Suppose I have a trained neural net

`nn1 = Flux.Chain(Dense(90, 60, relu), Dense(60, 30, relu), Dense(30,1)).`

I am aware that I can access the weights corresponding to the first 2 layers by params(nn1[1]), params(nn1[2]).

However, I then set up

`nn2 = Flux.Chain(Dense(90, 60, relu), Dense(60, 30, relu), Dense(30,2))`

.

Note that nn1 and nn2 have the same structure in first 2 layers.

Can I initialize the weights of the first two layers with

```
params(nn2[1]) = params(nn1[1])
params(nn2[2]) = params(nn1[2])
```

Skoffer
#2
I wonder what is the most idiomatic answer to this question, but you may go to low level.

Since `Dense`

is just an ordinary immutable structure which contains data, you can change it’s weights directly.

```
nn2[1].W .= nn1[1].W
nn2[1].b .= nn1[1].b
```

or you can wrap it (unsafe) function

```
function Base.:setindex!(c::Chain, d::Dense, i)
c.layers[i].W .= d.W
c.layers[i].b .= d.b
end
nn2[1] = nn1[1]
nn2[2] = nn1[2]
```

But I think it’s better to construct `nn2`

from `nn1`

directly

```
nn2 = Flux.Chain(deepcopy(nn1[1:2])..., Dense(30, 2))
```

Skoffer
#3
Also you may use `loadparams!`

function.

```
Flux.loadparams!(nn2[1:2], params(nn1[1:2]))
```

It is more generic approach, since it doesn’t rely on internal structure of `Dense`

. If you want, you may add some sugar

```
Base.:setindex!(c::Chain, x, i) = Flux.loadparams!(c[i], params(x))
nn2[1:2] = nn1[1:2]
```