def test_tfclassifier(self): """ First test with the TFClassifier. :return: """ # Build a TFClassifier # Define input and output placeholders input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1]) output_ph = tf.placeholder(tf.int32, shape=[None, 10]) # Define the tensorflow graph conv = tf.layers.conv2d(input_ph, 4, 5, activation=tf.nn.relu) conv = tf.layers.max_pooling2d(conv, 2, 2) fc = tf.contrib.layers.flatten(conv) # Logits layer logits = tf.layers.dense(fc, 10) # Train operator loss = tf.reduce_mean( tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=output_ph)) optimizer = tf.train.AdamOptimizer(learning_rate=0.01) train = optimizer.minimize(loss) # Tensorflow session and initialization sess = tf.Session() sess.run(tf.global_variables_initializer()) # Get MNIST (x_train, y_train), (x_test, y_test) = self.mnist # Train the classifier tfc = TFClassifier((0, 1), input_ph, logits, output_ph, train, loss, None, sess) tfc.fit(x_train, y_train, batch_size=BATCH_SIZE, nb_epochs=2) # Attack # TODO Launch with all possible attacks attack_params = { "attacker": "newtonfool", "attacker_params": { "max_iter": 5 } } up = UniversalPerturbation(tfc) x_train_adv = up.generate(x_train, **attack_params) self.assertTrue((up.fooling_rate >= 0.2) or not up.converged) x_test_adv = x_test + up.v self.assertFalse((x_test == x_test_adv).all()) train_y_pred = np.argmax(tfc.predict(x_train_adv), axis=1) test_y_pred = np.argmax(tfc.predict(x_test_adv), axis=1) self.assertFalse((np.argmax(y_test, axis=1) == test_y_pred).all()) self.assertFalse((np.argmax(y_train, axis=1) == train_y_pred).all())
def test_tfclassifier(self): """ First test with the TFClassifier. :return: """ # Build a TFClassifier # Define input and output placeholders self._input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1]) self._output_ph = tf.placeholder(tf.int32, shape=[None, 10]) # Define the tensorflow graph conv = tf.layers.conv2d(self._input_ph, 4, 5, activation=tf.nn.relu) conv = tf.layers.max_pooling2d(conv, 2, 2) fc = tf.contrib.layers.flatten(conv) # Logits layer self._logits = tf.layers.dense(fc, 10) # Train operator self._loss = tf.reduce_mean(tf.losses.softmax_cross_entropy(logits=self._logits, onehot_labels=self._output_ph)) optimizer = tf.train.AdamOptimizer(learning_rate=0.01) self._train = optimizer.minimize(self._loss) # Tensorflow session and initialization self._sess = tf.Session() self._sess.run(tf.global_variables_initializer()) # Get MNIST batch_size, nb_train, nb_test = 100, 1000, 10 (x_train, y_train), (x_test, y_test), _, _ = load_mnist() x_train, y_train = x_train[:nb_train], y_train[:nb_train] x_test, y_test = x_test[:nb_test], y_test[:nb_test] # Train the classifier tfc = TFClassifier((0, 1), self._input_ph, self._logits, self._output_ph, self._train, self._loss, None, self._sess) tfc.fit(x_train, y_train, batch_size=batch_size, nb_epochs=2) # Attack nf = NewtonFool(tfc) nf.set_params(max_iter=5) x_test_adv = nf.generate(x_test) self.assertFalse((x_test == x_test_adv).all()) y_pred = tfc.predict(x_test) y_pred_adv = tfc.predict(x_test_adv) y_pred_bool = y_pred.max(axis=1, keepdims=1) == y_pred y_pred_max = y_pred.max(axis=1) y_pred_adv_max = y_pred_adv[y_pred_bool] self.assertTrue((y_pred_max >= y_pred_adv_max).all())
def test_fit_predict(self): # Get MNIST (x_train, y_train), (x_test, y_test), _, _ = load_mnist() x_train, y_train = x_train[:NB_TRAIN], y_train[:NB_TRAIN] x_test, y_test = x_test[:NB_TEST], y_test[:NB_TEST] # Test fit and predict tfc = TFClassifier(None, self._input_ph, self._logits, self._output_ph, self._train, self._loss, None, self._sess) tfc.fit(x_train, y_train, batch_size=100, nb_epochs=1) preds = tfc.predict(x_test) preds_class = np.argmax(preds, axis=1) trues_class = np.argmax(y_test, axis=1) acc = np.sum(preds_class == trues_class) / len(trues_class) print("\nAccuracy: %.2f%%" % (acc * 100)) self.assertGreater(acc, 0.1)
