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[P] See RNN: Kernel-, Gate-, Channel-wise Visualization of Gradients, Weights, and Activations

[P] See RNN: Kernel-, Gate-, Channel-wise Visualization of Gradients, Weights, and Activations

DL is more than shooting in the dark and seeing what sticks; to this end, I present the first comprehensive RNN visualization API for Keras & TensorFlow layers, See RNN:

  • Per-gate, per-kernel, per-channel, and per-direction visuals
  • Gradients, weights, and activations visuals
  • Applicable to CNNs & other meaningfully-related layers

Why use? Introspection – is powerful for debugging, regularizing, and understanding NNs. For example, how can you tell whether your RNN is learning long-term dependencies? Monitor gradients: if a non-zero gradient flows through every timestep, then every timestep contributes to updating weights, so the RNN doesn’t ignore parts of sequences and is forced to learn from them. Or just because the visuals are rather pretty.

Numerous examples explored w/ image results in the link. Functionalities are fully-documented, and are compatible w/ TF 1.15.0 & Keras 2.2.5-, and TF 2.0.0+ & Keras 2.3.0+. Quickstart sandbox code included.

Feedback is welcome.

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https://preview.redd.it/lm7o59f1lv341.png?width=1170&format=png&auto=webp&s=cb7bd53f51b931dc514b5431c9a1afe687bb37f8

https://preview.redd.it/g7p8es25lv341.png?width=1065&format=png&auto=webp&s=0ef3580189c633ccd0a8ca1f0bc640b54be7392b

https://preview.redd.it/yjmciimnhv341.png?width=1025&format=png&auto=webp&s=69f37b3a66e3821015ec2120ca897f8e97af2223

https://preview.redd.it/52q9u4clhv341.png?width=1060&format=png&auto=webp&s=0e4a75bd6ca167e74b5837f981072ff4187ef82f

submitted by /u/OverLordGoldDragon
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.