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Information Bottleneck: Exact Analysis of (Quantized) Neural Networks

This repository consists of tools for applying information bottleneck (IB) analysis to neural networks, and specifically for exact IB analysis in quantized neural networks. The repo contains experiments for the ICLR 2022 paper [1]:

Stephan Sloth Lorenzen, Christian Igel & Mads Nielsen. Information Bottleneck: Exact Analysis of (Quantized Neural Networks.

The study applies and exact version of the IB analysis for neural networks as investigated and discussed in [2,3].

Requirements

The code has been tested using (Arch) Linux, but runs in Windows with only few modifications. The following python libraries are required:

• numpy
• pandas
• tensorflow
• tensorflow_model_optimization

Furthermore, lualatex is required for making the plots.

Overview

The important directories and files in the repository are:

• IB: Module containing the code for models, training procedure and computation of mutual information (MI).
• IB/experiment.py: Code for training procedure.
• IB/util/estimator.py: Code for MI estimation/computation.
• IB/models/models.py: Networks analysed in the experiments.
• IB/data: Contains code for loading MNIST [4] and the synthetic data set of [2].
• experiment: Directory with experiments contained in the paper [1].
• experiment/Makefile: Linux Makefile for running the experiments and making plots (see below).
• experiment/helpers/{binning,quantize}.py: Helper python scripts for running binning and quantization experiments.

Experiments from [1]

A Makefile is supplied in the experiment directory for running the experiments of [1] and creating plots. Before running any experiments, cd to experiment and run

make link
make data

This will link the IB module and collect the MNIST data set [4].

Running experiments

To run the experiments contained in the main body of [1], use:

make quantize

To run the experiments from the appendix of [1], use:

make binning         # Non-quantized/Binning experiments, Appendix A
make bit-width       # 4-/32-bit quantization experiments, Appendix C
make quantize-prefit # Quantization with randomized prefitting, Appendix D
make quantize-archs  # Quantization of different MNIST architectures, Appendix E

Please note, that the experiments above may run for a long time and require a significant amount of memory. Most experiments run for many iterations and computes/estimates MI between large quantities. Time per iteration may be minutes (synthetic data) or more than 12 hours (convolutional MNIST network).

Plotting results

To create the plots from [1] (corresponding to the above experiments), use:

make plot-quantize   # Main body and Appendix B
make plot-binning    # Appendix A
make plot-bit-width  # Appendix C
make plot-prefit     # Appendix D
make plot-archs      # Appendix E
make plot-accuracy   # Accuracy plots, Appendix F

The plots are made using lualatex and will take a few minutes to compile, as many data points are plotted for each information plane.

Running other experiments

Other experiments can be run using the module directly (note, some options are left out here, use --help for the full list):

python -m IB experiment [-h] [-n NETWORK] [-af ACT_FUNC] [-lr LR] [-e EPOCHS] [-q]
                              [-b BITS] [-d DATA] [-r REPEATS]

options:
  -n NETWORK     Network to use.
  -af ACT_FUNC   Activation function.
  -lr LR         Learning rate used in training.
  -e EPOCHS      Number of epochs.
  -q             Quantize the model (changes default binning strategy!).
  -b BITS        Number of bits for quantization, if -q set, must be in (4,8).
  -d DATA        Data for experiment
  -r REPEATS     Number of experiment repeats

Alternatively, one may use the python helper scripts in experiment/helpers:

python helpers/binning.py <experiment> [prefit=0] [repeats=50]
python helpers/quantize.py <experiment> <bits> [prefit=0] [repeats=50]

where experiment is one of SYN-{Tanh,Relu} or MNIST-{4x10,Conv,Bottleneck-2,Bottleneck-4,HourGlass}, bits are the precision of the quantization (4, 8 or 32), and prefit is the number of randomized prefitting epochs (0 means no prefitting).

Acknowledgement

Please cite our paper, if you found the code useful in your work/publication:

@inproceedings{
lorenzen2022information,
title={Information Bottleneck: Exact Analysis of (Quantized) Neural Networks}, author={Stephan Sloth Lorenzen and Christian Igel and Mads Nielsen}, booktitle={International Conference on Learning Representations}, year={2022}, url={https://openreview.net/forum?id=kF9DZQQrU0w}
}

References

[1] Stephan Sloth Lorenzen, Christian Igel, & Mads Nielsen. Information Bottleneck: Exact Analysis in (Quantized) Neural Networks. ICLR (2022).

[2] Ravid Shwartz-Ziv & Naftali Tishby. Opening the black box of deep neural networks via information. arXiv (2017).

[3] Andrew M. Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, & Brendan D. Tracey. On the information bottleneck theory of deep learning. ICLR (2018).

[4] Li Deng. The MNIST database of handwritten digit images for machine learning research. IEEE Signal Processing Magazine (2021).