ยป Keras API reference / Callbacks API / ReduceLROnPlateau

ReduceLROnPlateau

ReduceLROnPlateau class

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
)

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 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, 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: 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.