# tfa.losses.npairs_multilabel_loss

Computes the npairs loss between multilabel data y_true and y_pred.

### Aliases:

tfa.losses.npairs_multilabel_loss(
y_true,
y_pred
)

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 takes each row of the pair-wise similarity matrix, y_pred, as logits and the remapped multi-class labels, y_true, as labels.

To deal with multilabel inputs, the count of label intersection is computed as follows:

L_{i,j} = | set_of_labels_for(i) \cap set_of_labels_for(j) |

Each row of the count based label matrix is further normalized so that each row sums to one.

y_true should be a binary indicator for classes. That is, if y_true[i, j] = 1, then ith sample is in jth class; if y_true[i, j] = 0, then ith sample is not in jth class.

The similarity matrix y_pred between two embedding matrices a and b with shape [batch_size, hidden_size] can be computed as follows:

# y_pred = a * b^T
y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)