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https://github.com/li012589/neuralCT
Pytorch implement of the paper Neural Canonical Transformation with Symplectic Flows
https://github.com/li012589/neuralCT
Last synced: 27 days ago
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Pytorch implement of the paper Neural Canonical Transformation with Symplectic Flows
- Host: GitHub
- URL: https://github.com/li012589/neuralCT
- Owner: li012589
- License: apache-2.0
- Created: 2019-09-23T09:19:02.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-03-09T08:57:26.000Z (over 4 years ago)
- Last Synced: 2024-03-21T14:21:05.866Z (3 months ago)
- Language: Jupyter Notebook
- Size: 8.36 MB
- Stars: 29
- Watchers: 6
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
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- awesome-stars - li012589/neuralCT - Pytorch implement of the paper Neural Canonical Transformation with Symplectic Flows (Jupyter Notebook)
README
PyTorch implement of the paper [Neural Canonical Transformation with Symplectic Flows](https://arxiv.org/abs/1910.00024).
A symplectic normalizing flow learns slow and nonlinear collective modes in the latent space. The model reveals dynamical information from statistical correlations in the phase space.
## Usage
### 0. Setup Guide
Both [`pytorch`](https://pytorch.org/) and [`numpy`](https://numpy.org/) are required, you can install them using [`anaconda`](http://anaconda.org).
*If you want to run the following demos, you need to download savings and datasets from Google Drive. To do this run:*
```bash
python download_demo.py
```
### 1. Phase Space Density Estimation
#### 1.1 Molecular Dynamics trajectory data
To train a neuralCT for molecular dynamics data, use `density_estimation_md.py`
```bash
python ./density_estimation_md.py -batch 200 -epoch 500 -fixy 2.3222 -dataset ./database/alanine-dipeptide-3x250ns-heavy-atom-positions.npz
```
**Key Options**
- **-cuda**: Which device to use with -1 standing for CPU, number bigger than -1 is N.O. of GPU;
- **-hdim**: Hidden dimension of mlps;
- **-numFlow**: Number of flows layers;
- **-nlayers**: Number of mlps layers in the rnvp;
- **-nmlp**: Number of dense layers in each mlp;
- **-smile**: SMILE expression of this molecular;
- **-dataset**: Path to the training data.
To see detailed options, run `python density_estimation_md.py -h`.
**Analysis Notebook**
[Alanine Dipeptide](3_AlanineDipeptide.ipynb)
#### 1.2 Image dataset
To train a neuralCT for machine learning dataset, use `density_estimation.py`.
```bash
python ./density_estimation.py -epochs 5000 -batch 200 -hdim 256 -nmlp 3 -nlayers 16 -dataset ./database/mnist.npz
```
**Key Options**
- **-cuda**: Which device to use with -1 standing for CPU, number bigger than -1 is N.O. of GPU;
- **-hdim**: Hidden dimension of mlps;
- **-numFlow**: Number of flows layers;
- **-nlayers**: Number of mlps layers in the rnvp;
- **-nmlp**: Number of dense layers in each mlp;
- **-n**: Number of dimensions of the training data;
- **-dataset**: Path to the training data.
To see detailed options, run`python density_estimation.py -h`.
**Analysis Notebook**
[MNIST concept compression](4_MNIST.ipynb)
### 2. Variational Free Energy Calculation
To train a neuralCT via the variational approach, use `variation.py`. Specify the name of the distribution with the **-source** option.
```bash
python ./variation.py -epochs 5000 -batch 200 -hdim 256 -nmlp 3 -nlayers 16 -source Ring2d
```
**Key Options**
- **-cuda**: Which device to use with -1 standing for CPU, number bigger than -1 is N.O. of GPU;
- **-hdim**: Hidden dimension of mlps;
- **-numFlow**: Number of flows layers;
- **-nlayers**: Number of mlps layers in the rnvp;
- **-nmlp**: Number of dense layers in each mlp;
- **-source**: Using which source, Ring2d or HarmonicChain.
To see detailed options, run `python variation.py -h`.
**Analysis Notebooks**
1. [Ring2D distribution](1_Ringworld.ipynb)
2. [Harmonic Chain](2_HarmonicChain.ipynb)
## Citation
````latex
@article{neuralCT,
Author = {Shuo-Hui Li, Chen-Xiao Dong, Linfeng Zhang, and Lei Wang},
Title = {Neural Canonical Transformation with Symplectic Flows},
Year = {2019},
Eprint = {arXiv:1910.00024},
}
````
## Contact
For questions and suggestions, please contact Shuo-Hui Li at [[email protected]](mailto:[email protected]).