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tfa.losses.npairs_multilabel_loss

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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)

See: http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf

Args:

  • y_true: Either 2-D integer Tensor with shape [batch_size, num_classes], or SparseTensor with dense shape [batch_size, num_classes]. If y_true is a SparseTensor, then it will be converted to Tensor via tf.sparse.to_dense first.

  • y_pred: 2-D float Tensor with shape [batch_size, batch_size] of similarity matrix between embedding matrices.

Returns:

  • npairs_multilabel_loss: float scalar.