JuliaDiff / JuliaDiff/ForwardDiff.jl
Defining different forward/backward pass behavior
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- Julia
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Description
I am trying to port Eric Jang's Gumbel-Softmax code from here:
http://blog.evjang.com/2016/11/tutorial-categorical-variational.html
The part that I am having trouble with is line 27 of the Tensorflow Implementation:
y = tf.stop_gradient(y_hard - y) + y
I believe the desired behavior is to use y_hard on the forward pass but use y to compute gradients on the backwards pass.
How can I achieve this in ForwardDiff (and ReverseDiff too)?
Thanks!
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Research direction
Start with line 27 of the linked TensorFlow implementation and compare the requested forward/backward behavior with ForwardDiff and ReverseDiff. Determine whether either project exposes a supported way to express this behavior; done means providing a clear, project-appropriate answer or defining the scope of a new feature.
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Assessment
- Tech stack
- julia, tensorflow
- Domain
- tooling
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100