I propose a different variant of `accuracy_thresh`

that can be used to calculate what fractions examples are classified perfectly in multiclass classification problems.

For the following, I will assume `thresh=0.5`

. The current `accuracy_thresh`

function calculates for each examples what fraction of classes where identified correctly. For example, in a face classification problem, if the true labels are “bald, not smiling” and the predicted labels are “bald, smiling”, then 50% of the classes where identified correctly. `accuracy_thresh`

computes the mean of these fractions over all examples.

Instead, it can be useful to only consider a classification a success if **all** the classes are identified correctly. Effectively, this means setting the above mentioned fractions to 0 if they are not 1, and then calculate the mean.

The function would look like this:

```
def multilabel_accuracy(y_pred, y_true, tresh):
y_pred = y_pred > tresh
y_true = y_true.byte()
return (y_pred == y_true).all(1).float().mean()
```

If this is deemed useful, I can create a pull request for this. It would also be possible to make this an optional parameter for `accuracy_thresh`

. What do you think?