def run_jsma(weight_fpath):
flags.DEFINE_boolean('viz_enabled', VIZ_ENABLED,
'Visualize adversarial ex.')
flags.DEFINE_integer('nb_epochs', NB_EPOCHS,
'Number of epochs to train model')
flags.DEFINE_integer('batch_size', BATCH_SIZE, 'Size of training batches')
flags.DEFINE_integer('source_samples', SOURCE_SAMPLES,
'Nb of test inputs to attack')
flags.DEFINE_float('learning_rate', LEARNING_RATE,
'Learning rate for training')
mnist_tutorial_jsma(weight_fpath,
viz_enabled=FLAGS.viz_enabled,
nb_epochs=FLAGS.nb_epochs,
batch_size=FLAGS.batch_size,
source_samples=FLAGS.source_samples,
learning_rate=FLAGS.learning_rate)
import matplotlib.pyplot as plt
plt.close(figure)
_ = grid_visual(grid_viz_data)
return report
def main(argv=None):
from cleverhans_tutorials import check_installation
check_installation(__file__)
mnist_tutorial_jsma(viz_enabled=FLAGS.viz_enabled,
nb_epochs=FLAGS.nb_epochs,
batch_size=FLAGS.batch_size,
source_samples=FLAGS.source_samples,
learning_rate=FLAGS.learning_rate)
if __name__ == '__main__':
flags.DEFINE_boolean('viz_enabled', VIZ_ENABLED,
'Visualize adversarial ex.')
flags.DEFINE_integer('nb_epochs', NB_EPOCHS,
'Number of epochs to train model')
flags.DEFINE_integer('batch_size', BATCH_SIZE, 'Size of training batches')
flags.DEFINE_integer('source_samples', SOURCE_SAMPLES,
'Nb of test inputs to attack')
flags.DEFINE_float('learning_rate', LEARNING_RATE,
'Learning rate for training')
tf.app.run()
def main(argv=None):
from cleverhans_tutorials import check_installation
check_installation(__file__)
mnist_tutorial_fgsm(viz_enabled=FLAGS.viz_enabled,
nb_epochs=FLAGS.nb_epochs,
batch_size=FLAGS.batch_size,
source_samples=FLAGS.source_samples,
learning_rate=FLAGS.learning_rate,
attack_iterations=FLAGS.attack_iterations,
model_path=FLAGS.model_path,
targeted=FLAGS.targeted)
if __name__ == '__main__':
flags.DEFINE_boolean('viz_enabled', VIZ_ENABLED,
'Visualize adversarial ex.')
flags.DEFINE_integer('nb_epochs', NB_EPOCHS,
'Number of epochs to train model')
flags.DEFINE_integer('batch_size', BATCH_SIZE, 'Size of training batches')
flags.DEFINE_integer('source_samples', SOURCE_SAMPLES,
'Number of test inputs to attack')
flags.DEFINE_float('learning_rate', LEARNING_RATE,
'Learning rate for training')
flags.DEFINE_string('model_path', MODEL_PATH,
'Path to save or load the model file')
flags.DEFINE_integer('attack_iterations', ATTACK_ITERATIONS,
'Number of iterations to run attack; 1000 is good')
flags.DEFINE_boolean('targeted', TARGETED,
'Run the tutorial in targeted mode?')
tf.app.run()
bim_test = np.expand_dims(bim_test, axis=3)
bim_test_decoded = model.predict(bim_test)
eval(bim_test)
eval(bim_test_decoded)
def main(argv=None):
from cleverhans_tutorials import check_installation
check_installation(__file__)
mnist_tutorial(nb_epochs=FLAGS.nb_epochs,
batch_size=FLAGS.batch_size,
learning_rate=FLAGS.learning_rate,
train_dir=FLAGS.train_dir,
filename=FLAGS.filename,
load_model=FLAGS.load_model)
if __name__ == '__main__':
flags.DEFINE_integer('nb_epochs', NB_EPOCHS,
'Number of epochs to train model')
flags.DEFINE_integer('batch_size', BATCH_SIZE, 'Size of training batches')
flags.DEFINE_float('learning_rate', LEARNING_RATE,
'Learning rate for training')
flags.DEFINE_string('train_dir', TRAIN_DIR,
'Directory where to save model.')
flags.DEFINE_string('filename', FILENAME, 'Checkpoint filename.')
flags.DEFINE_boolean('load_model', LOAD_MODEL,
'Load saved model or train.')
