tf_initialise_model function

Initialise model weights to a global parameter fit

Initialises a compiled reservr_keras_model weights such that the predictions are equal to, or close to, the distribution parameters given by params.

tf_initialise_model(
  model,
  params,
  mode = c("scale", "perturb", "zero", "none")
)

Arguments

• model: A reservr_compiled_model obtained by tf_compile_model().

• params: A list of distribution parameters compatible with model.

• mode: An initialisation mode

  • scale: Initialise the biases according to params and the kernels uniform on [-0.1, 0.1] * bias scale.
  • perturb: Initialise the biases according to params and leave the kernels as is.
  • zero: Initialise the biases according to params and set the kernel to zero.
  • none: Don't modify the weights.

Returns

Invisibly model with changed weights

Examples

dist <- dist_exponential()
group <- sample(c(0, 1), size = 100, replace = TRUE)
x <- dist$sample(100, with_params = list(rate = group + 1))
global_fit <- fit(dist, x)

if (interactive()) {
  library(keras3)
  l_in <- layer_input(shape = 1L)
  mod <- tf_compile_model(
    inputs = list(l_in),
    intermediate_output = l_in,
    dist = dist,
    optimizer = optimizer_adam(),
    censoring = FALSE,
    truncation = FALSE
  )
  tf_initialise_model(mod, global_fit$params)
  fit_history <- fit(
    mod,
    x = group,
    y = x,
    epochs = 200L
  )

  predicted_means <- predict(mod, data = as_tensor(c(0, 1), config_floatx()))
}