Defining Customised Layers

Here we will try and describe usage of some more advanced features that Flux provides to give more control over model building.

Custom Model Example

Here is a basic example of a custom model. It simply adds the input to the result from the neural network.

struct CustomModel{T <: Chain} # Parameter to avoid type instability

function (m::CustomModel)(x)
  # Arbitrary code can go here, but note that everything will be differentiated.
  # Zygote does not allow some operations, like mutating arrays.

  return m.chain(x) + x

# Call @layer to allow for training. Described below in more detail.
Flux.@layer CustomModel

Notice that we parameterized the type of the chain field. This is necessary for fast Julia code, so that that struct field can be given a concrete type. Chains have a type parameter fully specifying the types of the layers they contain. By using a type parameter, we are freeing Julia to determine the correct concrete type, so that we do not need to specify the full, possibly quite long, type ourselves.

You can then use the model like:

chain = Chain(Dense(10 => 10, relu), Dense(10 => 10))
model = CustomModel(chain)
model(rand(Float32, 10))

For an intro to Flux and automatic differentiation, see this tutorial.

Customising Parameter Collection for a Model

Taking reference from our example Affine layer from the basics.

By default all the fields in the Affine type are collected as its parameters, however, in some cases it may be desired to hold other metadata in our "layers" that may not be needed for training, and are hence supposed to be ignored while the parameters are collected. With Flux, the way to mark some fields of our layer as trainable is through overloading the trainable function:

julia> struct Affine

julia> Affine(in::Int, out::Int) = Affine(randn(out, in), randn(out));

julia> (m::Affine)(x) = m.W * x .+ m.b;

julia> Flux.@layer Affine

julia> a = Affine(Float32[1 2; 3 4; 5 6], Float32[7, 8, 9])
Affine(Float32[1.0 2.0; 3.0 4.0; 5.0 6.0], Float32[7.0, 8.0, 9.0])

julia> Flux.trainable(a) # default behavior
(W = Float32[1.0 2.0; 3.0 4.0; 5.0 6.0], b = Float32[7.0, 8.0, 9.0])

julia> Flux.trainable(a::Affine) = (; W = a.W)  # returns a NamedTuple using the field's name

julia> Flux.trainable(a)
(W = Float32[1.0 2.0; 3.0 4.0; 5.0 6.0],)

Only the fields returned by trainable will be seen by Flux.setup and Flux.update! for training. But all fields wil be seen by gpu and similar functions, for example:

julia> a |> f16
Affine(Float16[1.0 2.0; 3.0 4.0; 5.0 6.0], Float16[7.0, 8.0, 9.0])

Note that there is no need to overload trainable to hide fields which do not contain numerical array (for example, activation functions, or Boolean flags). These are always ignored by training.

The exact same method of trainable can also be defined using the macro, for convenience:

Flux.@layer Affine trainable=(W,)

There is a second, more severe, kind of restriction possible. This is not recommended, but is included here for completeness. Calling Functors.@functor Affine (W,) means that all no exploration of the model will ever visit the other fields: They will not be moved to the GPU by gpu, and their precision will not be changed by f32. This requires the struct to have a corresponding constructor that accepts only W as an argument.

Custom multiple input or output layer

Sometimes a model needs to receive several separate inputs at once or produce several separate outputs at once. In other words, there multiple paths within this high-level layer, each processing a different input or producing a different output. A simple example of this in machine learning literature is the inception module.

We could have a struct that stores the weights of along each path and implement the joining/splitting in the forward pass function. That would mean a new struct for each different block, e.g. one would have a TransformerBlock struct for a transformer block, and a ResNetBlock struct for a ResNet block, each block being composed by smaller sub-blocks. This is often the simplest and cleanest way to implement complex models.

This guide instead will show you how to construct a high-level layer (like Chain) that is made of multiple sub-layers for each path.

Multiple inputs: a custom Join layer

Our custom Join layer will accept multiple inputs at once, pass each input through a separate path, then combine the results together. Note that this layer can already be constructed using Parallel, but we will first walk through how do this manually.

We start by defining a new struct, Join, that stores the different paths and a combine operation as its fields.

using Flux
using CUDA

# custom join layer
struct Join{T, F}

# allow Join(op, m1, m2, ...) as a constructor
Join(combine, paths...) = Join(combine, paths)

Notice again that we parameterized the type of the combine and paths fields. In addition to the performance considerations of concrete types, this allows either field to be Vectors, Tuples, or one of each - we don't need to pay attention to which.

The next step is to use Flux.@layer to make our struct behave like a Flux layer. This is important so that calling Flux.setup on a Join maps over the underlying trainable arrays on each path.

Flux.@layer Join

Finally, we define the forward pass. For Join, this means applying each path in paths to each input array, then using combine to merge the results.

(m::Join)(xs::Tuple) = m.combine(map((f, x) -> f(x), m.paths, xs)...)
(m::Join)(xs...) = m(xs)

Lastly, we can test our new layer. Thanks to the proper abstractions in Julia, our layer works on GPU arrays out of the box!

model = Chain(
                   Chain(Dense(1 => 5, relu), Dense(5 => 1)), # branch 1
                   Dense(1 => 2),                             # branch 2
                   Dense(1 => 1)                              # branch 3
              Dense(4 => 1)
             ) |> gpu

xs = map(gpu, (rand(1), rand(1), rand(1)))

# returns a single float vector with one value

This Join layer is available from the Fluxperimental.jl package.

Using Parallel

Flux already provides Parallel that can offer the same functionality. In this case, Join is going to just be syntactic sugar for Parallel.

Join(combine, paths) = Parallel(combine, paths)
Join(combine, paths...) = Join(combine, paths)

# use vararg/tuple version of Parallel forward pass
model = Chain(
                   Chain(Dense(1 => 5, relu), Dense(5 => 1)),
                   Dense(1 => 2),
                   Dense(1 => 1)
              Dense(4 => 1)
             ) |> gpu

xs = map(gpu, (rand(1), rand(1), rand(1)))

# returns a single float vector with one value

Multiple outputs: a custom Split layer

Our custom Split layer will accept a single input, then pass the input through a separate path to produce multiple outputs.

We start by following the same steps as the Join layer: define a struct, use Flux.@layer, and define the forward pass.

using Flux
using CUDA

# custom split layer
struct Split{T}

Split(paths...) = Split(paths)

Flux.@layer Split

(m::Split)(x::AbstractArray) = map(f -> f(x), m.paths)

Now we can test to see that our Split does indeed produce multiple outputs.

model = Chain(
              Dense(10 => 5),
              Split(Dense(5 => 1, tanh), Dense(5 => 3, tanh), Dense(5 => 2))
             ) |> gpu

# returns a tuple with three float vectors

A custom loss function for the multiple outputs may look like this:

using Statistics

# assuming model returns the output of a Split
# x is a single input
# ys is a tuple of outputs
function loss(x, ys, model)
  # rms over all the mse
  ŷs = model(x)
  return sqrt(mean(Flux.mse(y, ŷ) for (y, ŷ) in zip(ys, ŷs)))

This Split layer is available from the Fluxperimental.jl package.