gaurav-gireesh commented on a change in pull request #12750: [MXNET -1030]
Cosine Embedding Loss
URL: https://github.com/apache/incubator-mxnet/pull/12750#discussion_r228599157
##########
File path: python/mxnet/gluon/loss.py
##########
@@ -767,3 +767,69 @@ 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
Review comment:
Addressed this. Thanks
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
[email protected]
With regards,
Apache Git Services
