UndefVarError: Order not defined Stacktrace: [1] top-level scope @ In[22]:23

Hi everyone,
I am new in using Julia and I want to replicate my neural network code that I have written in Matlab in Julia, so I ask ChatGPT to write a simple code, but when I run it in Julia it returns me an error, can anyone, please, help me to figure out how to solve the problem, below is the code and an error message.

using Flux, Optim, LineSearches

Define the input data and the target outputs

x = [0.1, 0.2, 0.3, 0.4]
y = [0.2, 0.4, 0.6, 0.8]

Define the number of hidden neurons

num_hidden = 5

Define the neural network architecture

model = Chain(Dense(length(x), num_hidden, relu), Dense(num_hidden, 1))

Define the loss function

loss(x, y) = Flux.mse(model(x), y)

Define the initial parameter values

init_params = Flux.params(model)

Load the LineSearches package

using LineSearches

Define the optimizer and set the options for the Levenberg-Marquardt algorithm

optimizer = LBFGS(linesearch = BackTracking(Order(3)), # set the line search
x_tol=1e-12, # set the tolerance for the solution
g_tol=1e-8, # set the tolerance for the gradient
iterations = 10000, # set the maximum number of iterations
alphaguess = 1.0, # set the initial step size
store_trace = true) # store the history of optimization

Train the neural network using the Levenberg-Marquardt algorithm

Flux.train!(loss, init_params, [(x, y)], optimizer, cb = () → println("Training loss: ", loss(x, y)))

UndefVarError: Order not defined

[1] top-level scope
@ In[22]:23

ChatGPT often produces broken code. The documentation for these packages is pretty good. I recommend using it. Here’s a code snippet from the readme GitHub - FluxML/Flux.jl: Relax! Flux is the ML library that doesn't make you tensor

using Flux, Plots
data = [([x], 2x-x^3) for x in -2:0.1f0:2]

model = Chain(Dense(1 => 23, tanh), Dense(23 => 1, bias=false), only)

optim = Flux.setup(Adam(), model)
for epoch in 1:1000
  Flux.train!((m,x,y) -> (m(x) - y)^2, model, data, optim)

plot(x -> 2x-x^3, -2, 2, legend=false)
scatter!(x -> model([x]), -2:0.1f0:2)

There’s much more available at Quick Start · Flux.