mlx.optimizers.linear_schedule#

linear_schedule(init: float, end: float, steps: int) → Callable#

Make a linear scheduler.

Parameters:

  • init (float) – Initial value.

  • end (float) – Final value.

  • steps (int) – Number of steps to apply the schedule over. The value is end for any steps beyond steps.

Example

>>> warmup = optim.linear_schedule(0, 1e-1, 100)
>>> optimizer = optim.Adam(learning_rate=warmup)
>>> optimizer.learning_rate
array(0.0, dtype=float32)
>>> for _ in range(101): optimizer.update({}, {})
...
>>> optimizer.learning_rate
array(0.1, dtype=float32)