train_PAN: Trains PAN model

In ModelOriented/FairPAN: Comfortable Tool for Making Fair Classifier with the Neural Networks

Description

Trains Predictive Adversarial Network model, which means that it proceeds with the mutual training of adversarial model on whole dataloader and classifier on a single mini batch. The result is a fairer classifier.

Usage

train_PAN(
  dsl,
  clf_model,
  adv_model,
  clf_optimizer,
  adv_optimizer,
  dev,
  sensitive_train,
  sensitive_test,
  n_ep_pan = 50,
  batch_size,
  learning_rate_adv,
  learning_rate_clf,
  lambda,
  verbose = TRUE,
  monitor = TRUE
)

Arguments

dsl	dataset_loader object for classificator network
clf_model	net, nn_module, classifier model (preferably after pretrain)
adv_model	net, nn_module, adversarial model (preferably after pretrain)
clf_optimizer	optimizer for classificator model from pretrain
adv_optimizer	optimizer for adversarial model from pretrain
dev	device used to computation ("cuda" or "cpu")
sensitive_train	integer vector of sensitive attribute used for training
sensitive_test	integer vector of sensitive attribute used for testing
n_ep_pan	number of epochs for PAN training
batch_size	batch size used in adversarial models dataset_loader
learning_rate_adv	learning rate of adversarial
learning_rate_clf	learning rate of classifier
lambda	parameter regulating learning process (intuition: the bigger it is, the fairer predictions and the worse accuracy of classifier).
verbose	logical indicating if we want to print monitored outputs or not
monitor	logical indicating if we want to monitor the learning process or not (monitoring tends to slow down the training proccess, but provides some useful info to adjust parameters and training process)

Value

NULL if monitor is FALSE, list of metrics if it is TRUE

ModelOriented/FairPAN documentation built on Dec. 17, 2021, 4:19 a.m.