```
Hello dear programmers,
```

I am still a beginner in Pytorch. I have found the following function from a repository. I thought that it is aimed to estimate some evaluation metrics such as recall, precision and F-score. However, when I used it in my training model, it gave me some weird values such as a recall of 6.5, precision of 0.08 and F-score of 0.085

def val(model,train_loader):

```
model.train(False)
overlap,Y_area,X_area,recall,precision = [],[],[],[],[]
for i, data in enumerate(train_loader):
x, y_true = data
if y_true.max()>0:
if torch.cuda.is_available():
x, y_true = x.cuda(), y_true.cuda()
output = model(x)
if output.max() > 0.5:
overlap.append(torch.sum(torch.round(output).mul(y_true)).item())
Y_area.append(torch.sum(y_true).item())
X_area.append(torch.sum(torch.round(output)).item())
recall.append(overlap[-1]/Y_area[-1])
precision.append(overlap[-1]/X_area[-1])
print(np.mean(overlap),np.mean(Y_area),np.mean(X_area),np.mean(recall),np.mean(precision))
model.train(True)
return np.mean(recall)*np.mean(precision)/(np.mean(recall)+np.mean(precision)+0.00001)
```

Please, could anyone help with an explanation of this function? I would be very grateful if you could also tell me how to adjust it properly.

Thank you very much for your time and guidance