anchor.diff:  My methodology is always to first start with a quick and simple baseline model and setup and end-end working code. In case of this challenge, this means:
   
   * Data related I/O - Loading, train/test splits, optional preprocessing blocks
  -* Baseline model: I used FCN encoder-decoder as they learn end-end, simple and showed good results on other segmenatation problems.
  +* Baseline model: I used FCN encoder-decoder as they learn end-end, simple and showed good results on other segmenatation problems. I also retrofitted it with batch normalization, ELU activation, and all that jazz.
   * Clean code: Try to organize code into classes. Having function blocks everywhere makes the code messy and error prone.
   * Submission code: Code to generate submission file.
   
  
negative.code: 
  Despite all this, the cleaned raining data manages to improve baseline model score to ~0.55.
  
  == Step3: Real work begins...
  
  
  
  