def get_parser(self):
"""
Get parser.
:return: parser
:rtype: argparse.ArgumentParser
"""
parser = argparse.ArgumentParser(description='Test attacks on classifier.')
parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing dataset.', type=str)
parser.add_argument('-train_images_file', default=paths.train_images_file(), help='HDF5 file containing dataset.', type=str)
parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing labels.', type=str)
parser.add_argument('-label_index', default=2, help='Column index in label file.', type=int)
parser.add_argument('-accuracy_file', default=paths.results_file('learned_decoder/accuracy'), help='Correctly classified test samples of classifier.', type=str)
parser.add_argument('-perturbations_file', default=paths.results_file('learned_decoder/perturbations'), help='HDF5 file containing perturbations.', type=str)
parser.add_argument('-success_file', default=paths.results_file('learned_decoder/success'), help='HDF5 file indicating attack success.', type=str)
parser.add_argument('-plot_directory', default=paths.experiment_dir('learned_decoder'), help='Path to PNG plot file for success rate.', type=str)
parser.add_argument('-results_file', default='', help='Path to pickled results file.', type=str)
parser.add_argument('-batch_size', default=128, help='Batch size of attack.', type=int)
parser.add_argument('-plot_manifolds', default=False, action='store_true', help='Whether to plot manifolds.')
parser.add_argument('-no_gpu', dest='use_gpu', action='store_false')
# Network.
parser.add_argument('-N_theta', default=6, help='Numer of transformations.', type=int)
return parser
def get_parser(self):
"""
Get parser.
:return: parser
:rtype: argparse.ArgumentParser
"""
parser = argparse.ArgumentParser(description='Detect attacks on classifier.')
parser.add_argument('-mode', default='svd', help='Mode.', type=str)
parser.add_argument('-database_file', default=paths.database_file(), help='HDF5 file containing font prototype images.', type=str)
parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file codes dataset.', type=str)
parser.add_argument('-test_theta_file', default=paths.test_theta_file(), help='HDF5 file containing transformations.', type=str)
parser.add_argument('-train_images_file', default=paths.train_images_file(), help='HDF5 file containing dataset.', type=str)
parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing dataset.', type=str)
parser.add_argument('-perturbations_file', default=paths.results_file('classifier/perturbations'), help='HDF5 file containing perturbations.', type=str)
parser.add_argument('-success_file', default=paths.results_file('classifier/success'), help='HDF5 file containing success indicators.', type=str)
parser.add_argument('-accuracy_file', default=paths.results_file('classifier/accuracy'), help='HDF5 file containing accuracy indicators.', type=str)
parser.add_argument('-batch_size', default=64, help='Batch size.', type=int)
parser.add_argument('-no_gpu', dest='use_gpu', action='store_false')
parser.add_argument('-pre_pca', default=20, help='PCA dimensionality reduction ebfore NN.', type=int)
parser.add_argument('-n_nearest_neighbors', default=50, help='Number of NNs to consider.', type=int)
parser.add_argument('-n_pca', default=10, help='Number of NNs to consider.', type=int)
parser.add_argument('-n_fit', default=100000, help='Training images to fit.', type=int)
parser.add_argument('-plot_directory', default=paths.experiment_dir('classifier/detection'), help='Plot directory.', type=str)
parser.add_argument('-max_samples', default=1000, help='Number of samples.', type=int)
# Some decoder parameters.
parser.add_argument('-decoder_files', default=paths.state_file('decoder'), help='Decoder files.', type=str)
parser.add_argument('-latent_space_size', default=10, help='Size of latent space.', type=int)
parser.add_argument('-decoder_architecture', default='standard', help='Architecture to use.', type=str)
parser.add_argument('-decoder_activation', default='relu', help='Activation function to use.', type=str)
parser.add_argument('-decoder_no_batch_normalization', default=False, help='Do not use batch normalization.', action='store_true')
parser.add_argument('-decoder_channels', default=16, help='Channels of first convolutional layer, afterwards channels are doubled.', type=int)
parser.add_argument('-decoder_dropout', default=False, action='store_true', help='Whether to use dropout.')
parser.add_argument('-decoder_units', default='1024,1024,1024,1024', help='Units for MLP.')
return parser
def get_parser(self):
"""
Get parser.
