ott.neural.networks.potentials.BasePotential

class ott.neural.networks.potentials.BasePotential(parent=<flax.linen.module._Sentinel object>, name=None)

Base class for the neural solver models (Linen).

Kept for backward compatibility with ExpectileNeuralDual and MongeGapEstimator. New code should use BaseDualPotential (NNX) instead.

Parameters:

• parent (Union[Module, Scope, _Sentinel, None])

• name (Optional[str])

Methods

apply(variables, *args[, rngs, method, ...])	Applies a module method to variables and returns output and modified variables.
clone(*[, parent, _deep_clone, _reset_names])	Creates a clone of this Module, with optionally updated arguments.
copy(*[, parent, name])	Creates a copy of this Module, with optionally updated arguments.
create_train_state(rng, optimizer, input, ...)	Create initial training state.
get_variable(col, name[, default])	Retrieves the value of a Variable.
has_rng(name)	Returns true if a PRNGSequence with name name exists.
has_variable(col, name)	Checks if a variable of given collection and name exists in this Module.
init(rngs, *args[, method, mutable, ...])	Initializes a module method with variables and returns modified variables.
init_with_output(rngs, *args[, method, ...])	Initializes a module method with variables and returns output and modified variables.
is_initializing()	Returns True if running under self.init(...) or nn.init(...)().
is_mutable_collection(col)	Returns true if the collection col is mutable.
lazy_init(rngs, *args[, method, mutable])	Initializes a module without computing on an actual input.
make_rng([name])	Returns a new RNG key from a given RNG sequence for this Module.
param(name, init_fn, *init_args[, unbox])
perturb(name, value[, collection])	Add an zero-value variable ('perturbation') to the intermediate value.
potential_gradient_fn(params)	Return a function returning a vector or the gradient of the potential.
potential_value_fn(params[, ...])	Return a function giving the value of the potential.
put_variable(col, name, value)	Updates the value of the given variable if it is mutable, or an error otherwise.
setup()	Initializes a Module lazily (similar to a lazy __init__).
sow(col, name, value[, reduce_fn, init_fn])
unbind()	Returns an unbound copy of a Module and its variables.
variable(col, name[, init_fn, unbox])

Attributes

is_potential	Indicates if the module implements a potential value or a vector field.
name
parent
path	Get the path of this Module.
scope
variables	Returns the variables in this module.