https://github.com/yoyolicoris/pytorch-wise-ale

https://github.com/yoyolicoris/pytorch-wise-ale

kl-divergence vae

Last synced: 5 months ago
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• Host: GitHub
• URL: https://github.com/yoyolicoris/pytorch-wise-ale
• Owner: yoyolicoris
• License: mit
• Created: 2019-05-31T06:54:07.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-06-21T13:01:43.000Z (over 6 years ago)
• Last Synced: 2025-05-11T21:52:09.485Z (5 months ago)
• Topics: kl-divergence, vae
• Language: Jupyter Notebook
• Size: 575 KB
• Stars: 5
• Watchers: 3
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Pytorch WiSE-ALE 

Implementation of [WiSE-ALE: Wide Sample Estimator for Approximate

Latent Embedding](https://arxiv.org/abs/1902.06160).

The project structure is brought from [pytorch-template](https://github.com/victoresque/pytorch-template).

## Quick start

An example config file `mnist.json`' is provided.

~~~~

{

    "name": "MNIST",

    "n_gpu": 1,

    "arch": {

        "type": "MNIST_VAE",

        "args": {}

    },

    "data_loader": {

        "type": "MnistDataLoader",

        "args":{

            "data_dir": "mnist_data/",

            "batch_size": 64,

            "shuffle": true,

            "validation_split": 0,

            "num_workers": 1

        }

    },

    "optimizer": {

        "type": "Adam",

        "args":{

            "lr": 0.001

        }

    },

    "loss": "WiSE_UB2",     // Default loss function is WiSE-UB. Type 'AEVB' will use vanilla VAE objective.

    "metrics": [

        "kl_div", "reconstruct"

    ],

    "lr_scheduler": {

        "type": "StepLR",

        "args": {

            "step_size": 60,

            "gamma": 0.5

        }

    },

    "trainer": {

        "epochs": 30,

        "save_dir": "mnist_saved/",

        "save_period": 10,

        "verbosity": 2,

        "monitor": "off",

        "early_stop": 0,

        "tensorboardX": true,

        "sample_size": 2

    }

}

~~~~

The setting is the same as in the paper appendix.

To train the model with example config:

```

python train.py -c mnist.json

```

The checkpoint files will be saved in `mnist_saved`. Other instructions please refer to pytorch-template.

## Example Files

* [mnist_visualization.ipynb](mnist_visualization.ipynb): A latent embedding visualization on mnist dataset.