tfm.optimization.PowerAndLinearDecayLrConfig

Configuration for DirectPower learning rate decay.

Inherits From: Config, ParamsDict

tfm.optimization.PowerAndLinearDecayLrConfig(
    default_params: dataclasses.InitVar[Optional[Mapping[str, Any]]] = None,
    restrictions: dataclasses.InitVar[Optional[List[str]]] = None,
    name: str = 'PowerAndLinearDecay',
    initial_learning_rate: Optional[float] = None,
    total_decay_steps: Optional[int] = None,
    power: float = -0.5,
    linear_decay_fraction: float = 0.1,
    offset: int = 0
)

The schedule has the following behavoir. Let offset_step = step - offset.

1) offset_step < 0, the actual learning rate equals initial_learning_rate. 2) offset_step <= total_decay_steps * (1 - linear_decay_fraction), the actual learning rate equals lr * offset_step^power. 3) total_decay_steps * (1 - linear_decay_fraction) <= offset_step < total_decay_steps, the actual learning rate equals lr * offset_step^power * (total_decay_steps - offset_step) / (total_decay_steps * linear_decay_fraction). 4) offset_step >= total_decay_steps, the actual learning rate equals zero.

name The name of the learning rate schedule. Defaults to PowerAndLinearDecay.
initial_learning_rate A float. The initial learning rate. Defaults to None.
total_decay_steps An int. The total number of steps for power + linear decay. Defaults to None.
power A float. The order of the polynomial. Defaults to -0.5, for sqrt decay.
linear_decay_fraction A float. In the last linear_decay_fraction steps, the learning rate will be multiplied by a linear decay. Defaults to 0.1.
offset An int. The offset applied to steps. Defaults to 0.
BUILDER

default_params Dataclass field
restrictions Dataclass field

Methods

as_dict

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as_dict()

Returns a dict representation of params_dict.ParamsDict.

For the nested params_dict.ParamsDict, a nested dict will be returned.

from_args

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@classmethod
from_args(
    *args, **kwargs
)

Builds a config from the given list of arguments.

from_json

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@classmethod
from_json(
    file_path: str
)

Wrapper for from_yaml.

from_yaml

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@classmethod
from_yaml(
    file_path: str
)

get

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get(
    key, value=None
)

Accesses through built-in dictionary get method.

lock

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lock()

Makes the ParamsDict immutable.

override

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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

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replace(
    **kwargs
)

Overrides/returns a unlocked copy with the current config unchanged.

validate

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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

  • a.a1 = 1 == b.ccc.a1 = 1
  • a.a2 = 2 <= b.bb.bb2 = 20

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__

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__contains__(
    key
)

Implements the membership test operator.

__eq__

__eq__(
    other
)

IMMUTABLE_TYPES (<class 'str'>, <class 'int'>, <class 'float'>, <class 'bool'>, <class 'NoneType'>)
RESERVED_ATTR ['_locked', '_restrictions']
SEQUENCE_TYPES (<class 'list'>, <class 'tuple'>)
default_params None
initial_learning_rate None
linear_decay_fraction 0.1
name 'PowerAndLinearDecay'
offset 0
power -0.5
restrictions None
total_decay_steps None