_train_start(trainer, *args, **kwargs)

Called before training, determines unique names for all lr schedulers in the case of multiple of the same type or in the case of multiple parameter groups

Raises

MisconfigurationException – If Trainer has no logger.