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Reduce learning rate when a metric has stopped improving.
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See Migration guide for more details.
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.
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001) model.fit(X_train, Y_train, callbacks=[reduce_lr])
| ||quantity to be monitored.|
| || factor by which the learning rate will be reduced. |
| ||number of epochs with no improvement after which learning rate will be reduced.|
| ||int. 0: quiet, 1: update messages.|
| || one of |
| ||threshold for measuring the new optimum, to only focus on significant changes.|
| ||number of epochs to wait before resuming normal operation after lr has been reduced.|
| ||lower bound on the learning rate.|
set_model( model )
set_params( params )
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Code samples licensed under the Apache 2.0 License.