diff --git a/tensorflow/python/keras/losses.py b/tensorflow/python/keras/losses.py
index f75e6af6e30..bda32897fc5 100644
--- a/tensorflow/python/keras/losses.py
+++ b/tensorflow/python/keras/losses.py
@@ -1728,12 +1728,13 @@ def cosine_similarity(y_true, y_pred, axis=-1):
 class CosineSimilarity(LossFunctionWrapper):
   """Computes the cosine similarity between labels and predictions.
 
-  Note that it is a negative quantity between -1 and 0, where 0 indicates
-  orthogonality and values closer to -1 indicate greater similarity. This makes
-  it usable as a loss function in a setting where you try to maximize the
-  proximity between predictions and targets. If either `y_true` or `y_pred`
-  is a zero vector, cosine similarity will be 0 regardless of the proximity
-  between predictions and targets.
+  Note that it is a number between -1 and 1. When it is a negative number
+  between -1 and 0, 0 indicates orthogonality and values closer to -1
+  indicate greater similarity. The values closer to 1 indicate greater
+  dissimilarity. This makes it usable as a loss function in a setting
+  where you try to maximize the proximity between predictions and targets.
+  If either `y_true` or `y_pred` is a zero vector, cosine similarity will be 0
+  regardless of the proximity between predictions and targets.
 
   `loss = -sum(l2_norm(y_true) * l2_norm(y_pred))`