ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=10, threshold=0.0001, threshold_mode='rel', cooldown=0, min_lr=0, eps=1e-08, verbose=False)¶
Reduce learning rate when a metric has stopped improving. Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. This scheduler reads a metrics quantity and if no improvement is seen for a ‘patience’ number of epochs, the learning rate is reduced.
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:
optimizer = flow.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) scheduler = flow.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min') for epoch in range(10): train(...) val_loss = validate(...) # Note that step should be called after validate() scheduler.step(val_loss)
__init__(optimizer, mode='min', factor=0.1, patience=10, threshold=0.0001, threshold_mode='rel', cooldown=0, min_lr=0, eps=1e-08, verbose=False)¶
Initialize self. See help(type(self)) for accurate signature.
Implement delattr(self, name).
Default dir() implementation.
Default object formatter.
Return getattr(self, name).
__init__(optimizer[, mode, factor, …])
This method is called when a class is subclassed.
Create and return a new object.
Helper for pickle.
Helper for pickle.
__setattr__(name, value, /)
Implement setattr(self, name, value).
Size of object in memory, in bytes.
Abstract classes can override this to customize issubclass().
_init_is_better(mode, threshold, threshold_mode)
Resets num_bad_steps counter and cooldown counter.
Whether the metric has improvement.
Loads the schedulers state.
Returns the state of the scheduler as a
Performs a single learning rate schedule step.
Whether the learning rate scheduler in cooldown phase.