Reduce learning rate when a metric has stopped improving.
Source:R/callbacks.R
callback_reduce_lr_on_plateau.RdModels often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. This callback monitors a quantity and if no improvement is seen for a 'patience' number of epochs, the learning rate is reduced.
Usage
callback_reduce_lr_on_plateau(
monitor = "val_loss",
factor = 0.1,
patience = 10L,
verbose = 0L,
mode = "auto",
min_delta = 1e-04,
cooldown = 0L,
min_lr = 0,
...
)Arguments
- monitor
String. Quantity to be monitored.
- factor
Float. Factor by which the learning rate will be reduced.
new_lr = lr * factor.- patience
Integer. Number of epochs with no improvement after which learning rate will be reduced.
- verbose
Integer. 0: quiet, 1: update messages.
- mode
String. One of
{'auto', 'min', 'max'}. In'min'mode, the learning rate will be reduced when the quantity monitored has stopped decreasing; in'max'mode it will be reduced when the quantity monitored has stopped increasing; in'auto'mode, the direction is automatically inferred from the name of the monitored quantity.- min_delta
Float. Threshold for measuring the new optimum, to only focus on significant changes.
- cooldown
Integer. Number of epochs to wait before resuming normal operation after the learning rate has been reduced.
- min_lr
Float. Lower bound on the learning rate.
- ...
For forward/backward compatability.
Value
A Callback instance that can be passed to fit.keras.src.models.model.Model().
Examples
reduce_lr <- callback_reduce_lr_on_plateau(monitor = 'val_loss', factor = 0.2,
patience = 5, min_lr = 0.001)
model %>% fit(x_train, y_train, callbacks = list(reduce_lr))