R: Deep Neural Network with Custom Loss Function

Question

(In R) Suppose I have a loss function, which takes a function as an input and evaluates it at a (fixed) series of transformations of a fixed data-set. Is it possible to integrate this into tensorflow and use it as a custom loss function for DNN regression? In order to perform Deep Learning, I'm currently using a tensorflow -> R interface.

Answer 1

The keras implementation of R allows you to use a custom loss function. However, the function needs to be implemented using a very specific syntax and should take in y_true and y_pred parameters. You can find a nice tutorial here. The following code would give you some intution:

model %>% compile(
  optimizer = "your-choice-of-optimezer",
  loss = custom_loss_function,
  metrics = c("your-choice-of-metric")
)

where

custom_loss_function <- function(y_true, y_pred) {
  K <- backend()
  ... # define your function using the backend K
}