:return: parser
:rtype: argparse.ArgumentParser
"""
parser = argparse.ArgumentParser(description='Test attacks on classifier.')
parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing dataset.', type=str)
parser.add_argument('-train_images_file', default=paths.train_images_file(), help='HDF5 file containing dataset.', type=str)
parser.add_argument('-test_theta_file', default=paths.results_file('test_theta'), help='HDF5 file containing dataset.', type=str)
parser.add_argument('-train_theta_file', default=paths.results_file('train_theta'), help='HDF5 file containing dataset.', type=str)
parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing labels.', type=str)
parser.add_argument('-decoder_files', default=paths.state_file('decoder'), help='Decoder files.', type=str)
parser.add_argument('-label_index', default=2, help='Column index in label file.', type=int)
parser.add_argument('-accuracy_file', default=paths.results_file('learned_decoder/accuracy'), help='Correctly classified test samples of classifier.', type=str)
parser.add_argument('-perturbations_file', default=paths.results_file('learned_decoder/perturbations'), help='HDF5 file containing perturbations.', type=str)
parser.add_argument('-success_file', default=paths.results_file('learned_decoder/success'), help='HDF5 file indicating attack success.', type=str)
parser.add_argument('-plot_directory', default=paths.experiment_dir('learned_decoder'), help='Path to PNG plot file for success rate.', type=str)
parser.add_argument('-results_file', default='', help='Path to pickled results file.', type=str)
parser.add_argument('-batch_size', default=128, help='Batch size of attack.', type=int)
parser.add_argument('-plot_manifolds', default=False, action='store_true', help='Whether to plot manifolds.')
parser.add_argument('-no_gpu', dest='use_gpu', action='store_false')
parser.add_argument('-bound', default=2, help='Bound to consider for samples in latent space.', type=float)
# Some decoder parameters.
parser.add_argument('-latent_space_size', default=10, help='Size of latent space.', type=int)
parser.add_argument('-decoder_architecture', default='standard', help='Architecture to use.', type=str)
parser.add_argument('-decoder_activation', default='relu', help='Activation function to use.', type=str)
parser.add_argument('-decoder_no_batch_normalization', default=False, help='Do not use batch normalization.', action='store_true')
parser.add_argument('-decoder_channels', default=16, help='Channels of first convolutional layer, afterwards channels are doubled.', type=int)
parser.add_argument('-decoder_dropout', default=False, action='store_true', help='Whether to use dropout.')
parser.add_argument('-decoder_units', default='1024,1024,1024,1024', help='Units for MLP.')
return parser
def get_parser(self):
"""
Get parser.
:return: parser
:rtype: argparse.ArgumentParser
"""
parser = argparse.ArgumentParser(
description='Test attacks on classifier.')
parser.add_argument('-classifier_file',
default=paths.state_file('classifier'),
help='Snapshot state file of classifier.',
type=str)
parser.add_argument('-test_images_file',
default=paths.test_images_file(),
help='HDF5 file containing dataset.',
type=str)
parser.add_argument('-test_codes_file',
default=paths.test_codes_file(),
help='HDF5 file containing dataset.',
type=str)
parser.add_argument('-label_index',
default=2,
help='Column index in label file.',
type=int)
parser.add_argument(
'-perturbations_file',
default=paths.results_file('classifier/perturbations'),
help='HDF5 file containing perturbations.',
type=str)
parser.add_argument('-success_file',
default=paths.results_file('classifier/success'),
help='HDF5 file indicating attack success.',
type=str)
parser.add_argument(
'-probabilities_file',
default=paths.results_file('classifier/probabilities'),
help='HDF5 file containing attack probabilities.')
