setMLP: Create setting for neural network model with python

In hxia/plp-git-demo: Package for patient level prediction using data in the OMOP Common Data Model

Description

Create setting for neural network model with python

Usage

setMLP(
  size = 4,
  alpha = c(0.3, 0.01, 0.001, 1e-06),
  maxIter = 2000,
  tol = 1e-04,
  learningRateInit = 0.001,
  nIterNoChange = 10,
  beta1 = 0.9,
  beta2 = 0.999,
  epsilon = c(1, 0.1, 1e-08),
  seed = NULL
)

Arguments

size	The number of hidden nodes
alpha	The l2 regularisation
maxIter	Maximum number of iterations. The solver iterates until convergence (determined by ‘tol’) or this number of iterations.
tol	Tolerance for the optimization
learningRateInit	The initial learning rate used. It controls the step-size in updating the weights.
nIterNoChange	Maximum number of epochs to not meet tol improvement.
beta1	Exponential decay rate for estimates of first moment vector in adam, should be in [0, 1).
beta2	Exponential decay rate for estimates of second moment vector in adam, should be in [0, 1).
epsilon	Value for numerical stability in adam.
seed	A seed for the model

Examples

## Not run: 
model.mlp <- setMLP(size=4, alpha=0.00001, seed=NULL)

## End(Not run)

hxia/plp-git-demo documentation built on March 19, 2021, 1:54 a.m.