Comments (4)
Hessians are slightly different - if you are using a DiffResult
, you need to pass it to the HessianConfig
. For example, this should work:
using DiffBase, ReverseDiff
f(x) = sum(sin, x)+prod(tan, x)*sum(sqrt, x);
x = rand(4);
result = DiffBase.HessianResult(x);
rcfg = ReverseDiff.HessianConfig(result, x); # let the HessianConfig know you'll be using `result`
ReverseDiff.hessian!(result, f, x, rcfg);
DiffBase.value(result)
DiffBase.gradient(result)
DiffBase.hessian(result)
This can be found in the tests, but isn't documented very well at all, and we don't have example code for it. The error message is also horrible.
We should document this better and provide examples. This might also be a case where we could easily add an informative error message. Alternatively, we might actually be able to allow HessianConfig(x)
to work for the second example, though it would be a less efficient way to use the configuration API.
I'll keep this issue open, but rename it to reflect the actual problem (poor docs + examples + error message).
from reversediff.jl.
Thanks a lot @jrevels, I understand the API for Hessians better via your clarifications. I have one more question; it is highly likely I do sth wrong in what follows. When I try to use a tape in the above example, it then breaks:
using DiffBase, ReverseDiff
f(x) = sum(sin, x)+prod(tan, x)*sum(sqrt, x);
x = rand(4);
result = DiffBase.HessianResult(x);
rcfg = ReverseDiff.HessianConfig(result, x); # let the HessianConfig know you'll be using `result`
ftape = ReverseDiff.HessianTape(f, x);
cftape = ReverseDiff.compile(ftape);
ReverseDiff.hessian!(result, cftape, x, rcfg);
DiffBase.value(result)
DiffBase.gradient(result)
DiffBase.hessian(result)
from reversediff.jl.
ReverseDiff.hessian!(result, cftape, x, rcfg)
should just be ReverseDiff.hessian!(result, cftape, x)
(this is probably another thing that could be better documented/throw better errors).
from reversediff.jl.
Thanks @jrevels, I now follow the API.
from reversediff.jl.
Related Issues (20)
- Error when using scalar vs. vector to operate on tracked inupt HOT 1
- Record `Broadcast.broadcasted` instead of `Broadcast.broadcast`
- MethodError: ReverseDiff.TrackedReal ... is ambiguous.
- double free crash with multi-threaded code only when using multiple threads
- @grad_from_chainrules macro fails when using multi-output functions HOT 2
- ReverseDiff documentation shows issue that has been fixed? Nested differentiation of a closure? HOT 1
- `MethodError: *(::Diagonal, ::ReverseDiff.TrackedArray)` is ambiguous.
- `@grad_from_chainrules` hygiene: cannot use custom types in method signature HOT 3
- Define `typemin` for tracked reals.
- ReverseDiff defines a huge number of methods. HOT 3
- Nested differentiation of closures yields incorrect results. Any news on the fix?
- Enhancement proposal: Modular tape caching HOT 16
- Bug: Derivative of transposed-vector times matrix is incorrect. HOT 5
- Strange bug when deferring to ChainRules HOT 1
- Add ChainRulesCore RuleConfig? HOT 1
- mean BigFloat precision
- MethodError: vcat(::ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ::Matrix{Float32}) is ambiguous. HOT 4
- Method ambiguities reported by Aqua
- DiffResults objects are not re-aliased properly HOT 2
- ERROR: LoadError: Some tests did not pass: 146 passed, 0 failed, 1 errored, 0 broken. HOT 1
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from reversediff.jl.