Thanks for your patience. I think it will be easier to explain my use case if I give an example.
Let’s say I have manufacturing data from 100 factories. All of these factories have 50 machines. For each machine, I have 3 features (time in use, machine type, number of times serviced). I can formulate my input tensor with the shape (100 factories, 50 machines, 3 features). I want the first layer of my network to have 50 nodes, each corresponding to one of the 50 machines. Each node in the first layer will take the 3 input features associated with one machine. So, each of the 3 features will have its own weight, which contributes to the activation of the “machine” node. This network can now be trained on an output label corresponding to each factory or as an autoencoder.
Now let’s say I have a pretrained model with this architecture, but I have a 4th feature that I want to incorporate. How can I add a 4th input to my “machine” layer?
I hope this is clear! Please tell me if anything needs clarification.