CalculatedContent / Weightwatcher

Weight Watcher

Current Version: 0.4

Weight Watcher analyzes the Fat Tails in the weight matrices of Deep Neural Networks (DNNs).

This tool can predict the trends in the generalization accuracy of a series of DNNs, such as VGG11, VGG13, ..., or even the entire series of ResNet models--without needing a test set !

This relies upon recent research into the Heavy (Fat) Tailed Self Regularization in DNNs

The tool lets one compute a averager capacity, or quality, metric for a series of DNNs, trained on the same data, but with different hyperparameters, or even different but related architectures. For example, it can predict that VGG19_BN generalizes better than VGG19, and better than VGG16_BN, VGG16, etc.

Types of Capacity Metrics:

There are 2 basic types metrics we use

• alpha (the average power law exponent)
• weighted alpha / log_alpha_norm (scale adjusted alpha metrics)

The average alpha can be used to compare one or more DNN models with different hyperparemeter settings, but of the same depth. The average weighted alpha is suitable for DNNs of differing depths.

Here is an example of the Weighted Alpha capacity metric for all the current pretrained VGG models.

Notice: we did not peek at the ImageNet test data to build this plot.

Frameworks supported

• Tensorflow 2.x / Keras
• PyTorch
• HuggingFace

Layers supported

• Dense / Linear / Fully Connected (and Conv1D)
• Conv2D

Installation

pip install weightwatcher

Usage

import weightwatcher as ww
import torchvision.models as models

model = models.vgg19_bn(pretrained=True)
watcher = ww.WeightWatcher(model=model)
details = watcher.analyze()
summary = watcher.get_summary(details)

It is as easy to run and generates a pandas dataframe with details (and plots) for each layer

and summary dict of generalization metrics

    {'log_norm': 2.11,
      'alpha': 3.06,
      'alpha_weighted': 2.78,
      'log_alpha_norm': 3.21,
      'log_spectral_norm': 0.89,
      'stable_rank': 20.90,
      'mp_softrank': 0.52}]

More examples are include the Demo Notebook

and will be made available shortly in a Jupyter book

Advanced Usage

The watcher object has several functions and analyze features described below

analyze( model=None, layers=[], min_evals=0, max_evals=None,
	 plot=True, randomize=True, mp_fit=True, ww2x=False):
...
describe(self, model=None, layers=[], min_evals=0, max_evals=None,
         plot=True, randomize=True, mp_fit=True, ww2x=False):
...
get_details()
get_summary(details) or get_summary()
get_ESD()
...
distances(model_1, model_2)

filter by layer types

ww.LAYER_TYPE.CONV2D |  ww.LAYER_TYPE.CONV2D |  ww.LAYER_TYPE.DENSE

as

details=watcher.analyze(layers=[ww.LAYER_TYPE.CONV2D])

filter by ids or name

details=watcher.analyze(layers=[20])

minimum, maximum number of eigenvalues of the layer weight matrix

Sets the minimum and maximum size of the weight matrices analyzed. Setting max is useful for a quick debugging.

details = watcher.analyze(min_evals=50, max_evals=500)

plots (for each layer)

Create ESD plots for each layer weight matrix to observe how well the power law fits work

details = watcher.analyze(plot=True)

compare layer ESD to randomized W matrix

The randomize option compares the ESD of the layer weight matrix (W) to the ESD of the randomized W matrix. This is good way to visualize the correlations in the true ESD.

details = watcher.analyze(randomize=True, plot=True)

fit ESDs to a Marchenko-Pastur (MP) distrbution

Attempts to the fit the ESD to an MP dist.

details = watcher.analyze(mp_fit=True, plot=True)

and reports the

num_spikes, mp_sigma, and mp_sofrank

Also works for randomized ESD and reports

rand_num_spikes, rand_mp_sigma, and rand_mp_sofrank

get the ESD for a specific layer, for visualization or further analysis

watcher.analyze()
esd = watcher.get_ESD()

describe a model

Describe a model and report the details dataframe, without analyzing it

details = watcher.describe(model=model)

get summary

Get the average metrics, as a summary (dict), from the given (or current) details dataframe

details = watcher.analyze(model=model)
summary = watcher.get_summary(model)

or just

watcher.analyze()
summary = watcher.get_summary()

compare 2 models

The new distances method reports the distances between 2 models, such as the norm between the initial weight matrices and the final, trained weight matrices

details = watcher.distances(initial_model, trained_model)

compatability with version 0.2x

The new 0.4 version of weightwatcher treats each layer as a single, unified set of eigenvalues. In contrast, the 0.2x versions split the Conv2D layers into n slices, 1 for each receptive field. The ww2x option provides results which are back-compatable with the 0.2x version of weightwatcher, with details provide for each slice for each layer.

details = watcher.analyze(ww2x=True)

Demo Notebook

How to Release

Publishing to the PyPI repository:

# 1. Check in the latest code with the correct revision number (__version__ in __init__.py)
vi weightwatcher/__init__.py # Increse release number, remove -dev to revision number
git commit
# 2. Check out latest version from the repo in a fresh directory
cd ~/temp/
git clone https://github.com/CalculatedContent/WeightWatcher
cd WeightWatcher/
# 3. Use the latest version of the tools
python -m pip install --upgrade setuptools wheel twine
# 4. Create the package
python setup.py sdist bdist_wheel
# 5. Test the package
twine check dist/*
# 6. Upload the package to PyPI
twine upload dist/*
# 7. Tag/Release in github by creating a new release (https://github.com/CalculatedContent/WeightWatcher/releases/new)

License

Apache License 2.0

Academic Presentations and Media Appearances

This tool is based on state-of-the-art research done in collaboration with UC Berkeley:

• Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning

• Traditional and Heavy Tailed Self Regularization in Neural Network Models

  • Notebook for above 2 papers (https://github.com/CalculatedContent/ImplicitSelfRegularization)

• ICML 2019 Theoretical Physics Workshop Paper

• Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks

  • Notebook for paper (https://github.com/CalculatedContent/PredictingTestAccuracies)

and has been presented at Stanford, UC Berkeley, etc:

• NERSC Summer 2018

• UC Berkeley/ICSI 12/13/2018

• Institute for Pure & Applied Mathematics (IPAM)

• Physics Informed Machine Learning

and major AI conferences like ICML, KDD, etc.

• KDD 2019 Workshop: Statistical Mechanics Methods for Discovering Knowledge from Production-Scale Neural Networks

• KDD 2019 Workshop: Slides

KDD2019 Workshop

KDD 2019 Workshop: Statistical Mechanics Methods for Discovering Knowledge from Production-Scale Neural Networks

KDD 2019 Workshop: Slides

Popular Popdcasts ands Blogs

and has been the subject many popular podcasts

• This Week in ML

• Data Science at Home Podcast

• Aggregate Intellect VLog

• Rebellion Research VLog

• Rebellion Research BLog

Latest paper and results

Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data

Repo for latest paper

Talk on latest results, Stanford ICME 2020

Slack Channel

We have a slack channel for the tool if you need help For an invite, please send an email to [email protected]

Contributors

Charles H Martin, PhD Calculation Consulting

Serena Peng

Consulting Practice

Calculation Consulting homepage

Calculated Content Blog