Hi,

I have a CNN model for the classification task with 11 classes.

I want to use `sklearn.metrics`

to print metrics like F1, precision, roc_auc, etc.

I’ve calculated y_real and y_pred as follow:

```
with torch.no_grad():
for inputs, targets in test_dl:
targets = targets[0]
inputs = inputs.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
predict += [preds.cpu().numpy()]
label += [targets.cpu().numpy()]
y_pred = np.concatenate(predict)
y_real = np.concatenate(label)
return y_real, y_pred
```

`y_real`

and `y_pred`

work ok and without any error for precision_score, recall_score, f1_score, for example:

`print(recall_score(y_real, y_pred))`

but for `roc_auc`

, it doesn’t accept `y_real`

and `y_pred`

:

```
print('ROC: {:.4f}\n'.format(roc_auc_score(y_real, y_pred)))
ValueError: multi_class must be in ('ovo', 'ovr')
```

I also tested with `multi_class='ovo'`

and `multi_class='ovr'`

, I got this error:

`AxisError: axis 1 is out of bounds for array of dimension 1`

The type of `y_real`

and `y_pred`

and their shape for both of them are:

```
<class 'numpy.ndarray'>
(550,)
```