tf_compile_model function

Compile a Keras model for truncated data under dist

tf_compile_model(
  inputs,
  intermediate_output,
  dist,
  optimizer,
  censoring = TRUE,
  truncation = TRUE,
  metrics = NULL,
  weighted_metrics = NULL
)

Arguments

• inputs: List of keras input layers

• intermediate_output: Intermediate model layer to be used as input to distribution parameters

• dist: A Distribution to use for compiling the loss and parameter outputs

• optimizer: String (name of optimizer) or optimizer instance. See optimizer_* family.

• censoring: A flag, whether the compiled model should support censored observations. Set to FALSE for higher efficiency. fit(...) will error if the resulting model is used to fit censored observations.

• truncation: A flag, whether the compiled model should support truncated observations. Set to FALSE for higher efficiency. fit(...) will warn if the resuting model is used to fit truncated observations.

• metrics: List of metrics to be evaluated by the model during training and testing. Each of these can be:

  • a string (name of a built-in function),

  • a function, optionally with a "name" attribute or

  • a Metric()

    instance. See the metric_* family of functions.

  Typically you will use metrics = c('accuracy'). A function is any callable with the signature result = fn(y_true, y_pred). To specify different metrics for different outputs of a multi-output model, you could also pass a named list, such as metrics = list(a = 'accuracy', b = c('accuracy', 'mse')). You can also pass a list to specify a metric or a list of metrics for each output, such as metrics = list(c('accuracy'), c('accuracy', 'mse'))

  or metrics = list('accuracy', c('accuracy', 'mse')). When you pass the strings 'accuracy' or 'acc', we convert this to one of metric_binary_accuracy(), metric_categorical_accuracy(), metric_sparse_categorical_accuracy() based on the shapes of the targets and of the model output. A similar conversion is done for the strings "crossentropy"

  and "ce" as well. The metrics passed here are evaluated without sample weighting; if you would like sample weighting to apply, you can specify your metrics via the weighted_metrics argument instead.

  If providing an anonymous R function, you can customize the printed name during training by assigning attr(<fn>, "name") <- "my_custom_metric_name", or by calling custom_metric("my_custom_metric_name", <fn>)

• weighted_metrics: List of metrics to be evaluated and weighted by sample_weight or class_weight during training and testing.

Returns

A reservr_keras_model that can be used to train truncated and censored observations from dist based on input data from inputs.

Examples

dist <- dist_exponential()
params <- list(rate = 1.0)
N <- 100L
rand_input <- runif(N)
x <- dist$sample(N, with_params = params)

if (interactive()) {
  tf_in <- keras3::layer_input(1L)
  mod <- tf_compile_model(
    inputs = list(tf_in),
    intermediate_output = tf_in,
    dist = dist,
    optimizer = keras3::optimizer_adam(),
    censoring = FALSE,
    truncation = FALSE
  )
}