nsl.configs.AdvNeighborConfig

Contains configuration for generating adversarial neighbors.

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.

Methods

__eq__

Return self==value.

__ge__

Automatically created by attrs.

__gt__

Automatically created by attrs.

__le__

Automatically created by attrs.

__lt__

Automatically created by attrs.

__ne__

Check equality and either forward a NotImplemented or return the result negated.