Perform sanity checks and follow good coding practices

**Have you checked the [list of proposed rules](https://github.com/Benjamin-Lee/deep-rules/issues?q=is%3Aissue+is%3Aopen+label%3Arule) to see if the rule has already been proposed?**

- [x] Yes

**Did you add yourself as a [contributor](https://github.com/Benjamin-Lee/deep-rules/blob/master/contributors.md) by making a pull request if this is your first contribution?**

- [x] Yes, I added myself or am already a contributor

**Feel free to elaborate, rant, and/or ramble.**
When coding DL models, it is important to maintain good software engineering practices. All code should be documented and include rigorous tests. Sanity checks are also useful. For instance, something is probably wrong (e.g. bug in code, ill posed problem, bad hyperparameters) if model training loss does not decrease (i.e. not overfitting) when considering a very small subset of the training data.

**Any citations for the rule?** (peer-reviewed literature preferred but not required)
- https://arxiv.org/pdf/1206.5533v2.pdf
- http://cs231n.github.io/neural-networks-3/#sanitycheck