title: Transfer learning from the pre-trained NASnet network. How to know the number of layers to freeze?
body: <p>In order to train a model for image classification (using Keras or Tensorflow) I want to retrain a certain number of layers of the NASNetMobile, using my own dataset of images. </p>
  
  <p>In this paper: <a href="https://arxiv.org/pdf/1707.07012.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1707.07012.pdf</a> (section A.7) we can read: "Additionally, all models use an auxiliary classifier located at 2/3 of the way up the network". </p>
  
  <p>Here the layers of the NasNetMobile that I want to do the transfer learning from: <a href="https://gist.github.com/didacroyo/a451c890b1f02822c7dd67c6f270f1d6" rel="nofollow noreferrer">https://gist.github.com/didacroyo/a451c890b1f02822c7dd67c6f270f1d6</a></p>
  
  <p>Then, based on the previous, should I freeze the bottom 1/3 of layers?
  (this is the first 250 layers)</p>