tensorflow / tensorflow/probability
gradient descent and performance issues with Tf 2.2
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Description
I am running an algorithm with tensorflow 2.2 and tensorflow-probability 0.10 using a gaussian process layer which is based on the example provided in google colab.
I am getting the following warnings which seem to be impacting my performance further. Before the upgrade I could get near 0 loss with 270 epochs. Now it has stopped improving altogether after a few hundred epochs and gets permanently stuck at 34. I believe that the problem may have something to do with this warning
WARNING:tensorflow:Gradients do not exist for variables ['variational_gaussian_process_6/observation_noise_variance:0'] when minimizing the loss.
I suspect that while the VGP distribution was made tape safe the VGP layer still doesnt appear to be tape safe. I am wondering if this is true and if so how I can get around this problem
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Research direction
Start with the Google Colab Gaussian process example and reproduce it under TensorFlow 2.2 and tensorflow-probability 0.10. Inspect the VGP layer around the reported observation_noise_variance gradient warning and compare its behavior with the VGP distribution. Done means identifying whether the warning causes the stalled loss and documenting a verified workaround or fix.
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Assessment
- Tech stack
- jupyter-notebook
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100