# Variational-Lstm-Autoencoder

**Repository Path**: yi-junquan/Variational-Lstm-Autoencoder

## Basic Information

- **Project Name**: Variational-Lstm-Autoencoder
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-12-16
- **Last Updated**: 2023-12-16

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Lstm-Variational-Auto-encoder

#  ![CI status](https://img.shields.io/cocoapods/l/AFNetworking.svg)

Variational auto-encoder for anomaly detection/features extraction, with lstm cells (stateless or stateful). 

## Installation

### Requirements


`$ pip install --upgrade git+https://github.com/Danyleb/Lstm-Variational-Auto-encoder.git`

## Usage

```python
from LstmVAE import LSTM_Var_Autoencoder
from LstmVAE import preprocess

preprocess(df) #return normalized df, check NaN values replacing it with 0

df = df.reshape(-1,timesteps,n_dim) #use 3D input, n_dim = 1 for 1D time series. 

vae = LSTM_Var_Autoencoder(intermediate_dim = 15,z_dim = 3, n_dim=1, stateful = True) #default stateful = False

vae.fit(df, learning_rate=0.001, batch_size = 100, num_epochs = 200, opt = tf.train.AdamOptimizer, REG_LAMBDA = 0.01,
            grad_clip_norm=10, optimizer_params=None, verbose = True)

"""REG_LAMBDA is the L2 loss lambda coefficient, should be set to 0 if not desired.
   optimizer_param : pass a dict = {}
"""

x_reconstructed, recons_error = vae.reconstruct(df, get_error = True) #returns squared error

x_reduced = vae.reduce(df) #latent space representation
```



## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.


## License
[MIT](https://choosealicense.com/licenses/mit/)

## References 
[Tutorial on variational Autoencoders](https://arxiv.org/pdf/1606.05908.pdf)

[A Multimodal Anomaly Detector for Robot-Assisted Feeding
Using an LSTM-based Variational Autoencoder](https://arxiv.org/pdf/1711.00614.pdf)

[Variational Autoencoder based Anomaly Detection
using Reconstruction Probability](http://dm.snu.ac.kr/static/docs/TR/SNUDM-TR-2015-03.pdf)

[The Generalized Reparameterization Gradient](http://www.cs.columbia.edu/~blei/papers/RuizTitsiasBlei2016b.pdf)