old_contents: #!/usr/bin/env python
  
  import neukrill_net.utils as utils
  import neukrill_net.image_processing as image_processing
  
  import csv
  import pickle
  from sklearn.externals import joblib
  import numpy as np
  import glob
  import os
  
  def main():
      settings = utils.Settings('settings.json')
  
      image_fname_dict = settings.image_fnames
  
      processing = lambda image: image_processing.resize_image(image, (48,48))
  
      X, names = utils.load_data(image_fname_dict, processing=processing,
                                 verbose=True)
  
      clf = joblib.load('model.pkl')
      p = clf.predict_proba(X)
  
      with open('submission.csv', 'w') as csv_out:
          out_writer = csv.writer(csv_out, delimiter=',')
          out_writer.writerow(['image'] + list(settings.classes))
          for index in range(len(names)):
              out_writer.writerow([names[index]] + list(p[index,]))
  
  if __name__ == '__main__':
      main()
  
  
new_contents: #!/usr/bin/env python
  
  import neukrill_net.utils as utils
  import neukrill_net.image_processing as image_processing
  
  import csv
  import pickle
  from sklearn.externals import joblib
  import numpy as np
  import glob
  import os
  
  def main():
      settings = utils.Settings('settings.json')
  
      image_fname_dict = settings.image_fnames
  
      processing = lambda image: image_processing.resize_image(image, (48,48))
  
      X, names = utils.load_data(image_fname_dict, processing=processing,
                                 verbose=True)
      
      clf = joblib.load('model.pkl')
      p = clf.predict_proba(X)
      
      utils.write_predictions('submission.csv', p, names, settings)
      
  
  if __name__ == '__main__':
      main()
  
  
subject: Swap to using submission prediction writer function