Hello everyone

Recently as part of one of the Kaggle Competitions, I needed to build a custom loss function which calculates the “Pearson’s correlation coefficients”.

Here is this loss function code:

```
class Regress_Loss_1(torch.nn.Module):
def __init__(self):
super(Regress_Loss_1,self).__init__()
def forward(self,x,y):
x = input
y = target
vx = x - torch.mean(x)
vy = y - torch.mean(y)
cost = torch.sum(vx * vy) / (torch.sqrt(torch.sum(vx ** 2)) * torch.sqrt(torch.sum(vy ** 2)))
loss = cost.mean()
loss = loss*(-1)
return loss
```

Now, after defining my learner, I am using this custom loss function to be used in my training as follows:

`learn_tfidf.crit = Regress_Loss_1`

While this does not give me any error, I wanted to check whether this is a right approach?

Best Regards

Abhik