forked from FluxML/Fluxperimental.jl
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- See discussion in FluxML/Flux.jl#2107 Co-authored-by: Michael Abbott <[email protected]>
- Loading branch information
1 parent
8650d54
commit 5c450b4
Showing
2 changed files
with
117 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,114 @@ | ||
""" | ||
@Magic(forward::Function; construct...) | ||
Creates a layer by specifying some code to construct the layer, run immediately, | ||
and (usually as a `do` block) a function for the forward pass. | ||
You may think of `construct` as keywords, or better as a `let` block creating local variables. | ||
Their names may be used within the body of the `forward` function. | ||
Here is a linear model: | ||
``` | ||
r = @Magic(w = rand(3)) do x | ||
w .* x | ||
end | ||
r([1, 1, 1]) # x is set to [1, 1, 1]. | ||
``` | ||
Here is a linear model with bias and activation: | ||
``` | ||
d = @Magic(in=5, out=7, W=randn(out, in), b=zeros(out), act=relu) do x | ||
y = W * x | ||
act.(y .+ b) | ||
end | ||
d(ones(5, 10)) # 7×10 Matrix as output. | ||
``` | ||
Finally, here is a simple MLP: | ||
``` | ||
using Flux | ||
n_in = 1 | ||
n_out = 1 | ||
nlayers = 3 | ||
model = @Magic( | ||
w1=Dense(n_in, 128), | ||
w2=[Dense(128, 128) for i=1:nlayers], | ||
w3=Dense(128, n_out), | ||
act=relu | ||
) do x | ||
embed = act(w1(x)) | ||
for w in w2 | ||
embed = act(w(embed)) | ||
end | ||
out = w3(embed) | ||
return out | ||
end | ||
model(randn(n_in, 32)) # 1×32 Matrix as output. | ||
``` | ||
We can train this model just like any `Chain`: | ||
``` | ||
data = [([x], 2x-x^3) for x in -2:0.1f0:2] | ||
optim = Flux.setup(Adam(), model) | ||
for epoch in 1:1000 | ||
Flux.train!((m,x,y) -> (m(x) - y)^2, model, data, optim) | ||
end | ||
``` | ||
""" | ||
macro Magic(fex, kwexs...) | ||
# check input | ||
Meta.isexpr(fex, :(->)) || error("expects a do block") | ||
isempty(kwexs) && error("expects keyword arguments") | ||
all(ex -> Meta.isexpr(ex, :kw), kwexs) || error("expects only keyword argumens") | ||
|
||
# make strings | ||
layer = "@Magic" | ||
setup = join(map(ex -> string(ex.args[1], " = ", ex.args[2]), kwexs), ", ") | ||
input = join(fex.args[1].args, ", ") | ||
block = string(Base.remove_linenums!(fex).args[2]) | ||
|
||
# edit expressions | ||
vars = map(ex -> ex.args[1], kwexs) | ||
assigns = map(ex -> Expr(:(=), ex.args...), kwexs) | ||
@gensym self | ||
pushfirst!(fex.args[1].args, self) | ||
addprefix!(fex, self, vars) | ||
|
||
# assemble | ||
return esc(quote | ||
let | ||
$(assigns...) | ||
$MagicLayer($fex, ($layer, $setup, $input, $block); $(vars...)) | ||
end | ||
end) | ||
end | ||
|
||
function addprefix!(ex::Expr, self, vars) | ||
for i = 1:length(ex.args) | ||
if ex.args[i] in vars | ||
ex.args[i] = :($self.$(ex.args[i])) | ||
else | ||
addprefix!(ex.args[i], self, vars) | ||
end | ||
end | ||
end | ||
addprefix!(not_ex, self, vars) = nothing | ||
|
||
struct MagicLayer{F,NT<:NamedTuple} | ||
fun::F | ||
strings::NTuple{4,String} | ||
variables::NT | ||
end | ||
MagicLayer(f::Function, str::Tuple; kw...) = MagicLayer(f, str, NamedTuple(kw)) | ||
(m::MagicLayer)(x...) = m.fun(m.variables, x...) | ||
MagicLayer(args...) = error("MagicLayer is meant to be constructed by the macro") | ||
Flux.@functor MagicLayer | ||
|
||
function Base.show(io::IO, m::MagicLayer) | ||
layer, setup, input, block = m.strings | ||
print(io, layer, "(", setup, ") do ", input) | ||
return print(io, block[6:end]) | ||
end |