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 prepare_dual_object(self): # Function to prepare dual object to be used for testing optimization. net_weights = [[[2, 2], [3, 3], [4, 4]], [[1, 1, 1], [-1, -1, -1]]] net_biases = [np.transpose(np.matrix([0, 0, 0])), np.transpose(np.matrix([0, 0]))] net_layer_types = ['ff_relu', 'ff'] nn_params1 = neural_net_params.NeuralNetParams(net_weights, net_biases, net_layer_types) test_input = np.transpose(np.matrix([0, 0])) true_class = 0 adv_class = 1 input_minval = 0 input_maxval = 0 epsilon = 0.1 # Creating dual variables to use for optimization lambda_pos = [tf.get_variable('lambda_pos0', initializer=np.random.uniform( 0, 0.1, size=(2, 1)).astype(np.float32)), tf.get_variable('lambda_pos1', initializer=np.random.uniform( 0, 0.1, size=(3, 1)).astype(np.float32))] lambda_neg = [tf.get_variable('lambda_neg0', initializer=np.random.uniform( 0, 0.1, size=(2, 1)).astype(np.float32)), tf.get_variable('lambda_neg1', initializer=np.random.uniform( 0, 0.1, size=(3, 1)).astype(np.float32))] lambda_quad = [tf.get_variable('lambda_quad0', initializer=np.random.uniform( 0, 0.1, size=(2, 1)).astype(np.float32)), tf.get_variable('lambda_quad1', initializer=np.random.uniform( 0, 0.1, size=(3, 1)).astype(np.float32))] lambda_lu = [tf.get_variable('lambda_lu0', initializer=np.random.uniform( 0, 0.1, size=(2, 1)).astype(np.float32)), tf.get_variable('lambda_lu1', initializer=np.random.uniform( 0, 0.1, size=(3, 1)).astype(np.float32))] nu = tf.reshape(tf.get_variable('nu', initializer=200.0, dtype=tf.float32), shape=(1, 1)) dual_var = {'lambda_pos': lambda_pos, 'lambda_neg': lambda_neg, 'lambda_quad': lambda_quad, 'lambda_lu': lambda_lu, 'nu': nu} dual_formulation_object = dual_formulation.DualFormulation(dual_var, nn_params1, test_input, true_class, adv_class, input_minval, input_maxval, epsilon) return dual_formulation_object
def test_get_psd_product(self): # Function to test implicit product with PSD matrix. net_weights = [[[2, 2], [3, 3], [4, 4]], [[1, 1, 1], [-1, -1, -1]]] net_biases = [ np.transpose(np.matrix([0, 0, 0])), np.transpose(np.matrix([0, 0])) ] net_layer_types = ['ff_relu', 'ff'] nn_params1 = neural_net_params.NeuralNetParams(net_weights, net_biases, net_layer_types) test_input = np.transpose(np.matrix([0, 0])) true_class = 0 adv_class = 1 input_minval = 0 input_maxval = 0 epsilon = 0.1 three_dim_tensor = tf.random_uniform(shape=(3, 1), dtype=tf.float32) two_dim_tensor = tf.random_uniform(shape=(2, 1), dtype=tf.float32) scalar = tf.random_uniform(shape=(1, 1), dtype=tf.float32) lambda_pos = [two_dim_tensor, three_dim_tensor] lambda_neg = lambda_pos lambda_quad = lambda_pos lambda_lu = lambda_pos nu = scalar dual_var = { 'lambda_pos': lambda_pos, 'lambda_neg': lambda_neg, 'lambda_quad': lambda_quad, 'lambda_lu': lambda_lu, 'nu': nu } dual_formulation_object = dual_formulation.DualFormulation( dual_var, nn_params1, test_input, true_class, adv_class, input_minval, input_maxval, epsilon) _, matrix_m = dual_formulation_object.get_full_psd_matrix() # Testing if the values match six_dim_tensor = tf.random_uniform(shape=(6, 1), dtype=tf.float32) implicit_product = dual_formulation_object.get_psd_product( six_dim_tensor) explicit_product = tf.matmul(matrix_m, six_dim_tensor) with tf.Session() as sess: [implicit_product_value, explicit_product_value ] = sess.run([implicit_product, explicit_product]) self.assertEqual(np.shape(implicit_product_value), np.shape(explicit_product_value)) self.assertLess( np.max(np.abs(implicit_product_value - explicit_product_value)), 1E-5)
def test_init(self): # Function to test initialization of NeuralNetParams object. # Valid params net_weights = [[[2, 2], [3, 3], [4, 4]], [1, 1, 1]] net_biases = [ np.transpose(np.matrix([0, 0, 0])), np.transpose(np.matrix([0, 0])) ] net_layer_types = ['ff_relu', 'ff'] nn_params1 = neural_net_params.NeuralNetParams(net_weights, net_biases, net_layer_types) self.assertIsNotNone(nn_params1) # Invalid params : list length net_biases = [0] with self.assertRaises(ValueError): neural_net_params.NeuralNetParams(net_weights, net_biases, net_layer_types) # Invalid params: layer types with self.assertRaises(ValueError): net_layer_types = ['ff_relu', 'ff_relu'] neural_net_params.NeuralNetParams(net_weights, net_biases, net_layer_types)
