def shake_shake_fgsm(): aparams = HParams() aparams.attack = "fgsm" aparams.attack_epsilons = [(i+1) * 0.1 for i in range(12)] aparams.add_hparam("clip_min", 0.0) aparams.add_hparam("clip_max", 255.0) return aparams
def resnet_fgsm(): aparams = HParams() aparams.attack = "fgsm" aparams.epsilon_name = "eps" aparams.attack_epsilons = [i * 0.8 for i in range(20)] aparams.add_hparam("clip_min", 0.0) aparams.add_hparam("clip_max", 255.0) return aparams
def transformer_weight_mjc(): hp = HParams() hp.add_hparam("strategy", "weight") hp.add_hparam("black_list", ["logits", "bias"]) hp.add_hparam("white_list", ["attention"]) hp.add_hparam("sparsities", [0.1 * i for i in range(8)]) hp.add_hparam("weight_sparsities", [0.1 * i for i in range(8)]) return hp
def resnet_weight_mjc(): hp = HParams() hp.add_hparam("strategy", "weight") hp.add_hparam("black_list", ["logits", "bias"]) hp.add_hparam("white_list", ["conv2d"]) hp.add_hparam("sparsities", [0.3 * i for i in range(1)]) hp.add_hparam("weight_sparsities", [0.3 * i for i in range(1)]) return hp
def resnet_weight(): hp = HParams() hp.add_hparam("strategy", "weight") hp.add_hparam("black_list", ["logits", "bias"]) hp.add_hparam("white_list", ["td_conv"]) hp.add_hparam("sparsities", [0.1 * i for i in range(10)]) return hp