Sequential neural network training with 1-D function layer

I'm trying to run a sequential neural network with input shape "ST". I have made a function layer, containing findpeaks. I'd like to perform findpeaks on the first dimension. When I try running the neural network, it tries to batch the data so that a 2-d SxT dlarray is passed into findpeaks, but findpeaks only accepts vectors as input.

Is my only option to put it into a for loop, or is there a way to parallelise it?

Thanks in advance.

[Matt J]

That doesn't appear to be your biggest problem. Your biggest problem is that the operations allowed in a functionLayer are restricted to the those compatible with dlarray input,

https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.functionlayer.html#mw_395e8693-0862-4258-8f0a-dfeb9681e485

But findpeaks is not in the list of supported functions for dlarrays,

https://www.mathworks.com/help/deeplearning/ug/list-of-functions-with-dlarray-support.html

Nor does it seem to me that it ever could be. A findpeaks operations is not obviously differentiable in any way.

Unless for some reason findpeaks is not intended to be part of the differentiable chain of operations done by the network, I think you probably need a different approach to whatever you are doing.