Bases: NoiseModule
Gaussian-noise addition module for DP-SGD.
This module uses either fast numpy pseudo-random number generation,
or slower cryptographically secure pseudo-random numbers (CSPRN).
Source code in declearn/optimizer/modules/_noise.py
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171 | class GaussianNoiseModule(NoiseModule):
"""Gaussian-noise addition module for DP-SGD.
This module uses either fast numpy pseudo-random number generation,
or slower cryptographically secure pseudo-random numbers (CSPRN).
"""
name: ClassVar[str] = "gaussian-noise"
def __init__(
self,
std: float = 1.0,
safe_mode: bool = True,
seed: Optional[int] = None,
) -> None:
"""Instantiate the gaussian noise module.
Parameters
----------
std: float, default=1.0,
Standard deviation of the gaussian noise.
safe_mode: bool, default=True
Whether to use cryptographically-secure pseudo-random numbers
(CSPRN) rather than the default numpy generator.
For experimental purposes, set flag to False, as generating CSPRN
is significantly slower.
seed: int or None, default=None
Seed used for initiliazing the non-secure random number generator.
If `safe_mode=True`, seed is ignored.
"""
self.std = std
super().__init__(safe_mode, seed)
def get_config(
self,
) -> Dict[str, Any]:
config = super().get_config()
config["std"] = self.std
return config
def _sample_noise(
self,
shape: Tuple[int, ...],
dtype: str,
) -> np.ndarray:
"""Sample a noise tensor from a Gaussian distribution."""
# Case when using CSPRN, that only provides uniform value sampling.
# REVISE: improve performance, possibly using torch's CSPRN lib
if isinstance(self._rng, SystemRandom):
value = [self._rng.random() for _ in range(np.prod(shape))]
array = np.array(value).reshape(shape).astype(dtype)
return scipy.stats.norm.ppf(array, scale=self.std)
# Case when using numpy RNG, that provides with gaussian sampling.
if isinstance(self._rng, np.random.Generator):
# false-positive; pylint: disable=no-member
return self._rng.normal(scale=self.std, size=shape).astype(dtype)
# Theoretically-unreachable case.
raise RuntimeError( # pragma: no cover
"Unexpected `GaussianeNoiseModule._rng` type."
)
|
__init__(std=1.0, safe_mode=True, seed=None)
Instantiate the gaussian noise module.
Parameters:
| Name |
Type |
Description |
Default |
std |
float
|
Standard deviation of the gaussian noise. |
1.0
|
safe_mode |
bool
|
Whether to use cryptographically-secure pseudo-random numbers
(CSPRN) rather than the default numpy generator.
For experimental purposes, set flag to False, as generating CSPRN
is significantly slower. |
True
|
seed |
Optional[int]
|
Seed used for initiliazing the non-secure random number generator.
If safe_mode=True, seed is ignored. |
None
|
Source code in declearn/optimizer/modules/_noise.py
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143 | def __init__(
self,
std: float = 1.0,
safe_mode: bool = True,
seed: Optional[int] = None,
) -> None:
"""Instantiate the gaussian noise module.
Parameters
----------
std: float, default=1.0,
Standard deviation of the gaussian noise.
safe_mode: bool, default=True
Whether to use cryptographically-secure pseudo-random numbers
(CSPRN) rather than the default numpy generator.
For experimental purposes, set flag to False, as generating CSPRN
is significantly slower.
seed: int or None, default=None
Seed used for initiliazing the non-secure random number generator.
If `safe_mode=True`, seed is ignored.
"""
self.std = std
super().__init__(safe_mode, seed)
|