I am getting this error -

```
output = model.forward(images)
---> 90 conf_matrix = confusion_matrix(output, labels, conf_matrix)
91 p = torch.nn.functional.softmax(output, dim=1)
92 prediction = torch.argmax(p, dim=1)
50 preds = torch.argmax(preds, 1)
51 for p, t in zip(preds, labels):
---> 52 conf_matrix[p, t] += 1
53
54 #print(conf_matrix)
```

I have implemented a Transfer Learning with VGG16 and tried to print precision, recall, and conf_matrix. I have printed Vgg16 from `models`

and used it.

```
model = models.vgg16(pretrained=True)
for param in model.parameters():
param.requires_grad = True
model.fc = nn.Sequential(nn.Linear(25088, 4096),
nn.ReLU(),
nn.Dropout(0.4),
nn.Linear(4096,4096),
nn.ReLU(),
nn.Dropout(0.4),
nn.Linear(4096,39),
nn.LogSoftmax(dim=1))
```

I am really not getting where is exactly the problme is with the Conf_matrix

```
def confusion_matrix(preds, labels, conf_matrix, title='Confusion matrix', cmap=plt.cm.Blues):
preds = torch.argmax(preds, 1)
for p, t in zip(preds, labels):
conf_matrix[p, t] += 1
#print(conf_matrix)
#plt.imshow(conf_matrix)
TP = conf_matrix.diag()
for c in range(n_classes):
idx = torch.ones(n_classes).byte()
idx[c] = 0
TN = conf_matrix[idx.nonzero()[:,None], idx.nonzero()].sum()
FP = conf_matrix[c, idx].sum()
FN = conf_matrix[idx, c].sum()
Recall = (TP[c] / (TP[c]+FN))
precision = (TP[c] / (TP[c]+FP))
f1 = (2 * ((precision * Recall)/(precision + Recall)))
#print('Class {}\nTP {}, TN {}, FP {}, FN {}'.format(c, TP[c], TN, FP, FN))
#print('Sensitivity = {}'.format(sensitivity))
#print('Specificity = {}'.format(specificity))
return conf_matrix
conf_matrix = torch.zeros(n_classes, n_classes)
```

Lastly, this is my training loop

```
for images, labels in dataloader_train:
#steps += 1
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
output = model.forward(images)
conf_matrix = confusion_matrix(output, labels, conf_matrix)
p = torch.nn.functional.softmax(output, dim=1)
prediction = torch.argmax(p, dim=1)
#loss = torch.nn.functional.nll_loss(torch.log(p), y)
loss = criterion(output, labels)
loss.backward()
optimizer.step()
train_loss += loss.item()*images.size(0)
```

Any help is appriciaed. Thank you.