Hello,

how do you access (or store) intermediate results such as weights per epoch in Flux? I have today started using it and was really surprised by how easy it is to implement thing. One thing I did not find and could not solve so far: how do you access results per epoch?

My below minimum working example is doing a simple linear regression. How could I access the weights after epoch i?

I have managed to store losses, however, I am not able to achieve the same for weights.

```
# Generate simple true data for MWE
nFeat = 10
X = rand(nFeat, 200) # rows = features, obs = columns
W = transpose(rand(1:10,nFeat))
b = rand(1:10,1)
y = W * X .+ b
# build simple model for MWE
s_in = size(X,1)
s_out = 1
m = Flux.Chain(
# only one layer for MWE...
Dense(s_in, s_out, identity)
)
loss(x,y) = Flux.mse(m(x),y)
opt = Flux.Descent()
my_losses = []
evalcb = () -> push!(my_losses, loss(X,y)) # works!
# FOR WEIGHTS
# my_weights
# evalcb = () -> push!(my_weights, [l.W for l in m.layers]) # stores only the final weights
# Train model
data = [(X,y)]
epochs = 1000
for i in 1:epochs
if i % 100 == 0
@show i
end
Flux.train!(loss, Flux.params(m), data, opt, cb=Flux.throttle(evalcb, 10))
end
```

I could not find anything in the documentation. As I am not an expert on Julia and/or ML it may be due to my lack of understanding, howeverâ€¦

Thanks for your help!