Hi Zowie Silva,
I understand that you are facing an error while using trainnet function for training a sequence-to-sequence neural network problem.
Use "analyzeNetwork" to look into the structure of your neural network.
From the above screenshot, it can be observed that the model is losing the "T" dimension after "gapool1D" layer. Due to which the model is expecting a numerical or categorical target value instead of sequence.
The Neural Network built by you is using globalAveragePooling1dLayer. As per the documentation of globalAveragePooling1dLayer, for time series and vector sequence input (data with three dimensions corresponding to the "C" (channel), "B" (batch), and "T" (time) dimensions), the layer pools over the "T" (time) dimension. That means, after the pooling, only "C" and "B" dimension will remain.
If you wish to do the average pooling along channel dimension, you can create a functionLayer and replace the "gapool1D" layer with it. Please refer to the following code snippet to modify your model:
lgraph = layerGraph();
functionLayer = functionLayer(@(X) mean(X, 1), 'Name', 'avgPoolChannel'); % defining function layer
tempLayers = [
sequenceInputLayer(4,"Name","input")
convolution1dLayer(4,32,"Name","conv1d","Padding","same") % using the function layer
functionLayer];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = lstmLayer(25,"Name","lstm");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = lstmLayer(25,"Name","lstm_1");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
concatenationLayer(1,2,"Name","concat")
lstmLayer(55,"Name","lstm_2")
dropoutLayer(0.5,"Name","drop")
fullyConnectedLayer(1,"Name","fc")
sigmoidLayer("Name","sigmoid")];
lgraph = addLayers(lgraph,tempLayers);
% clean up helper variable
clear tempLayers;
lgraph = connectLayers(lgraph,"avgPoolChannel","lstm"); % connect to function layer
lgraph = connectLayers(lgraph,"avgPoolChannel","lstm_1"); % connect to function layer
lgraph = connectLayers(lgraph,"lstm","concat/in1");
lgraph = connectLayers(lgraph,"lstm_1","concat/in2");
Please refer to the following documentation links to learn more about:
- globalAveragePooling1dLayer: https://www.mathworks.com/help/releases/R2023b/deeplearning/ref/nnet.cnn.layer.globalaveragepooling1dlayer.html
- functionLayer: https://www.mathworks.com/help/releases/R2023b/deeplearning/ref/nnet.cnn.layer.functionlayer.html
- trainnet: https://in.mathworks.com/help/releases/R2023b/deeplearning/ref/trainnet.html
Hope this helps!
