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()
)

Value

No return value.

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.