update fix

            for k, v in six.iteritems(feed_dict)

        }

        n_samples = max(feed_dict[self.membership.out_tensor]) + 1

        feed_dict[self._training_placeholder] = 0.0

        feed_results = self.session.run(outputs, feed_dict=feed_dict)

        if len(feed_results) > 1:

      else:

        return final_results

  def predict_proba_on_generator(self, generator, transformers=[],

                                 outputs=None):

    if not self.built:

      self.build()

    with self._get_tf("Graph").as_default():

      out_tensors = [x.out_tensor for x in self.outputs]

      results = []

      for feed_dict in generator:

        feed_dict = {

            self.layers[k.name].out_tensor: v

            for k, v in six.iteritems(feed_dict)

        }

        n_samples = max(feed_dict[self.membership.out_tensor]) + 1

        feed_dict[self._training_placeholder] = 1.0  ##

        result = np.array(self.session.run(out_tensors, feed_dict=feed_dict))

        if len(result.shape) == 3:

          result = np.transpose(result, axes=[1, 0, 2])

        if len(transformers) > 0:

          result = undo_transforms(result, transformers)

        results.append(result[:n_samples])

      return np.concatenate(results, axis=0)

  def evaluate(self, dataset, metrics, transformers=[], per_task_metrics=False):

    if not self.built:

      self.build()