You’ve framed your problem, prepared your datasets, designed your models and revved up your GPUs. With bated breath, you start training your neural network, hoping to return in a few days to great results.

When you do return though, you find yourself faced with a very different picture. Your network seems to do no better than random selection. Or, if it is a classification model, has curiously learned to classify all entries to a single dominant category. You scratch your head wondering what went wrong, and hit a wall. What’s more, since you’re programming at a higher layer of abstraction, you have no intuitive sense for what’s going on with your matrices and activation functions.

This isn’t a problem faced only by beginners. Empirically, it happens to even the more experienced among us, especially as the complexity of models, the dataset and the core problem increases. So if you find yourself in this situation, don’t fret. To tackle this, we’ve put together a little checklist that might help you find a way out of this hole. This was written specifically in the context of image classification, but the advice is generic enough to apply to all types of networks.

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