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Library APIs for Neural Structured Learning.
Classes
class GenNeighbor
: Abstract class for generating neighbors.
Functions
adv_regularizer(...)
: Calculates adversarial loss from generated adversarial samples.
apply_feature_mask(...)
: Applies a feature mask on features
if the feature_mask
is not None
.
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 sources
and targets
.
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 embeddings
by replicate_times
.
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.