def main(_):
net_weights, net_biases, net_layer_types = read_weights.read_weights(
FLAGS.checkpoint, FLAGS.model_json)
nn_params = neural_net_params.NeuralNetParams(net_weights, net_biases,
net_layer_types)
print(nn_params.sizes)
dual_var = utils.initialize_dual(nn_params,
FLAGS.init_dual_file,
init_nu=FLAGS.init_nu)
# Reading test input and reshaping
with tf.gfile.Open(FLAGS.test_input) as f:
test_input = np.load(f)
test_input = np.reshape(test_input, [np.size(test_input), 1])
if FLAGS.adv_class == -1:
start_class = 0
end_class = FLAGS.num_classes
else:
start_class = FLAGS.adv_class
end_class = FLAGS.adv_class + 1
for adv_class in range(start_class, end_class):
print('Adv class', adv_class)
if adv_class == FLAGS.true_class:
continue
dual = dual_formulation.DualFormulation(dual_var, nn_params,
test_input, FLAGS.true_class,
adv_class, FLAGS.input_minval,
FLAGS.input_maxval,
FLAGS.epsilon)
dual.set_differentiable_objective()
dual.get_full_psd_matrix()
optimization_params = {
'init_penalty': FLAGS.init_penalty,
'large_eig_num_steps': FLAGS.large_eig_num_steps,
'small_eig_num_steps': FLAGS.small_eig_num_steps,
'inner_num_steps': FLAGS.inner_num_steps,
'outer_num_steps': FLAGS.outer_num_steps,
'beta': FLAGS.beta,
'smoothness_parameter': FLAGS.smoothness_parameter,
'eig_learning_rate': FLAGS.eig_learning_rate,
'optimizer': FLAGS.optimizer,
'init_learning_rate': FLAGS.init_learning_rate,
'learning_rate_decay': FLAGS.learning_rate_decay,
'momentum_parameter': FLAGS.momentum_parameter,
'print_stats_steps': FLAGS.print_stats_steps,
'stats_folder': FLAGS.stats_folder,
'projection_steps': FLAGS.projection_steps
}
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
optimization_object = optimization.Optimization(
dual, sess, optimization_params)
optimization_object.prepare_one_step()
is_cert_found = optimization_object.run_optimization()
if not is_cert_found:
print('Current example could not be verified')
exit()
print('Example successfully verified')
def main(_):
# pylint: disable=missing-docstring
tf.logging.set_verbosity(FLAGS.verbosity)
start_time = time.time()
# Initialize neural network based on config files
input_shape = [FLAGS.num_rows, FLAGS.num_columns, FLAGS.num_channels]
nn_params = nn.load_network_from_checkpoint(FLAGS.checkpoint,
FLAGS.model_json, input_shape)
tf.logging.info('Loaded neural network with size of layers: %s',
nn_params.sizes)
tf.logging.info('Loaded neural network with input shapes: %s',
nn_params.input_shapes)
tf.logging.info('Loaded neural network with output shapes: %s',
nn_params.output_shapes)
dual_var = utils.initialize_dual(nn_params,
FLAGS.init_dual_file,
init_nu=FLAGS.init_nu)
# Reading test input and reshaping
with tf.gfile.Open(FLAGS.test_input) as f:
test_input = np.load(f)
test_input = np.reshape(test_input, [np.size(test_input), 1])
if FLAGS.adv_class == -1:
start_class = 0
end_class = FLAGS.num_classes
else:
start_class = FLAGS.adv_class
end_class = FLAGS.adv_class + 1
for adv_class in range(start_class, end_class):
tf.logging.info('Running certification for adversarial class %d',
adv_class)
if adv_class == FLAGS.true_class:
continue
optimization_params = {
'init_penalty': FLAGS.init_penalty,
'large_eig_num_steps': FLAGS.large_eig_num_steps,
'small_eig_num_steps': FLAGS.small_eig_num_steps,
'inner_num_steps': FLAGS.inner_num_steps,
'outer_num_steps': FLAGS.outer_num_steps,
'beta': FLAGS.beta,
'smoothness_parameter': FLAGS.smoothness_parameter,
'eig_learning_rate': FLAGS.eig_learning_rate,
'optimizer': FLAGS.optimizer,
'init_learning_rate': FLAGS.init_learning_rate,
'learning_rate_decay': FLAGS.learning_rate_decay,
'momentum_parameter': FLAGS.momentum_parameter,
'print_stats_steps': FLAGS.print_stats_steps,
'stats_folder': FLAGS.stats_folder,
'projection_steps': FLAGS.projection_steps,
'eig_type': FLAGS.eig_type,
'has_conv': nn_params.has_conv,
'lanczos_steps': FLAGS.lanczos_steps
}
lzs_params = {
'min_iter': MIN_LANCZOS_ITER,
'max_iter': FLAGS.lanczos_steps
}
with tf.Session() as sess:
dual = dual_formulation.DualFormulation(
sess, dual_var, nn_params, test_input, FLAGS.true_class,
adv_class, FLAGS.input_minval, FLAGS.input_maxval,
FLAGS.epsilon, lzs_params)
optimization_object = optimization.Optimization(
dual, sess, optimization_params)
is_cert_found = optimization_object.run_optimization()
if not is_cert_found:
print('Example could not be verified')
exit()
print('Example successfully verified')
print('Elapsed time: ' + str(time.time() - start_time))