szha commented on a change in pull request #12750: [MXNET -1030] Cosine
Embedding Loss
URL: https://github.com/apache/incubator-mxnet/pull/12750#discussion_r228392711
##########
File path: python/mxnet/gluon/loss.py
##########
@@ -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.
+
+ .. math::
+
+ L = \sum_i \begin{cases} 1 - {cos\_sim({input1}_i, {input2}_i)} &
\text{ if } {label}_i = 1\\
+ {cos\_sim({input1}_i, {input2}_i)} & \text{ if }
{label}_i = -1 \end{cases}\\
+ cos\_sim(input1, input2) =
\frac{{input1}_i.{input2}_i}{||{input1}_i||.||{input2}_i||}
+
+ `input1`, `input2` can have arbitrary shape as long as they have the same
number of elements.
+
+ Parameters
+ ----------
+ weight : float or None
+ Global scalar weight for loss.
+ batch_axis : int, default 0
+ The axis that represents mini-batch.
+ margin : float
+ Margin of separation between correct and incorrect pair.
+
+
+ Inputs:
+ - **input1**: a tensor with arbitrary shape
+ - **input2**: another tensor with same shape as pred to which input1 is
+ compared for similarity and loss calculation
+ - **sample_weight**: element-wise weighting tensor. Must be
broadcastable
+ to the same shape as input1. For example, if input1 has shape (64,
10)
+ and you want to weigh each sample in the batch separately,
+ sample_weight should have shape (64, 1).
+ - **label**: A 1-D tensor indicating for each pair input1 and input2,
target label is 1 or -1
Review comment:
Label is not the last input variable. It's before the sample_weight, and the
second last variable
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