Simple denoising autoencoder for 1D data

I'm trying to set up a simple denoising autoencoder with Matlab for 1D data. As currently there is no specialised input layer for 1D data the imageInputLayer() function has to be used:

function net = DenoisingAutoencoder(data)
[N, n] = size(data);

%setting up input
X = zeros([n 1 1 N]);
for i = 1:n
    for j = 1:N
        X(i, 1, 1, j) = data(j,i);
    end
end

% noisy X : 1/10th of elements are set to 0
Xnoisy = X;
mask1 = (mod(randi(10, size(X)), 7) ~= 0); 
Xnoisy = Xnoisy .* mask1;

layers = [imageInputLayer([n 1 1]) fullyConnectedLayer(n) regressionLayer()];

opts = trainingOptions('sgdm');
net = trainNetwork(X, Xnoisy, layers, opts);
However, the code fails with this error message:

The output size [1 1 n] of the last layer doesn't match the response size [ n 1 1].

Any thoughts on how should the input / layers should be reconfigured? If the fullyConnectedLayer is left out then the code runs fine, but obviously then I'm left without the hidden layer.

[Georgios Papageorgiou]

I assume N is the number of data and n is you data_size. I think if you make:

1. X 1xnx1xN
2. The input layer: imageInputLayer([1 n])
3. X_noisy of dimension Nxn and finally,
4. net = trainNetwork(Xnoisy, X, layers, opts);

it should work. Make sure at the end that your inpout of your Denoising Autoencoder is the noisy data and the desired output is your "clean" data. A similar version that I implemented in MATLAB works fine for me and the dimensions match usign the regressionLayer like this.