Merge pull request #1500 from peastman/seqtoseq

            tf.minimum(1.0, anneal_frac * anneal_frac), name='kl_scale')

      else:

        kl_scale = 1.0

      loss += 0.5 * kl_scale * layers.ReduceMean(layers.ReduceSum(kl, axis=1))

      loss += 0.5 * kl_scale * layers.ReduceMean(kl)

    return loss

  def fit_sequences(self,

    for i, sequence in enumerate(sequences):

      for j, token in enumerate(sequence):

        labels[i, j, self._output_dict[token]] = 1

      if lengths[i] < self._max_output_length:

        labels[i, lengths[i], end_marker_index] = 1

      for j in range(lengths[i], self._max_output_length):

        labels[i, j, end_marker_index] = 1

    return labels

  def _batch_elements(self, elements):

  def test_int_sequence(self):

    """Test learning to reproduce short sequences of integers."""

    sequence_length = 10

    sequence_length = 8

    tokens = list(range(10))

    s = dc.models.SeqToSeq(

        tokens,