title: tensorflow high level api vs low level api
body: <p>The question is simple.</p>
  
  <p>Should one go for low level APIs or high level API in tensorflow in his/her own projects?</p>
  
  <p>If I go for high level APIs only in my Neural Network implementation: Will I e.g. be able to apply such solutions like dropout, batch normalization, learning decay, etc.? Or is it inevitable to start using lower level APIs for such cases?</p>
  
  <p>My view so far is that the tensorflow community does a great job in almost everything they do but I unfortunately cannot say the same thing for their documentation. It really takes time to clearly figure out how they thought when implementing the API. Then the question is if it is really worth the time I and many others spend to make things work? Or should we always search for solutions in the high level APIs?</p>
  
  <p>It is not clear to me either where the boundary goes between high level APIs and low level APIs.</p>