We have spent considerable time discussing layers of a network; we have learned that layers are made up of weights, configured in such a way that they can transform an input into a desirable output. These weights come at a cost, though; each one (by default) is a 32-bit floating-point number with a typical model, especially in computer vision, having millions resulting in networks that are hundreds of megabytes in size. On top of that; it's plausible that your application will have multiple models (with this chapter being a good example, requiring a model for each style). 

Fortunately, our model in this chapter has a moderate number of weights and weighs in at a mere 2.2 MB; but this is possibly an exception. So we'll use this chapter as an excuse to explore some ways we can reduce our model's weights. But before doing so, let's quickly discuss why, even though it's probably obvious. The three main reasons why you should be conscious of your model's size include...