testfunction.py

def test():
    model.eval()
    test_acc = 0.0
    for i, (images, labels) in enumerate(test_loader):

        if cuda_avail:
            images = Variable(images.cuda())
            labels = Variable(labels.cuda())

        # Predict classes using images from the test set
        outputs = model(images)
        _, prediction = torch.max(outputs.data, 1)
        
        test_acc += torch.sum(prediction == labels.data)

    # Compute the average acc and loss over all 10000 test images
    test_acc = test_acc / 10000

    return test_acc