((encoder_fw_outputs,
  encoder_bw_outputs),
 (encoder_fw_final_state,
  encoder_bw_final_state)) = (
    tf.nn.bidirectional_dynamic_rnn(cell_fw=encoder_cell,
                                    cell_bw=encoder_cell,
                                    inputs=encoder_inputs_embedded,
                                    sequence_length=encoder_inputs_length,
                                    dtype=tf.float64, time_major=True)
    )

dec_outputs, states = tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq(enc_inp, dec_inp, cell, vocab_size, vocab_size, embedding_size, feed_previous=True)

max_batches = 3001
batches_in_epoch = 1000

try:
    for batch in range(max_batches):
        fd = next_feed()
        _, l = sess.run([train_op, loss], fd)
        loss_track.append(l)

        if batch == 0 or batch % batches_in_epoch == 0:
            print('batch {}'.format(batch))
            print('  minibatch loss: {}'.format(sess.run(loss, fd)))
            predict_ = sess.run(decoder_prediction, fd)
            for i, (inp, pred) in enumerate(zip(fd[encoder_inputs].T, predict_.T)):
                print('  sample {}:'.format(i + 1))
                print('    input     > {}'.format(inp))
                print('    predicted > {}'.format(pred))
                if i >= 2:
                    break
            print()

except KeyboardInterrupt:
    print('training interrupted')

---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-42-d7706e679a79> in <module>()
      5     for batch in range(max_batches):
      6         fd = next_feed()
----> 7         _, l = sess.run([train_op, loss], fd)
      8         loss_track.append(l)
      9 

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
    765     try:
    766       result = self._run(None, fetches, feed_dict, options_ptr,
--> 767                          run_metadata_ptr)
    768       if run_metadata:
    769         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
    963     if final_fetches or final_targets:
    964       results = self._do_run(handle, final_targets, final_fetches,
--> 965                              feed_dict_string, options, run_metadata)
    966     else:
    967       results = []

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1013     if handle is None:
   1014       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1015                            target_list, options, run_metadata)
   1016     else:
   1017       return self._do_call(_prun_fn, self._session, handle, feed_dict,

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _do_call(self, fn, *args)
   1033         except KeyError:
   1034           pass
-> 1035       raise type(e)(node_def, op, message)
   1036 
   1037   def _extend_graph(self):

InvalidArgumentError: AttrValue must not have reference type value of float_ref
	 for attr 'tensor_type'
	; NodeDef: Variable/Adam_2/_459 = _Recv[_start_time=0, client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_1721_Variable/Adam_2", tensor_type=DT_FLOAT_REF, _device="/job:localhost/replica:0/task:0/cpu:0"](^Adam_1/update_Variable/mul_2); Op<name=_Recv; signature= -> tensor:tensor_type; attr=tensor_type:type; attr=tensor_name:string; attr=send_device:string; attr=send_device_incarnation:int; attr=recv_device:string; attr=client_terminated:bool,default=false; is_stateful=true>
	 [[Node: Variable/Adam_2/_459 = _Recv[_start_time=0, client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_1721_Variable/Adam_2", tensor_type=DT_FLOAT_REF, _device="/job:localhost/replica:0/task:0/cpu:0"](^Adam_1/update_Variable/mul_2)]]