Create setting for neural network model with python

setMLPTorch(size = c(500, 1000), w_decay = c(5e-04, 0.005), epochs = c(20,
  50), seed = 0, class_weight = 0, mlp_type = "MLP",
  autoencoder = FALSE, vae = FALSE)

Arguments

size

The number of hidden nodes

w_decay

The l2 regularisation

epochs

The number of epochs

seed

A seed for the model

class_weight

The class weight used for imbalanced data: 0: Inverse ratio between positives and negatives -1: Focal loss

mlp_type

The type of multiple layer network, inlcuding MLP and SNN (self-normalizing neural network)

autoencoder

First learn stakced autoencoder for input features, then train MLP on the encoded features.

vae

First learn stakced varational autoencoder for input features, then train MLP on the encoded features.

Examples

# NOT RUN {
model.mlpTorch <- setMLPTorch()
# }