tensorflow / tensorflow/probability
Feature request: Different activation functions for different hidden layers in tfp.bijectors.AutoregressiveNetwork
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Description
I would like to propose the following enhancement. In tfp.bijectors.AutoregressiveNetwork (https://www.tensorflow.org/probability/api_docs/python/tfp/bijectors/AutoregressiveNetwork) there does not seem to be a way to specify different activations for different hidden layers. Specifically, hidden_units allows for the specification of a network with multiple hidden layers. E.g., [10, 10] specifies two hidden layers with 10 units each. activation though does not take a list of activations. Instead, it takes a single activation function, which it applies to all hidden layers. It would be useful to be able to specify different activations for different hidden layers.
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First steps
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Research direction
Start with the tfp.bijectors.AutoregressiveNetwork API and its handling of hidden_units and activation. Trace the implementation and existing tests, then verify that a separate activation can be selected for each hidden layer while preserving the current single-activation behavior.
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Assessment
- Tech stack
- tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Clearly specified
- Newbie friendliness
- 45/100