ModelGenerator {SignacX} | R Documentation |
Generates an ensemble of neural network models.
Description
ModelGenerator
generates an ensemble of neural network models
each trained to classify cellular phenotypes using the reference data set.
Usage
ModelGenerator(
R,
N = 1,
num.cores = 1,
verbose = TRUE,
hidden = 1,
set.seed = TRUE,
seed = "42"
)
Arguments
R |
Reference data set returned by |
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. |
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
## Not run:
# 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")
## End(Not run)
[Package SignacX version 2.2.5 Index]