Updated "tf.keras.losses.cosine_similarity()" docs

tensorflow/python/keras/losses.py

@@ -1681,27 +1681,24 @@ def poisson(y_true, y_pred):
 def cosine_similarity(y_true, y_pred, axis=-1):
   """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))`
 
   Usage:
 
-  >>> y_true = [[0., 1.], [1., 1.]]
-  >>> y_pred =[[1., 0.], [1., 1.]]
+  >>> y_true = [[0., 1.], [1., 1.], [1., 1.]]
+  >>> y_pred = [[1., 0.], [1., 1.], [-1., -1.]]
   >>> loss = tf.keras.losses.cosine_similarity(y_true, y_pred, axis=1)
-  >>> # l2_norm(y_true) = [[0., 1.], [1./1.414], 1./1.414]]]
-  >>> # l2_norm(y_pred) = [[1., 0.], [1./1.414], 1./1.414]]]
-  >>> # l2_norm(y_true) . l2_norm(y_pred) = [[0., 0.], [0.5, 0.5]]
-  >>> # loss = -sum(l2_norm(y_true) . l2_norm(y_pred), axis=1)
-  >>> #       = -[0. + 0., 0.5 + 0.5]
   >>> loss.numpy()
-  array([-0., -0.999], dtype=float32)
+  array([-0., -0.999, 0.999], dtype=float32)
 
   Args:
     y_true: Tensor of true targets.