How would you make an intentionally bad CNN?

Hey folks,

I’m a data science lecturer and for one of my assignments this year, I want to challenge my students to fix and optimise a CNN coded in keras/TF. The gist is I need to code up a model that is BAD—something full of processing bottlenecks to slow it down, and hyperparameters that hamper the models ability to learn anything. The students will get the model, and will be tasked with “fixing” it—tidying up the input pipeline so that it runs efficiently and adjust the model parameters so that it actually fits properly.

I have a few ideas already, mostly setting up the input pipeline in a convoluted order, using suboptimal activations, etc. But I’m curious to hear other suggestions!