class TestTFClassifier(unittest.TestCase): """ This class tests the functionalities of the Tensorflow-based classifier. """ @classmethod def setUpClass(cls): # Get MNIST (x_train, y_train), (x_test, y_test), _, _ = load_mnist() x_train, y_train = x_train[:NB_TRAIN], y_train[:NB_TRAIN] x_test, y_test = x_test[:NB_TEST], y_test[:NB_TEST] cls.mnist = (x_train, y_train), (x_test, y_test) def setUp(self): # Set master seed master_seed(1234) # Define input and output placeholders input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1]) output_ph = tf.placeholder(tf.int32, shape=[None, 10]) learning = tf.placeholder(tf.bool) # Define the tensorflow graph conv = tf.layers.conv2d(input_ph, 16, 5, activation=tf.nn.relu) conv = tf.layers.max_pooling2d(conv, 2, 2) fc = tf.contrib.layers.flatten(conv) # Logits layer logits = tf.layers.dense(fc, 10) # Train operator loss = tf.reduce_mean( tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=output_ph)) optimizer = tf.train.AdamOptimizer(learning_rate=0.01) train = optimizer.minimize(loss) # Tensorflow session and initialization self.sess = tf.Session() self.sess.run(tf.global_variables_initializer()) # Create classifier and fit self.classifier = TFClassifier((0, 1), input_ph, logits, output_ph, train, loss, learning, self.sess) # Get MNIST (x_train, y_train), (x_test, y_test) = self.mnist self.classifier.fit(x_train, y_train, batch_size=100, nb_epochs=3) def tearDown(self): self.sess.close() def test_fit_predict(self): # Get MNIST (x_train, y_train), (x_test, y_test) = self.mnist # Test fit and predict preds = self.classifier.predict(x_test) preds_class = np.argmax(preds, axis=1) trues_class = np.argmax(y_test, axis=1) acc = np.sum(preds_class == trues_class) / len(trues_class) logger.info('Accuracy after fitting: %.2f%%', (acc * 100)) self.assertGreater(acc, 0.1) tf.reset_default_graph() def test_fit_generator(self): from art.data_generators import TFDataGenerator # Get MNIST (x_train, y_train), (x_test, y_test) = self.mnist # Create Tensorflow data generator x_tensor = tf.convert_to_tensor(x_train.reshape(10, 100, 28, 28, 1)) y_tensor = tf.convert_to_tensor(y_train.reshape(10, 100, 10)) dataset = tf.data.Dataset.from_tensor_slices((x_tensor, y_tensor)) iterator = dataset.make_initializable_iterator() data_gen = TFDataGenerator(sess=self.sess, iterator=iterator, iterator_type='initializable', iterator_arg={}, size=1000, batch_size=100) # Test fit and predict self.classifier.fit_generator(data_gen, nb_epochs=1) preds = self.classifier.predict(x_test) preds_class = np.argmax(preds, axis=1) trues_class = np.argmax(y_test, axis=1) acc = np.sum(preds_class == trues_class) / len(trues_class) logger.info('Accuracy after fitting: %.2f%%', (acc * 100)) self.assertGreater(acc, 0.1) tf.reset_default_graph() def test_fit_generator(self): from art.classifiers.keras import generator_fit from art.data_generators import KerasDataGenerator labels = np.argmax(self.mnist[1][1], axis=1) acc = np.sum( np.argmax(self.classifier.predict(self.mnist[1][0]), axis=1) == labels) / NB_TEST logger.info('Accuracy: %.2f%%', (acc * 100)) gen = generator_fit(self.mnist[0][0], self.mnist[0][1], batch_size=100) data_gen = KerasDataGenerator(generator=gen, size=NB_TRAIN, batch_size=100) self.classifier.fit_generator(generator=data_gen, nb_epochs=2) acc2 = np.sum( np.argmax(self.classifier.predict(self.mnist[1][0]), axis=1) == labels) / NB_TEST logger.info('Accuracy: %.2f%%', (acc2 * 100)) self.assertTrue(acc2 >= .8 * acc) def test_nb_classes(self): # Start to test self.assertTrue(self.classifier.nb_classes == 