defense.py

# Load the classification model by passing the model, that
# you'd like to train, in `model` argument
advreg_wrapper.load_classification(model=cmodel, 
                                   optimizer="adam", 
                                   learning_rate=0.001)

advreg_wrapper.load_inference(optimizer="adam", learning_rate=0.0001)

# begins training in an adversarial manner (with defense if _lambda > 0)
advreg_wrapper.begin_advreg_training(datahandler=datahandler, 
                          		 	_lambda=3, 
                          		 	regloss="mse", 
                                device="cpu",
                                epochs_wo_defense=1, 
                          		 	epochs_w_defense=49,#49, 
                          		 	iterations_wo_defense=100, 
                          		 	iterations_w_defense=76,
                          		 	classifier_batches=1, 
                          		 	attack_batches=10, 
                          		 	decay_learning_rate=False)

# begins testing
advreg_wrapper.begin_testing(datahandler=datahandler, 
							 attack_data_percentage=50, training_size=20000)