def test_set_differentiable_objective(self): # Function to test the function that sets the differentiable objective. net_weights = [[[2, 2], [3, 3], [4, 4]], [[1, 1, 1], [-1, -1, -1]]] net_biases = [ np.transpose(np.matrix([0, 0, 0])), np.transpose(np.matrix([0, 0])) ] net_layer_types = ['ff_relu', 'ff'] nn_params1 = neural_net_params.NeuralNetParams(net_weights, net_biases, net_layer_types) test_input = np.transpose(np.matrix([0, 0])) true_class = 0 adv_class = 1 input_minval = 0 input_maxval = 0 epsilon = 0.1 three_dim_tensor = tf.random_uniform(shape=(3, 1), dtype=tf.float32) two_dim_tensor = tf.random_uniform(shape=(2, 1), dtype=tf.float32) scalar = tf.random_uniform(shape=(1, 1), dtype=tf.float32) lambda_pos = [two_dim_tensor, three_dim_tensor] lambda_neg = lambda_pos lambda_quad = lambda_pos lambda_lu = lambda_pos nu = scalar dual_var = { 'lambda_pos': lambda_pos, 'lambda_neg': lambda_neg, 'lambda_quad': lambda_quad, 'lambda_lu': lambda_lu, 'nu': nu } dual_formulation_object = dual_formulation.DualFormulation( dual_var, nn_params1, test_input, true_class, adv_class, input_minval, input_maxval, epsilon) dual_formulation_object.set_differentiable_objective() self.assertEqual(dual_formulation_object.scalar_f.shape.as_list(), [1]) self.assertEqual( dual_formulation_object.unconstrained_objective.shape.as_list(), [1, 1]) self.assertEqual(dual_formulation_object.vector_g.shape.as_list(), [5, 1])
def test_forward_pass(self): # Function to test forward pass of nn_params. net_weights = [[[2, 2], [3, 3], [4, 4]], [1, 1, 1]] net_biases = [ np.transpose(np.matrix([0, 0, 0])), np.transpose(np.matrix([0, 0])) ] net_layer_types = ['ff_relu', 'ff'] nn_params = neural_net_params.NeuralNetParams(net_weights, net_biases, net_layer_types) input_vector = tf.random_uniform(shape=(2, 1), dtype=tf.float32) output_vector = nn_params.forward_pass(input_vector, 0) self.assertEqual(output_vector.shape.as_list(), [3, 1]) output_vector_2 = nn_params.forward_pass(input_vector, 0, is_abs=True) self.assertEqual(output_vector_2.shape.as_list(), [3, 1]) input_vector_trans = tf.random_uniform(shape=(3, 1), dtype=tf.float32) output_vector_3 = nn_params.forward_pass(input_vector_trans, 0, is_transpose=True) self.assertEqual(output_vector_3.shape.as_list(), [2, 1])
def test_get_full_psd_matrix(self): # Function to test product with PSD matrix. net_weights = [[[2, 2], [3, 3], [4, 4]], [[1, 1, 1], [-1, -1, -1]]] net_biases = [ np.transpose(np.matrix([0, 0, 0])), np.transpose(np.matrix([0, 0])) ] net_layer_types = ['ff_relu', 'ff'] nn_params1 = neural_net_params.NeuralNetParams(net_weights, net_biases, net_layer_types) test_input = np.transpose(np.matrix([0, 0])) true_class = 0 adv_class = 1 input_minval = 0 input_maxval = 0 epsilon = 0.1 three_dim_tensor = tf.random_uniform(shape=(3, 1), dtype=tf.float32) two_dim_tensor = tf.random_uniform(shape=(2, 1), dtype=tf.float32) scalar = tf.random_uniform(shape=(1, 1), dtype=tf.float32) lambda_pos = [two_dim_tensor, three_dim_tensor] lambda_neg = lambda_pos lambda_quad = lambda_pos lambda_lu = lambda_pos nu = scalar dual_var = { 'lambda_pos': lambda_pos, 'lambda_neg': lambda_neg, 'lambda_quad': lambda_quad, 'lambda_lu': lambda_lu, 'nu': nu } dual_formulation_object = dual_formulation.DualFormulation( dual_var, nn_params1, test_input, true_class, adv_class, input_minval, input_maxval, epsilon) matrix_h, matrix_m = dual_formulation_object.get_full_psd_matrix() self.assertEqual(matrix_h.shape.as_list(), [5, 5]) self.assertEqual(matrix_m.shape.as_list(), [6, 6])
def test_init(self): # Function to test initialization of dual formulation class. net_weights = [[[2, 2], [3, 3], [4, 4]], [[1, 1, 1], [-1, -1, -1]]] net_biases = [ np.transpose(np.matrix([0, 0, 0])), np.transpose(np.matrix([0, 0])) ] net_layer_types = ['ff_relu', 'ff'] nn_params1 = neural_net_params.NeuralNetParams(net_weights, net_biases, net_layer_types) test_input = np.transpose(np.matrix([0, 0])) true_class = 0 adv_class = 1 input_minval = 0 input_maxval = 0 epsilon = 0.1 three_dim_tensor = tf.random_uniform(shape=(3, 1), dtype=tf.float32) two_dim_tensor = tf.random_uniform(shape=(2, 1), dtype=tf.float32) scalar = tf.random_uniform(shape=(1, 1), dtype=tf.float32) lambda_pos = [two_dim_tensor, three_dim_tensor] lambda_neg = lambda_pos lambda_quad = lambda_pos lambda_lu = lambda_pos nu = scalar dual_var = { 'lambda_pos': lambda_pos, 'lambda_neg': lambda_neg, 'lambda_quad': lambda_quad, 'lambda_lu': lambda_lu, 'nu': nu } dual_formulation_object = dual_formulation.DualFormulation( dual_var, nn_params1, test_input, true_class, adv_class, input_minval, input_maxval, epsilon) self.assertIsNotNone(dual_formulation_object)