View source on GitHub |
Configuration for optimizer.
Inherits From: OneOfConfig
, Config
, ParamsDict
tfm.optimization.OptimizerConfig(
default_params: dataclasses.InitVar[Optional[Mapping[str, Any]]] = None,
restrictions: dataclasses.InitVar[Optional[List[str]]] = None,
type: Optional[str] = None,
sgd: tfm.optimization.SGDConfig
= dataclasses.field(default_factory=opt_cfg.SGDConfig),
sgd_experimental: tfm.optimization.SGDExperimentalConfig
= dataclasses.field(default_factory=opt_cfg.SGDExperimentalConfig),
adam: tfm.optimization.AdamConfig
= dataclasses.field(default_factory=opt_cfg.AdamConfig),
adam_experimental: tfm.optimization.AdamExperimentalConfig
= dataclasses.field(default_factory=opt_cfg.AdamExperimentalConfig),
adamw: tfm.optimization.AdamWeightDecayConfig
= dataclasses.field(default_factory=opt_cfg.AdamWeightDecayConfig),
adamw_experimental: tfm.optimization.AdamWeightDecayExperimentalConfig
= dataclasses.field(default_factory=opt_cfg.AdamWeightDecayExperimentalConfig),
lamb: tfm.optimization.LAMBConfig
= dataclasses.field(default_factory=opt_cfg.LAMBConfig),
rmsprop: tfm.optimization.RMSPropConfig
= dataclasses.field(default_factory=opt_cfg.RMSPropConfig),
lars: tfm.optimization.LARSConfig
= dataclasses.field(default_factory=opt_cfg.LARSConfig),
adagrad: tfm.optimization.AdagradConfig
= dataclasses.field(default_factory=opt_cfg.AdagradConfig),
slide: tfm.optimization.SLIDEConfig
= dataclasses.field(default_factory=opt_cfg.SLIDEConfig),
adafactor: tfm.optimization.AdafactorConfig
= dataclasses.field(default_factory=opt_cfg.AdafactorConfig)
)
Methods
as_dict
as_dict()
Returns a dict representation of OneOfConfig.
For the nested base_config.Config, a nested dict will be returned.
from_args
@classmethod
from_args( *args, **kwargs )
Builds a config from the given list of arguments.
from_json
@classmethod
from_json( file_path: str )
Wrapper for from_yaml
.
from_yaml
@classmethod
from_yaml( file_path: str )
get
get()
Returns selected config based on the value of type.
If type is not set (None), None is returned.
lock
lock()
Makes the ParamsDict immutable.
override
override(
override_params, is_strict=True
)
Override the ParamsDict with a set of given params.
Args | |
---|---|
override_params
|
a dict or a ParamsDict specifying the parameters to be overridden. |
is_strict
|
a boolean specifying whether override is strict or not. If
True, keys in override_params must be present in the ParamsDict. If
False, keys in override_params can be different from what is currently
defined in the ParamsDict. In this case, the ParamsDict will be extended
to include the new keys.
|
replace
replace(
**kwargs
)
Overrides/returns a unlocked copy with the current config unchanged.
validate
validate()
Validate the parameters consistency based on the restrictions.
This method validates the internal consistency using the pre-defined list of restrictions. A restriction is defined as a string which specifies a binary operation. The supported binary operations are {'==', '!=', '<', '<=', '>', '>='}. Note that the meaning of these operators are consistent with the underlying Python immplementation. Users should make sure the define restrictions on their type make sense.
For example, for a ParamsDict like the following
a:
a1: 1
a2: 2
b:
bb:
bb1: 10
bb2: 20
ccc:
a1: 1
a3: 3
one can define two restrictions like this ['a.a1 == b.ccc.a1', 'a.a2 <= b.bb.bb2']
What it enforces are | |
---|---|
|
Raises | |
---|---|
KeyError
|
if any of the following happens (1) any of parameters in any of restrictions is not defined in ParamsDict, (2) any inconsistency violating the restriction is found. |
ValueError
|
if the restriction defined in the string is not supported. |
__contains__
__contains__(
key
)
Implements the membership test operator.
__eq__
__eq__(
other
)