tf.contrib.losses.metric_learning.lifted_struct_loss
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Computes the lifted structured loss.
tf.contrib.losses.metric_learning.lifted_struct_loss(
labels, embeddings, margin=1.0
)
The loss encourages the positive distances (between a pair of embeddings
with the same labels) to be smaller than any negative distances (between a
pair of embeddings with different labels) in the mini-batch in a way
that is differentiable with respect to the embedding vectors.
See: https://arxiv.org/abs/1511.06452
Args |
|---|
labels
|
1-D tf.int32 Tensor with shape [batch_size] of
multiclass integer labels.
|
embeddings
|
2-D float Tensor of embedding vectors. Embeddings should not
be l2 normalized.
|
margin
|
Float, margin term in the loss definition.
|
Returns |
|---|
lifted_loss
|
tf.float32 scalar.
|
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Last updated 2020-10-01 UTC.
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View source on GitHub