title: Regarding number of non-trainable layers in transfer learning
body: <p>I am experimenting VGG16 and InceptionV3 model for my dataset. </p>
  
  <p>One kernel I saw in Kaggle which uses InceptionV3 model had set the 51 initial layers as non-trainable. </p>
  
  <p>On the other hand, somewhere I saw a VGG16 model was used and except last 4 layers all were set as non-trainable. </p>
  
  <p>I am looking for a common generalized approach about how many layers should I set as non-trainable for any model. </p>
  
  <p>Or any basic checklist-list sort of things from which I could decide for my problem statement.</p>
  
  <p>I saw this <a href="https://stackoverflow.com/questions/48572918/transfer-learning-from-the-pre-trained-nasnet-network-how-to-know-the-number-of">Transfer learning from the pre-trained NASnet network. How to know the number of layers to freeze?</a> but could not understand the answer as why that particular point was selected</p>