piiswrong commented on a change in pull request #7304: gluon bce & ctc losses
URL: https://github.com/apache/incubator-mxnet/pull/7304#discussion_r131712363
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File path: python/mxnet/gluon/loss.py
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@@ -142,6 +144,46 @@ def hybrid_forward(self, F, output, label,
sample_weight=None):
return F.mean(loss, axis=self._batch_axis, exclude=True)
+class BinaryCrossEntropyLoss(Loss):
+ r"""The cross-entropy loss for binary classification.
+
+ BCE loss is useful when training logistic regression.
+
+ .. math::
+ loss(o, t) = - 1/n \sum_i (t[i] * log(o[i]) + (1 - t[i]) * log(1 -
o[i]))
+
+
+ Parameters
+ ----------
+ from_sigmoid : bool, default is `False`
+ Whether the input is from the output of sigmoid. Set this to false
will make
+ the loss calculate sigmoid and then BCE, which is more numerically
stable through
+ log-sum-exp trick.
+ weight : float or None
+ Global scalar weight for loss.
+ sample_weight : Symbol or None
+ Per sample weighting. Must be broadcastable to
+ the same shape as loss. For example, if loss has
+ shape (64, 10) and you want to weight each sample
+ in the batch, `sample_weight` should have shape (64, 1).
+ batch_axis : int, default 0
+ The axis that represents mini-batch.
+ """
+ def __init__(self, from_sigmoid=False, weight=None, batch_axis=0,
**kwargs):
+ super(BinaryCrossEntropyLoss, self).__init__(weight, batch_axis,
**kwargs)
+ self._from_sigmoid = from_sigmoid
+
+ def hybrid_forward(self, F, output, label, sample_weight=None):
+ label = label.reshape((-1, 1))
Review comment:
see l1loss for how to reshape
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