|View source on GitHub|
Library APIs for Neural Structured Learning.
class GenNeighbor: Abstract class for generating neighbors.
adv_regularizer(...): Calculates adversarial loss from generated adversarial samples.
apply_feature_mask(...): Applies a feature mask on
features if the
feature_mask is not
decay_over_time(...): Returns a decayed value of
init_value over time.
gen_adv_neighbor(...): Generates adversarial neighbors for the given input and loss.
get_target_indices(...): Selects targeting classes for adversarial attack (classification only).
jensen_shannon_divergence(...): Adds a Jensen-Shannon divergence to the training procedure.
kl_divergence(...): Adds a KL-divergence to the training procedure.
maximize_within_unit_norm(...): Solves the maximization problem weights^T*x with the constraint norm(x)=1.
normalize(...): Normalizes the values in
tensor with respect to a specified vector norm.
pairwise_distance_wrapper(...): A wrapper to compute the pairwise distance between
project_to_ball(...): Projects batched tensors to a ball with the given radius in the given norm.
random_in_norm_ball(...): Generates a random sample in a norm ball, conforming the given structure.
replicate_embeddings(...): Replicates the given
strip_neighbor_features(...): Strips graph neighbor features from a feature dictionary.
unpack_neighbor_features(...): Extracts sample features, neighbor features, and neighbor weights.
virtual_adv_regularizer(...): Calculates virtual adversarial loss for the given input.