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(1), batchSize = c(100),
  epochs = c(100), earlyStoppingMinDelta = c(1e-04),
  earlyStoppingPatience = c(10), useVae = T,
  vaeDataSamplingProportion = 0.1, vaeValidationSplit = 0.2,
  vaeBatchSize = 100L, vaeLatentDim = 10L, vaeIntermediateDim = 256L,
  vaeEpoch = 100L, vaeEpislonStd = 1, seed = NULL)

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

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.

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

seed

Random seed used by deep learning model

Examples

# NOT RUN {
model.CIReNN <- setCIReNN()
# }