sequenceInputLayer() Dimensions of arrays being concatenated are not consistent

Question

I try to create an LSTM model. I get following error:

Error using vertcat Dimensions of arrays being concatenated are not consistent. Error in source (line 9) sequenceInputLayer(33)

What should be the input of sequenceInputLayer and its size?

Data = csvread('newData.csv');
num_timesteps = size(Data,1)
num_features = size(Data,2)
Data = normalize(Data);
numHiddenUnits = 200;
size(Data)
layers = [
    sequenceInputLayer(33)
    lstmLayer(numHiddenUnits,'OutputMode','sequence')
    fullyConnectedLayer(50)
    dropoutLayer(0.5)
    fullyConnectedLayer(num_features),regressionLayer];
maxEpochs = 60;
miniBatchSize = 20;
options = trainingOptions('adam', ...
    'MaxEpochs',maxEpochs, ...
    'MiniBatchSize',miniBatchSize, ...
    'InitialLearnRate',0.001, ...
    'GradientThreshold',1, ...
    'Shuffle','never', ...
    'Plots','training-progress',...
    'Verbose',0);
% net = trainNetwork(Data,Data,layers,options);

Eliahu Aaron Eliahu Aaron · Accepted Answer · 2019-07-02T12:33:02

The problem is not in sequenceInputLayer, the problem is in the way you are creating the layers array.

Replace:

layers = [
    sequenceInputLayer(33)
    lstmLayer(numHiddenUnits,'OutputMode','sequence')
    fullyConnectedLayer(50)
    dropoutLayer(0.5)
    fullyConnectedLayer(num_features),regressionLayer];

With:

layers = [
    sequenceInputLayer(33)
    lstmLayer(numHiddenUnits,'OutputMode','sequence')
    fullyConnectedLayer(50)
    dropoutLayer(0.5)
    fullyConnectedLayer(num_features),
    regressionLayer];

Explanation: In an array declaration, when adding elements in new lines (or separating by ;) you are crating a columns vector, when separating by ,, you are crating a row vector. Somehow you mixed them up.

sequenceInputLayer() Dimensions of arrays being concatenated are not consistent

1 Answers