TensorFlow 1 version View source on GitHub

Reduce learning rate when a metric has stopped improving.

Inherits From: Callback

Models 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.


reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,
                              patience=5, min_lr=0.001), Y_train, callbacks=[reduce_lr])

monitor quantity to be monitored.
factor factor by which the learning rate will be reduced. new_lr = lr * factor
patience number of epochs with no improvement after which learning rate will be reduced.
verbose int. 0: quiet, 1: update messages.
mode one of {auto, 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; in auto mode, the direction is automatically inferred from the name of the monitored quantity.
min_delta threshold for measuring the new optimum, to only focus on significant changes.
cooldown number of epochs to wait before resuming normal operation after lr has been reduced.
min_lr lower bound on the learning rate.



View source


View source


View source