title: Dataset directly from tf.train.SequenceExample
body: <p>I'm working with a <code>NER</code>-like sequence tagging in <code>tensorflow</code> and decided to try <code>tf.data</code> to see if I can get IO performance improvements with my model.</p>
  
  <p>At the moment I am employing <code>TFRecordWriter</code> to preprocess and save my training/validation data, which is a <code>tf.train.SequenceExample()</code> serialized to string. I then load it with <code>tf.data.TFRecordDataset</code>, parse/shuffle/padded_batch it and get on with training, which works fine.</p>
  
  <p><strong>Question is</strong>:</p>
  
  <ul>
  <li>is there a convenient way to make the <code>dataset</code> without first <code>serializing</code> and saving the SeuquenceExamples to <code>tfrecord</code> file?</li>
  <li>It seems to be an unnecessary step when I'll be running the predictions on new data. I've tried playing with <code>tf.data.Dataset.from_tensor_slices()</code>, but it seems not suitable in this scenario as the inputs are sequences of different lengths that are not padded yet.</li>
  </ul>