parser.add_argument('-results_file',
default='',
help='Path to pickled results file.',
type=str)
parser.add_argument('-plot_directory',
default=paths.experiment_dir('classifier'),
help='Path to PNG plot file for success rate.',
type=str)
parser.add_argument('-plot_manifolds',
default=False,
action='store_true',
help='Whether to plot manifolds.')
parser.add_argument('-no_gpu', dest='use_gpu', action='store_false')
parser.add_argument('-batch_size',
default=128,
help='Batch size of attack.',
type=int)
# Some network parameters.
parser.add_argument('-network_architecture',
default='standard',
help='Classifier architecture to use.',
type=str)
parser.add_argument('-network_activation',
default='relu',
help='Activation function to use.',
type=str)
parser.add_argument('-network_no_batch_normalization',
default=False,
help='Do not use batch normalization.',
action='store_true')
parser.add_argument(
'-network_channels',
default=16,
help=
'Channels of first convolutional layer, afterwards channels are doubled.',
type=int)
parser.add_argument('-network_dropout',
default=False,
action='store_true',
help='Whether to use dropout.')
parser.add_argument('-network_units',
default='1024,1024,1024,1024',
help='Units for MLP.')
return parser
def train(self, base_directory, method, label):
"""
Train VAE-GAN.
"""
encoder_file = paths.state_file('%s/encoder' % base_directory)
decoder_file = paths.state_file('%s/decoder' % base_directory)
classifier_file = paths.state_file('%s/classifier' % base_directory)
reconstruction_file = paths.results_file('%s/reconstructions' %
base_directory)
interpolation_file = paths.results_file('%s/interpolation' %
base_directory)
random_file = paths.results_file('%s/random' % base_directory)
log_file = paths.log_file('%s/vaegan' % base_directory)
results_file = paths.pickle_file('%s/results' % base_directory)
training_file = paths.results_file('%s/training' % base_directory)
testing_file = paths.results_file('%s/testing' % base_directory)
error_file = paths.image_file('%s/error' % base_directory)
gradient_file = paths.image_file('%s/gradient' % base_directory)
test_results_file = paths.pickle_file('%s/test_results' %
base_directory)
if not os.path.exists(encoder_file) or not os.path.exists(
decoder_file):
if method.find('vaegan') >= 0:
arguments = [
'-train_images_file=%s' % self.train_images_file,
'-train_codes_file=%s' % self.train_codes_file,
'-test_images_file=%s' % self.test_images_file,
'-test_codes_file=%s' % self.test_codes_file,
'-label_index=%d' % self.label_index,
'-label=%d' % label,
'-encoder_file=%s' % encoder_file,
'-decoder_file=%s' % decoder_file,
'-classifier_file=%s' % classifier_file,
'-reconstruction_file=%s' % reconstruction_file,
'-interpolation_file=%s' % interpolation_file,
'-random_file=%s' % random_file,
'-log_file=%s' % log_file,
'-results_file=%s' % results_file,
'-training_file=%s' % training_file,
'-testing_file=%s' % testing_file,
'-error_file=%s' % error_file,
'-gradient_file=%s' % gradient_file,
'-latent_space_size=%d' % self.args.latent_space_size,
'-beta=%g' % self.args.beta,
'-gamma=%g' % self.args.gamma,
'-epochs=%d' % self.args.epochs,
'-eta=%g' % self.args.eta,
] + self.training_parameters + self.network_parameters + self.classifier_parameters
log(arguments)
if method == 'vaegan2':
train = TrainVAEGAN2(arguments)
else:
raise NotImplementedError()
train.main()
elif method == 'vae':
arguments = [
'-train_images_file=%s' % self.train_images_file,
'-train_codes_file=%s' % self.train_codes_file,
'-test_images_file=%s' % self.test_images_file,
'-test_codes_file=%s' % self.test_codes_file,
'-label_index=%d' % self.label_index,
'-label=%d' % label,
'-encoder_file=%s' % encoder_file,
'-decoder_file=%s' % decoder_file,
'-reconstruction_file=%s' % reconstruction_file,
'-interpolation_file=%s' % interpolation_file,
'-random_file=%s' % random_file,
'-log_file=%s' % log_file,
'-results_file=%s' % results_file,
'-training_file=%s' % training_file,
'-testing_file=%s' % testing_file,
'-error_file=%s' % error_file,
'-latent_space_size=%d' % self.args.latent_space_size,
'-beta=%g' % self.args.beta,
'-epochs=%d' % self.args.epochs,
] + self.training_parameters + self.network_parameters
