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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,
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. |
Methods
in_cooldown
in_cooldown()
set_model
set_model(
model
)
set_params
set_params(
params
)