Schedules¶
This module holds various schedules for parameters such as the step rate or momentum for gradient descent.
A schedule is implemented as an iterator. This allows it to have iterators
of infinite length. It also makes it possible to manipulate scheduls with
the itertools
python module, e.g. for chaining iterators.

climin.schedule.
decaying
(start, decay)¶ Return an iterator of exponentially decaying values.
The first value is
start
. Every further value is obtained by multiplying the last one by a factor ofdecay
.Examples
>>> from climin.schedule import decaying >>> s = decaying(10, .9) >>> [next(s) for i in range(5)] [10.0, 9.0, 8.100000000000001, 7.290000000000001, 6.561]

climin.schedule.
linear_annealing
(start, stop, n_steps)¶ Return an iterator that anneals linearly to a point linearly.
The first value is
start
, the last value isstop
. The annealing will be linear overn_steps
iterations. After that,stop
is yielded.Examples
>>> from climin.schedule import linear_annealing >>> s = linear_annealing(1, 0, 4) >>> [next(s) for i in range(10)] [1.0, 0.75, 0.5, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]

climin.schedule.
repeater
(iter, n)¶ Return an iterator that repeats each element of iter exactly n times before moving on to the next element.
Examples
>>> from climin.schedule import repeater >>> s = repeater([1, 2, 3], 2) >>> [next(s) for i in range(6)] [1, 1, 2, 2, 3, 3]

class
climin.schedule.
SutskeverBlend
(max_momentum, stretch=250)¶ Class representing a schedule that stepwise increases from zero to a maximum value, as described in [sutskever2013importance].
Examples
>>> from climin.schedule import SutskeverBlend >>> s = iter(SutskeverBlend(0.9, 2)) >>> [next(s) for i in range(10)] [0.5, 0.75, 0.75, 0.8333333333333333, 0.8333333333333333, 0.875, 0.875, 0.9, 0.9, 0.9]
[sutskever2013importance] On the importance of initialization and momentum in deep learning, Sutskever et al (ICML 2013)