Hey guys, currently re-reading chapter 04_mnist_basics and I got stumped on the following example:

Suppose we had three images which we knew were a 3, a 7, and a 3. And suppose our model predicted with high confidence (

`0.9`

) that the first was a 3, with slight confidence (`0.4`

) that the second was a 7, and with fair confidence (`0.2`

), but incorrectly, that the last was a 7.

They then created two variables to represent the predictions and the targets:

```
trgts = tensor([1,0,1])
prds = tensor([0.9, 0.4, 0.2])
```

Then after creating the loss function:

```
def mnist_loss(predictions, targets):
return torch.where(targets==1, 1-predictions, predictions).mean()
```

They ran it as follows:

```
Input: torch.where(trgts==1, 1-prds, prds)
Output: tensor([0.1000, 0.4000, 0.8000])
```

To calculate the final loss as a scalar, they ran:

```
Input: mnist_loss(prds,trgts)
Output: tensor(0.4333)
```

**Here is what stumped me:**

Afterwards it was taught that by changing the prediction for the one “false” target from *0.2* to *0.8*, it would cause the loss to decrease which indicates a better prediction.

Indeed after writing the code, it does:

```
Input: mnist_loss(tensor([0.9, 0.4, 0.8]),trgts)
Output: tensor(0.2333)
```

This obviously does not make sense. Since the “model” predicted the wrong target with a high level of confidence, that should indicate that the model is not performing so well and should thus increase the loss, not lower it.

I believe I am missing something here and I think it lies within how the torch.where function seems to work. I’m not 100% sure though. Any help is greatly appreciated.