message: Remove unnecesarry min in Keras losses
  This removes an unnecessary comparision since `K.clip(x, min_value, None)` is equivalent to `K.maximum(x, min_value)`.
diff: @@ -793,7 +793,7 @@ def mean_absolute_percentage_error(y_true, y_pred): # pylint: disable=missing-d
  y_pred = ops.convert_to_tensor(y_pred)
  y_true = math_ops.cast(y_true, y_pred.dtype)
  diff = math_ops.abs(
  - (y_true - y_pred) / K.clip(math_ops.abs(y_true), K.epsilon(), None))
  + (y_true - y_pred) / K.maximum(math_ops.abs(y_true), K.epsilon()))
  return 100. * K.mean(diff, axis=-1)
  @@ -806,8 +806,8 @@ def mean_absolute_percentage_error(y_true, y_pred): # pylint: disable=missing-d
  def mean_squared_logarithmic_error(y_true, y_pred): # pylint: disable=missing-docstring
  y_pred = ops.convert_to_tensor(y_pred)
  y_true = math_ops.cast(y_true, y_pred.dtype)
  - first_log = math_ops.log(K.clip(y_pred, K.epsilon(), None) + 1.)
  - second_log = math_ops.log(K.clip(y_true, K.epsilon(), None) + 1.)
  + first_log = math_ops.log(K.maximum(y_pred, K.epsilon()) + 1.)
  + second_log = math_ops.log(K.maximum(y_true, K.epsilon()) + 1.)
  return K.mean(math_ops.squared_difference(first_log, second_log), axis=-1)
  