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Google's TF-Coder Tool Automates Machine Learning Model Design

By VentureBeat

August 13, 2020

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Google Brain researchers have created an automated coding tool for machine learning frameworks like TensorFlow, that outperforms humans on challenging development tasks.

Google's TF-Coder synthesizes tensor manipulation programs from input and output examples and natural language descriptions, using per-operation weights to enumerate over TensorFlow expressions in order of increased complexity; a novel type- and value-based filtering system oversees constraints imposed by the TensorFlow library.

TF-Coder weighs 134 tensor-manipulation operations of the 500 in TensorFlow, and accommodates problems involving compositions of four or five different operations and data structures of 10 or more components.

The researchers said TF-Coder realized "superhuman" performance on a range of real problems from question-and-answer website StackOverflow, completing tasks that human programmers would spend minutes to hours on.

The team said, "TF-Coder can help both machine learning beginners and experienced practitioners in writing tricky tensor transformation programs that are common in deep learning pipelines."

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