Generates an ensemble of neural network models.

Source: R/ModelGenerator.R

ModelGenerator generates an ensemble of neural network models each trained to classify cellular phenotypes using the reference data set.

ModelGenerator(
  R,
  N = 1,
  num.cores = 1,
  verbose = TRUE,
  hidden = 1,
  set.seed = TRUE,
  seed = "42"
)

Arguments

R

Reference data set returned by GetTrainingData_HPCA
N

Number of neural networks to train. Default is 1.
num.cores

Number of cores to use for parallel computing. Default is 1.
verbose

if TRUE, code will report outputs. Default is TRUE.
hidden

Number of hidden layers in the neural network. Default is 1.
set.seed

If TRUE, seed is set to ensure reproducibility of these results. Default is TRUE.
seed

if set.seed is TRUE, the seed can be set. Default is 42.

Value

A list, each containing N neural network models

See also

[SignacFast()] for a function that uses the models generated by this function.

Examples

if (FALSE) {
# download training data set from GitHub
Ref = GetTrainingData_HPCA()

# train a stack of 1,800 neural network models
Models = ModelGenerator(R = Ref, N = 100, num.cores = 4)

# save models
save(Models, file = "models.rda")
}