Anomaly Detection from Head and Abdominal Fetal ECG -- A Case study of IOT anomaly detection using Generative Adversarial Networks

This DoNut Network contains uses The variational auto-encoder ("Auto-Encoding Variational Bayes",Kingma, D.P. and Welling) which is a deep Bayesian network, with observed variable x and latent variable z. The VAE is generated using TFSnippet (library for writing and testing tensorflow models). The generative process of Auto-Encoder is initiated with parameter z with prior distribution p(z), and a hidden network h(z), then uses observed variable x with distribution p(x h(z)). The posterior inference p(z x), variational inference techniques are adopted, to train a separated distribution q(z h(x)). Here each Sequential function creates a multi-layer perception, with 2 hidden layers of 50 units and RELU activation.