JuliaDiff / JuliaDiff/ReverseDiff.jl
Get a error when calculating the gradient for LSTM
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- Julia
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Description
Hi, I build a network. ReverseDiff.gradient works well for Dense layer. However, when I changed to a LSTM ReverseDiff.gradient(loss_mean, params), it throw an error, MethodError: no method matching (::Flux.LSTMCell{ReverseDiff.TrackedArray{Float64, Float64, 2, Matrix{Float64}, Matrix{Float64}}, ReverseDiff.TrackedArray{Float64, Float64, 2, Matrix{Float64}, Matrix{Float64}}...
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Research direction
Reproduce the reported call using ReverseDiff.gradient(loss_mean, params) with a Flux LSTM rather than a Dense layer. Start from the Flux.LSTMCell MethodError and compare the failing LSTM case with the working Dense case. Done means calculating the LSTM gradient without this MethodError.
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Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100