fancy weighted log loss for keras

fancylogloss.py

def wlogloss(yt, yp):
  '''Weighted log loss for each example in batch.'''

  # Weight false negative errors. This should decrease as recall increases.
  # Get the mean (softmax) activation of outputs that should be positive.
  meanpos = K.sum(yp * yt) / (K.sum(yt) + K.epsilon()) 
  # This is the maximum up-weighting value for false negative errors.
  wfnmax = 20. 
  # Compute the false negative multiplier between 0 and wfnmax. Maybe clipping this at (1, wfnmax) would be smarter.
  wfnmult = (1. - meanpos) * wfnmax 

  # Weight false positive errors - same idea.
  ytinv = K.abs(yt - 1)
  meanneg = K.sum(yp * ytinv) / (K.sum(ytinv) + K.epsilon())
  wfpmax = 20.
  wfpmult = meanneg * wfpmax

  # Define the weight matrix - one weight for every value of yp.
  # Multiply the original yt by your false-negative error, then multiply the inverse of yt by your false positive error.
  # Then add them together to get the weight mask/matrix.
  wmat = (yt * wfnmult) + (ytinv * wfpmult)
  
  # Compute the log-loss for every value of yp.
  errmat = yt * K.log(yp + K.epsilon()) + ((1 - yt) * K.log(1 - yp + K.epsilon()))
  errmat = errmat * -1
  
  # Multiply each value's loss by its corresponding weight in the weight matrix.
  return K.mean(errmat * wmat)