title: TensorRT/TFlite sample implementation
body: <p>Having a trained '.h5' Keras model file, I'm trying to optimize inference time:</p>
  
  <p>Explored 2 options:</p>
  
  <ol>
  <li>Accelerated inference via TensorRT</li>
  <li>'int8' Quantization.</li>
  </ol>
  
  <p>At this point I can convert the model file to TensorFlow protobuf '.pb' format, but as a sidenote, it also contains custom objects of few layers.</p>
  
  <p>Saw a few articles on TensorRT conversion and TFLite conversion, but I don't seem to find a robust implementation that's legible. Can someone explain how that's done (TFLite/Keras Quantization or TensorRT) to use the same model for faster inference.</p>
  
  <p>(Open for other suggestions to improve inference speed supported in TensorFlow and Keras)</p>