SRCL_2_train_neural_network_with_confounder: Training the monotonistic neural network with a confounder...

In ekstroem/SRCL: Synergistic Risk Contributions Learning

Description

This function trains the monotonistic neural network with a confounder connected to the output layer. This functions allows one to divide the training process into several steps.

Usage

SRCL_2_train_neural_network_with_confounder(
  X,
  Y,
  C,
  model,
  lr = 0.01,
  epochs = 50000,
  patience = 500,
  plot_and_evaluation_frequency = 50
)

Arguments

X	The exposure data
Y	The outcome data
C	The confounder data
model	The fitted monotonistic neural network
lr	Learning rate
epochs	Epochs
patience	The number of epochs allowed without an improvement in performance.
plot_and_evaluation_frequency	The interval for plotting the performance and checking the patience

Examples

#See the example under SRCL_0_synthetic_data

ekstroem/SRCL documentation built on Sept. 5, 2020, 8:59 p.m.