nsl.configs.make_adv_reg_config

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Creates an nsl.configs.AdvRegConfig object.

Used in the notebooks

Used in the tutorials

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.

An nsl.configs.AdvRegConfig object.