逆时间衰减
InverseTimeDecay class
tf_keras.optimizers.schedules.InverseTimeDecay(
initial_learning_rate, decay_steps, decay_rate, staircase=False, name=None
)
A LearningRateSchedule that uses an inverse time decay schedule.
When training a model, it is often useful to lower the learning rate as
the training progresses. This schedule applies the inverse decay function
to an optimizer step, given a provided initial learning rate.
It requires a step value to compute the decayed learning rate. You can
just pass a TensorFlow variable that you increment at each training step.
The schedule is a 1-arg callable that produces a decayed learning rate when passed the current optimizer step. This can be useful for changing the learning rate value across different invocations of optimizer functions. It is computed as:
def decayed_learning_rate(step):
return initial_learning_rate / (1 + decay_rate * step / decay_step)
or, if staircase is True, as:
def decayed_learning_rate(step):
return initial_learning_rate / (1 + decay_rate * floor(step / decay_step))
You can pass this schedule directly into a tf.keras.optimizers.Optimizer
as the learning rate.
Example
Fit a TF-Keras model when decaying 1/t with a rate of 0.5:
...
initial_learning_rate = 0.1
decay_steps = 1.0
decay_rate = 0.5
learning_rate_fn = keras.optimizers.schedules.InverseTimeDecay(
initial_learning_rate, decay_steps, decay_rate)
model.compile(optimizer=tf.keras.optimizers.SGD(
learning_rate=learning_rate_fn),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(data, labels, epochs=5)
Returns
A 1-arg callable learning rate schedule that takes the current optimizer
step and outputs the decayed learning rate, a scalar Tensor of the same
type as initial_learning_rate.