error: Invalid input data. Invalid number of spatial dimensions. Layer expects 0 but received 2.

I got 6960 traing dataset. each data size is 11890*2(double)

full code here

%% load data
Path1 = fullfile('E:\','m2020','kin2','trainset');
Path2 = fullfile('E:\','m2020','kin2','testset');
dsTrain = myDatastore(Path1)

%% define network
inputSize = dsTrain.SequenceDimension;
numClasses = dsTrain.NumClasses;
numHiddenUnits = 100;
layers = [ ...
    sequenceInputLayer(inputSize, 'Name','input')
    bilstmLayer(numHiddenUnits,'Name','bilstm')
    fullyConnectedLayer(numClasses,'Name','fc')
    sigmoidLayer('Name','sigmoid')

    ];
lgraph = layerGraph(layers);
dlnet = dlnetwork(lgraph)
% spec
numEpochs = 50;
miniBatchSize = 128;

initialLearnRate = 0.01;
decay = 0.01;
momentum = 0.9;

%% define minibatch
mbq = minibatchqueue(dsTrain,...
    'MiniBatchSize',miniBatchSize,...
    'MiniBatchFcn',@preprocessMiniBatch,...
    'MiniBatchFormat',{'SSCB',''});
figure
lineLossTrain = animatedline('Color',[0.85 0.325 0.098]);
ylim([0 inf])
xlabel("Iteration")
ylabel("Loss")
grid on
velocity = [];

%%
iteration = 0;
start = tic;

% Loop over epochs.
for epoch = 1:numEpochs
    % Shuffle data.
    shuffle(mbq);
    
    % Loop over mini-batches.
    while hasdata(mbq)
        iteration = iteration + 1;
        
        % Read mini-batch of data.
        [dlX, dlY] = next(mbq);
        
        % Evaluate the model gradients, state, and loss using dlfeval and the
        % modelGradients function and update the network state.
        [gradients,state,loss] = dlfeval(@modelGradients,dlnet,dlX,dlY);
       
        dlnet.State = state;
        
        % Determine learning rate for time-based decay learning rate schedule.
        learnRate = initialLearnRate/(1 + decay*iteration);
        
        % Update the network parameters using the SGDM optimizer.
        [dlnet,velocity] = sgdmupdate(dlnet,gradients,velocity,learnRate,momentum);
        
        % Display the training progress.
        D = duration(0,0,toc(start),'Format','hh:mm:ss');
        addpoints(lineLossTrain,iteration,loss)
        title("Epoch: " + epoch + ", Elapsed: " + string(D))
        drawnow
    end
end

this code is almost same with mathworks example(https://kr.mathworks.com/help/deeplearning/ug/train-network-using-custom-training-loop.html)

but the example is using imagedataset so i needed to change some

the error occurs at dlfeval.

I noticed forward(dlnet,dlX) making this error in modelgradients

the error says:

layer 'input' : Invalid input data. Invalid number of spatial dimensions. Layer expects 0 but received 2.

I guess it happens only to sequenceinputlayer.

what's the 0 and 2 meaning? how can I fix it?

[Van Vy]

Did you solve your problem? I have similar problem.

If you're done your problem, please share with me.

Thanks in advance

[yanqi liu]

yes，sir，may be upload the data to do some analysis，such as

https://ww2.mathworks.cn/matlabcentral/answers/1603220-number-of-observations-in-x-and-y-disagree-for-convolution-neural-network?s_tid=srchtitle

[zahoor m]

Layer 'input': Invalid input data. Invalid number of spatial dimensions. Layer expects 2 but received

0.