title: What are the scenarios for which the various TensorFlow data loading idioms apply?
body: <p>I have a TensorFlow deep learning workflow in which I have a fairly simple data reading and feeding pipeline built using regular NumPy; but I see that TensorFlow offers a large number of functions for loading data and building a data pipeline. I wonder though what scenarios these target. It seems there are two:</p>
  
  <ol>
  <li>learning that involves very large real world datasets, and </li>
  <li>networks built with the with the <a href="https://github.com/tflearn/tflearn" rel="nofollow noreferrer">high-level TensorFlow API</a>.</li>
  </ol>
  
  <p>It seems that the benefits of using <a href="https://www.tensorflow.org/programmers_guide/reading_data" rel="nofollow noreferrer">"reading" as opposed to "feeding"</a> (e.g. functions such as <code>tf.train.shuffle_batch</code>, but even simple helpers like <code>tf.one_hot</code>) apply to the former, while much of the documentation for <a href="https://www.tensorflow.org/get_started/input_fn" rel="nofollow noreferrer">things like input functions</a>, seems targeted exclusively at the latter.</p>
  
  <p>In my case, where I'm working with files that can easily be read using standard Python and can efficiently be loaded into memory all at once, feeding simply using <code>np.array</code>s works fine, and the other approaches either seem far too complex (reading pipelines; which are actually quite slow, to the extent I've tired them)  or inappropriate (the "high-level" API, given that I mostly use the "low level" API).</p>
  
  <p>Am I correct in concluding that given that I already use TensorFlow proper (not the low level learning API) and that feeding NumPy arrays meets my needs, there's no good reason to bother with either of the alternative approaches? Is it fair to say that the intended targets for these approaches are different from mine? </p>
  
  <p>Or is there a another taxonomy that better factors the various TensorFlow data loading idioms, and the scenarios to which they apply?</p>