Hello. While predicting probability using sigmoid activation output will be in the form of

Prediction. Label

Tensor([0.95,0.123]) tensor([1.,0.])

Tensor([0.96,0.42]) tensor([0.,1.])

…so on

Can u please tell me why that 2 probability values are displayed?. And in Label why once it well display 1,0 fit some and 0,1 for some images?

Because you have 2 classes.

Yes. Now how can I classify using these values?

Tell me how can I classify images that belongs to class 0 and also class 1?

Typically you would classify with whatever probability is highest.

```
Tensor([0.95,0.123]) tensor([1.,0.])
Tensor([0.96,0.42]) tensor([0.,1.])
```

Would mean both examples are classified as class 0. Second one has ground truth label of class 1.

I didn’t understood Sir. How it is classified as class 0. Because in second case probability value is more for label 0. In first case it is opposite .