survivalmodels: build_pytorch_net – R documentation

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build_pytorch_net

Build a Pytorch Multilayer Perceptron

Description

Utility function to build an MLP with a choice of activation function and weight initialization with optional dropout and batch normalization.

Usage

build_pytorch_net(
  n_in,
  n_out,
  nodes = c(32, 32),
  activation = "relu",
  act_pars = list(),
  dropout = 0.1,
  bias = TRUE,
  batch_norm = TRUE,
  batch_pars = list(eps = 1e-05, momentum = 0.1, affine = TRUE),
  init = "uniform",
  init_pars = list()
)

Arguments

`n_in`	`(integer(1))` Number of input features.
`n_out`	`(integer(1))` Number of targets.
`nodes`	`(numeric())` Hidden nodes in network, each element in vector represents number of hidden nodes in respective layer.
`activation`	`(character(1)\|list())` Activation function, can either be a single character and the same function is used in all layers, or a list of length `length(nodes)`. See get_pycox_activation for options.
`act_pars`	`(list())` Passed to get_pycox_activation.
`dropout`	`(numeric())` Optional dropout layer, if `NULL` then no dropout layer added otherwise either a single numeric which will be added to all layers or a vector of differing drop-out amounts.
`bias`	`(logical(1))` If `TRUE` (default) then a bias parameter is added to all linear layers.
`batch_norm`	`(logical(1))` If `TRUE` (default) then batch normalisation is applied to all layers.
`batch_pars`	`(list())` Parameters for batch normalisation, see `reticulate::py_help(torch$nn$BatchNorm1d)`.
`init`	`(character(1))` Weight initialization method. See get_pycox_init for options.
`init_pars`	`(list())` Passed to get_pycox_init.

Details

This function is a helper for R users with less Python experience. Currently it is limited to simple MLPs. More advanced networks will require manual creation with reticulate.

Examples

if (requireNamespaces("reticulate")) {
  build_pytorch_net(4L, 2L, nodes = c(32, 64, 32), activation = "selu")

  # pass parameters to activation and initializer functions
  build_pytorch_net(4L, 2L, activation = "elu", act_pars = list(alpha = 0.1),
  init  = "kaiming_uniform", init_pars = list(mode = "fan_out"))
}

survivalmodels

Models for Survival Analysis

v0.1.11

MIT + file LICENSE

Authors

Raphael Sonabend [aut, cre] (<https://orcid.org/0000-0001-9225-4654>)

Initial release