message: Add an example to keras backend categorical crossentropy documentation.
diff: @@ -4210,6 +4210,20 @@ def categorical_crossentropy(target, output, from_logits=False, axis=-1):
  Raises:
  ValueError: if `axis` is neither -1 nor one of the axes of `output`.
  +
  + Example:
  + ```python:
  + import tensorflow as tf
  + from tensorflow.keras import backend as K
  + a = tf.constant([1., 0., 0., 0., 1., 0., 0., 0., 1.], shape=[3,3])
  + print("a: ", a)
  + b = tf.constant([.9, .05, .05, .5, .89, .6, .05, .01, .94], shape=[3,3])
  + print("b: ", b)
  + loss = K.categorical_crossentropy(a, b)
  + print('Loss: ', loss) #Loss: tf.Tensor([0.10536055 0.8046684 0.06187541], shape=(3,), dtype=float32)
  + loss = K.categorical_crossentropy(a, a)
  + print('Loss: ', loss) #Loss: tf.Tensor([1.1920929e-07 1.1920929e-07 1.1920929e-07], shape=(3,), dtype=float32)
  + ```
  """
  if not from_logits:
  if (isinstance(output, (ops.EagerTensor, variables_module.Variable)) or
  