Official implementation of "Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks", NeurIPS 2021.

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Deep Learning PHDimGeneralization
Overview

PHDimGeneralization

Official implementation of "Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks", NeurIPS 2021.

Overview

This package provides computation of ph dimension of neural network trajectories. In particular, computation is done in topology.py. The code to produce the analysis experiments are given in train_analysis.py, and the code to produce the regularization experiments are given in train_reqularize.py.

Requirements

The baseline code requires PyTorch, which can be installed directly through a software package manager like pip or conda. However, the topological PH requirements are a bit more complex.

CPU (non-Differentiable)

The function calculate_ph_dim, which computes topology on CPU and is not differentiable, requires Ripser. This can be installed using

pip install Cython
pip install Ripser

GPU (Differentiable)

The function calculate_ph_dim_gpu, which computes topology on GPU and is differentiable, requires TorchPH. This is more difficult to install (due to various dependencies including C++ version). We recommend take a look at the installation page.

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Comments
  • Fix scripts

    Fix scripts

    First of all, congratulations to all the authors for getting this awesome paper out.

    I was highly interested and tried to reproduce some of the experiments as described in the README.md and came across some errors that were easily fixed in 7544b71e51af01611546ee51a90ad2243c5cd560 and 4f2cb4cab1833c3871df7cc4d6d73cdf81a5d495.

    The changes proposed in this PR can be summarized as follows :-

    • Earlier the path flag was set to /share/cuvl/aaron/ which on any other system would require permission error and thus I changed the path to a generic data/ dir that will be created within the project directory itself.
    • While instantiating models, the module fc was used rather than the functions fc_mnist and fc_cifar in their respective sections.

    Hope these minimal changes which make the code reproducible would be acceptable to the maintainers.

    opened by SauravMaheshkar 0
Owner
Tolga Birdal
Postdoctoral Research Fellow at Stanford University. Interested in three or higher dimensional geometries and learning there.
Tolga Birdal
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