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A LearningRateSchedule that uses a cosine decay schedule.
See Migration guide for more details.
tf.keras.optimizers.schedules.CosineDecay( initial_learning_rate, decay_steps, alpha=0.0, name=None )
See Loshchilov & Hutter, ICLR2016, SGDR: Stochastic Gradient Descent with Warm Restarts.
When training a model, it is often useful to lower the learning rate as
the training progresses. This schedule applies a cosine 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 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): step = min(step, decay_steps) cosine_decay = 0.5 * (1 + cos(pi * step / decay_steps)) decayed = (1 - alpha) * cosine_decay + alpha return initial_learning_rate * decayed