config_optimizer: Creation of customized optimizer objects

In cito: Building and Training Neural Networks

Creation of customized optimizer objects

Description

Helps you create custom optimizer for dnn. It is recommended to set learning rate in dnn.

Usage

config_optimizer(
  type = c("adam", "adadelta", "adagrad", "rmsprop", "rprop", "sgd"),
  verbose = FALSE,
  ...
)

Arguments

type	character string defining which optimizer should be used. See Details.
verbose	If TRUE, additional information about scheduler will be printed to console
...	additional arguments to be passed to optimizer. See Details.

Details

different optimizer need different variables, this function will tell you how the variables are set. For more information see the corresponding functions:

• adam: optim_adam

• adadelta: optim_adadelta

• adagrad: optim_adagrad

• rmsprop: optim_rmsprop

• rprop: optim_rprop

• sgd: optim_sgd

Value

object of class cito_optim to give to dnn

Examples

if(torch::torch_is_installed()){
library(cito)

# create optimizer object
opt <- config_optimizer(type = "adagrad",
                        lr_decay = 1e-04,
                        weight_decay = 0.1,
                        verbose = TRUE)

# Build and train  Network
nn.fit<- dnn(Sepal.Length~., data = datasets::iris, optimizer = opt)

}

cito documentation built on May 29, 2024, 1:28 a.m.