message: Documents that users can provide logits instead of probabilities in SparceCategoricalAccuracy metric.
diff: @@ -651,7 +651,8 @@ class CategoricalAccuracy(MeanMetricWrapper):
  For example, if `y_true` is [[0, 0, 1], [0, 1, 0]] and `y_pred` is
  [[0.1, 0.9, 0.8], [0.05, 0.95, 0]] then the categorical accuracy is 1/2 or .5.
  If the weights were specified as [0.7, 0.3] then the categorical accuracy
  - would be .3.
  + would be .3. You can provide logits of classes as `y_pred`, since argmax of
  + logits and probabilities are same.
  This metric creates two local variables, `total` and `count` that are used to
  compute the frequency with which `y_pred` matches `y_true`. This frequency is
  @@ -701,7 +702,8 @@ class SparseCategoricalAccuracy(MeanMetricWrapper):
  For example, if `y_true` is [[2], [1]] and `y_pred` is
  [[0.1, 0.9, 0.8], [0.05, 0.95, 0]] then the categorical accuracy is 1/2 or .5.
  If the weights were specified as [0.7, 0.3] then the categorical accuracy
  - would be .3.
  + would be .3. You can provide logits of classes as `y_pred`, since argmax of
  + logits and probabilities are same.
  This metric creates two local variables, `total` and `count` that are used to
  compute the frequency with which `y_pred` matches `y_true`. This frequency is
  