scheduler-param | R Documentation |
Parameters for neural network learning rate schedulers These parameters are used for constructing neural network models.
rate_initial(range = c(-3, -1), trans = transform_log10())
rate_largest(range = c(-1, -1/2), trans = transform_log10())
rate_reduction(range = c(1/5, 1), trans = NULL)
rate_steps(range = c(2, 10), trans = NULL)
rate_step_size(range = c(2, 20), trans = NULL)
rate_decay(range = c(0, 2), trans = NULL)
rate_schedule(values = values_scheduler)
values_scheduler
range |
A two-element vector holding the defaults for the smallest and largest possible values, respectively. If a transformation is specified, these values should be in the transformed units. |
trans |
A |
values |
A character string of possible values. See |
An object of class character
of length 5.
These parameters are often used with neural networks via
parsnip::mlp(engine = "brulee")
.
The details for how the brulee schedulers change the rates:
schedule_decay_time()
: rate(epoch) = initial/(1 + decay \times epoch)
schedule_decay_expo()
: rate(epoch) = initial\exp(-decay \times epoch)
schedule_step()
: rate(epoch) = initial \times reduction^{floor(epoch / steps)}
schedule_cyclic()
: cycle = floor( 1 + (epoch / 2 / step size) )
,
x = abs( ( epoch / step size ) - ( 2 * cycle) + 1 )
, and
rate(epoch) = initial + ( largest - initial ) * \max( 0, 1 - x)
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