# Flux/Lux - Combining two neural networks
**URL:** https://discourse.julialang.org/t/flux-lux-combining-two-neural-networks/83868
**Category:** Machine Learning
**Tags:** question
**Created:** [July 7, 2022, 1:05am UTC](https://discourse.julialang.org/t/flux-lux-combining-two-neural-networks/83868 "2022-07-07T01:05:16Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@raphaelchinchilla](https://discourse.julialang.org/u/raphaelchinchilla)
#### Post date: [July 7, 2022, 1:05am UTC](https://discourse.julialang.org/t/flux-lux-combining-two-neural-networks/83868/1 "2022-07-07T01:05:16Z")
I want to design a neural network whose output is the combination of the outputs of two neural networks; a very simple example would be the output of two Dense layers.
Intuitively, I would want to write something like
`model=Dense(2,2)+Dense(2,2)`
which throws an error.
An alternative would be to do something like (in Flux)
```julia
model_1=Dense(2,2)
model_2=Dense(2,2)
model(x)=model_1(x)+model_2(x)
```
but then `model` is not a layer, so I cannot use some API such as Flux.params or Optimisers.destructure (to site just a few).
Is there a way to do it in a more elegant way which would return me an object like `Chain` or similar?
PS1: I am currently using Lux, but I wrote the example in Flux for convenience. I imagine a solution in one would work in the other with the appropriate modification
PS2: Just to be sure that no one says it, I know that by linearity I could train the model in a single `Dense` layer. I am trying to do this with more complicated models.
---
### Author:  [@mcabbott](https://discourse.julialang.org/u/mcabbott)
#### Post date: [July 7, 2022, 1:21am UTC](https://discourse.julialang.org/t/flux-lux-combining-two-neural-networks/83868/2 "2022-07-07T01:21:51Z")
I think you want `Parallel`:
```julia
julia> m1 = Dense([1 2; 3 4]);
julia> m2 = Dense([5 6; 7 8]);
julia> x = [9, 10];
julia> m1(x)
2-element Vector{Int64}:
29
67
julia> m2(x)
2-element Vector{Int64}:
105
143
julia> m3 = Parallel(+, m1, m2)
Parallel(
+,
Dense(2 => 2), # 6 parameters
Dense(2 => 2), # 6 parameters
) # Total: 4 arrays, 12 parameters, 352 bytes.
julia> m3(x)
2-element Vector{Int64}:
134
210
```