Cost function that isn't just y_pred, y_true?

I have a cost function that in addition to using the overall network output, needs to multiply it by another function of the network weights (it’s actually the partial derivative of the output with respect to one of the inputs). Custom cost functions are parameterised as f(y_true, y_pred), and so cannot be used to provide this second function of the weights that I want.

I’ve seen a similar issue before where @shamidreza states that they had to use Theano for this functionality.

Is it still the case that this is the best option? I’ve only used Keras in R before so have no experience with either TensorFlow or Theano, would either be suitable in R?

[hgaiser]

It sounds like this example implements what you need. It’s not ideal in my opinion, feels a bit like a workaround, but it appears to work.

[stulacy]

I’m going to close this issue as the custom Layer method method suggested by @hgaiser works for my use case. Essentially rather than adding a loss function explicitly, you create a custom layer that calculates the loss. When using the network for prediction you create a new model that uses an earlier layer as the output of interest. The K.gradients function also provided the differentiation that I required.