Hi,
The error you’re encountering seems to be related to the format of your input data. It suggests that the input sequences must be numeric or categorical arrays. It’s important to ensure that your sequence data ‘XTrainD’ and feature data ‘XTrainf’ are in the correct format.
Try using as a workaround 'sequenceInputLayer' and 'featureInputLayer' in a multiple-input deep convolutional neural network.
Here is an example workaround script for defining the 'sequenceInputLayer' and 'featureInputLayer':
dsX1Train = arrayDatastore(XTrainD, 'OutputType', 'same'); % For sequence data
dsX2Train = arrayDatastore(XTrainf, 'OutputType', 'same'); % For feature data
dsTTrain = arrayDatastore(XTrainL, 'OutputType', 'same'); % For labels
dsTrain = combine(dsX1Train, dsX2Train, dsTTrain);
layers = [
sequenceInputLayer([1 1 800], 'Name', 'sequence_input')
% Add layers for sequence processing here
fullyConnectedLayer(128, 'Name', 'fc_seq')
featureInputLayer(1, 'Name', 'feature_input')
% Add layers for feature processing here
fullyConnectedLayer(128, 'Name', 'fc_feat')
concatenationLayer(1, 2, 'Name', 'concat')
fullyConnectedLayer(64, 'Name', 'fc1')
reluLayer('Name', 'relu1')
fullyConnectedLayer(numClasses, 'Name', 'fc2')
softmaxLayer('Name', 'softmax')
classificationLayer('Name', 'classOutput')
];
lgraph = layerGraph(layers);
lgraph = connectLayers(lgraph, 'fc_seq', 'concat/in1');
lgraph = connectLayers(lgraph, 'fc_feat', 'concat/in2');
- Ensure the sequence data format is CBT and the feature data format is BC.
- Create and combine arrayDatastore objects for sequences, features, and labels.
- Define the network architecture with appropriate input layers and combine them.
- Use trainingOptions with InputDataFormats set to {'CBT', 'BC'}.
- Train the network using trainNetwork.
Refer to know more about:
- featureInputLayer: https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.featureinputlayer.html
- sequenceInputLayer: https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.sequenceinputlayer.html
I hope this helps!
