More fixes

    if not self.built:

      self.build()

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

      with tf.Session() as sess:

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

      results = []

      for feed_dict in generator:

            for k, v in six.iteritems(feed_dict)

        }

        feed_dict[self._training_placeholder] = 1.0  ##

          result = np.array(sess.run(out_tensors, feed_dict=feed_dict))

        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:

    if not self.built:

      self.build()

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

      with tf.Session() as sess:

        saver = tf.train.Saver()

        self._initialize_weights(sess, saver)

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

      results = []

      for feed_dict in generator:

            for k, v in six.iteritems(feed_dict)

        }

        feed_dict[self._training_placeholder] = 0.0

          result = np.array(sess.run(out_tensors, feed_dict=feed_dict))

        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])

        result = undo_transforms(result, transformers)