HyperparametersMixin

class pytorch_lightning.core.mixins.HyperparametersMixin

Bases: object

save_hyperparameters(*args, ignore=None, frame=None, logger=True)

Save arguments to hparams attribute.

Parameters

• args (Any) – single object of dict, NameSpace or OmegaConf or string names or arguments from class __init__

• ignore (Union[Sequence[str], str, None]) – an argument name or a list of argument names from class __init__ to be ignored

• frame (Optional[frame]) – a frame object. Default is None

• logger (bool) – Whether to send the hyperparameters to the logger. Default: True

Example::

>>> from pytorch_lightning.core.mixins import HyperparametersMixin
>>> class ManuallyArgsModel(HyperparametersMixin):
...     def __init__(self, arg1, arg2, arg3):
...         super().__init__()
...         # manually assign arguments
...         self.save_hyperparameters('arg1', 'arg3')
...     def forward(self, *args, **kwargs):
...         ...
>>> model = ManuallyArgsModel(1, 'abc', 3.14)
>>> model.hparams
"arg1": 1
"arg3": 3.14

>>> from pytorch_lightning.core.mixins import HyperparametersMixin
>>> class AutomaticArgsModel(HyperparametersMixin):
...     def __init__(self, arg1, arg2, arg3):
...         super().__init__()
...         # equivalent automatic
...         self.save_hyperparameters()
...     def forward(self, *args, **kwargs):
...         ...
>>> model = AutomaticArgsModel(1, 'abc', 3.14)
>>> model.hparams
"arg1": 1
"arg2": abc
"arg3": 3.14

>>> from pytorch_lightning.core.mixins import HyperparametersMixin
>>> class SingleArgModel(HyperparametersMixin):
...     def __init__(self, params):
...         super().__init__()
...         # manually assign single argument
...         self.save_hyperparameters(params)
...     def forward(self, *args, **kwargs):
...         ...
>>> model = SingleArgModel(Namespace(p1=1, p2='abc', p3=3.14))
>>> model.hparams
"p1": 1
"p2": abc
"p3": 3.14

>>> from pytorch_lightning.core.mixins import HyperparametersMixin
>>> class ManuallyArgsModel(HyperparametersMixin):
...     def __init__(self, arg1, arg2, arg3):
...         super().__init__()
...         # pass argument(s) to ignore as a string or in a list
...         self.save_hyperparameters(ignore='arg2')
...     def forward(self, *args, **kwargs):
...         ...
>>> model = ManuallyArgsModel(1, 'abc', 3.14)
>>> model.hparams
"arg1": 1
"arg3": 3.14

Return type

None

property hparams: Union[pytorch_lightning.utilities.parsing.AttributeDict, MutableMapping]

The collection of hyperparameters saved with save_hyperparameters(). It is mutable by the user. For the frozen set of initial hyperparameters, use hparams_initial.

Return type

Union[AttributeDict, MutableMapping]

Returns

Mutable hyperparameters dictionary

property hparams_initial: pytorch_lightning.utilities.parsing.AttributeDict

The collection of hyperparameters saved with save_hyperparameters(). These contents are read-only. Manual updates to the saved hyperparameters can instead be performed through hparams.

Returns

immutable initial hyperparameters

Return type

AttributeDict