tensorflow / tensorflow/probability
Simple network with fipout layers does not work with mixed precision
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Description
Dear tfp devs
Firstly, thank you for this magnificent package!
I have tried using mixed precision the same way as with base tf(it works on tf). Unfortunately an error occurs:
Tensor conversion requested dtype float32 for Tensor with dtype float16: <tf.Tensor 'Cast:0' shape=(None, 300, 300, 1) dtype=float16>
Model consist of conv and dense flipout layers.
tfp: 0.12.1
tensorflow running on an official docker container
Regards, Jakub
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reproducing the reported mixed-precision setup with convolutional and dense Flipout layers using tfp 0.12.1 and the official TensorFlow Docker container. Trace the float16-to-float32 conversion that produces the reported error; the issue is done when this model runs under mixed precision without that dtype error.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- tensorflow
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100