If the effectiveness of a deep neural network in image classification is compared to a chef refining their recipe, then the relationship between the number of layers in the neural network and the quality of the dish can be summarized as: A:B :: C:?

The analogy says that the more layers a neural network has, the better the image‑classification performance, just as a chef who adds more steps to a recipe can improve the final dish. In this comparison, the “number of layers” is the first part of the pair (A), and the “quality of the dish” is the second part (B). The same relationship can be applied to another cooking element: the amount of seasoning (C) leads to the taste of the dish (? ). Thus the analogy reads: number of layers : dish quality :: amount of seasoning : taste.