train = TrainVariationalAutoEncoder(arguments)
train.main()
else:
raise NotImplementedError()
train_theta_file = paths.results_file('%s/train_theta' %
base_directory)
test_theta_file = paths.results_file('%s/test_theta' % base_directory)
if self.args.reevaluate or not os.path.exists(test_results_file):
test = TestVariationalAutoEncoder([
'-train_images_file=%s' % self.train_images_file,
'-test_images_file=%s' % self.test_images_file,
'-train_codes_file=%s' % self.train_codes_file,
'-test_codes_file=%s' % self.test_codes_file,
'-train_theta_file=%s' % train_theta_file,
'-test_theta_file=%s' % test_theta_file,
'-label_index=%d' % self.label_index,
'-label=%d' % label,
'-encoder_file=%s' % encoder_file,
'-decoder_file=%s' % decoder_file,
'-batch_size=256',
'-results_file=%s' % test_results_file,
'-reconstruction_file=',
'-random_file=',
'-interpolation_file=',
'-output_directory=',
'-latent_space_size=%d' % self.args.latent_space_size,
] + self.network_parameters)
test.main()
results = test.results
else:
results = utils.read_pickle(test_results_file)
self.results[base_directory] = (results['reconstruction_error'],
results['code_mean'],
results['code_var'])
if utils.display():
random_directory = paths.experiment_dir(
'%s/random/' % base_directory, experiment=self.experiment())
if self.args.reevaluate or not os.path.exists(random_directory):
visualize_mosaic = VisualizeMosaic([
'-images_file=%s' % random_file,
'-output_directory=%s' % random_directory,
])
visualize_mosaic.main()
random_directory = paths.experiment_dir(
'%s/random_/' % base_directory, experiment=self.experiment())
# if self.args.reevaluate or not os.path.exists(random_directory):
visualize_individual = VisualizeIndividual([
'-images_file=%s' % random_file,
'-output_directory=%s' % random_directory,
])
visualize_individual.main()
if utils.display():
reconstruction_directory = paths.experiment_dir(
'%s/reconstruction/' % base_directory,
experiment=self.experiment())
if self.args.reevaluate or not os.path.exists(
reconstruction_directory):
visualize_mosaic = VisualizeMosaic([
'-images_file=%s' % reconstruction_file,
'-output_directory=%s' % reconstruction_directory,
])
visualize_mosaic.main()
reconstruction_directory = paths.experiment_dir(
'%s/reconstruction_/' % base_directory,
experiment=self.experiment())
# if self.args.reevaluate or not os.path.exists(reconstruction_directory):
visualize_individual = VisualizeIndividual([
'-images_file=%s' % reconstruction_file,
'-output_directory=%s' % reconstruction_directory,
])
visualize_individual.main()
if utils.display():
interpolation_directory = paths.experiment_dir(
'%s/interpolation/' % base_directory,
experiment=self.experiment())
if self.args.reevaluate or not os.path.exists(
interpolation_directory):
visualize_mosaic = VisualizeMosaic([
'-images_file=%s' % interpolation_file,
'-output_directory=%s' % interpolation_directory,
])
visualize_mosaic.main()
interpolation_directory = paths.experiment_dir(
'%s/interpolation_/' % base_directory,
experiment=self.experiment())
# if self.args.reevaluate or not os.path.exists(reconstruction_directory):
visualize_individual = VisualizeIndividual([
'-images_file=%s' % interpolation_file,
'-output_directory=%s' % interpolation_directory,
])
visualize_individual.main()
if utils.display():
original_directory = paths.experiment_dir(
'%s/original/' % base_directory, experiment=self.experiment())
if self.args.reevaluate or not os.path.exists(original_directory):
visualize_mosaic = VisualizeMosaic([
'-images_file=%s' % self.test_images_file,
'-codes_file=%s' % self.test_codes_file,
'-label_index=%d' % self.label_index,
'-label=%d' % label,
'-output_directory=%s' % original_directory,
])
visualize_mosaic.main()
original_directory = paths.experiment_dir(
'%s/original_/' % base_directory, experiment=self.experiment())
# if self.args.reevaluate or not os.path.exists(reconstruction_directory):
visualize_individual = VisualizeIndividual([
'-images_file=%s' % self.test_images_file,
'-codes_file=%s' % self.test_codes_file,
'-label_index=%d' % self.label_index,
'-label=%d' % label,
'-output_directory=%s' % original_directory,
])
visualize_individual.main()
def get_parser(self):
"""
Get parser.
