


Computes the npairs loss between multilabel data y_true and 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:

a = tf.constant([[1, 2],
                [3, 4],
                [5, 6]], dtype=tf.float16)
b = tf.constant([[5, 9],
                [3, 6],
                [1, 8]], dtype=tf.float16)
y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)
<tf.Tensor: shape=(3, 3), dtype=float16, numpy=
array([[23., 15., 17.],
   [51., 33., 35.],
   [79., 51., 53.]], dtype=float16)>

<... Note: constants a & b have been used purely for example purposes and have no significant value ...>


name (Optional) name for the loss.




config Output of get_config().

A Loss instance.


Returns the config dictionary for a Loss instance.


Invokes the Loss instance.



ValueError If the shape of sample_weight is invalid.