I have the code to evaluate the model 
def evaluate_model(model, test_loader, criterion, best_model_name):
model.load_state_dict(torch.load(best_model_name))
# Specify the heartbeat classes from above
classes = {
0: 'N - Normal Beat',
1: 'S - Supraventricular premature or ectopic beat',
2: 'V - Premature ventricular contraction',
3: 'F - Fusion of ventricular and normal beat',
4: 'Q - Unclassified beat'}
# track test loss
test_loss = 0.0
class_correct = list(0. for i in range(5))
class_total = list(0. for i in range(5))
model.eval()
# iterate over test data
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
#print(data.float())
output = model(data.float())
# calculate the batch loss
loss = criterion(output, target.long())
# 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_tensor = pred.eq(target.data.view_as(pred))
correct = np.squeeze(correct_tensor.numpy()) if not train_on_gpu else np.squeeze(correct_tensor.cpu().numpy())
# calculate test accuracy for each object class
for i in range(batch_size):
label = target.data[i].int()
class_correct[label] += correct[i].item()
class_total[label] += 1
# average test loss
test_loss = test_loss/len(test_loader.dataset)
print('Test Loss: {:.6f}\n'.format(test_loss))
for i in range(5):
if class_total[i] > 0:
print('Test Accuracy of %5s: %2d%% (%2d/%2d)' % (
classes[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)))
When I run
evaluate_model(model_1, test_loader_1, criterion, ‘model_ecg_heartbeat_categorization_1.pt’)
I get the output as:
criterion is NLLLoss and batch size is 32
How do I correct it ??Pls help !!