In your example the your output has the same “probability” for all three classes, i.e. the logits have the same value.

Their probability should therefore be approx `[0.33, 0.33, 0.33]`

.

Since you are using `LogSoftmax`

we can check, if this is true by calling `exp`

on it (thus getting rid of the `log`

):

```
print(m(input))
> tensor([[-1.0986, -1.0986, -1.0986]], grad_fn=<LogSoftmaxBackward>)
print(m(input).exp())
> tensor([[0.3333, 0.3333, 0.3333]], grad_fn=<ExpBackward>)
```

You will get the same values every time you pass the same logits into `LogSoftmax`

.

Now we just have to get the right index using `target`

, multiply with `-1`

, and end up with a loss value of `1.0986`

.