:return: parser
:rtype: argparse.ArgumentParser
"""
parser = argparse.ArgumentParser(
description='Visualize attacks on decoder and classifier.')
parser.add_argument('-database_file',
default=paths.database_file(),
help='HDF5 file containing font prototype images.',
type=str)
parser.add_argument('-test_theta_file',
default=paths.test_theta_file(),
help='HDF5 file containing transformations.',
type=str)
parser.add_argument('-test_codes_file',
default=paths.test_codes_file(),
help='HDF5 file containing codes.',
type=str)
parser.add_argument('-test_images_file',
default=paths.test_images_file(),
help='HDF5 file containing images.',
type=str)
parser.add_argument('-label_index',
default=2,
help='Label index.',
type=int)
parser.add_argument('-classifier_file',
default=paths.state_file('classifier'),
help='Snapshot state file of classifier.',
type=str)
parser.add_argument(
'-perturbations_file',
default=paths.results_file('decoder/perturbations'),
help='HDF5 file containing perturbations.',
type=str)
parser.add_argument('-success_file',
default=paths.results_file('decoder/success'),
help='HDF5 file containing perturbations.',
type=str)
parser.add_argument(
'-accuracy_file',
default=paths.results_file('decoder/accuracy'),
help='Correctly classified test samples of classifier.',
type=str)
parser.add_argument(
'-output_directory',
default=paths.experiment_dir('decoder/perturbations'),
help='Directory to store visualizations.',
type=str)
parser.add_argument('-batch_size',
default=128,
help='Batch size of attack.',
type=int)
parser.add_argument('-no_gpu', dest='use_gpu', action='store_false')
# Some network parameters.
parser.add_argument('-network_architecture',
default='standard',
help='Classifier architecture to use.',
type=str)
parser.add_argument('-network_activation',
default='relu',
help='Activation function to use.',
type=str)
parser.add_argument('-network_no_batch_normalization',
default=False,
help='Do not use batch normalization.',
action='store_true')
parser.add_argument(
'-network_channels',
default=16,
help=
'Channels of first convolutional layer, afterwards channels are doubled.',
type=int)
parser.add_argument('-network_dropout',
default=False,
action='store_true',
help='Whether to use dropout.')
parser.add_argument('-network_units',
default='1024,1024,1024,1024',
help='Units for MLP.')
return parser
def get_parser(self):
"""
Get parser.
