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.

nsl.lib.gen_adv_neighbor

Args

Returns

Raises