[GitHub] szha commented on a change in pull request #9265: Add label_from_zero_one argument to LogisticLoss

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

szha commented on a change in pull request #9265: Add label_from_zero_one argument to LogisticLoss
URL: https://github.com/apache/incubator-mxnet/pull/9265#discussion_r161412513
 
 

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 File path: python/mxnet/gluon/loss.py
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 @@ -619,16 +619,19 @@ class LogisticLoss(Loss):
         L = \sum_i \log(1 + \exp(- {pred}_i \cdot {label}_i))
 
     where `pred` is the classifier prediction and `label` is the target tensor
-    containing values -1 or 1. `pred` and `label` can have arbitrary shape as
-    long as they have the same number of elements.
+    containing values -1 or 1 (0 or 1 if `label_format` is binary).
+     `pred` and `label` 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.
-
+    label_format : str, default 'signed'
+        Can be either 'signed' or 'binary'. If the label_format is 'signed', all label values should
+        be either -1 or 1. If the label_format is 'binary', all label values should be either
+        0 or 1.
 
     Inputs:
         - **pred**: prediction tensor with arbitrary shape.
 
 Review comment:
   The docstring below should have been updated to reflect the introduction of `label_format`

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