API

NeuralNetworkBlock(; n_input = 1, n_output = 1,
    chain = multi_layer_feed_forward(n_input, n_output),
    rng = Xoshiro(0),
    init_params = Lux.initialparameters(rng, chain),
    eltype = Float64,
    name)

Create a component neural network as a System.

SymbolicNeuralNetwork(; n_input = 1, n_output = 1,
    chain = multi_layer_feed_forward(n_input, n_output),
    rng = Xoshiro(0),
    init_params = Lux.initialparameters(rng, chain),
    nn_name =  :NN,
    nn_p_name = :p,
    eltype = Float64)

Create symbolic parameter for a neural network and one for its parameters. Example:

chain = multi_layer_feed_forward(2, 2)
NN, p = SymbolicNeuralNetwork(; chain, n_input=2, n_output=2, rng = StableRNG(42))

The NN and p are symbolic parameters that can be used later as part of a system. To change the name of the symbolic variables, use nn_name and nn_p_name. To get the predictions of the neural network, use

pred ~ NN(input, p)

where pred and input are a symbolic vector variable with the lengths n_output and n_input.

To use this outside of an equation, you can get the default values for the symbols and make a similar call

defaults(sys)[sys.NN](input, nn_p)

where sys is a system (e.g. ODESystem) that contains NN, input is a vector of n_input length and nn_p is a vector representing parameter values for the neural network.

To get the underlying Lux model you can use get_network(defaults(sys)[sys.NN]) or

multi_layer_feed_forward(; n_input, n_output, width::Int = 4,
    depth::Int = 1, activation = tanh, use_bias = true, initial_scaling_factor = 1e-8)

Create a Lux.jl Chain for use in NeuralNetworkBlocks. The weights of the last layer are multiplied by the initial_scaling_factor in order to make the initial contribution of the network small and thus help with achieving a stable starting position for the training.