Tangent: Automatic Differentiation Using Source Code Transformation in Python. Automatic differentiation (AD) is an essential primitive for machine learning programming systems. Tangent is a new library that performs AD using source code transformation (SCT) in Python. It takes numeric functions written in a syntactic subset of Python and NumPy as input, and generates new Python functions which calculate a derivative. This approach to automatic differentiation is different from existing packages popular in machine learning, such as TensorFlow and Autograd. Advantages are that Tangent generates gradient code in Python which is readable by the user, easy to understand and debug, and has no runtime overhead. Tangent also introduces abstractions for easily injecting logic into the generated gradient code, further improving usability.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
- Dan Moldovan, James M Decker, Fei Wang, Andrew A Johnson, Brian K Lee, Zachary Nado, D Sculley, Tiark Rompf, Alexander B Wiltschko: AutoGraph: Imperative-style Coding with Graph-based Performance (2018) arXiv
- Guillaume Baudart, Martin Hirzel, Kiran Kate, Louis Mandel, Avraham Shinnar: Yaps: Python Frontend to Stan (2018) arXiv
- Bart van Merrienboer, Alexander B. Wiltschko, Dan Moldovan: Tangent: Automatic Differentiation Using Source Code Transformation in Python (2017) arXiv