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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