Add label_from_zero_one argument to LogisticLoss (apache#9265)

* add use_zero_one argument to logisticloss

* add comment

* revise name

* update

* update

python/mxnet/gluon/loss.py

@@ -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.
@@ -643,11 +646,17 @@ class LogisticLoss(Loss):
         - **loss**: loss tensor with shape (batch_size,). Dimenions other than
           batch_axis are averaged out.
     """
-    def __init__(self, weight=None, batch_axis=0, **kwargs):
+    def __init__(self, weight=None, batch_axis=0, label_format='signed', **kwargs):
         super(LogisticLoss, self).__init__(weight, batch_axis, **kwargs)
+        self._label_format = label_format
+        if self._label_format not in ["signed", "binary"]:
+            raise ValueError("label_format can only be signed or binary, recieved %s."
+                             % label_format)
 
     def hybrid_forward(self, F, pred, label, sample_weight=None):
         label = _reshape_like(F, label, pred)
+        if self._label_format == 'binary':
+            label = 2 * label - 1  # Transform label to be either -1 or 1
         loss = F.log(1.0 + F.exp(-pred * label))
         loss = _apply_weighting(F, loss, self._weight, sample_weight)
         return F.mean(loss, axis=self._batch_axis, exclude=True)