tf_initialise_model: Initialise model weights to a global parameter fit

In reservr: Fit Distributions and Neural Networks to Censored and Truncated Data

Initialise model weights to a global parameter fit

Description

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

Usage

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.

Value

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()))
}

reservr documentation built on June 24, 2024, 5:10 p.m.