learning_rate_schedule_piecewise_constant_decay | R Documentation |
LearningRateSchedule
that uses a piecewise constant decay schedule.The function returns a 1-arg callable to compute the piecewise constant when passed the current optimizer step. This can be useful for changing the learning rate value across different invocations of optimizer functions.
learning_rate_schedule_piecewise_constant_decay(
boundaries,
values,
name = "PiecewiseConstant"
)
boundaries |
A list of Python numbers with strictly increasing entries, and with all elements having the same type as the optimizer step. |
values |
A list of Python numbers that specifies the values for the
intervals defined by |
name |
A string. Optional name of the operation. Defaults to
|
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 the boundary tensors.
The output of the 1-arg function that takes the step
is values[0]
when step <= boundaries[0]
,
values[1]
when step > boundaries[0]
and step <= boundaries[1]
,
..., and values[-1]
when step > boundaries[-1]
.
use a learning rate that's 1.0 for the first 100001 steps, 0.5 for the next 10000 steps, and 0.1 for any additional steps.
step <- 0 boundaries <- c(100000, 110000) values <- c(1.0, 0.5, 0.1) learning_rate_fn <- learning_rate_schedule_piecewise_constant_decay( boundaries, values) # Later, whenever we perform an optimization step, we pass in the step. learning_rate <- learning_rate_fn(step)
You can pass this schedule directly into a optimizer
as the learning rate. The learning rate schedule is also serializable and
deserializable using keras$optimizers$schedules$serialize
and
keras$optimizers$schedules$deserialize
.
ValueError: if the number of elements in the boundaries
and values
lists do not match.
Other optimizer learning rate schedules:
LearningRateSchedule()
learning_rate_schedule_cosine_decay()
learning_rate_schedule_cosine_decay_restarts()
learning_rate_schedule_exponential_decay()
learning_rate_schedule_inverse_time_decay()
learning_rate_schedule_polynomial_decay()
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