Latest Edit:
In order to model the LSTM regression networks with 2-D data, the input should be a Nx1 cell, N being the number of observations. Each cell entry should then comprise a HxWxCxS array, where H = height, W=width, C=channels and S= sequence length. The responses, Y, can be a NxR matrix, where N = observations, R = number of responses (or output of the network) for sequence-to-one problems or a Nx1 cell array of RxS responses for sequence-to-sequence problems.
If in the above script S=54 and it's a sequence-to-sequence task with N=1, then the fix is simple:
X = {rand(13,11,1,54)};
Y = {rand(1,54)};
net = trainNetwork(X,Y,lgraph,options);
If N=54, and it's a sequence-to-one task, then I see two issues:
1. I don't see the S dimension. Assuming N=54 is the number of observations, I see only 54 arrays of 13x11x1 dimensions but I don't see a sequence of these arrays.
2. The 'OutputMode' of the bilstmLayer is set to 'sequence'. This would correspond to a sequence-to-sequence problem but the responses are only Nx1, so I assume the 'OutputMode' needs to be changed to 'last' to correspond to a sequence-to-one task.
For example, if you change the input and response to (where S=10):
X = arrayfun(@(x)rand(13,11,1,10),1:54,'UniformOutput',false)';
Y = rand(54,1);
Where, H=13,W=11,C=1,S=10,N=54,R=1, and change the OutputMode to 'last', then you can train the network.
bilstmLayer(100,'OutputMode', 'last','Name', 'bilstm')
Earlier suggestion:
Your layers itself has some errors, refer to the attached image for the errors. Use analyzeNetwork to check for the errors in your network as follows:
analyzeNetwork(layers)
I would suggest you to fix the errors in your network and then train the network. Since I don't have the value of the variable vars.numHiddenUnits, I used the value 200 for the bilstmLayer.
Refer to the example Train Convolutional Neural Network for Regression, and check the size of XTrain & YTrain to get an idea on what should be the dimension of the input data and the target data.
