jaxopt.OptaxSolver
- class jaxopt.OptaxSolver(fun, opt, value_and_grad=False, pre_update=None, maxiter=500, tol=0.001, verbose=0, implicit_diff=False, implicit_diff_solve=None, has_aux=False, jit='auto', unroll='auto')[source]
Optax solver.
- Parameters
fun (Callable) –
opt (NamedTuple) –
value_and_grad (bool) –
pre_update (Optional[Callable]) –
maxiter (int) –
tol (float) –
verbose (int) –
implicit_diff (bool) –
implicit_diff_solve (Optional[Callable]) –
has_aux (bool) –
jit (Union[str, bool]) –
unroll (Union[str, bool]) –
- fun
a function of the form
fun(params, *args, **kwargs)
, whereparams
are parameters of the model,*args
and**kwargs
are additional arguments.- Type
Callable
- opt
the optimizer to use, an optax.GradientTransformation, which is just a NamedTuple with
init
andupdate
functions.- Type
NamedTuple
- value_and_grad
whether
fun
just returns the value (False) or both the value and gradient (True).- Type
bool
- has_aux
whether
fun
outputs auxiliary data or not. Ifhas_aux
is False,fun
is expected to bescalar-valued.
- If
has_aux
is True, then we have one of the following two cases.
If
value_and_grad
is False, the output should bevalue, aux = fun(...)
. Ifvalue_and_grad == True
, the output should be(value, aux), grad = fun(...)
. At each iteration of the algorithm, the auxiliary outputs are storedin
state.aux
.- Type
bool
- If
- pre_update
a function to execute before Optax’s update. The function signature must be
params, state = pre_update(params, state, *args, **kwargs)
.- Type
Optional[Callable]
- maxiter
maximum number of solver iterations.
- Type
int
- tol
tolerance to use.
- Type
float
- verbose
whether to print error on every iteration or not. verbose=True will automatically disable jit.
- Type
int
- implicit_diff
whether to enable implicit diff or autodiff of unrolled iterations.
- Type
bool
- implicit_diff_solve
the linear system solver to use.
- Type
Optional[Callable]
- jit
whether to JIT-compile the optimization loop (default: “auto”).
- Type
Union[str, bool]
- unroll
whether to unroll the optimization loop (default: “auto”).
- Type
Union[str, bool]
- __init__(fun, opt, value_and_grad=False, pre_update=None, maxiter=500, tol=0.001, verbose=0, implicit_diff=False, implicit_diff_solve=None, has_aux=False, jit='auto', unroll='auto')
- Parameters
fun (Callable) –
opt (NamedTuple) –
value_and_grad (bool) –
pre_update (Optional[Callable]) –
maxiter (int) –
tol (float) –
verbose (int) –
implicit_diff (bool) –
implicit_diff_solve (Optional[Callable]) –
has_aux (bool) –
jit (Union[str, bool]) –
unroll (Union[str, bool]) –
- Return type
None
Methods
__init__
(fun, opt[, value_and_grad, ...])attribute_names
()attribute_values
()init_state
(init_params, *args, **kwargs)Initialize the solver state.
l2_optimality_error
(params, *args, **kwargs)Computes the L2 optimality error.
optimality_fun
(params, *args, **kwargs)Optimality function mapping compatible with
@custom_root
.run
(init_params, *args, **kwargs)Runs the optimization loop.
run_iterator
(init_params, iterator, *args, ...)Runs the optimization loop over an iterator.
update
(params, state, *args, **kwargs)Performs one iteration of the optax solver.
Attributes
- init_state(init_params, *args, **kwargs)[source]
Initialize the solver state.
- Parameters
init_params (
Any
) – pytree containing the initial parameters.*args – additional positional arguments to be passed to
fun
.**kwargs – additional keyword arguments to be passed to
fun
.
- Return type
OptaxState
- Returns
state
- l2_optimality_error(params, *args, **kwargs)
Computes the L2 optimality error.
- optimality_fun(params, *args, **kwargs)[source]
Optimality function mapping compatible with
@custom_root
.
- run(init_params, *args, **kwargs)
Runs the optimization loop.
- Parameters
init_params (
Any
) – pytree containing the initial parameters.*args – additional positional arguments to be passed to the update method.
**kwargs – additional keyword arguments to be passed to the update method.
- Return type
OptStep
- Returns
(params, state)
- run_iterator(init_params, iterator, *args, **kwargs)
Runs the optimization loop over an iterator.
- Parameters
init_params (
Any
) – pytree containing the initial parameters.iterator – iterator generating data batches.
*args – additional positional arguments to be passed to
fun
.**kwargs – additional keyword arguments to be passed to
fun
.
- Return type
OptStep
- Returns
(params, state)
- update(params, state, *args, **kwargs)[source]
Performs one iteration of the optax solver.
- Parameters
params (
Any
) – pytree containing the parameters.state (
NamedTuple
) – named tuple containing the solver state.*args – additional positional arguments to be passed to
fun
.**kwargs – additional keyword arguments to be passed to
fun
.
- Return type
OptStep
- Returns
(params, state)