A simple library which allows you to do algebraic manipulations on neural network models/layers defined in Lux.jl, for example if l1 and l2 are Lux layers, then

l3 = @lift_nn 2*l1 + l2

defines a Lux layer l3 such that for an input x, the layer produces an output l3(x) = 2*l1(x) + l2(x).

Technical Motivation

Given two real-valued functions f and g which have the same domain, we can define an algebra over the real numbers such that h=f+g defines another real-valued function h. Because parameterized neural networks can be defined as real-valued functions over the real numbers (excluding their parameters), then we can "lift" this algebra to an algebra over neural networks.

Basic Functionality

using AlgebraOfNNs
using Lux
l1 = Dense(16=>32)
l2 = Dense(16=>32)
l3 = @lift_nn 2*l1+l2

will call constructors for Lux's Parallel and Chain layers such that

julia> l3
        Dense(16 => 32),                # 544 parameters
    Dense(16 => 32),                    # 544 parameters
)         # Total: 1_088 parameters,
          #        plus 0 states.


  • Works generically for any function call, not just algebraic operators (i.e., recall that in Julia even arithmetic operations like a+b+c are themselves simply n-ary function calls +(a,b,c))
  • Does not introduce any new state or learnable parameters