How to create a loss function with 3 inputs in Flux?

How could I create a loss function with 3 inputs like loss(w,x,y)?
Could someone show me an example?

Have you run into an issue using a 3 input loss function? loss(w, x, y) should work just fine. For example:

loss(m, x, y) = Flux.mse(m(x), y)

Since you mentioned Flux and the Flux doc for Flux.train! says the below

If d is a tuple of arguments to loss call loss(d…), else call loss(d).

I imagine, you can have as many inputs as you want as long as you pass in the data = [(w, x, y), (w1, x1, y1) etc etc]

Full Doc as below

  train!(loss, params, data, opt; cb)

  For each datapoint d in data, compute the gradient of loss with respect to params through backpropagation and call
  the optimizer opt.

  **If d is a tuple of arguments to loss call loss(d...), else call loss(d).**

  A callback is given with the keyword argument cb. For example, this will print "training" every 10 seconds (using

  train!(loss, params, data, opt, cb = throttle(() -> println("training"), 10))

  The callback can call Flux.stop to interrupt the training loop.

  Multiple optimisers and callbacks can be passed to opt and cb as arrays.