LearningRateScheduler
- 원본 링크 : https://keras.io/api/callbacks/learning_rate_scheduler/
- 최종 확인 : 2024-11-25
LearningRateScheduler class
keras.callbacks.LearningRateScheduler(schedule, verbose=0)Learning rate scheduler.
At the beginning of every epoch, this callback gets the updated learning
rate value from schedule function provided at __init__, with the current
epoch and current learning rate, and applies the updated learning rate on
the optimizer.
Arguments
- schedule: A function that takes an epoch index (integer, indexed from 0) and current learning rate (float) as inputs and returns a new learning rate as output (float).
- verbose: Integer. 0: quiet, 1: log update messages.
Example
>>> # This function keeps the initial learning rate for the first ten epochs
>>> # and decreases it exponentially after that.
>>> def scheduler(epoch, lr):
... if epoch < 10:
... return lr
... else:
... return lr * ops.exp(-0.1)
>>>
>>> model = keras.models.Sequential([keras.layers.Dense(10)])
>>> model.compile(keras.optimizers.SGD(), loss='mse')
>>> round(model.optimizer.learning_rate, 5)
0.01
>>> callback = keras.callbacks.LearningRateScheduler(scheduler)
>>> history = model.fit(np.arange(100).reshape(5, 20), np.zeros(5),
... epochs=15, callbacks=[callback], verbose=0)
>>> round(model.optimizer.learning_rate, 5)
0.00607