Hi @paulkirkland, and welcome back to the Nengo forums.
If by multi-channel input, you mean that the Keras layer accepts multiple inputs (e.g., the
tf.keras.layers.Add()), then no, NengoDL doesn’t natively know how to deal with these types of Keras layers. As discussed in this forum thread, you’ll need to write your own
nengo_dl.TensorNode, or create a custom class for the NengoDL converter (see this forum thread for an example, or refer to the NengoDL documentation).
If you provide some code, or a description of which Keras layers you are intending to convert, I can probably give you more directed suggestions to help you with your implementation.
I just realized you might also be talking about multi-channel as a description of inputs (e.g., RGB images, where R, G, and B form individual channels). In that case, flattening the images should work. If your inputs work within a Keras network, there should be very few reasons why it wouldn’t also work when converted to a Nengo network using the NengoDL converter.
Once again, some code that demonstrates the behaviour you are observing vs. what you are expecting would be helpful.