tf.app.run()
sess, x, y, preds_2_adv, x_train, y_train, args=eval_params
)
report.train_adv_train_adv_eval = accuracy
return report
def main(argv=None):
from cleverhans_tutorials import check_installation
check_installation(__file__)
mnist_tutorial(
nb_epochs=FLAGS.nb_epochs,
batch_size=FLAGS.batch_size,
learning_rate=FLAGS.learning_rate,
train_dir=FLAGS.train_dir,
filename=FLAGS.filename,
load_model=FLAGS.load_model,
)
if __name__ == "__main__":
flags.DEFINE_integer("nb_epochs", NB_EPOCHS, "Number of epochs to train model")
flags.DEFINE_integer("batch_size", BATCH_SIZE, "Size of training batches")
flags.DEFINE_float("learning_rate", LEARNING_RATE, "Learning rate for training")
flags.DEFINE_string("train_dir", TRAIN_DIR, "Directory where to save model.")
flags.DEFINE_string("filename", FILENAME, "Checkpoint filename.")
flags.DEFINE_boolean("load_model", LOAD_MODEL, "Load saved model or train.")
tf.app.run()
check_installation(__file__)
mnist_tutorial_cw(
viz_enabled=FLAGS.viz_enabled,
nb_epochs=FLAGS.nb_epochs,
batch_size=FLAGS.batch_size,
source_samples=FLAGS.source_samples,
learning_rate=FLAGS.learning_rate,
attack_iterations=FLAGS.attack_iterations,
model_path=FLAGS.model_path,
targeted=FLAGS.targeted,
)
if __name__ == "__main__":
flags.DEFINE_boolean("viz_enabled", VIZ_ENABLED, "Visualize adversarial ex.")
flags.DEFINE_integer("nb_epochs", NB_EPOCHS, "Number of epochs to train model")
flags.DEFINE_integer("batch_size", BATCH_SIZE, "Size of training batches")
flags.DEFINE_integer(
"source_samples", SOURCE_SAMPLES, "Number of test inputs to attack"
)
flags.DEFINE_float("learning_rate", LEARNING_RATE, "Learning rate for training")
flags.DEFINE_string("model_path", MODEL_PATH, "Path to save or load the model file")
flags.DEFINE_integer(
"attack_iterations",
ATTACK_ITERATIONS,
"Number of iterations to run attack; 1000 is good",
)
flags.DEFINE_boolean("targeted", TARGETED, "Run the tutorial in targeted mode?")
tf.app.run()
f = {x: flags.FLAGS[x].value for x in dir(flags.FLAGS)}
HParams = namedtuple('HParams', f.keys())
hparams = HParams(**f)
run_trainer(hparams)
if __name__ == '__main__':
flags.DEFINE_integer('train_start', 0,
'Index of first training set example.')
flags.DEFINE_integer('train_end', 60000,
'Index of last training set example.')
flags.DEFINE_integer('test_start', 0, 'Index of first test set example.')
flags.DEFINE_integer('test_end', 10000, 'Index of last test set example.')
flags.DEFINE_integer('nb_epochs', 6, 'Number of epochs to train model.')
flags.DEFINE_integer('batch_size', 128, 'Size of training batches.')
flags.DEFINE_boolean('adv_train', False,
'Whether to do adversarial training.')
flags.DEFINE_boolean('save', True, 'Whether to save from a checkpoint.')
flags.DEFINE_string('save_dir', 'runs/X', 'Location to store logs/model.')
flags.DEFINE_string('model_type', 'madry',
'Model type: basic|madry|resnet_tf.')
flags.DEFINE_string(
'attack_type_train', 'MadryEtAl_y_multigpu',
'Attack type for adversarial training:\
FGSM|MadryEtAl{,_y}{,_multigpu}.')
flags.DEFINE_string('attack_type_test', 'FGSM',
'Attack type for test: FGSM|MadryEtAl{,_y}.')
flags.DEFINE_string('dataset', 'mnist', 'Dataset mnist|cifar10.')
flags.DEFINE_boolean(
'only_adv_train', False,
'Do not train with clean examples when adv training.')
flags.DEFINE_integer('save_steps', 50, 'Save model per X steps.')
return report
def main(argv=None):
from cleverhans_tutorials import check_installation
check_installation(__file__)
mnist_tutorial_jsma(
viz_enabled=FLAGS.viz_enabled,
nb_epochs=FLAGS.nb_epochs,
batch_size=FLAGS.batch_size,
source_samples=FLAGS.source_samples,
learning_rate=FLAGS.learning_rate,
)
if __name__ == "__main__":
flags.DEFINE_boolean("viz_enabled", VIZ_ENABLED,
"Visualize adversarial ex.")
flags.DEFINE_integer("nb_epochs", NB_EPOCHS,
"Number of epochs to train model")
flags.DEFINE_integer("batch_size", BATCH_SIZE, "Size of training batches")
flags.DEFINE_integer("source_samples", SOURCE_SAMPLES,
"Nb of test inputs to attack")
flags.DEFINE_float("learning_rate", LEARNING_RATE,
"Learning rate for training")
tf.app.run()