:return: parser
:rtype: argparse.ArgumentParser
"""
parser = argparse.ArgumentParser(
description='Test attacks on classifier.')
parser.add_argument('-test_images_file',
default=paths.test_images_file(),
help='HDF5 file containing dataset.',
type=str)
parser.add_argument('-train_images_file',
default=paths.train_images_file(),
help='HDF5 file containing dataset.',
type=str)
parser.add_argument('-test_theta_file',
default=paths.test_theta_file(),
help='HDF5 file containing dataset.',
type=str)
parser.add_argument('-train_theta_file',
default=paths.train_theta_file(),
help='HDF5 file containing dataset.',
type=str)
parser.add_argument('-test_codes_file',
default=paths.test_codes_file(),
help='HDF5 file containing labels.',
type=str)
parser.add_argument('-label_index',
default=2,
help='Column index in label file.',
type=int)
parser.add_argument(
'-accuracy_file',
default=paths.results_file('classifier/accuracy'),
help='Correctly classified test samples of classifier.',
type=str)
parser.add_argument(
'-perturbations_file',
default=paths.results_file('classifier/perturbations'),
help='HDF5 file containing perturbations.',
type=str)
parser.add_argument('-success_file',
default=paths.results_file('classifier/success'),
help='HDF5 file indicating attack success.',
type=str)
parser.add_argument('-results_file',
default='',
help='Path to pickled results file.',
type=str)
parser.add_argument('-plot_directory',
default=paths.experiment_dir('classifier'),
help='Path to PNG plot file for success rate.',
type=str)
parser.add_argument('-plot_manifolds',
default=False,
action='store_true',
help='Whether to plot manifolds.')
parser.add_argument('-latent',
default=False,
action='store_true',
help='Latent statistics.')
return parser
def get_parser(self):
"""
Get parser.
:return: parser
:rtype: argparse.ArgumentParser
"""
parser = argparse.ArgumentParser(
description='Attack decoder and classifier.')
parser.add_argument('-test_images_file',
default=paths.test_images_file(),
help='HDF5 file containing dataset.',
type=str)
parser.add_argument('-test_codes_file',
default=paths.test_codes_file(),
help='HDF5 file containing codes.',
type=str)
parser.add_argument('-label_index',
default=2,
help='Label index.',
type=int)
parser.add_argument('-classifier_file',
default=paths.state_file('classifier'),
help='Snapshot state file of classifier.',
type=str)
parser.add_argument('-output_directory',
default=paths.experiment_dir('output'),
help='Output directory.',
type=str)
parser.add_argument('-log_file',
default=paths.log_file('learned_decoder/attacks'),
help='Log file.',
type=str)
parser.add_argument('-objective',
default='UntargetedF0',
help='Objective to use.',
type=str)
parser.add_argument(
'-max_samples',
default=10,
help='How many samples from the test set to attack.',
type=int)
parser.add_argument('-no_gpu', dest='use_gpu', action='store_false')
parser.add_argument('-N_theta',
default=6,
help='Numer of transformations.',
type=int)
parser.add_argument('-translation_x',
default='-0.1,0.1',
type=str,
help='Minimum and maximum translation in x.')
parser.add_argument('-translation_y',
default='-0.1,0.1',
type=str,
help='Minimum and maximum translation in y')
parser.add_argument('-shear_x',
default='-0.25,0.25',
type=str,
help='Minimum and maximum shear in x.')
parser.add_argument('-shear_y',
default='-0.25,0.25',
type=str,
help='Minimum and maximum shear in y.')
parser.add_argument('-scale',
default='0.95,1.05',
type=str,
help='Minimum and maximum scale.')
parser.add_argument('-rotation',
default='%g,%g' % (-math.pi / 4, math.pi / 4),
type=str,
help='Minimum and maximum rotation.')
parser.add_argument('-color',
default=0.5,
help='Minimum color value, maximum is 1.',
type=float)
# Some network parameters.
parser.add_argument('-network_architecture',
default='standard',
help='Classifier architecture to use.',
type=str)
parser.add_argument('-network_activation',
default='relu',
help='Activation function to use.',
type=str)
parser.add_argument('-network_no_batch_normalization',
default=False,
help='Do not use batch normalization.',
action='store_true')
parser.add_argument(
'-network_channels',
default=16,
help=
'Channels of first convolutional layer, afterwards channels are doubled.',
type=int)
parser.add_argument('-network_dropout',
default=False,
action='store_true',
help='Whether to use dropout.')
parser.add_argument('-network_units',
default='1024,1024,1024,1024',
help='Units for MLP.')
return parser
def get_parser(self):
"""
Get parser.
