message: Clarify -1 is multiplied on cosine similarity
diff: @@ -1922,16 +1922,16 @@ def poisson(y_true, y_pred):
  ])
  @dispatch.add_dispatch_support
  def cosine_similarity(y_true, y_pred, axis=-1):
  - """Computes the cosine similarity between labels and predictions.
  + """Computes the negative cosine similarity between labels and predictions.
  - 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.
  + Note that it is a number between -1 and 1. As -1 is multiplied,
  + values closer to -1 indicate greater similarity and 0 indicates
  + orthogonality, when it is a negative number between -1 and 0.
  + 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))`
  @@ -1949,7 +1949,7 @@ def cosine_similarity(y_true, y_pred, axis=-1):
  axis: Axis along which to determine similarity.
  Returns:
  - Cosine similarity tensor.
  + Negative Cosine similarity tensor.
  """
  y_true = nn.l2_normalize(y_true, axis=axis)
  y_pred = nn.l2_normalize(y_pred, axis=axis)
  @@ -1958,15 +1958,16 @@ def cosine_similarity(y_true, y_pred, axis=-1):
  @keras_export('keras.losses.CosineSimilarity')
  class CosineSimilarity(LossFunctionWrapper):
  - """Computes the cosine similarity between labels and predictions.
  -
  - 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.
  + """Computes the negative cosine similarity between labels and predictions.
  +
  + Note that it is a number between -1 and 1. As -1 is multiplied,
  + values closer to -1 indicate greater similarity and 0 indicates
  + orthogonality, when it is a negative number between -1 and 0.
  + 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))`
  