Autoencoder implementation using keras and tensorflow. Ran with python 2.7.16

How to start:

• Explore datasets with data-exploration.py
• Preprocess datasets with data-preprocessing.py
• You are able to run ae.py for regular autoencoder or vae.py for a variational autoencoder.py (after training, the model is saved in the models directory)
• Use title of model inside model-analyser.py to process results

References:

1. Ellison, D. (2018) Fraud Detection Using Autoencoders in Keras with a TensorFlow Backend. Retrieved from: https://www.datascience.com/blog/fraud-detection-with-tensorflow
2. Schreyer, M. Sattarov, T. Borth, Dengel, A. Reimer, B. (2017). Detection of Anomalies in Large-Scale Accounting Data using Deep Autoencoder Networks Retrieved from: https://arxiv.org/pdf/1709.05254.pdf
3. Pumsirirat, A. Yan, L. (2018) Credit Card Fraud Detection using Deep Learning based on Auto-Encoderand Restricted Boltzmann Machine. Retrieved from: https://pdfs.semanticscholar.org/01be/7624aa0e0251182593350a984411c2e5128a.pdf
4. An, j. Cho, S. Variational Autoencoder based Anomaly Detectionusing Reconstruction Probability. Retrieved from: https://pdfs.semanticscholar.org/0611/46b1d7938d7a8dae70e3531a00fceb3c78e8.pdf