# ReverseDiff over Zygote to differentiate neural network using GalacticOptim - Dimension mismatch
**URL:** https://discourse.julialang.org/t/reversediff-over-zygote-to-differentiate-neural-network-using-galacticoptim-dimension-mismatch/59744
**Category:** Machine Learning
**Created:** [April 21, 2021, 4:24pm UTC](https://discourse.julialang.org/t/reversediff-over-zygote-to-differentiate-neural-network-using-galacticoptim-dimension-mismatch/59744 "2021-04-21T16:24:33Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@RynoLaubscher](https://discourse.julialang.org/u/RynoLaubscher)
#### Post date: [April 21, 2021, 4:24pm UTC](https://discourse.julialang.org/t/reversediff-over-zygote-to-differentiate-neural-network-using-galacticoptim-dimension-mismatch/59744/1 "2021-04-21T16:24:33Z")
Hi All, I am struggling a bit with using GalacticOptim.AutoReverseDiff to find the gradients over a loss function which internally calculates a gradient using Zygote. Running ForwardDiff over the function works fine but is quite slow. I have created a mwe as seen below along with the error message. Any help would be appreciated to get around this or find an alternative approach.
```julia
using Zygote
using GalacticOptim
using Flux
using LinearAlgebra
using Statistics
using StatsBase
l1 = Dense(2, 10, tanh)
l2 = Dense(10, 4, identity)
network = Chain(l1, l2)
pars = Flux.params(network)
vec_pars, re = Flux.destructure(network)
function loss(x, p)
network = re(x)
u = Zygote.gradient(d -> sum(network(d)), p)[1]
sum(u)
end
data = rand(2, 1000)
loss(vec_pars, data)
adtype = GalacticOptim.AutoReverseDiff()
optf = GalacticOptim.OptimizationFunction(loss, adtype)
optprob = GalacticOptim.OptimizationProblem(optf, vec_pars, data)
result_ad = GalacticOptim.solve(optprob, ADAM(0.001), maxiters=1)
```
```julia
DimensionMismatch("array could not be broadcast to match destination")
check_broadcast_shape at broadcast.jl:520 [inlined]
check_broadcast_axes at broadcast.jl:523 [inlined]
check_broadcast_axes at broadcast.jl:527 [inlined]
instantiate at broadcast.jl:269 [inlined]
materialize! at broadcast.jl:894 [inlined]
materialize! at broadcast.jl:891 [inlined]
apply!(o::ADAM, x::Vector{Float32}, Δ::Vector{Float64}) at optimisers.jl:179
update!(opt::ADAM, x::Vector{Float32}, x̄::Vector{Float64}) at train.jl:23
update!(opt::ADAM, xs::Params, gs::Zygote.Grads) at train.jl:29
__solve(prob::OptimizationProblem{true, OptimizationFunction{true, GalacticOptim.AutoReverseDiff, typeof(loss), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float32}, Matrix{Float64}, Nothing, Nothing, Nothing, Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}}, opt::ADAM, data::Base.Iterators.Cycle{Tuple{GalacticOptim.NullData}}; maxiters::Int64, cb::Function, progress::Bool, save_best::Bool, kwargs::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}) at solve.jl:106
__solve at solve.jl:66 [inlined]
__solve at solve.jl:66 [inlined]
#solve#468 at solve.jl:3 [inlined]
(::CommonSolve.var"#solve##kw")(::NamedTuple{(:maxiters,), Tuple{Int64}}, ::typeof(solve), ::OptimizationProblem{true, OptimizationFunction{true, GalacticOptim.AutoReverseDiff, typeof(loss), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float32}, Matrix{Float64}, Nothing, Nothing, Nothing, Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}}, ::ADAM) at solve.jl:3
top-level scope at MWE.jl:27
eval at boot.jl:360 [inlined]
```
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [April 21, 2021, 7:14pm UTC](https://discourse.julialang.org/t/reversediff-over-zygote-to-differentiate-neural-network-using-galacticoptim-dimension-mismatch/59744/2 "2021-04-21T19:14:15Z")
Open an issue on GalacticOptim.jl. I’ll want to dig into this and having it in my email will make it easier to track. @dhairyagandhi96 and @mohamed82008 this should be a fun one.
---
### Author:  [@RynoLaubscher](https://discourse.julialang.org/u/RynoLaubscher)
#### Post date: [April 21, 2021, 7:21pm UTC](https://discourse.julialang.org/t/reversediff-over-zygote-to-differentiate-neural-network-using-galacticoptim-dimension-mismatch/59744/3 "2021-04-21T19:21:16Z")
Will do @ChrisRackauckas, thanks. 👍