Calculates adversarial loss from generated adversarial samples.
nsl.lib.adv_regularizer(
adv_neighbors, target_scores, model_fn, loss_fn
)
Args |
|---|
adv_neighbors
|
dense float32 tensor, with two possible shapes: (a)
[batch_size, feat_len] for pointwise samples, or (b)
[batch_size, seq_len, feat_len] for sequence samples.
|
target_scores
|
target tensor used to compute loss.
|
model_fn
|
a method that has input tensor (same shape as adv_neighbors),
is_train and reuse as inputs, and returns predicted logits.
|
loss_fn
|
a loss function that has target and prediction as inputs, and
returns a float32 scalar.
|
Returns |
|---|
adv_loss
|
a float32 denoting the adversarial loss.
|
View source on GitHub