tfm.optimization.PolynomialLrConfig
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Configuration for polynomial learning rate decay.
Inherits From: Config, ParamsDict
View aliases
Main aliases
tfm.optimization.PolynomialLrConfig(
default_params: dataclasses.InitVar[Optional[Mapping[str, Any]]] = None,
restrictions: dataclasses.InitVar[Optional[List[str]]] = None,
name: str = 'PolynomialDecay',
initial_learning_rate: Optional[float] = None,
decay_steps: Optional[int] = None,
end_learning_rate: float = 0.0001,
power: float = 1.0,
cycle: bool = False,
offset: int = 0
)
This class is a containers for the polynomial learning rate decay configs.
Attributes | |
|---|---|
name
|
The name of the learning rate schedule. Defaults to PolynomialDecay. |
initial_learning_rate
|
A float. The initial learning rate. Defaults to None. |
decay_steps
|
A positive integer that is used for decay computation. Defaults to None. |
end_learning_rate
|
A float. The minimal end learning rate. |
power
|
A float. The power of the polynomial. Defaults to linear, 1.0. |
cycle
|
A boolean, whether or not it should cycle beyond decay_steps. Defaults to False. |
offset
|
An int. The offset applied to steps. Defaults to 0. |
BUILDER
|
|
default_params
|
Dataclass field |
restrictions
|
Dataclass field |
Methods
as_dict
as_dict()
Returns a dict representation of params_dict.ParamsDict.
For the nested params_dict.ParamsDict, a nested dict will be returned.
from_args
@classmethodfrom_args( *args, **kwargs )
Builds a config from the given list of arguments.
from_json
@classmethodfrom_json( file_path: str )
Wrapper for from_yaml.
from_yaml
@classmethodfrom_yaml( file_path: str )
get
get(
key, value=None
)
Accesses through built-in dictionary get method.
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
)
Class Variables | |
|---|---|
| IMMUTABLE_TYPES |
(<class 'str'>,
<class 'int'>,
<class 'float'>,
<class 'bool'>,
<class 'NoneType'>)
|
| RESERVED_ATTR |
['_locked', '_restrictions']
|
| SEQUENCE_TYPES |
(<class 'list'>, <class 'tuple'>)
|
| cycle |
False
|
| decay_steps |
None
|
| default_params |
None
|
| end_learning_rate |
0.0001
|
| initial_learning_rate |
None
|
| name |
'PolynomialDecay'
|
| offset |
0
|
| power |
1.0
|
| restrictions |
None
|