nsl.lib.gen_adv_neighbor

Generates adversarial neighbors for the given input and loss.

nsl.lib.gen_adv_neighbor(
    input_features,
    labeled_loss,
    config,
    raise_invalid_gradient=False,
    gradient_tape=None,
    pgd_model_fn=None,
    pgd_loss_fn=None,
    pgd_labels=None,
    use_while_loop=False
)

This function implements the following operation: adv_neighbor = input_features + adv_step_size * gradient where adv_step_size is the step size (analogous to learning rate) for searching/calculating adversarial neighbor.

Args
input_features	a dense (float32) tensor, a list of dense tensors, or a dictionary of feature names and dense tensors. The shape of the tensor(s) should be either: (a) pointwise samples: [batch_size, feat_len], or (b) sequence samples: [batch_size, seq_len, feat_len]. Note that only dense (float) tensors in input_features will be perturbed and all other features (int, string, or SparseTensor) will be kept as-is in the returning adv_neighbor.
labeled_loss	A scalar tensor of floating point type calculated from true labels (or supervisions).
config	A nsl.configs.AdvNeighborConfig object containing the following hyperparameters for generating adversarial samples. 'feature_mask': mask (with 0-1 values) applied on the graident. 'adv_step_size': step size to find the adversarial sample. 'adv_grad_norm': type of tensor norm to normalize the gradient.
raise_invalid_gradient	(optional) A Boolean flag indicating whether to raise an error when gradients cannot be computed on any input feature. There are three cases where this error may happen: (1) The feature is a SparseTensor. (2) The feature has a non-differentiable dtype, like string or integer. (3) The feature is not involved in loss computation. If set to False (default), those inputs without gradient will be ignored silently and not perturbed.
gradient_tape	A tf.GradientTape object watching the calculation from input_features to labeled_loss. Can be omitted if running in graph mode.
pgd_model_fn	The model to generate adversaries for. Generates predictions for a given set of inputs, in the shape of input_features.
pgd_loss_fn	The loss function. Takes samples of labels and a model predictions.
pgd_labels	labels for the input features. This should have shape [batch_size, 1]. Required to generate adversaries with PGD, unused otherwise.
use_while_loop	A Boolean indicating whether the PGD steps should be done in a tf.while_loop. This can potentially reduce memory footprint, but is not compatible to pgd_model_fn with side effects. (default=False)

Returns
adv_neighbor	The perturbed example, with the same shape and structure as input_features.
adv_weight	A dense Tensor with shape of [batch_size, 1], representing the weight for each neighbor.

Raises
ValueError	In case of raise_invalid_gradient is set and some of the input features cannot be perturbed. See raise_invalid_gradient for situations where this can happen.