Deep Learning Network Debugging Made Easy
Deep Learning Network Debugging Made Easy
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 πππ§π¬π¨π«πππ§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.
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