def __init__(self,
hyper_params=None,
reuse=False,
is_saving=True,
init_graph=True,
mode='train',
name='JOINT_DEFENSE'):
self.is_saving = is_saving
self.init_graph = init_graph
self.mode = mode
if hyper_params is None:
hyper_params = ADV_TRAIN_HP
self.hp_params = utils.ParamWrapper(hyper_params)
self.threshold = None # get_median()
# attack initilization
if not (os.path.exists(config.get('dataset', 'dataX'))
and os.path.exists(config.get('dataset', 'datay'))
and os.path.exists(config.get('dataset', 'normalizer'))):
dataX, datay = self.data_preprocess()
utils.dump_joblib(dataX, config.get('dataset', 'dataX'))
utils.dump_joblib(datay, config.get('dataset', 'datay'))
self.normalizer = utils.read_joblib(config.get('dataset',
'normalizer'))
input_dim = len(self.normalizer.data_min_)
self.inner_maximizer = PGDAdam(self,
input_dim,
self.normalizer,
verbose=False,
**AUG_PARAM)
super(JointDefense, self).__init__(hyper_params, reuse, self.is_saving,
self.init_graph, self.mode, name)
def __init__(self,
hyper_params = None,
reuse = False,
is_saving = True,
init_graph = True,
mode = 'train',
name = 'DAE_RPST_LEARN_DNN'):
self.is_saving = is_saving
self.init_graph = init_graph
self.mode = mode
if hyper_params is None:
hyper_params = DAE_TRAIN_HP
# initilization
if not (os.path.exists(config.get('dataset', 'dataX')) and
os.path.exists(config.get('dataset', 'datay')) and
os.path.exists(config.get('dataset', 'normalizer'))):
dataX, datay = self.data_preprocess()
utils.dump_joblib(dataX, config.get('dataset', 'dataX'))
utils.dump_joblib(datay, config.get('dataset', 'datay'))
self.normalizer = utils.read_joblib(config.get('dataset', 'normalizer'))
input_dim = len(self.normalizer.data_min_)
self.inner_maximizer = PGDAdam(self, input_dim, self.normalizer, verbose=False, **AUG_PARAM)
super(DAE_RPST_DNN, self).__init__(hyper_params, reuse,
self.is_saving, self.init_graph, self.mode, name)
def normalize_data(X, is_fitting=False):
"""Normalize data using minmaxscalar"""
if not os.path.exists(config.get('dataset', 'normalizer')) and is_fitting:
minmax_norm = MinMaxScaler()
normalizer = minmax_norm.fit(X)
utils.dump_joblib(
normalizer,
config.get('dataset', 'normalizer'),
)
normalizer = utils.read_joblib(config.get('dataset', 'normalizer'))
x_clipped = np.clip(X,
a_min=normalizer.data_min_,
a_max=normalizer.data_max_)
X_normlized = normalizer.transform(x_clipped)
return X_normlized
def __init__(self,
hyper_params=None,
reuse=False,
is_saving=True,
init_graph=True,
mode='train',
name='BASIC_DNN'):
super(BasicDNN, self).__init__()
self.is_saving = is_saving
self.init_graph = init_graph
self.reuse = reuse
self.model_name = name
try:
assert mode == 'train' or mode == 'test'
except:
raise AssertionError("Two modes: 'train' or 'test', not both.")
self.mode = mode
if hyper_params is not None:
self.hp_params_dict = hyper_params
self.hp_params = utils.ParamWrapper(hyper_params)
else:
self.hp_params_dict = DNN_HP
self.hp_params = utils.ParamWrapper(DNN_HP)
if self.is_saving:
self.save_dir = config.get("experiments", self.model_name.lower())
if not (os.path.exists(config.get('dataset', 'dataX'))
and os.path.exists(config.get('dataset', 'datay'))
and os.path.exists(config.get('dataset', 'normalizer'))):
dataX, datay = self.data_preprocess()
utils.dump_joblib(dataX, config.get('dataset', 'dataX'))
utils.dump_joblib(datay, config.get('dataset', 'datay'))
self.normalizer = utils.read_joblib(config.get('dataset',
'normalizer'))
# DNN based model
self.input_dim = len(self.normalizer.data_min_)
self.hidden_layers = self.hp_params.hidden_units
self.output_dim = self.hp_params.output_dim
tf.set_random_seed(self.hp_params.random_seed)
if self.init_graph:
self.model_graph(reuse=reuse)