I defined the following model with a dictionary containing weights

```
mutable struct Affine
params
end
Affine(in::Integer, out::Integer) =
Affine(Dict("W"=>randn(out, in), ("b"=>randn(out))))
# Overload call, so the object can be used as a function
(m::Affine)(x) = m.params["W"] * x .+ m.params["b"]
a = Affine(1, 1)
#a.params
#Dict{String,Array{Float64,N} where N} with 2 entries:
# "W" => [0.559984]
# "b" => [0.841258]
```

Then I tried to specify params[“W”] as trainable

```
Flux.@functor Affine
Flux.trainable(a::Affine) = (a.params["W"],)
grads = Flux.gradient(() -> a(3)[1], params(a))
for p in grads.grads
println(p)
println("--")
end
# Output
Pair{Any,Any}([0.5599836013031454], nothing)
--
Pair{Any,Any}(Dict{String,Array{Float64,N} where N}("W" => [0.5599836013031454],"b" => [0.841258238965482]), Dict{Any,Any}("W" => [3.0],"b" => [1.0]))
--
```

But gradient seems to be taken with respect to both parameters.

Can I choose only one I need?