:return: parser
:rtype: argparse.ArgumentParser
"""
parser = argparse.ArgumentParser(
description='Attack decoder and classifier.')
parser.add_argument('-test_images_file',
default=paths.test_images_file(),
help='HDF5 file containing dataset.',
type=str)
parser.add_argument('-test_codes_file',
default=paths.test_codes_file(),
help='HDF5 file containing codes.',
type=str)
parser.add_argument('-test_theta_file',
default=paths.results_file('test_theta'),
help='HDF5 file containing theta.',
type=str)
parser.add_argument('-label_index',
default=2,
help='Label index.',
type=int)
parser.add_argument('-classifier_file',
default=paths.state_file('classifier'),
help='Snapshot state file of classifier.',
type=str)
parser.add_argument('-decoder_files',
default=paths.state_file('decoder'),
help='Decoder state file.',
type=str)
parser.add_argument('-output_directory',
default=paths.experiment_dir('output'),
help='Output directory.',
type=str)
parser.add_argument('-log_file',
default=paths.log_file('learned_decoder/attacks'),
help='Log file.',
type=str)
parser.add_argument('-attack',
default='UntargetedBatchL2ClippedGradientDescent',
help='Attack to try.',
type=str)
parser.add_argument('-objective',
default='UntargetedF6',
help='Objective to use.',
type=str)
parser.add_argument('-max_attempts',
default=1,
help='Maximum number of attempts per attack.',
type=int)
parser.add_argument(
'-max_samples',
default=10,
help='How many samples from the test set to attack.',
type=int)
parser.add_argument('-epsilon',
default=0.1,
help='Epsilon allowed for attacks.',
type=float)
parser.add_argument('-c_0',
default=0.,
help='Weight of norm.',
type=float)
parser.add_argument(
'-c_1',
default=0.1,
help=
'Weight of bound, if not enforced through clipping or reparameterization.',
type=float)
parser.add_argument('-c_2',
default=0.5,
help='Weight of objective.',
type=float)
parser.add_argument('-max_iterations',
default=100,
help='Number of iterations for attack.',
type=int)
parser.add_argument(
'-max_projections',
default=5,
help='Number of projections for alternating projection.',
type=int)
parser.add_argument('-base_lr',
default=0.005,
help='Learning rate for attack.',
type=float)
parser.add_argument('-no_gpu', dest='use_gpu', action='store_false')
parser.add_argument('-on_manifold',
dest='on_manifold',
action='store_true')
# Some network parameters.
parser.add_argument('-network_architecture',
default='standard',
help='Classifier architecture to use.',
type=str)
parser.add_argument('-network_activation',
default='relu',
help='Activation function to use.',
type=str)
parser.add_argument('-network_no_batch_normalization',
default=False,
help='Do not use batch normalization.',
action='store_true')
parser.add_argument(
'-network_channels',
default=16,
help=
'Channels of first convolutional layer, afterwards channels are doubled.',
type=int)
parser.add_argument('-network_dropout',
default=False,
action='store_true',
help='Whether to use dropout.')
parser.add_argument('-network_units',
default='1024,1024,1024,1024',
help='Units for MLP.')
# Some decoder parameters.
parser.add_argument('-latent_space_size',
default=10,
help='Size of latent space.',
type=int)
parser.add_argument('-decoder_architecture',
default='standard',
help='Architecture to use.',
type=str)
parser.add_argument('-decoder_activation',
default='relu',
help='Activation function to use.',
type=str)
parser.add_argument('-decoder_no_batch_normalization',
default=False,
help='Do not use batch normalization.',
action='store_true')
parser.add_argument(
'-decoder_channels',
default=16,
help=
'Channels of first convolutional layer, afterwards channels are doubled.',
type=int)
parser.add_argument('-decoder_units',
default='1024,1024,1024,1024',
help='Units for MLP.')
return parser