Last updated: 2022-09-01, 17:25
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
)
当 metric 停止改善时，降低学习率（learning rate, LR）。
在学习停滞时，将学习率降低 2-10 倍往往对训练有利。该 callback 监视某个指标，如果在 patience 个 epochs 该指标都没有改善，就降低 LR。
monitor
要监视的量值。
factor
学习率降低的因子， new_lr = lr * factor。
patience
如果在 patience 个 epochs 后没有改善，就降低学习率。
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
学习率最小值。
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,
patience=5, min_lr=0.001)
model.fit(X_train, Y_train, callbacks=[reduce_lr])
- https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ReduceLROnPlateau
- https://keras.io/api/callbacks/reduce_lr_on_plateau/
Original: https://blog.csdn.net/Marryvivien/article/details/126954678
Author: CrazyPhilo
Title: ReduceLROnPlateau
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