FluxTrainingHyperparameter interface
"""
HyperParameter{T}
A hyperparameter is any state that influences the
training and is not a parameter of the model.
Hyperparameters can be scheduled using the [`Scheduler`](#)
callback.
"""
abstract
type
HyperParameter
{
T
}
end
"""
sethyperparameter!(learner, H, value) -> learner
Sets hyperparameter `H` to `value` on `learner`, returning
the modified learner.
"""
function
sethyperparameter!
end
"""
stateaccess(::Type{HyperParameter})
Defines what `Learner` state is accessed when calling
`sethyperparameter!` and `gethyperparameter`. This is needed
so that [`Scheduler`](#) can access the state.
"""
stateaccess
(
::
Type
{
HyperParameter
}
)
=
(
)Implementations
"""
abstract type LearningRate <: HyperParameter
Hyperparameter for the optimizer's learning rate.
See [`Scheduler`](#) and [hyperparameter scheduling](./docs/tutorials/hyperparameters.md).
"""
abstract
type
LearningRate
<:
HyperParameter
{
Float64
}
end
stateaccess
(
::
Type
{
LearningRate
}
)
=
(
optimizer
=
Write
(
)
,
)
function
sethyperparameter!
(
learner
,
::
Type
{
LearningRate
}
,
value
)
learner
.
optimizer
=
setlearningrate!
(
learner
.
optimizer
,
value
)
return
learner
end
function
setlearningrate!
(
optimizer
::
Flux
.
Optimise
.
AbstractOptimiser
,
value
)
optimizer
.
eta
=
value
optimizer
end
function
setlearningrate!
(
optimizer
,
value
)
@
set
optimizer
.
eta
=
value
end