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.