def test()

in code/train.py [0:0]

• 19 lines of code
• 2 McCabe index (conditional complexity)

def test(model, test_loader, device):
    model.eval()
    losses = []
    correct_predictions = 0
    loss_fn = nn.CrossEntropyLoss().to(device)
    tmp_eval_accuracy, eval_accuracy = 0, 0
    
    with torch.no_grad():
        for batch in test_loader:
            b_input_ids = batch['input_ids'].to(device)
            b_input_mask = batch['attention_mask'].to(device)
            b_labels = batch['targets'].to(device)

            outputs = model(b_input_ids,attention_mask=b_input_mask)
            _, preds = torch.max(outputs, dim=1)
            loss = loss_fn(outputs, b_labels)
            correct_predictions += torch.sum(preds == b_labels)
            losses.append(loss.item())
            
    print('\nTest set: Validation loss: {:.4f}, Validation Accuracy: {:.0f}%\n'.format(
        np.mean(losses),
        100. * correct_predictions.double() / len(test_loader.dataset)))