pytest-visual VS receptive_field_analysis_toolbox

I’ve developed a tool called pytest-visual, aiming to make ML code testing more efficient and meaningful. Traditional unit testing often misses visual and functional aspects of ML workflows such as data augmentation and model structures.

On a more personal note, the interaction of input resolution and receptive field allows you to pretty accurately determine if your network is too deep. I created an OpenSource-library for people to check this out: https://github.com/MLRichter/receptive_field_analysis_toolbox. Also, I found out in this publication that the intrinsic dimensionality of the data inside a layer can be analyzed in life during training pretty efficiently and used as a guideline to adjust the width of the network. So, there are some ways to guide neural architecture design and there maybe are more to come, but that's just me being optimistic about my own research.