title: Loading a trained model from Python to C++ in Tensorflow 1.2
body: <p>I'm looking to run a basic fully-connected neural network for the MNIST dataset with the C++ API v1.2 from Tensorflow. I have trained the model and exported it using <code>tf.train.Saver()</code> in Python. This gave me a checkpoint file, a data file, an index file and a meta file.</p>
  
  <p>I know that the data file contains the saved variables while the meta file contains the graph from using Tensorboard on a previous project. </p>
  
  <blockquote>
    <p>However, I am not sure what is the recommended way to load those files
    and run the trained model in a C++ environment in v1.2, since all the
    tutorials and questions I've found are for older versions which differ
    substantially.</p>
  </blockquote>
  
  <p>I've found that <code>tensorflow::ops::Restore</code> should be the method to do such a thing, but I know that inference in Tensorflow isn't well supported, as such I am not certain what parameters should I give it in order to receive the trained model that I can just put into a <code>session-&gt;Run()</code> and receive an accuracy statement when fed test data.</p>