title: How to inference Tensorflow model with input queue pipeline?
body: <p>I am struggling with my Tensorflow model. I have trained it using <em>tf.PaddingFIFOQueue</em> and then I mainly used this tutorial: <a href="https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-and-serve-it-with-a-python-api-d4f3596b3adc#.dykqbzqek" rel="nofollow noreferrer">https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-and-serve-it-with-a-python-api-d4f3596b3adc#.dykqbzqek</a>, for freezing the graph with its variables and later on loading it into a library for inferencing the model. </p>
  
  <p>My problem is that I don't really know how to run the model for predicting once it is loaded. In the case of just a placeholder as an input it is just required to get the input and output variables and then run the model:</p>
  
  <pre><code># We load the graph
  graph_path = ...
  graph = load_graph(graph_path)
  
  # We launch a Session
  with tf.Session(graph=graph) as sess:
    # Note: we didn't initialize/restore anything, everything is stored in the graph_def
    y_out = sess.run(y, feed_dict={
      x: [[3, 5, 7, 4, 5, 1, 1, 1, 1, 1]] # &lt; 45
    })
    print(y_out) # [[ False ]] Yay, it works!
  </code></pre>
  
  <p>In this example it really looks straightforward but for the use case with input pipeline I didn't really figure out how to make it work. I didn't even find anything related. I some one could give me a hint how this should be done, or how people normally use Tensorflow in production would be really helpful.</p>