Custom Loss Function in R Keras

Question

I want to calculate weighted mean squared error, where weights is one vector in the data. I wrote a custom code based on the suggestions available on stack overflow.

The function is provided below:

weighted_mse <- function(y_true, y_pred,weights){
  # convert tensors to R objects
  K        <- backend()
  y_true   <- K$eval(y_true)
  y_pred   <- K$eval(y_pred)
  weights  <- K$eval(weights)

  # calculate the metric
  loss <- sum(weights*((y_true - y_pred)^2)) 

  # convert to tensor
  return(K$constant(loss))
  }

However, I am not sure how to pass the custom function to the compiler. It would be great if someone can help me. Thank you.

model      <- model %>% compile(
                loss = 'mse', 
                optimizer = 'rmsprop',
                metrics = 'mse')

Regards

Daniel Möller · Accepted Answer · 2018-07-19T19:53:56.910

You can't eval in loss funtions. This will break the graph.

You should just use the sample_weight parameter of the fit method: https://keras.rstudio.com/reference/fit.html

##not sure if this is valid R, but 
##at some point you will call `fit` for training with `X_train` and `Y_train`, 
##so, just add the weights.
history <- model$fit(X_train, Y_train, ..., sample_weight = weights)

That's all (don't use a custom loss).

Just for knowledge - Passing loss functions to compile

Only works for functions taking y_true and y_pred. (Not necessary if you're using sample_weights)

model      <- model %>% compile(
            loss = weighted_mse, 
            optimizer = 'rmsprop',
            metrics = 'mse')

But this won't work, you need something similar to the wrapper created by @spadarian.

Also, it will be very complicated to keep a correlation between your data and the weights, both because Keras will divide your data in batches and also because the data will be shuffled.

Okay. Thank you. So sample_weight uses those weights in the calculation of loss function? So for instance, mse using sample_weight is equivalent to weighted mse? I notice that my fit and prediction is way worse using sample_weight, hence I am asking. — Sumit, Jul 19 '18 at 20:04
Yes, using `sample_weight` + `mse` is the same as using `weighted_mse`. — Daniel Möller, Jul 19 '18 at 23:38

spadarian · Answer 2 · 2018-07-23T04:43:45.817

I haven't used Keras with R but, following the example from the documentation, probably this should work:

weighted_mse <- function(y_true, y_pred, weights){
    K        <- backend()
    weights  <- K$variable(weights)
    # calculate the metric
    loss <- K$sum(weights * (K$pow(y_true - y_pred, 2))) 
    loss
}

metric_weighted_mse <- custom_metric("weighted_mse", function(y_true, y_pred) {
    weighted_mse(y_true, y_pred, weights)
})

model <- model %>% compile(
    loss = 'mse', 
    optimizer = 'rmsprop',
    metrics = metric_weighted_mse)

Note that I'm using a wrapper for the loss function because it has an extra parameter. Also, the loss function process the inputs as tensors, that is why you should convert the weights with K$variable(weights).

I am getting the following error, when I use your function. Error in py_call_impl(callable, dots$args, dots$keywords) : RuntimeError: Evaluation error: AttributeError: 'function' object has no attribute 'eval'. — Sumit, Jul 19 '18 at 19:54

Custom Loss Function in R Keras

2 Answers2