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Autoencoder TensorFlow Python Data Function for Spotfire® 1.0.0

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This is a Python data function employing Autoencoder method. Autoencoder is a versatile deep learning model that is used in anomaly detection and dimension reduction.


This data function uses Tensorflow with the Keras API for implementation; both are popular deep-learning libraries. The data function allows a user to configure different datasets, configure different neural network architectures, train and save the neural network model, and score new data using the trained models. The DXP includes further analysis of model features contributing towards reconstruction errors and uses reconstruction errors to find a statistical golden batch of data.

We would like to point you to anomaly detection template which is also leveraging TensorFlow autoencoder methods. 


Installing the data function

Follow the online guide available here to register a data function in Spotfire.


Configuring the data function

Each data function may require inputs from the Spotfire analysis and will return outputs to the Spotfire analysis. Each data function needs to be configured once the data function is registered. To learn about how to configure data functions in Spotfire please view this video:

For more information on Spotfire visit the Spotfire training page.

Initial Release (version 1.0.0)

Published: November 2021

Initial release includes:

  • Data function
  • Dxp with example usage
  • Documentation
  • License information

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