anirudhacharya commented on a change in pull request #12750: [MXNET -1030]
Cosine Embedding Loss
URL: https://github.com/apache/incubator-mxnet/pull/12750#discussion_r223538731
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File path: python/mxnet/gluon/loss.py
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@@ -706,3 +706,77 @@ def hybrid_forward(self, F, pred, positive, negative):
axis=self._batch_axis, exclude=True)
loss = F.relu(loss + self._margin)
return _apply_weighting(F, loss, self._weight, None)
+
+class CosineEmbeddingLoss(Loss):
+ r"""For a target label 1 or -1, vectors target and pred, the function
computes the cosine distance
+ between the vectors. This can be interpretted as how similar/dissimilar
two input vectors are.
+
+ .. math::
+ L = \begin{gather*}
Review comment:
isn't the loss a cumulative sum over all data points? so the formula should
begin with `\sum_i`
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