SPOTMisc (version 1.19.52)

startMnistRun: Start hyperparameter optimization runs with spot based on MNIST data

Description

Runs to compare deep learning models. Note: Number of epochs is limited: model <- "dl"; cfg <- getModelConf(model = model); cfg$upper[6] <- 5

Usage

startMnistRun(
  runNr = "000",
  SPOTVersion = "2.11.4",
  SPOTMiscVersion = "1.19.6",
  encoding = "tensor",
  network = "cnn",
  timebudget = 60,
  data.seed = 1,
  prop = 2/3,
  batch_size = 32,
  tuner.seed = 1,
  returnValue = "validationLoss",
  initSizeFactor = 1,
  spotModel = buildKriging,
  spotOptim = optimDE,
  lower = NULL,
  upper = NULL,
  noise = TRUE,
  OCBA = FALSE,
  OCBABudget = 0,
  multiStart = 2,
  multFun = 200,
  handleNAsMethod = handleNAsMean,
  imputeCriteriaFuns = list(is.infinite, is.na, is.nan),
  krigingTarget = "ei",
  krigingUseLambda = TRUE,
  krigingReinterpolate = TRUE,
  defaultAsStartingPoint = TRUE,
  plots = FALSE,
  Rinit = 1,
  replicates = 1,
  resDummy = FALSE,
  verbosity = 0
)

Arguments

runNr

character, specifies the run number. Default: "000"

SPOTVersion

smallest package version number

SPOTMiscVersion

smallest package version number

encoding

encoding: "oneHot" od "tensor". Default: "tensor"

network

network: "dl" odr "cnn". Default: "cnn"

timebudget

time budget Default: 3600 (secs)

data.seed

1

prop

proportion 2 / 3

batch_size

batch size (for dl) 32

tuner.seed

seed for SPOT 1

returnValue

"validationLoss"

initSizeFactor

multiplier for the initial design size 2

spotModel

buildKriging

spotOptim

optimDE

lower

NULL

upper

NULL

noise

TRUE

OCBA

TRUE

OCBABudget

3

multiStart

2

multFun

200

handleNAsMethod

handleNAsMean

imputeCriteriaFuns

list(is.infinite, is.na, is.nan)

krigingTarget

"ei"

krigingUseLambda

TRUE

krigingReinterpolate

FALSE

defaultAsStartingPoint

FALSE

plots

FALSE

Rinit

2

replicates

2

resDummy

FALSE

verbosity

0

Examples

# \donttest{
### These examples require an activated Python environment as described in
### Bartz-Beielstein, T., Rehbach, F., Sen, A., and Zaefferer, M.:
### Surrogate Model Based Hyperparameter Tuning for Deep Learning with SPOT,
### June 2021. http://arxiv.org/abs/2105.14625.
PYTHON_RETICULATE <- FALSE
if(PYTHON_RETICULATE){
library("dplyr")
library("farff")
library("GGally")
library("keras")
library("tensorflow")
library("Metrics")
library("mlr")
library("reticulate")
library("rpart")
library("rpart.plot")
library("SPOT")
library("SPOTMisc")
library("tfdatasets")
library("rsample")
startMnistRun(timebudget=60, initSizeFactor = 1, verbosity = 1)
startMnistRun(timebudget=60, encoding="tensor", network="cnn")
}
# }