tf.keras.callbacks.ReduceLROnPlateau

TensorFlow 1 version View source on GitHub

Reduce learning rate when a metric has stopped improving.

Inherits From: Callback

tf.keras.callbacks.ReduceLROnPlateau(
    monitor='val_loss', factor=0.1, patience=10, verbose=0, mode='auto',
    min_delta=0.0001, cooldown=0, min_lr=0, **kwargs
)

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.

Example:

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

Arguments:

  • 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.

Methods

in_cooldown

View source

in_cooldown()

set_model

View source

set_model(
    model
)

set_params

View source

set_params(
    params
)