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

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.

lifted_loss tf.float32 scalar.