Multiple input (sequence and static) to a dag network

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Mona Ramadan 2020-3-2

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https://ww2.mathworks.cn/matlabcentral/answers/508573-multiple-input-sequence-and-static-to-a-dag-network

编辑： Yildirim Kocoglu 2020-11-27

I want to train a DAG_net with two inputs, the dag network is shown below:

My two inputs are : a sequential timeseries data with 17 features for 60 training examples. The other input is a static feature for the 60 training samples. Here is a screenshot of my training data

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Yildirim Kocoglu 2020-11-27

编辑：Yildirim Kocoglu 2020-11-27

在 MATLAB Online 中打开

Mona, have you by any chance solved this issue?

I'm trying to do the same exact thing with similar data as yours except my output is also a sequence.

I got the add/concatonate layer idea from a post I read online and a paper which shows the architecture as shown below:

My architecture looks similar to yours as below:

My training code looks something like this (to show it needs 2 inputs to train):

net = trainNetwork({XTrain(1:510,:);Static_features(1:510,:)'},YTrain(1:510,:),lgraph,options);

I use the same lgraph object as plotted above and XTrain (for temporal inputs) and Static_features (for static inputs) and a single output "YTrain" that should be a sequence similar to XTrain with multiple cells (similar to your XTrain). This is a prediction network that predicts the next time step value (YTrain) from the previous time step (XTrain).

However, when I try to train the network I get this error (next line of error says: if sequenceInputLayer is present, it can't accept any other type of input layer):

 Network: Incompatible layer types. The network contains layer types not supported with recurrent layers.
    Detected recurrent layers:
        layer 'bilstm1' (BiLSTM)
        layer 'bilstm2' (BiLSTM)
        layer 'bilstm3' (BiLSTM)
        layer 'bilstm4' (BiLSTM)
        and 1 other layers.
        

For static_input I use an imageInputLayer but, I tried every layer that exists in Matlab 2020a (apparently featureInputLayer is not avaiable in 2020a) and it still gives me the same error.

I searched for every post I believe and can't seem to find a tutorial nor an example showing how to achieve this (if it is possible). The closest thing I found was using CNN-RNN but, that is with images (frames of a video) and has a single input layer.

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