train.ReduceLROnPlateau

class train.ReduceLROnPlateau(*args: Any, **kwargs: Any)[source]

Extends PyTorch ReduceLROnPlateau by exponential smoothing of the monitored metric

Parameters:

  • optimizer (Optimizer) – Wrapped optimizer.

  • mode (str) – One of min, max. In min mode, lr will be reduced when the quantity monitored has stopped decreasing; in max mode it will be reduced when the quantity monitored has stopped increasing. Default: ‘min’.

  • factor (float) – Factor by which the learning rate will be reduced. new_lr = lr * factor. Default: 0.1.

  • patience (int) – Number of epochs with no improvement after which learning rate will be reduced. For example, if patience = 2, then we will ignore the first 2 epochs with no improvement, and will only decrease the LR after the 3rd epoch if the loss still hasn’t improved then. Default: 10.

  • threshold (float) – Threshold for measuring the new optimum, to only focus on significant changes. Default: 1e-4.

  • threshold_mode (str) – One of rel, abs. In rel mode, dynamic_threshold = best * ( 1 + threshold ) in ‘max’ mode or best * ( 1 - threshold ) in min mode. In abs mode, dynamic_threshold = best + threshold in max mode or best - threshold in min mode. Default: ‘rel’.

  • cooldown (int) – Number of epochs to wait before resuming normal operation after lr has been reduced. Default: 0.

  • min_lr (float or list) – A scalar or a list of scalars. A lower bound on the learning rate of all param groups or each group respectively. Default: 0.

  • eps (float) – Minimal decay applied to lr. If the difference between new and old lr is smaller than eps, the update is ignored. Default: 1e-8.

  • verbose (bool) – If True, prints a message to stdout for each update. Default: False.

  • smoothing_factor – smoothing_factor of exponential moving average