def val_test(dataloader, model):
    # Get dataset size
    dataset_size = len(dataloader.dataset)

    # Turn off gradient calculation for validation
    with torch.no_grad():
        # Loop over dataset
        correct = 0
        wrong_preds = []
        for (images, labels) in dataloader:
            images, labels = images.to(device), labels.to(device)

            # Get raw values from model
            output = model(images)

            # Derive prediction
            y_pred = output.argmax(1)

            # Count correct classifications over all batches
            correct += (y_pred == labels).type(torch.float32).sum().item()

            # Save wrong predictions (image, pred_lbl, true_lbl)
            for i, _ in enumerate(labels):
                if y_pred[i] != labels[i]:
                    wrong_preds.append((images[i], y_pred[i], labels[i]))

        # Calculate accuracy
        acc = correct / dataset_size

    return acc, wrong_preds