I understand that you want to train a multi-input, single-output network using the “trainNetwork” function.
You can achieve this by combining the two input arrays and the output array into a single datastore and passing the combined datastore to the “trainNetwork” function.
Arrays can be converted to datastores using the “arrayDatastore” function. Each of the “arrayDatastore” objects can then be combined into a single datastore using the “combine” function. For example, if your input arrays are X1 and X2 and the output array is Y, you can try this:
X1Datastore = arrayDatastore(X1);
X2Datastore = arrayDatastore(X2);
labelDatastore = arrayDatastore(Y);
trainDatastore = combine(X1Datastore, X2Datastore, labelDatastore);
Once you have the combined datastore, you can pass it to the “trainNetwork” function as follows:
net = trainNetwork(trainDatastore, Networklayers,options);
You can also refer to the following link for a complete example on how to train multi-input networks: https://www.mathworks.com/help/deeplearning/ug/train-network-on-image-and-feature-data.html.
Please note that the example uses the “trainnet” function instead of the “trainNetwork” function since, starting with MATLAB R2024a, the “trainNetwork” function is not recommended. The “trainnet” function has certain advantages over “trainNetwork” which are mentioned here: https://www.mathworks.com/help/deeplearning/ref/trainnetwork.html#mw_868305a5-132e-4203-9214-860584bdcfdd.
Please refer to the following resources for further information:
- “arrayDatastore” documentation - https://www.mathworks.com/help/matlab/ref/matlab.io.datastore.arraydatastore.html.
- “combine” documentation - https://www.mathworks.com/help/matlab/ref/matlab.io.datastore.combine.html.
- “trainNetwork” documentation - https://www.mathworks.com/help/deeplearning/ref/trainnetwork.html.
- “trainnet” documentation - https://www.mathworks.com/help/deeplearning/ref/trainnet.html.
Hope this helps!
