sandeep-krishnamurthy commented on a change in pull request #12750: [MXNET
-1030] Cosine Embedding Loss
URL: https://github.com/apache/incubator-mxnet/pull/12750#discussion_r228410529
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File path: python/mxnet/gluon/loss.py
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@@ -767,3 +767,71 @@ def hybrid_forward(self, F, pred, target,
sample_weight=None, epsilon=1e-08):
loss += stirling_factor
loss = _apply_weighting(F, loss, self._weight, sample_weight)
return F.mean(loss)
+
+
+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.
Review comment:
nit: interpreted
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