Custom loss

[turgut090]

No description provided.

[turgut090]

E.g.

my_loss = nn_loss(function(inputs, targets, alpha=0.8, gamma=2L, smooth=1L) {
  #comment out if your model contains a sigmoid or equivalent activation layer
  #inputs = Functional$sigmoid(inputs)       
  
  #flatten label and prediction tensors
  inputs = inputs$view(-1L)
  targets = targets$view(-1L)
  
  #first compute binary cross-entropy 
  BCE = Functional$binary_cross_entropy(inputs, targets, reduction='mean')
  BCE_EXP = torch$exp(-BCE)
  focal_loss = alpha * (1-BCE_EXP)^gamma * BCE
  focal_loss
},name = 'BCE')

learn = Learner(dls, model, loss_func=my_loss, metrics=accuracy())

based on https://www.kaggle.com/bigironsphere/loss-function-library-keras-pytorch