Create setting for CIReNN model

Create setting for CIReNN model

setCIReNN(
  numberOfRNNLayer = c(1),
  units = c(128, 64),
  recurrentDropout = c(0.2),
  layerDropout = c(0.2),
  lr = c(1e-04),
  decay = c(1e-05),
  outcomeWeight = c(0),
  batchSize = c(100),
  epochs = c(100),
  earlyStoppingMinDelta = c(1e-04),
  earlyStoppingPatience = c(10),
  bayes = T,
  useDeepEnsemble = F,
  numberOfEnsembleNetwork = 5,
  useVae = T,
  vaeDataSamplingProportion = 0.1,
  vaeValidationSplit = 0.2,
  vaeBatchSize = 100L,
  vaeLatentDim = 10L,
  vaeIntermediateDim = 256L,
  vaeEpoch = 100L,
  vaeEpislonStd = 1,
  useGPU = FALSE,
  maxGPUs = 2,
  seed = 1234
)

Arguments

numberOfRNNLayer	The number of RNN layer, only 1, 2, or 3 layers available now. eg. 1, c(1,2), c(1,2,3)
units	The number of units of RNN layer - as a list of vectors
recurrentDropout	The reccurrent dropout rate (regularisation)
layerDropout	The layer dropout rate (regularisation)
lr	Learning rate
decay	Learning rate decay over each update.
outcomeWeight	The weight of the outcome class in the loss function. Default is 0, which will be replaced by balanced weight.
batchSize	The number of data points to use per training batch
epochs	Number of times to iterate over dataset
earlyStoppingMinDelta	minimum change in the monitored quantity to qualify as an improvement for early stopping, i.e. an absolute change of less than min_delta in loss of validation data, will count as no improvement.
earlyStoppingPatience	Number of epochs with no improvement after which training will be stopped.
bayes	logical (either TRUE or FALSE) value for using Bayesian Drop Out Layer to measure uncertainty. If it is TRUE, both Epistemic and Aleatoric uncertainty will be measured through Bayesian Drop Out layer
useDeepEnsemble	logical (either TRUE or FALSE) value for using Deep Ensemble (Lakshminarayanan et al., 2017) to measure uncertainty. It cannot be used together with Bayesian deep learing.
numberOfEnsembleNetwork	Integer. Number of network used for Deep Ensemble (Lakshminarayanan et al recommended 5).
useVae	logical (either TRUE or FALSE) value for using Variational AutoEncoder before RNN
vaeDataSamplingProportion	Data sampling proportion for VAE
vaeValidationSplit	Validation split proportion for VAE
vaeBatchSize	batch size for VAE
vaeLatentDim	Number of latent dimesion for VAE
vaeIntermediateDim	Number of intermediate dimesion for VAE
vaeEpoch	Number of times to interate over dataset for VAE
vaeEpislonStd	Epsilon
useGPU	logical (either TRUE or FALSE) value. If you have GPUs in your machine, and want to use multiple GPU for deep learning, set this value as TRUE
maxGPUs	Integer, If you will use GPU, how many GPUs will be used for deep learning in VAE? GPU parallelisation for deep learning will be activated only when parallel vae is true. Integer >= 2 or list of integers, number of GPUs or list of GPU IDs on which to create model replicas.
seed	Random seed used by deep learning model

Examples

if (FALSE) {
model.CIReNN <- setCIReNN()
}