def validate()

in ASLRecognition/scripts/train.py [0:0]

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

def validate(model, dataloader):
    print('Validating')
    model.eval()
    running_loss = 0.0
    running_correct = 0
    with torch.no_grad():
        for i, data in tqdm(enumerate(dataloader), total=int(len(test_data)/dataloader.batch_size)):
            data, target = data[0].to(device), data[1].to(device)
            outputs = model(data)
            loss = criterion(outputs, target)

            running_loss += loss.item()
            _, preds = torch.max(outputs.data, 1)
            running_correct += (preds == target).sum().item()

        val_loss = running_loss/len(dataloader.dataset)
        val_accuracy = 100. * running_correct/len(dataloader.dataset)
        print(f'Val Loss: {val_loss:.4f}, Val Acc: {val_accuracy:.2f}')

        return val_loss, val_accuracy