Gradient Descent, each datapoint is an array, in Flux

General Usage
1

I have been trying to use Flux.jl library for simple Machine learning. But in my case, my each data point is an array of some fixed length greater than 1000. And so I haven’t been able to convert the following basic gradient descent algorithm with 1 hidden layer consisting of 100 neurones.

n=100      
x1 = randn(length(10000))       
learning_rate = 0.1


W1 = randn(n, 1)
W2 = randn(1, n)

X1_W1 = randn(n, length(x1))
X1_W1_s = randn(n, length(x1))
X2_W2 = randn(1, length(x1))

dw1 = randn(n, 1)
dw2 = randn(1, n)

error = 0

for i in 1:10
      X1_W1 .= W1 * x1'   # FeedForward
      X1_W1_s .= sigmoid.(X1_W1)        # FeedForward

      X2_W2 .= W2 * X1_W1_s

      error = norm(X2_W2 .- Sin_)/sqrt(length(X2_W2))

      println(error)

      dw2 .= (0.5/length(X2_W2)) .* ( X1_W1_s * (X2_W2' .- Sin_))'
      W2 .= W2 .- learning_rate .* dw2
      dw1 .= (0.5/length(X2_W2)) .* ( X1_W1_s * (X2_W2' .- Sin_))' * X1_W1_s .* (1 .- X1_W1_s) * x1
      W1 .= W1 .- learning_rate .*dw1
      W1 .= W1/norm(W1)
      W2 .= W2/norm(W2)
end

Is there a way someone could be generous enough to help convert the above to the Flux paradigm, deeply appreciate it. Thank you.