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https://github.com/ikostrikov/pytorch-flows
PyTorch implementations of algorithms for density estimation
https://github.com/ikostrikov/pytorch-flows
deep-learning density-estimation neural-networks probabilities pytorch
Last synced: 2 months ago
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PyTorch implementations of algorithms for density estimation
- Host: GitHub
- URL: https://github.com/ikostrikov/pytorch-flows
- Owner: ikostrikov
- License: mit
- Created: 2018-09-01T19:58:17.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2021-05-13T19:59:29.000Z (about 3 years ago)
- Last Synced: 2024-01-25T21:05:40.971Z (5 months ago)
- Topics: deep-learning, density-estimation, neural-networks, probabilities, pytorch
- Language: Python
- Homepage:
- Size: 52.7 KB
- Stars: 565
- Watchers: 19
- Forks: 80
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-stars - ikostrikov/pytorch-flows - PyTorch implementations of algorithms for density estimation (Python)
- Awesome-pytorch-list - pytorch-flows
- awesome-normalizing-flows - pytorch-flows
- my-awesome-stars - ikostrikov/pytorch-flows - PyTorch implementations of algorithms for density estimation (Python)
- Awesome-pytorch-list-CNVersion - pytorch-flows
- awesome-stars - ikostrikov/pytorch-flows - PyTorch implementations of algorithms for density estimation (Python)
- awesome-energy-based-model - `pytorch-flows`
README
# pytorch-flows
A PyTorch implementations of [Masked Autoregressive Flow](https://arxiv.org/abs/1705.07057) and
some other invertible transformations from [Glow: Generative Flow with Invertible 1x1 Convolutions](https://arxiv.org/pdf/1807.03039.pdf) and [Density estimation using Real NVP](https://arxiv.org/abs/1605.08803).For MAF, I'm getting results similar to ones reported in the paper. GLOW requires some work.
## Run
```bash
python main.py --dataset POWER
```Available datasets are POWER, GAS, HEPMASS, MINIBONE and BSDS300. For the moment, I removed MNIST and CIFAR10 because I have plans to add pixel-based models later.
## Datasets
The datasets are taken from the [original MAF repository](https://github.com/gpapamak/maf#how-to-get-the-datasets). Follow the [instructions](https://github.com/gpapamak/maf#how-to-get-the-datasets) to get them.
## Tests
Tests check invertibility, you can run them as
```bash
pytest flow_test.py
```