Detecting Over-Trained Layers in Deep Neural Networks with WeightWatcher

Are you struggling with training a Deep Neural Network (DNN) and finding that your model is over-trained or not performing well? It can be challenging to pinpoint where the issue lies, but there is a tool that can help with that. WeightWatcher is an open-source, data-free diagnostic tool for analyzing trained DNNs, based on research into Why Deep Learning Works in collaboration with UC Berkeley.

By using WeightWatcher, you can inspect the weight matrices of your layers to see if they are converging properly and detect if a layer is over-trained. The tool uses the alpha metric, which measures how Heavy-Tailed a layer is. If the alpha drops below 2, it suggests that the layer may be over-trained.

In a carefully-designed experiment with a 3-layer Multi-Layer Perceptron (MLP) trained on MNIST, different batch sizes were used to induce over-training. The experiments were made deterministic for reproducibility, and the training was controlled to ensure smooth and systematic changes in training and test accuracy.

Analyzing the layers using WeightWatcher, the alpha metric was compared to the test accuracy for the hidden layer. The results showed that as the test accuracy increased, the alpha metric decreased, and when the test accuracy dropped, the alpha fell below 2. This indicates that WeightWatcher can detect which layer is over-trained, a unique capability not found in other approaches.

The theory behind the alpha metric is based on fitting the spectral density to a Power Law distribution, with lower alphas indicating Very Heavy-Tailed layers. When a layer is Very Heavy-Tailed, it means the layer weight matrix is atypical and cannot describe any data except the training data, leading to potential over-training.

While interpreting and applying the results of WeightWatcher may require some experimentation and careful design, it can be a valuable tool for identifying and addressing over-training in DNNs. If you’re working on AI, ML, or Data Science projects and need assistance, consider reaching out for consulting services and hands-on support.

Overall, WeightWatcher offers a unique and insightful approach to detecting over-trained layers in DNNs, providing a valuable tool for improving model performance.