Custom layer with two parameters function on Core ML

Question

Thanks to this great article(http://machinethink.net/blog/coreml-custom-layers/), I understood how to write converting using coremltools and Lambda with Keras custom layer. But, I cannot understand on the situation, function with two parameters.

#python
def scaling(x, scale):
    return x * scale

Keras layer is here.

#python
up = conv2d_bn(mixed,
                   K.int_shape(x)[channel_axis],
                   1,
                   activation=None,
                   use_bias=True,
                   name=name_fmt('Conv2d_1x1'))
x = Lambda(scaling, # HERE !!
           output_shape=K.int_shape(up)[1:],
           arguments={'scale': scale})(up)
x = add([x, up])

On this situation, how can I write func evaluate(inputs: [MLMultiArray], outputs: [MLMultiArray]) in custom MLCustomLayer class on Swift? I understand just in one parameter function situation, like this,

#swift 
func evaluate(inputs: [MLMultiArray], outputs: [MLMultiArray]) throws {
  for i in 0..<inputs.count {
    let input = inputs[i]
    let output = outputs[i]

    for j in 0..<input.count {
      let x = input[j].floatValue
      let y = x / (1 + exp(-x))
      output[j] = NSNumber(value: y)
    }
  }  
}

How about two parameters function, like x * scale?

Full code is here.

• Converting to Core ML model with custom layer https://github.com/osmszk/dla_team14/blob/master/facenet/coreml/CoremlTest.ipynb
• Network model by Keras https://github.com/osmszk/dla_team14/blob/master/facenet/code/facenet_keras_v2.py

Thank you.

Answer 1

It looks like scale is a hyperparameter, not a learnable parameter, is that correct?

In that case, you need to add scale to the parameters dictionary for the custom layer. Then in your Swift class, scale will also be inside the parameters dictionary that is passed into your init(parameters) function. Store it inside a property and then in evaluate(inputs, outputs) read from that property again.

My blog post actually shows how to do this. ;-)