title: How to turn off dropout for testing in Tensorflow?
body: <p>I am fairly new to Tensorflow and ML in general, so I hereby apologize for a (likely) trivial question. </p>
  
  <p>I use the dropout technique to improve learning rates of my network, and it seems to work just fine. Then, I would like to test the network on some data to see if it works like this:</p>
  
  <pre><code>   def Ask(self, image):
          return self.session.run(self.model, feed_dict = {self.inputPh: image})
  </code></pre>
  
  <p>Obviously, it yields different results each time as the dropout is still in place. One solution I can think of is to create two separate models - one for a training and the other one for an actual later use of the network, however, such a solution seems impractical to me.</p>
  
  <p>What's the common approach to solving this problem? </p>