| Variational Auto Encoder(VAE) |
| ============================= |
| |
| This folder contains a tutorial which implements the Variational Auto Encoder in MXNet using the MNIST handwritten digit |
| recognition dataset. Model built is referred from [Auto-Encoding Variational Bayes](https://arxiv.org/abs/1312.6114/) |
| paper. This paper introduces a stochastic variational inference and learning algorithm that scales to large datasets. |
| |
| Prerequisites: |
| To run this example, you need: |
| - [Jupyter Notebook](http://jupyter.org/index.html) |
| - Matplotlib |
| |
| Files in this folder: |
| - **VAE_example.ipynb** : Jupyter notebook which explains concept of VAE step by step and also shows how to use |
| MXNet-based VAE class(from VAE.py) to do the training directly. |
| |
| - **VAE.py** : Contains class which implements the Variational Auto Encoder. This is used in the above tutorial. |
| |
| In VAE, the encoder becomes a variational inference network that maps the data to a distribution |
| for the hidden variables, and the decoder becomes a generative network that maps the latent variables back to the data. |
| The network architecture shown in the tutorial uses Gaussian MLP as an encoder and Bernoulli MLP as a decoder. |
