Evaluate the average accuracy for the whole test dataset and find out which classes performed well, and the classes that did not perform well.

Finish the code and calculate the accuracy for each class.

 

 

class_correct = list(0. for i in range(10))

class_total = list(0. for i in range(10))

with torch.no_grad():

for data in testloader:

images, labels = data

outputs = net(images)

_, predicted = torch.max(outputs, 1)

c = (predicted == labels).squeeze()

for i in range(4):

label = labels[i]

class_correct[label] += c[i].item()

# To do