tf.contrib.losses.metric_learning.npairs_loss_multilabel

Computes the npairs loss with multilabel data.

tf.contrib.losses.metric_learning.npairs_loss_multilabel(
    sparse_labels, embeddings_anchor, embeddings_positive, reg_lambda=0.002,
    print_losses=False
)

Npairs loss expects paired data where a pair is composed of samples from the same labels and each pairs in the minibatch have different labels. The loss has two components. The first component is the L2 regularizer on the embedding vectors. The second component is the sum of cross entropy loss which takes each row of the pair-wise similarity matrix as logits and the remapped one-hot labels as labels. Here, the similarity is defined by the dot product between two embedding vectors. S_{i,j} = f(x_i)^T f(x_j)

To deal with multilabel inputs, we use the count of label intersection i.e. L_{i,j} = | set_of_labels_for(i) \cap set_of_labels_for(j) | Then we normalize each rows of the count based label matrix so that each row sums to one.

Args
sparse_labels	List of 1-D Boolean SparseTensor of dense_shape [batch_size/2, num_classes] labels for the anchor-pos pairs.
embeddings_anchor	2-D Tensor of shape [batch_size/2, embedding_dim] for the embedding vectors for the anchor images. Embeddings should not be l2 normalized.
embeddings_positive	2-D Tensor of shape [batch_size/2, embedding_dim] for the embedding vectors for the positive images. Embeddings should not be l2 normalized.
reg_lambda	Float. L2 regularization term on the embedding vectors.
print_losses	Boolean. Option to print the xent and l2loss.

Returns
npairs_loss	tf.float32 scalar.

Raises
TypeError	When the specified sparse_labels is not a SparseTensor.