Hello, I am a beginner in Deep Learning and PyTorch. If my question is not relevant or does not follow the community guidelines, please pardon me.

I am studying the following Kaggle kernel and trying to replicate it:

https://www.kaggle.com/piantic/plant-pathology-2020-pytorch-for-beginner

In the section titled, “PreTrainedModels”, the author uses a resnet34 model and writes the following loss function class:

```
class DenseCrossEntropy(nn.Module):
def __init__(self):
super(DenseCrossEntropy, self).__init__()
def forward(self, logits, labels):
logits = logits.float()
labels = labels.float()
logprobs = F.log_softmax(logits, dim=-1)
loss = -labels * logprobs
loss = loss.sum(-1)
return loss.mean()
```

I’ve read about Cross Entropy Loss function and I think I get the basic gist of it. But I do not understand what’s going on in this class. Can someone please explain what’s going on?

I can see its calculating the log softmax but then why the multiplication with the labels? And why loss.sum(-1)?

This is a Kaggle competition where given a plant image, we have to predict 4 classes (healthy or 3 types of diseases).

Thank you so much for your help!