def _check_and_download_file(uri: str, basename: str, *paths: str) -> str:
"""Check and download the file from given URI."""
dir_path = os.path.join(*paths)
file_path = os.path.join(dir_path, basename)
if not os.path.isdir(dir_path):
make_directory(dir_path)
if not os.path.isfile(file_path):
logger.info("Could not find %s. Downloading it now...", basename)
get_file(basename, uri, path=dir_path)
return file_path
def save_classifier(classifier, file_path="./model/"):
"""Saves classifier in the given location
:param classifier: Model to save
:param str file_path: Path to file
"""
make_directory(file_path.rsplit('/', 1)[0])
# Save classifier params
with open(os.path.join(file_path, 'params.json'), 'w') as fp:
params = {
"class_name": type(classifier).__name__,
"defences": classifier.defences
}
json.dump(params, fp)
# Serialize model to JSON
with open(os.path.join(file_path, "model.json"), "w") as json_file:
model_json = classifier.model.to_json()
json_file.write(model_json)
# Serialize weights to HDF5
classifier.model.save_weights(os.path.join(file_path, "weights.h5"))
# Save compilation params to json
if classifier.comp_param:
with open(os.path.join(file_path, 'comp_par.json'), 'w') as fp:
try:
json.dump(classifier.comp_param, fp)
except:
fp.seek(0)
json.dump(
{
"loss": 'categorical_crossentropy',
"optimizer": "sgd",
"metrics": ['accuracy']
}, fp)
fp.truncate()
v_print = get_verbose_print(args.verbose)
alpha = 0.05 # constant for random perturbation
# get dataset
(X_train, Y_train), (X_test, Y_test), min_, max_ = load_dataset(args.dataset)
session = tf.Session()
k.set_session(session)
# Load classification model
MODEL_PATH = os.path.join(os.path.abspath(args.load), "")
classifier = load_classifier(MODEL_PATH, "best-weights.h5")
if args.save:
SAVE_ADV = os.path.join(os.path.abspath(args.save), args.adv_method)
make_directory(SAVE_ADV)
with open(os.path.join(SAVE_ADV, "readme.txt"), "w") as wfile:
wfile.write("Model used for crafting the adversarial examples is in " +
MODEL_PATH)
v_print("Adversarials crafted with", args.adv_method, "on", MODEL_PATH,
"will be saved in", SAVE_ADV)
if args.adv_method in ['fgsm', "vat", "rnd_fgsm"]:
eps_ranges = {
'fgsm': [e / 10 for e in range(1, 11)],
'rnd_fgsm': [e / 10 for e in range(1, 11)],
'vat': [1.5, 2.1, 5, 7, 10]
}
def setUp(self):
make_directory("./tests/")
classifier.compile(comp_params)
if args.save is not False:
if args.save:
MODEL_PATH = os.path.abspath(args.save)
else:
if args.defences:
defences = "-".join(args.defences)
else:
defences = ""
MODEL_PATH = os.path.join(os.path.abspath(DATA_PATH), "classifiers",
args.dataset, args.classifier, args.act,
defences)
v_print("Classifier saved in", MODEL_PATH)
make_directory(MODEL_PATH)
# Save best classifier weights
# checkpoint = ModelCheckpoint(os.path.join(FILEPATH,"best-weights.{epoch:02d}-{val_acc:.2f}.h5"),
# monitor='val_acc', verbose=1, save_best_only=True, mode='max')
checkpoint = ModelCheckpoint(os.path.join(MODEL_PATH, "best-weights.h5"),
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
# Remote monitor
monitor = TensorBoard(log_dir=os.path.join(MODEL_PATH, 'logs'),
write_graph=False)
callbacks_list = [checkpoint, monitor]
else:
