# How to check and read Confusion matrix?

**URL:** <https://discuss.pytorch.org/t/how-to-check-and-read-confusion-matrix/41835>\
**Category:** Uncategorized\
**Created:** [April 6, 2019, 5:15pm UTC](https://discuss.pytorch.org/t/how-to-check-and-read-confusion-matrix/41835 "2019-04-06T17:15:20Z")\
**Posts on this page:** 1\
**Showing post:** 10

<div class="post-metadata">

**Author:** ![Deb\_Prakash\_Chatterj](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/deb_prakash_chatterj/32/10121_2.png) [@Deb\_Prakash\_Chatterj](https://discuss.pytorch.org/u/Deb_Prakash_Chatterj)\
**Post date:** [April 11, 2019, 1:31pm UTC](https://discuss.pytorch.org/t/how-to-check-and-read-confusion-matrix/41835/10 "2019-04-11T13:31:49Z")

</div>

> If you just want to print it at the end, past the `print` statements at the desired place (e.g. after the training loop).

So, it will be like this right?

```auto
for epoch in range(epochs):
  
  running_loss = 0
  model.train()
  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()
    
    running_loss += loss.item()
    
  #if steps % print_every == 0:
  valid_loss = 0
  accuracy = 0
  model.eval()
  #print(conf_matrix)( **NOT HERE RIGHT?** )
  for images, labels in dataloader_test:
    optimizer.zero_grad()
    with torch.no_grad():
       
      images, labels = images.to(device), labels.to(device)

      output = model.forward(images)
      p = torch.nn.functional.softmax(output, dim=1)
      prediction = torch.argmax(p, dim=1)
      loss = criterion(output, labels)
          
      valid_loss += loss.item()
          
      ps = torch.exp(output)
         
      top_p, top_class = ps.topk(1, dim = 1)
      equals = top_class == labels.view(*top_class.shape)
      accuracy += torch.mean(equals.type(torch.FloatTensor))
  
  print(conf_matrix)      
  print("Epoch: {}/{} " .format(epoch+1, epochs))
  print("Train loss: {:.4f}.. " .format(running_loss/len(dataloader_train)))
  print("Valid loss: {:.4f}.. " .format(valid_loss/len(dataloader_test)))
  print("Accuracy: {:.4f}.. " .format(accuracy/len(dataloader_test)))
  model.train()

```

> Also, it looks like you are computing the training and validation stats together, which is a bad idea. Usually you would re-create the confusion matrix for each dataset.

So, do I have made two different functions for confusion matrix? If not then what should I do? I have stopped all the print statements in the def Confusion\_matrix. Thanks.

---

_[View the full topic](https://discuss.pytorch.org/t/how-to-check-and-read-confusion-matrix/41835)._