nsl.configs.make_adv_reg_config
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Creates an nsl.configs.AdvRegConfig
object.
nsl.configs.make_adv_reg_config(
multiplier=attr.fields(AdvRegConfig).multiplier.default,
feature_mask=attr.fields(AdvNeighborConfig).feature_mask.default,
adv_step_size=attr.fields(AdvNeighborConfig).adv_step_size.default,
adv_grad_norm=attr.fields(AdvNeighborConfig).adv_grad_norm.default,
clip_value_min=attr.fields(AdvNeighborConfig).clip_value_min.default,
clip_value_max=attr.fields(AdvNeighborConfig).clip_value_max.default,
pgd_iterations=attr.fields(AdvNeighborConfig).pgd_iterations.default,
pgd_epsilon=attr.fields(AdvNeighborConfig).pgd_epsilon.default,
random_init=attr.fields(AdvNeighborConfig).random_init.default
)
Used in the notebooks
Args |
multiplier
|
multiplier to adversarial regularization loss. Defaults to 0.2.
|
feature_mask
|
mask w/ values in [0, 1] applied on the gradient. Its shape
should be the same as (or broadcastable to) that of the input features.
If the input features are in a collection (e.g. list or dictionary), this
field should also be a collection of the same structure. Input features
corresponding to mask values of 0.0 are not perturbed. Setting this
field to None is equivalent to setting a mask value of 1.0 for all input
features.
|
adv_step_size
|
step size to find the adversarial sample. Defaults to 0.001.
|
adv_grad_norm
|
type of tensor norm to normalize the gradient. Input will be
converted to NormType when applicable (e.g., a value of 'l2' will be
converted to nsl.configs.NormType.L2 ). Defaults to L2 norm.
|
clip_value_min
|
minimum value to clip the features after perturbation. The
shape should be the same as (or broadcastable to) input features. If the
input features are in a collection (e.g. list or dictionary), this field
should also be a collection with the same structure. An omitted or
None -valued entry in the collection indicates no constraint on the
corresponding feature.
|
clip_value_max
|
maximum value to clip the feature after perturbation. (See
clip_value_min for the structure and shape limitations.)
|
pgd_iterations
|
number of attack iterations for Projected Gradient Descent
(PGD) attack. Defaults to 1, which resembles the Fast Gradient Sign Method
(FGSM) attack.
|
pgd_epsilon
|
radius of the epsilon ball to project back to. Only used in
Projected Gradient Descent (PGD) attack.
|
random_init
|
Apply a random perturbation before FGSM/PGD steps. Default set
to False for no random initialization being applied.
|
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Last updated 2022-10-28 UTC.
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