Improves documentation of labels and logits arguments in hinge loss methods .

PiperOrigin-RevId: 197487120

diff --git a/tensorflow/contrib/losses/python/losses/loss_ops.py b/tensorflow/contrib/losses/python/losses/loss_ops.py
index bdad34a..651de4e 100644 (file)
--- a/tensorflow/contrib/losses/python/losses/loss_ops.py
+++ b/tensorflow/contrib/losses/python/losses/loss_ops.py
@@ -482,9 +482,12 @@ def hinge_loss(logits, labels=None, scope=None):
   """Method that returns the loss tensor for hinge loss.
 
   Args:
-    logits: The logits, a float tensor.
+    logits: The logits, a float tensor. Note that logits are assumed to be
+      unbounded and 0-centered. A value > 0 (resp. < 0) is considered a positive
+      (resp. negative) binary prediction.
     labels: The ground truth output tensor. Its shape should match the shape of
-      logits. The values of the tensor are expected to be 0.0 or 1.0.
+      logits. The values of the tensor are expected to be 0.0 or 1.0. Internally
+      the {0,1} labels are converted to {-1,1} when calculating the hinge loss.
     scope: The scope for the operations performed in computing the loss.
 
   Returns:

diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py
index 9fc545c..de9b3c6 100644 (file)
--- a/tensorflow/python/ops/losses/losses_impl.py
+++ b/tensorflow/python/ops/losses/losses_impl.py
@@ -334,8 +334,11 @@ def hinge_loss(labels, logits, weights=1.0, scope=None,
 
   Args:
     labels: The ground truth output tensor. Its shape should match the shape of
-      logits. The values of the tensor are expected to be 0.0 or 1.0.
-    logits: The logits, a float tensor.
+      logits. The values of the tensor are expected to be 0.0 or 1.0. Internally
+      the {0,1} labels are converted to {-1,1} when calculating the hinge loss.
+    logits: The logits, a float tensor. Note that logits are assumed to be
+      unbounded and 0-centered. A value > 0 (resp. < 0) is considered a positive
+      (resp. negative) binary prediction.
     weights: Optional `Tensor` whose rank is either 0, or the same rank as
       `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
       be either `1`, or the same as the corresponding `losses` dimension).