https://github.com/tommoral/adopty

https://github.com/tommoral/adopty

Last synced: 7 days ago
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• Host: GitHub
• URL: https://github.com/tommoral/adopty
• Owner: tomMoral
• Created: 2018-03-28T13:25:16.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-01-20T17:44:50.000Z (almost 5 years ago)
• Last Synced: 2024-11-02T09:51:36.864Z (14 days ago)
• Language: Jupyter Notebook
• Size: 6.92 MB
• Stars: 19
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Code to reproduce the NeurIPS submission : "_Learning step sizes for unfolded sparse coding_"

## Compat

This package has been developed and tested with `python3.6`. It is therefore not guaranteed to work with earlier versions of python.

## Install the repository on your machine

This package can easily be installed using `pip`, with the following command:

```bash

pip install numpy

pip install -e .

```

This will install the package and all its dependencies, listed in `requirements.txt`. To test that the installation has been successful, you can install `pytest` and run the test suite using

```

pip install pytest

pytest

```

## Reproducing the figures of the paper

Figure 2 - Convergence curves between ISTA, FISTA and OISTA.

```bash

python examples/comparison_oracle_ista.py

```

Figure 4 - Learn a small network and display the learned step as well as the distribution of 1/L_S at each layer on the training set.

```bash

python examples/plot_learned_steps.py

```

Figure 5 - Show that when learning a LISTA network with 40 layers, the last layers look like SLISTA layers, where only the step size differs from ISTA. This verifies numerically theorem 4.4.

```bash

python examples/plot_dict_similarity.py

```

Figure 6 - comparison of ISTA, LISTA, ALISTA and SLISTA on large scale problems. Note that this experiment can take up to 24h to be generated. You can tweak the parameter N_JOB and N_GPU in `examples/run_comparison_networks.py` to accelerate the computations by parallelization and using GPU. Note that if both are set, you will have N_JOB / N_GPU jobs running on each GPU, which should be chosen reasonably.

```bash

python examples/run_comparison_networks.py

python examples/plot_comparison_networks.py

```

## Reproducing figures from the appendix

Figure E.1 - Plot the distribution of L_S / L as a function of the cardinal of S.

```bash

python examples/plot_sparsity_distribution.py

```

Figure E.2 - Plot histograms of number of iterations for ISTA, FISTA and OISTA.

```bash

python examples/run_comparison_iterative.py

python examples/plot_comparison_iterative.py

```

Figure E.3 - Plot the test loss relatively to the number of training points used to optimized LISTA or SLISTA.

```bash

python examples/plot_learning_curves.py

```