nsl.configs.AdvNeighborConfig

Contains configuration for generating adversarial neighbors.

nsl.configs.AdvNeighborConfig(
    feature_mask=attr_dict['feature_mask'].default,
    adv_step_size=attr_dict['adv_step_size'].default,
    adv_grad_norm=attr_dict['adv_grad_norm'].default,
    clip_value_min=attr_dict['clip_value_min'].default,
    clip_value_max=attr_dict['clip_value_max'].default,
    pgd_iterations=attr_dict['pgd_iterations'].default,
    pgd_epsilon=attr_dict['pgd_epsilon'].default,
    random_init=attr_dict['random_init'].default
)

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. Default set to 0.001.
adv_grad_norm type of tensor norm to normalize the gradient. Input will be converted to nsl.configs.NormType when applicable (e.g., 'l2' -> nls.configs.NormType.L2). Default set 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.

Methods

__eq__

__eq__(
    other
)

Method generated by attrs for class AdvNeighborConfig.

__ge__

__ge__(
    other
)

Method generated by attrs for class AdvNeighborConfig.

__gt__

__gt__(
    other
)

Method generated by attrs for class AdvNeighborConfig.

__le__

__le__(
    other
)

Method generated by attrs for class AdvNeighborConfig.

__lt__

__lt__(
    other
)

Method generated by attrs for class AdvNeighborConfig.

__ne__

__ne__(
    other
)

Method generated by attrs for class AdvNeighborConfig.