[GitHub] anirudhacharya commented on a change in pull request #13612: add pos_weight for SigmoidBinaryCrossEntropyLoss

[GitBox <gi...@apache.org>]

anirudhacharya commented on a change in pull request #13612: add pos_weight for SigmoidBinaryCrossEntropyLoss
URL: https://github.com/apache/incubator-mxnet/pull/13612#discussion_r242279400
 
 

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 File path: python/mxnet/gluon/loss.py
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 @@ -227,13 +238,20 @@ def __init__(self, from_sigmoid=False, weight=None, batch_axis=0, **kwargs):
         super(SigmoidBinaryCrossEntropyLoss, self).__init__(weight, batch_axis, **kwargs)
         self._from_sigmoid = from_sigmoid
 
-    def hybrid_forward(self, F, pred, label, sample_weight=None):
+    def hybrid_forward(self, F, pred, label, sample_weight=None, pos_weight=None):
         label = _reshape_like(F, label, pred)
         if not self._from_sigmoid:
             # We use the stable formula: max(x, 0) - x * z + log(1 + exp(-abs(x)))
-            loss = F.relu(pred) - pred * label + F.Activation(-F.abs(pred), act_type='softrelu')
+            if pos_weight is None:
 
 Review comment:
   can default `pos_weight` be set to 1 since it is a multiplicative factor and update the formulas accordingly. Then we can do away with this `if else` statement.

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