Comments (2)
Incidentally it's easy to do forward-over-reverse with Zygote's API.
using Flux
y, back = Tracker.forward(x -> [3x^2, 5x+2], 5)
Tracker.gradient(x -> back(x)[1], [1, 1]) # [30, 5]
This probably won't actually work with Zygote yet, since nested AD doesn't work in all cases, but it will eventually.
from zygote.jl.
Zygote is reverse mode, so the output of forward
calculates a vector-Jacobian product. For forward you should probably check out ForwardDiff.jl.
It would be good to add utilities for full Jacobians though.
from zygote.jl.
Related Issues (20)
- Spurious "Output is complex, so the gradient is not defined" error HOT 2
- NaN in gradient of abs() on complex 0 HOT 1
- Pullback on mean() gives illegal memory access code 700 HOT 31
- test
- Type unstable gradients (@code_warntype) HOT 1
- Type unstable gradients HOT 1
- Zygote gradients different from ForwardDiff/ReverseDiff on Julia 1.10-rc2 HOT 3
- try/catch is not supported when attempting to use `remake` with Zygote HOT 1
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- `gradient` broken for `(*)(::Diagonal{Real}, ::Matrix{Complex}, ::Diagonal{Real})` when updating Julia 1.8 -> 1.9 HOT 6
- Method ambiguities reported by Aqua
- slow/high allocation gradient with mapreduce and iterators HOT 11
- error in summation of product iterator HOT 2
- `sort(x; rev=true)` is not supported HOT 1
- Incorrect gradients for `plan_rfft(x) * x` HOT 2
- Gradient of scalar function of gradient giving mutating array error HOT 4
- `sum` with CUDA and view on array errors HOT 3
- Cannot take gradient of sort on 2D CuArray HOT 1
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from zygote.jl.