#######load the saved model for testing the trained network######
model.load_state_dict(torch.load(‘model_FER_CAN.pt’))
   # initialize lists to monitor test loss and accuracy

test_loss = 0.0
class_correct = list(0. for i in range(len(classes)))
class_total = list(0. for i in range(len(classes)))
model.eval() # prep model for evaluation
for data, target in test_loader:
# move tensors to GPU if CUDA is available
if train_on_gpu:
data, target = data.cuda(), target.cuda()
# forward pass: compute predicted outputs by passing inputs to the model
output = model(data)
# calculate the loss
loss = criterion(output, target)
# update test loss
test_loss += loss.item()*data.size(0)
# convert output probabilities to predicted class
_, pred = torch.max(output, 1)
# compare predictions to true label
correct = np.squeeze(pred.eq(target.data.view_as(pred)))
###calculate test accuracy for each object class
print(len(target))
for i in range(len(target)):
    label = target.data[i]
    class_correct[label] += correct[i].item()
    class_total[label] += 1

calculate and print avg test loss
test_loss = test_loss/len(test_loader.sampler)
print(‘Test Loss: {:.6f}\n’.format(test_loss))
for i in range(len(classes)):
if class_total[i] > 0:
print(‘Test Accuracy of %5s: %2d%% (%2d/%2d)’ % (
str(i), 100 * class_correct[i] / class_total[i],
np.sum(class_correct[i]), np.sum(class_total[i])))
else:
print(‘Test Accuracy of %5s: N/A (no training examples)’ % (classes[i]))
print(’\nTest Accuracy (Overall): %2d%% (%2d/%2d)’ % (
100. * np.sum(class_correct) / np.sum(class_total),
np.sum(class_correct), np.sum(class_total)))
error is ==========================================================
class_correct[label] += correct[i].item()
IndexError: invalid index of a 0-dim tensor. Use tensor.item() to convert a 0-dim tensor to a Python number