WeightWatcher (WW): is an open-source, diagnostic tool for analyzing Deep Neural Networks (DNN), without needing access to training or even test data. It can be used to:
- analyze pre/trained pyTorch, Keras, DNN models (Conv2D and Dense layers)
- monitor models, and the model layers, to see if they are over-trained or over-parameterized
- predict test accuracies across different models, with or without training data
- detect potential problems when compressing or fine-tuning pretrained models
- layer warning labels: over-trained; under-trained
ad well several new experimental model transformations, including:
- SVDSmoothing: builds a model that can be used to predict test accuracies, but only with the training data.
- SVDSharpness: removes Correlation Traps, which arise from sub-optimal regularization pre-trained models.
You may install the latest / Trunk from testpypi
python3 -m pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple weightwatcher
The testpypi version usually has the most recent updates, including experimental methods qnd bug fixes
WeightWatcher is based on theoretical research (done injoint with UC Berkeley) into Why Deep Learning Works, based on our Theory of Heavy Tailed Self-Regularization (HT-SR). It uses ideas from Random Matrix Theory (RMT), Statistical Mechanics, and Strongly Correlated Systems.
More details and demos can be found on the Calculated Content Blog
pip install weightwatcher
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}]
WW computes several Scale and Shape metrics for each layer Weight matrix W, as described in our papers (see below)
These are reported in a details dataframe, including:
- N, M: Matrix or Tensor Slice Dimensions
- D: Quality of the (Truncated) Power law fit (D is the Kolmogorov Smirnov Distance metric)
- num_spikes: number of spikes outside the bulk region of the ESD, when fit to an MP distribution
The layer metrics are be averaged in the summary statistics:
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
summary = watcher.get_summary()
The summary statistics can be used to gauge the test error of a series of pre/trained models, without needing access to training or test data.
- average alpha can be used to compare one or more DNN models with different hyperparemeter settings θ, but of the same depth.
- average log spectral norm is useful to compare models of different depths L
- average weighted alpha and log alpha norm are suitable for DNNs of differing hyperparemeters θ and depths L simultaneously.
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, savefig=True):
...
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)
WW creates plots for each layer weight matrix to observe how well the power law fits work
details = watcher.analyze(plot=True)
For each layer, Weightwatcher plots the ESD--a histogram of the eigenvalues of the layer correlation matrix X=WTW. It then fits the tail of ESD to a (Truncated) Power Law, and plots these fits on different axes. The metrics (above) characterize the Shape and Scale of each ESD.
Weightwatcher can detect the signatures of overtraining in specific layers of a pre/trained Deep Neural Networks.
The weightwatcher alpha metric can be used to detect when to apply early stopping. When the average alpha (summary statistic) drops below 2.0, this indicates that the model may be overtrained and early stopping is necesary.
Below is an example of this, showing training loss and test lost curves for a small Transformer model, trained from scratch, along with the average alpha summary statistic.
We can see that as the training and test losses decrease, so does alpha. But when the test loss saturates and then starts to increase, alpha drops below 2.0.
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)
Fig (a) is well trained; Fig (b) may be over-trained. That orange spike on the far right is the tell-tale clue; it's caled a Correlation Trap.
A Correlation Trap is characterized by Fig (b); here the actual (green) and random (red) ESDs look almost identical, except for a small shelf of correlation (just right of 0). And for the random (red) ESD, the largest eigenvalue (orange) is far to the right of and seperated from the bulk of the ESD.
Weightwatcher will analyze your model, layer-by-layer, and show you where these kind of problems may be lurking.
WeightWatcher (WW)can be used to compare the test error for a series of models, trained on the similar dataset, but with different hyperparameters, or even different but related architectures.
Our Theory of HT-SR predicts that models with smaller PL exponents alpha , on average, correspond to models that generalize better.
The WW summary metric alpha (α) can predict the generalization Δ error when varying the model hyperparmeters θ (like batch size, learning rate, momentum, etc)
whereas the summary metric weighed alpha can predict the generalization error Δ when varying hyperparmeters θ and depth L
Here is an example of the Weighted Alpha capacity metric for all the current pretrained VGG models.
This can be reppduced with the Demo Notebook
Notice: we did not peek at the ImageNet test data to build this plot.
Smoothed models can be used to predict test accuracies, by evaluating the training accuracy on the smoothed model.
smoothed_model = watcher.SVDSmoothing(model=...)
Sharpned models can be used when fine-tuning pre-trained models that have not been fully optimized yet.
sharpemed_model = watcher.SVDSharpness(model=...)
Sample notebooks are provided for each new feature
ww.LAYER_TYPE.CONV2D | ww.LAYER_TYPE.CONV2D | ww.LAYER_TYPE.DENSE
as
details=watcher.analyze(layers=[ww.LAYER_TYPE.CONV2D])
details=watcher.analyze(layers=[20])
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)
The mp_fit option tells WW to fit each layer ESD as a Random Matrix as a Marchenko-Pastur (MP) distribution, as described in our papers on HT-SR.
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
watcher.analyze()
esd = watcher.get_ESD()
Describe a model and report the details dataframe, without analyzing it
details = watcher.describe(model=model)
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)
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)
Saves the layer ESD plots for each layer
watcher.analyze(savefig=True)
generating 4 files per layer
ww.layer#.esd1.png ww.layer#.esd2.png ww.layer#.esd3.png ww.layer#.esd4.png
- Tensorflow 2.x / Keras
- PyTorch
- HuggingFace
- Dense / Linear / Fully Connected (and Conv1D)
- Conv2D
-
rankloss is currently not working , may be always set to 0
-
the embedded powerlaw packages may show warning messages; you can ignore these
/home/xander/anaconda3/envs/my_model/lib/python3.7/site-packages/powerlaw.py:700: RuntimeWarning: divide by zero encountered in true_divide (Theoretical_CDF * (1 - Theoretical_CDF))
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)
This tool is based on state-of-the-art research done in collaboration with UC Berkeley:
-
Traditional and Heavy Tailed Self Regularization in Neural Network Models
- Notebook for above 2 papers (https://github.com/CalculatedContent/ImplicitSelfRegularization)
-
- Notebook for paper (https://github.com/CalculatedContent/PredictingTestAccuracies)
and has been presented at Stanford, UC Berkeley, etc:
and has been the subject many popular podcasts
We have a slack channel for the tool if you need help For an invite, please send an email to [email protected]
Charles H Martin, PhD Calculation Consulting