Documented scheduler module

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Brandon Rozek 2020-03-20 17:59:56 -04:00
parent ea62ccf389
commit 720bb1b051
3 changed files with 50 additions and 1 deletions

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Hyperparameter Scheduling
=========================
.. automodule:: rltorch.scheduler
.. autoclass:: rltorch.scheduler.LinearScheduler
:members:
.. autoclass:: rltorch.scheduler.ExponentialScheduler
:members:

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from .Scheduler import Scheduler
class ExponentialScheduler(Scheduler):
r"""
A exponential scheduler that given a certain number
of iterations, spaces the values between
a start and an end point in an exponential order.
Notes
-----
The forumula used to produce the value :math:`y` is based on the number of
times you call `next`. (denoted as :math:`i`)
:math:`y(1) = initial\_value`
:math:`y(i) = y(1) \cdot base^{i - 1}`
:math:`base = \sqrt[iterations]{\frac{end\_value}{initial\_value}}`.
Another property is that :math:`y(iterations) = end\_value`.
Parameters
----------
initial_value : number
The first value returned in the schedule.
end_value: number
The value returned when the maximum number of iterations are reached
iterations: int
The total number of iterations
"""
def __init__(self, initial_value, end_value, iterations):
super(ExponentialScheduler, self).__init__(initial_value, end_value, iterations)
self.base = (end_value / initial_value) ** (1.0 / iterations)

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from .Scheduler import Scheduler
class LinearScheduler(Scheduler):
r"""
A linear scheduler that given a certain number
of iterations, equally spaces the values between
a start and an end point.
Notes
-----
The forumula used to produce the value :math:`y` is based on the number of
times you call `next`. (denoted as :math:`i`)
:math:`y(i) = slope \cdot (i - 1) + initial\_value`
where :math:`slope = \frac{end\_value - initial\_value)}{iterations}`.
Parameters
----------
initial_value : number
The first value returned in the schedule.
end_value: number
The value returned when the maximum number of iterations are reached
iterations: int
The total number of iterations
"""
def __init__(self, initial_value, end_value, iterations):
super(LinearScheduler, self).__init__(initial_value, end_value, iterations)
self.slope = (end_value - initial_value) / iterations