LogLoss on Kaggle
<SCODE>LogLoss= −1/n∑i=1 to n [yi log(ŷ i) + (1−yi) log(1−ŷ i)],

where:
  n   : is the number of patients in the test set
  ŷi  : is the predicted probability of the image belonging to a patient with cancer
  yi  : is 1 if the diagnosis is cancer, 0 otherwise
<ECODE> <SCODE>torch.nn MLSM Loss:
loss(x, y) = - sum_i (y[i] log( exp(x[i]) / (1 + exp(x[i])))
                      + (1-y[i]) log(1/(1+exp(x[i])))) / x:nElement()
<ECODE> Thanks!
FuriouslyCurious
Hi Torchies, Bumping an old question… if someone has a paper / presentation comparing different loss functions and their effects, it will really help me out. Thanks!
FuriouslyCurious