message: fixed missing minus sign
  fixed `cosine_similarity` documentation showing an averaging over instances
  synchronized `cosine_similarity` and `CosineSimilarity` docs
diff: @@ -1698,8 +1698,8 @@ def 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 = mean(sum(l2_norm(y_true) . l2_norm(y_pred), axis=1))
  - >>> # = ((0. + 0.) + (0.5 + 0.5)) / 2
  + >>> # 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)
  @@ -1718,9 +1718,16 @@ def cosine_similarity(y_true, y_pred, axis=-1):
  @keras_export('keras.losses.CosineSimilarity')
  class CosineSimilarity(LossFunctionWrapper):
  - """Computes the cosine similarity between `y_true` and `y_pred`.
  + """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.
  - `loss = -sum(y_true * y_pred)`
  + `loss = -sum(l2_norm(y_true) * l2_norm(y_pred))`
  Usage:
  @@ -1732,7 +1739,7 @@ class CosineSimilarity(LossFunctionWrapper):
  >>> # 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 = mean(sum(l2_norm(y_true) . l2_norm(y_pred), axis=1))
  - >>> # = ((0. + 0.) + (0.5 + 0.5)) / 2
  + >>> # = -((0. + 0.) + (0.5 + 0.5)) / 2
  >>> cosine_loss(y_true, y_pred).numpy()
  -0.5
  