I read your paper 'Evaluating Differentially Private Machine Learning in Practice' and it is very interesting work.
when I run your code I find that in evaluating_dpml.py, it seems in line 22,
pred_y, membership, test_classes, classifier, aux = train_target_model(
dataset=dataset,
epochs=args.target_epochs,
batch_size=args.target_batch_size,
learning_rate=args.target_learning_rate,
clipping_threshold=args.target_clipping_threshold,
n_hidden=args.target_n_hidden,
l2_ratio=args.target_l2_ratio,
model=args.target_model,
privacy=args.target_privacy,
dp=args.target_dp,
epsilon=args.target_epsilon,
delta=args.target_delta,
save=args.save_model
),
should add 'args' to target_train_model()
Hi Dr Barga,
I read your paper 'Evaluating Differentially Private Machine Learning in Practice' and it is very interesting work.
when I run your code I find that in evaluating_dpml.py, it seems in line 22, pred_y, membership, test_classes, classifier, aux = train_target_model( dataset=dataset, epochs=args.target_epochs, batch_size=args.target_batch_size, learning_rate=args.target_learning_rate, clipping_threshold=args.target_clipping_threshold, n_hidden=args.target_n_hidden, l2_ratio=args.target_l2_ratio, model=args.target_model, privacy=args.target_privacy, dp=args.target_dp, epsilon=args.target_epsilon, delta=args.target_delta, save=args.save_model ), should add 'args' to target_train_model()