I’m a beginner in Computer Vision, Pytorch and working on the multi class classification problem ( MNIST), how do I adjust the penalty for misclassifying, for example, a 5 as a 6, because a human would do similar mistake as they look pretty close ( for a terrible image), or 1 as 7 in few images. The weight parameter in loss function is not really suitable for this, as it’s for imbalanced datasets and I’m not dealing with imbalanced datasets. Do I need to write few if conditions in the custom loss functions or is there a built in way to define less penalty for classifying it wrong between few classes.