Hi everyone,

I’m a beginner with Flux, and I want to use it to approximate a high-dimensional input function. However, I’m running into a dimension mismatch error. Here’s a MWE, where I’m just trying to approximate f(x,y) = x^2 + y^2.

```
using Flux
using Surrogates
using Statistics
using Pipe
#Defining toy function to approximate
f(x) = x[1].^2 + x[2].^2;
#Generating 2D sample inputs
n_samples = 100;
lower_bound = [-1.0, -1.0];
upper_bound = [1.0, 1.0];
xys = Surrogates.sample(n_samples, lower_bound, upper_bound, SobolSample()) #Sobol sampling - gives me a 100 element array of 2 element arrays
rawInputs = convert(Vector{Tuple{Float32, Float32}}, xys)
#Corresponding outputs
rawOutputs = @pipe [[f(xy)] for xy in xys] |>
convert(Vector{Vector{Float32}}, _);
#Defining neural network
dim_input = 2; #it's a 2 dimensional input (x and y)
dim_ouptut = 1; #1 dimensional output (f(x,y))
Q1 = 784; #Number of nodes for the first hidden layer
Q2 = 50; #Number of nodes for the second hidden layer
# Two inputs, one output
model = Chain(Dense(2,Q1,relu),
Dense(Q1,Q2,relu),
Dense(Q2,1,identity))
# Define loss function and weights
loss(x, y) = Flux.Losses.mse(model(collect(x)), y);
lr = 0.001; # learning rate
opt = Descent(lr);
epochs = 1000; # Define the number of epochs
trainingLosses = zeros(epochs);# Initialize a vector to keep track of the training progress
ps = Flux.params(model) #initialize weigths
trainingData = [(rawInputs, rawOutputs)];
# Training loop
@time for ii in 1:epochs
Flux.train!(loss, ps, trainingData, opt)
end
ERROR: DimensionMismatch: layer Dense(2 => 784, relu) expects size(input, 1) == 2, but got 100-element Vector{Tuple{Float32, Float32}}
```

FYI, I know that using

```
trainingData = zip(rawInputs, rawOutputs);
```

instead of

```
trainingData = [(rawInputs, rawOutputs)];
```

resolves the issue, but I eventually want to put this onto my GPU and I’d run into a “scalar indexing is disallowed” error message.

Thank you for your help!