Unable to evaluate a Lux model at a specific parameter set.

[TorkelE]

Say that I have a Lux model

using Lux
model = Lux.Chain(
    Lux.Dense(1 => 4, Lux.softplus),
    Lux.Dense(4 => 4, Lux.softplus),
    Lux.Dense(4 => 1, Lux.softplus)
)

Two questions:

1. is there a way to learn how many parameters this model have? All function I apply returns various structured things which I cannot really understand what they contain.
2. Lets say my model has 5 parameters and 1 input, how do I, given that I have these in normal vectors:

input = [1.0]
p_vals = [1.0, -2.0, 0.5, 5.0, -1.5]

get the output of my neural network for these parameter values and output?

I have spent the last couple of days trying to get something like this to work, but I really have not been able to. i have been trying to make a basic example where I can train a model using optimization (where I have a neural network and some other things I want to optimize), and then build from there. Unfortunatley I cannot get this to work. Doc pages I have been reading include
https://lux.csail.mit.edu/stable/introduction/
https://lux.csail.mit.edu/stable/tutorials/beginner/1_Basics
https://docs.sciml.ai/Optimization/stable/optimization_packages/optimization/#Train-NN-with-Sophia
https://lux.csail.mit.edu/stable/tutorials/beginner/5_OptimizationIntegration
https://lux.csail.mit.edu/stable/tutorials/beginner/2_PolynomialFitting

[TorkelE]

(I realise that I might need to create a component array from my vector, if that is the case I don't mind if that is required)

[avik-pal]

For the interface check https://lux.csail.mit.edu/stable/manual/interface#Layer-Interface.

1. Use parameterlength for the number of parameters.
2. You should construct an initial random parameter set using Lux.setup and update those parameter values with the values you want. https://lux.csail.mit.edu/stable/manual/interface#Parameter-Interface describes the expectations from the parameters struct.