# Problem relating to use gradient descends two models using GPU

**URL:** <https://discourse.julialang.org/t/problem-relating-to-use-gradient-descends-two-models-using-gpu/121911>\
**Category:** General Usage\
**Created:** [October 29, 2024, 11:33am UTC](https://discourse.julialang.org/t/problem-relating-to-use-gradient-descends-two-models-using-gpu/121911 "2024-10-29T11:33:38Z")\
**Posts on this page:** 1\
**Showing post:** 3

<div class="post-metadata">

**Author:** ![Yaozhong\_Liu](https://avatars.discourse-cdn.com/v4/letter/y/a587f6/32.png) [@Yaozhong\_Liu](https://discourse.julialang.org/u/Yaozhong_Liu)\
**Post date:** [October 29, 2024, 11:54am UTC](https://discourse.julialang.org/t/problem-relating-to-use-gradient-descends-two-models-using-gpu/121911/3 "2024-10-29T11:54:17Z")

</div>

Sorry, My fault.  
I mistakenly used trianloader…though I still didn’t know how to use it now.  
Anyway, it will run smoothly without using trainloader:

```julia
using CUDA
using Flux
using Random
Random.seed!(333)
model1 = Chain(
    Dense(3, 3) |> gpu
)

model2 = Chain(
    model1,
    Dense(ones(3,3),true,relu) |> gpu
)
model3 = Chain(
    model1,
    Dense(ones(3,3),true,relu) |> gpu
)
A = [1.0,2.0,3.0] |> gpu
B = [1.0,1.0,1.0] |> gpu
label = [10,10,10] |> gpu
opt = Adam(0.1)
opt_stats = Flux.setup(opt, (model2, model3))
for i in 1:5
    global l1 = 0
    global l2 = 0
    gs = gradient(model2, model3) do m2, m3
        l1 += Flux.mse(m2(A),label)
        l2 = Flux.mse(m3(B),label)
        allLoss = l1 + l2
        @show l1
        @show l2
        @show allLoss
    end
    Flux.update!(opt_stats, (model2,model3), gs)
end

```

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