Hello ptrblck,
I an trying to create a custom loss function in CNN for regression. The input is a binary image (600x600) which the background is black, and foreground is white. The ground truth associated with each input is an image with color range from 0 t 255 which is normalized between 0 and 1.
x =Input, ground truth=y and predicted output=y_hat
I tried to penalize the foreground by custom loss function below, but it didn’t improve the result. I am wondering whether my idea is right or not, if yes what’s wrong with my custom function?
mse = nn.MSELoss(reduction=‘mean’)
def criterion(y, y_hat, x, loss, weight=0.1):
y_hat_modified = torch.where(x[:,0:1]==1, weight*y_hat,y_hat) # x[:,0:1] is input
return loss(y,y_hat_modified)
I created a topic for it and you can see more detailed info there.
custom loss function for regression in cnn