robustness 1.1

  • Added ability to superclass ImageNet to make custom datasets (docs)
  • Added shuffle_train and shuffle_test options to make_loaders()
  • Added support for cyclic learning rate (--custom-schedule cyclic via command line or {"custom_schedule": "cyclic"} from Python
  • Added support for transfer learning/partial parameter updates, robustness.train.train_model() now takes update_params argument, list of parameters to update
  • Allow random_start (random start for adversarial attacks) to be set via command line
  • Change defaults for ImageNet training (200 epochs instead of 350)
  • Small fixes/refinements to module