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Deep Learning Network Debugging Made Easy

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Deep Learning Network Debugging Made Easy

Avi Chawla
Dec 9, 2022
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Aligning the shape of tensors (or vectors/matrices) in a network can be challenging at times.

As the network grows, it is common to lose track of dimensionalities in a complex expression.

Instead of explicitly printing tensor shapes to debug, use π“πžπ§π¬π¨π«π’πžπ§π¬π¨π«. It generates an elegant visualization for each statement executed within its block. This makes dimensionality tracking effortless and quick.

In case of errors, it augments default error messages with more helpful details. This further speeds up the debugging process.

Read more: Documentation

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The code snippets for the posts I have shared here are available on GitHub. Check out this repository: GitHub.

I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn.

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