Project Status: Active – The project has reached a stable, usable state and is being actively developed. CI codecov

Explicit Parameterization of Flux Layers


] add ExplicitFluxLayers

Getting Started

using ExplicitFluxLayers, Random, Optimisers

# Seeding
rng = Random.default_rng()
Random.seed!(rng, 0)

# Construct the layer
model = EFL.Chain(
    EFL.Dense(128, 256, tanh),
        EFL.Dense(256, 1, tanh),
        EFL.Dense(1, 10)

# Parameter and State Variables
ps, st = EFL.setup(rng, model) .|> EFL.gpu

# Dummy Input
x = rand(rng, Float32, 128, 2) |> EFL.gpu

# Run the model
y, st = EFL.apply(model, x, ps, st)

# Gradients
gs = gradient(p -> sum(EFL.apply(model, x, p, st)[1]), ps)[1]

# Optimization
st_opt = Optimisers.setup(Optimisers.ADAM(0.0001), ps)
st_opt, ps = Optimisers.update(st_opt, ps, gs)

Design Principles

  • Layers must be immutable -- i.e. they cannot store any parameters/states but rather stores information to construct them

  • Layers return a Tuple containing the result and the updated state

  • Layers are pure functions

  • Given same inputs the outputs must be same -- stochasticity is controlled by seeds passed in the state variables

  • Easily extendible for Custom Layers: Each Custom Layer should be a subtype of either:

    a. AbstractExplicitLayer: Useful for Base Layers and needs to define the following functions

    1. initialparameters(rng::AbstractRNG, layer::CustomAbstractExplicitLayer) -- This returns a ComponentArray/NamedTuple containing the trainable parameters for the layer.
    2. initialstates(rng::AbstractRNG, layer::CustomAbstractExplicitLayer) -- This returns a NamedTuple containing the current state for the layer. For most layers this is typically empty. Layers that would potentially contain this include BatchNorm, Recurrent Neural Networks, etc.
    3. parameterlength(layer::CustomAbstractExplicitLayer) & statelength(layer::CustomAbstractExplicitLayer) -- These can be automatically calculated, but it is recommended that the user defines these.

    b. AbstractExplicitContainerLayer: Used when the layer is storing other AbstractExplicitLayers or AbstractExplicitContainerLayers. This allows good defaults of the dispatches for functions mentioned in the previous point.

Why use ExplicitFluxLayers over Flux?

  • Large Neural Networks
    • For small neural networks we recommend SimpleChains.jl.
    • For SciML Applications (Neural ODEs, Deep Equilibrium Models) solvers typically expect a monolithic parameter vector. Flux enables this via its destructure mechanism, however, it often leads to weird bugs. EFL forces users to make an explicit distinction between state variables and parameter variables to avoid these issues.
    • Comes battery-included for distributed training using FluxMPI.jl
  • Sensible display of Custom Layers -- Ever wanted to see Pytorch like Network printouts or wondered how to extend the pretty printing of Flux's layers. ExplicitFluxLayers handles all of that by default.
  • Less Bug-ridden Code
    • No arbitrary internal mutations -- all layers are implemented as pure functions.
    • All layers are deterministic given the parameter and state -- if the layer is supposed to be stochastic (say Dropout), the state must contain a seed which is then updated after the function call.
  • Easy Parameter Manipulation -- Wondering why Flux doesn't have WeightNorm, SpectralNorm, etc. The implicit parameter handling makes it extremely hard to pass parameters around without mutations which AD systems don't like. With ExplicitFluxLayers implementing them is outright simple.

Usage Examples

ExplicitFluxLayers is exclusively focused on designing Neural Network Architectures. All other parts of the DL training/evaluation pipeline should be offloaded to the following frameworks:

If you found any other packages useful, please open a PR and add them to this list.

Implemented Layers

We don't have a Documentation Page as of now. But all these functions have docs which can be access in the REPL help mode.

  • Chain, Parallel, SkipConnection, BranchLayer, PairwiseFusion
  • Dense, Diagonal
  • Conv, MaxPool, MeanPool, GlobalMaxPool, GlobalMeanPool, Upsample, AdaptiveMaxPool, AdaptiveMeanPool
  • BatchNorm, WeightNorm, GroupNorm
  • ReshapeLayer, SelectDim, FlattenLayer, NoOpLayer, WrappedFunction
  • Dropout, VariationalHiddenDropout


  • Support Recurrent Neural Networks
  • Add wider support for Flux Layers
    • Convolution --> ConvTranspose, CrossCor
    • Upsampling --> PixelShuffle
    • General Purpose --> Maxout, Bilinear, Embedding, AlphaDropout
    • Normalization --> LayerNorm, InstanceNorm
  • Port tests over from Flux