WeightWatcher: Empirical Quality Metrics for Deep Neural Networks

We introduce the weightwatcher (ww), a python tool for a python tool for computing quality metrics of trained, and pretrained, Deep Neural Netwworks. This blog describes how to use the tool in practice; see our most recent paper for even more details. The summary contains the Power Law exponent (), as well as several log norm metrics, as explained in our papers, and below. Each value represents an empirical quality metric that can be used to gauge the gross effectiveness of the model, as compared to similar models. We can use these metrics to compare models across a common architecture series, such as the VGG series, the ResNet series, etc. These can be applied to trained models, pretrained models, and/or even fine-tuned models.