title: TensorFlow: How to predict from a SavedModel?
body: <p>I have exported a <code>SavedModel</code> and now I with to load it back in and make a prediction. It was trained with the following features and labels:</p>
  
  <pre><code>F1 : FLOAT32
  F2 : FLOAT32
  F3 : FLOAT32
  L1 : FLOAT32
  </code></pre>
  
  <p>So say I want to feed in the values <code>20.9, 1.8, 0.9</code> get a single <code>FLOAT32</code> prediction. How do I accomplish this? I have managed to successfully load the model, but I am not sure how to access it to make the prediction call.</p>
  
  <pre><code>with tf.Session(graph=tf.Graph()) as sess:
      tf.saved_model.loader.load(
          sess,
          [tf.saved_model.tag_constants.SERVING],
          "/job/export/Servo/1503723455"
      )
  
      # How can I predict from here?
      # I want to do something like prediction = model.predict([20.9, 1.8, 0.9])
  </code></pre>
  
  <p>This question is not a duplicate of the question posted <a href="https://stackoverflow.com/questions/46098863">here</a>. This question focuses on a minimal example of performing inference on a <code>SavedModel</code> of any model class (not just limited to <code>tf.estimator</code>) and the syntax of specifying input and output node names.</p>