This Julia package provides learning rate schedule types for training deep learning models.

Schedules are iterable, and return the learning rate at each iteration. For example, a linear schedule that decreases the learning rate from 1.0 to 0.6 with 4 steps can be created and used as follows:

julia> using LearningSchedules

julia> linear_schedule = Linear(1.0, 0.6, 4)

julia> for (i, r) in zip(1:6, linear_schedule) # would repeat infinitely without zipping
           println(r)
       end
1.0
0.9
0.8
0.7
0.6
0.6 # final value repeats infinitely

In this package, schedules are stateless, meaning they are immutable and do not store any information about the current iteration. The schedule state is instead passed around in the underlying iterate calls. A state can still be binded to a schedule using Iterators.Stateful (which is exported by this package) like so:

julia> linear_with_state = linear_schedule(); # creates a stateful iterator from the linear schedule

julia> next_rate(linear_with_state)
1.0

julia> next_rate!(linear_with_state)
1.0

julia> next_rate!(linear_with_state)
0.9

For more schedule types, see the documentation.