# VAE_anomaly_test

**Repository Path**: shenghsin/VAE_anomaly_test

## Basic Information

- **Project Name**: VAE_anomaly_test
- **Description**: No description available
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-04
- **Last Updated**: 2022-01-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Variational autoencoder for anomaly detection

This repo contains my personal implementation of Variational autoencoder
 in tensorflow for anomaly detection, that follow
 [Variational Autoencoder based Anomaly Detection using Reconstruction Probability by Jinwon An, Sungzoon Cho](https://www.semanticscholar.org/paper/Variational-Autoencoder-based-Anomaly-Detection-An-Cho/061146b1d7938d7a8dae70e3531a00fceb3c78e8)
 <br>
 In order to make work the variational autoencoder for anomaly detection i've to change the last layer of the decoder
 from a simple fully connected layer to two layers that estimate mean and variance
 of x~ ( _p(x|z)_ )
<br>

## How To Train a Model

- Define your dataset into dataset.py and put in output into the function _get_dataset_
- Eventually change encoder and decoder inside _VAE.py_ to fits your data layout
- Run in a terminal _python train.py_ and specify required at least _--input-size_ (pass -h to see all optional parameters)
- Trained model, parameters and Tensorboard log goes into the folder _run/{id}_ where _{id}_ is an integer from 0 to +inf
- After the model training run _tensorboard --logdir=run_ to check all the training results

## How to make predictions:
Once the model is trained (suppose for simplicity that it is under _run/0/_ ) just load and predict with this code snippet:
```python
import torch

#load X_test
model = VAEAnomaly(input_size=50, latent_size=32)
# could load input_size and latent_size also 
# from run/0/train_config.yaml
model.load_state_dict(torch.load('run/0/model.pth'))
# load saved parameters from a run
outliers = model.is_anomaly(X_test)
```