Lstm-Variational-Auto-encoder

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

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

References

Tutorial on variational Autoencoders

A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder

Variational Autoencoder based Anomaly Detection using Reconstruction Probability

The Generalized Reparameterization Gradient