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Modularity by Inputs and Outputs

This is the code repository accompanying the paper

Lange, R. D., Rolnick, D. S., and Kording, K. (2022) "Clustering units in neural networks: upstream vs downstream information." TMLR. https://openreview.net/forum?id=Euf7KofunK

High-level structure

1. Model definitions and training

PyTorch models are defined in models/mnist.py and models/cifar10.py. Models are wrapped by a Pytorch Lightning module models.LitWrapper, which handles loading a specific model or dataset. Training is done by train.py, which is called for a range of hyperparameter configurations by train.sh.

Training needs to be run before moving on to step 2.

2. Computing modularity

As detailed in the paper, we analyze "modularity" of a set of units (e.g. all units in a layer) by

  1. computing pairwise similarity scores of units
  2. clustering units together by maximizing the Q score from Newman (2006).

Step 1 is done by functions in associations.py and step 2 is done by functions in modularity.py.

Running eval.py does the following:

  • loads a model from a checkpoint
  • computes a variety of performance statistics such as validation accuracy, weight norms, etc
  • computes a variety of modularity statistics by calling functions from associations.py and modularity.py
  • saves results back into the same checkpoint file

The file eval.sh is a shell script that demonstrates how we call eval.py for each checkpoint in a directory.

3. Loading and plotting results

As mentioned above, eval.py loads a checkpoint, computes a variety of statistics including modules (clusters), and saves the result back into the checkpoint file. This means that eval.sh needs to be run on a set of checkpoints before notebooks can be run to plot the results. The file analysis.py handles the process of loading statistics computed by eval.py into a pandas DataFrame.

The notebook notebooks/analysis_sandbox.ipynb was used to generate most figures in the paper. This notebook's structure primarily involves calling analysis.load_data_as_table() to load precomputed information from a set of checkpoints into a DataFrame, then the rest is a variety of ways of slicing and plotting the results.