loss_npairs_multilabel: Npairs multilabel loss

In tfaddons: Interface to 'TensorFlow SIG Addons'

Description

Computes the npairs loss between multilabel data 'y_true' and 'y_pred'.

Usage

loss_npairs_multilabel(name = "npairs_multilabel_loss")

Arguments

name	Optional name for the op.

Details

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 'i'th sample is in 'j'th class; if 'y_true[i, j] = 0', then 'i'th sample is not in 'j'th 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) “'

Value

npairs_multilabel_loss: float scalar.

See

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

tfaddons documentation built on July 2, 2020, 2:12 a.m.