jax_privacy.experimental.training.TrainingState

class jax_privacy.experimental.training.TrainingState(*, step, params, opt_state, noise_state)

Bases: Mapping

Container for the state of the training loop.

Methods

__init__
from_tuple
get	Retrieves the corresponding layout by the string key.
items	D.items() -> a set-like object providing a view on D's items
keys	D.keys() -> a set-like object providing a view on D's keys
replace
to_tuple
values	D.values() -> an object providing a view on D's values

Attributes

step
params
opt_state
noise_state

step: int

params: Union[Array, ndarray, bool, number, Iterable[Union[Array, ndarray, bool, number, Iterable[ArrayTree], Mapping[Any, ArrayTree]]], Mapping[Any, Union[Array, ndarray, bool, number, Iterable[ArrayTree], Mapping[Any, ArrayTree]]]]

opt_state: Union[Array, ndarray, bool, number, Iterable[Union[Array, ndarray, bool, number, Iterable[ArrayTree], Mapping[Any, ArrayTree]]], Mapping[Any, Union[Array, ndarray, bool, number, Iterable[ArrayTree], Mapping[Any, ArrayTree]]]]

noise_state: Union[Array, ndarray, bool, number, Iterable[Union[Array, ndarray, bool, number, Iterable[ArrayTree], Mapping[Any, ArrayTree]]], Mapping[Any, Union[Array, ndarray, bool, number, Iterable[ArrayTree], Mapping[Any, ArrayTree]]]]

from_tuple()

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

replace(**kwargs)

to_tuple()

values() → an object providing a view on D's values