Hey guys, I am making a ROC graph for my multi-class classification problem. I have FPR and TPR and I am following the tutorial from scikit learn, printing multiclass ROC curve. For this, I am getting the prediction values from each and every epoch and I am taking labels as y_test. This is my code for ROC(Initial stage) -

```
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
from scipy import interp
# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(nb_classes):
fpr[i], tpr[i], _ = roc_curve(labels[:, i], prediction[:, i]) # here change y_test to labels
roc_auc[i] = auc(fpr[i], tpr[i])
# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = roc_curve(labels.ravel(), prediction.ravel()) # and here
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
```

Now it is telling that -

```
Automatically created module for IPython interactive environment
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-34-bda71c8fb3a6> in <module>()
17 roc_auc = dict()
18 for i in range(nb_classes):
---> 19 fpr[i], tpr[i], _ = roc_curve(labels[:, i], prediction[:, i]) # here change y_test to labels
20 roc_auc[i] = auc(fpr[i], tpr[i])
21 # Compute micro-average ROC curve and ROC area
IndexError: too many indices for tensor of dimension 1
```

I have checked about shape of prediction and labels. Those are both `torch.Size([32])`

.

I wonder why it is throwing an error. Can anyone of you help? Thanks.