10) tf.reset_default_graph() def test_input_shape(self): # Start to test self.assertTrue( np.array(self.classifier.input_shape == (28, 28, 1)).all()) tf.reset_default_graph() def test_class_gradient(self): # Get MNIST (_, _), (x_test, _) = self.mnist # Test all gradients label = None grads = self.classifier.class_gradient(x_test) self.assertTrue( np.array(grads.shape == (NB_TEST, 10, 28, 28, 1)).all()) self.assertTrue(np.sum(grads) != 0) # Test 1 gradient label = 5 grads = self.classifier.class_gradient(x_test, label=5) self.assertTrue(np.array(grads.shape == (NB_TEST, 1, 28, 28, 1)).all()) self.assertTrue(np.sum(grads) != 0) # Test a set of gradients label = array label = np.random.randint(5, size=NB_TEST) grads = self.classifier.class_gradient(x_test, label=label) self.assertTrue(np.array(grads.shape == (NB_TEST, 1, 28, 28, 1)).all()) self.assertTrue(np.sum(grads) != 0) def test_loss_gradient(self): # Get MNIST (_, _), (x_test, y_test) = self.mnist # Test gradient grads = self.classifier.loss_gradient(x_test, y_test) self.assertTrue(np.array(grads.shape == (NB_TEST, 28, 28, 1)).all()) self.assertTrue(np.sum(grads) != 0) tf.reset_default_graph() def test_layers(self): # Get MNIST (_, _), (x_test, _) = self.mnist # Test and get layers layer_names = self.classifier.layer_names logger.debug(layer_names) self.assertTrue(layer_names == [ 'conv2d/Relu:0', 'max_pooling2d/MaxPool:0', 'Flatten/flatten/Reshape:0', 'dense/BiasAdd:0' ]) for i, name in enumerate(layer_names): act_i = self.classifier.get_activations(x_test, i) act_name = self.classifier.get_activations(x_test, name) self.assertAlmostEqual(np.sum(act_name - act_i), 0) self.assertTrue( self.classifier.get_activations(x_test, 0).shape == (20, 24, 24, 16)) self.assertTrue( self.classifier.get_activations(x_test, 1).shape == (20, 12, 12, 16)) self.assertTrue( self.classifier.get_activations(x_test, 2).shape == (20, 2304)) self.assertTrue( self.classifier.get_activations(x_test, 3).shape == (20, 10)) tf.reset_default_graph() def test_save(self): import os import re path = 'tmp' filename = 'model.ckpt' self.classifier.save(filename, path=path) self.assertTrue(os.path.isfile(os.path.join(path, filename + '.meta'))) self.assertTrue(os.path.isfile(os.path.join(path, filename + '.index'))) # Remove saved files for f in os.listdir(path): if re.search(filename, f): os.remove(os.path.join(path, f)) def test_set_learning(self): tfc = self.classifier self.assertTrue(tfc._feed_dict == {}) tfc.set_learning_phase(False) self.assertFalse(tfc._feed_dict[tfc._learning]) tfc.set_learning_phase(True) self.assertTrue(tfc._feed_dict[tfc._learning]) self.assertTrue(tfc.learning_phase)
def test_tfclassifier(self): """ First test with the TFClassifier. :return: """ # Build a TFClassifier # Define input and output placeholders input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1]) output_ph = tf.placeholder(tf.int32, shape=[None, 10]) # Define the tensorflow graph conv = tf.layers.conv2d(input_ph, 4, 5, activation=tf.nn.relu) conv = tf.layers.max_pooling2d(conv, 2, 2) fc = tf.contrib.layers.flatten(conv) # Logits layer logits = tf.layers.dense(fc, 10) # Train operator loss = tf.reduce_mean( tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=output_ph)) optimizer = tf.train.AdamOptimizer(learning_rate=0.01) train = optimizer.minimize(loss) # Tensorflow session and initialization sess = tf.Session() sess.run(tf.global_variables_initializer()) # Get MNIST (x_train, y_train), (x_test, y_test) = self.mnist # Train the classifier tfc = TFClassifier((0, 1), input_ph, logits, output_ph, train, loss, None, sess) tfc.fit(x_train, y_train, batch_size=BATCH_SIZE, nb_epochs=10) # First attack clinfm = CarliniLInfMethod(classifier=tfc, targeted=True, max_iter=10, eps=0.5) params = {'y': random_targets(y_test, tfc.nb_classes)} x_test_adv = clinfm.generate(x_test, **params) self.assertFalse((x_test == x_test_adv).all()) self.assertTrue((x_test_adv <= 1.0001).all()) self.assertTrue((x_test_adv >= -0.0001).all()) target = np.argmax(params['y'], axis=1) y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1) logger.debug('CW0 Target: %s', target) logger.debug('CW0 Actual: %s', y_pred_adv) logger.info('CW0 Success Rate: %.2f', (sum(target == y_pred_adv) / float(len(target)))) self.assertTrue((target == y_pred_adv).any()) # Second attack clinfm = CarliniLInfMethod(classifier=tfc, targeted=False, max_iter=10, eps=0.5) params = {'y': random_targets(y_test, tfc.nb_classes)} x_test_adv = clinfm.generate(x_test, **params) self.assertTrue((x_test_adv <= 1.0001).all()) self.assertTrue((x_test_adv >= -0.0001).all()) target = np.argmax(params['y'], axis=1) y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1) logger.debug('CW0 Target: %s', target) logger.debug('CW0 Actual: %s', y_pred_adv) logger.info('CW0 Success Rate: %.2f', (sum(target != y_pred_adv) / float(len(target)))) self.assertTrue((target != y_pred_adv).any()) # Third attack clinfm = CarliniLInfMethod(classifier=tfc, targeted=False, max_iter=10, eps=0.5) params = {} x_test_adv = clinfm.generate(x_test, **params) self.assertFalse((x_test == x_test_adv).all()) self.assertTrue((x_test_adv <= 1.0001).all()) self.assertTrue((x_test_adv >= -0.0001).all()) y_pred = np.argmax(tfc.predict(x_test), axis=1) y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1) logger.debug('CW0 Target: %s', y_pred) logger.debug('CW0 Actual: %s', y_pred_adv) logger.info('CW0 Success Rate: %.2f', (sum(y_pred != y_pred_adv) / float(len(y_pred)))) self.assertTrue((y_pred != y_pred_adv).any()) # First attack without batching clinfmwob = CarliniLInfMethod(classifier=tfc, targeted=True, max_iter=10, eps=0.5, batch_size=1) params = {'y': random_targets(y_test, tfc.nb_classes)} x_test_adv = clinfmwob.generate(x_test, **params) self.assertFalse((x_test == x_test_adv).all()) self.assertTrue((x_test_adv <= 1.0001).all()) self.assertTrue((x_test_adv >= -0.0001).all()) target = np.argmax(params['y'], axis=1) y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1) logger.debug('CW0 Target: %s', target) logger.debug('CW0 Actual: %s', y_pred_adv) logger.info('CW0 Success Rate: %.2f', (sum(target == y_pred_adv) / float(len(target)))) self.assertTrue((target == y_pred_adv).any()) # Second attack without batching clinfmwob = CarliniLInfMethod(classifier=tfc, targeted=False, max_iter=10, eps=0.5, batch_size=1) params = {'y': random_targets(y_test, tfc.nb_classes)} x_test_adv = clinfmwob.generate(x_test, **params) self.assertTrue((x_test_adv <= 1.0001).all()) self.assertTrue((x_test_adv >= -0.0001).all()) target = np.argmax(params['y'], axis=1) y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1) logger.debug('CW0 Target: %s', target) logger.debug('CW0 Actual: %s', y_pred_adv) logger.info('CW0 Success Rate: %.2f', (sum(target != y_pred_adv) / float(len(target)))) self.assertTrue((target != y_pred_adv).any()) # Third attack without batching clinfmwob = CarliniLInfMethod(classifier=tfc, targeted=False, max_iter=10, eps=0.5, batch_size=1) params = {} x_test_adv = clinfmwob.generate(x_test, **params) self.assertFalse((x_test == x_test_adv).all()) self.assertTrue((x_test_adv <= 1.0001).all()) self.assertTrue((x_test_adv >= -0.0001).all()) y_pred = np.argmax(tfc.predict(x_test), axis=1) y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1) logger.debug('CW0 Target: %s', y_pred) logger.debug('CW0 Actual: %s', y_pred_adv) logger.info('CW0 Success Rate: %.2f', (sum(y_pred != y_pred_adv) / float(len(y_pred)))) self.assertTrue((y_pred != y_pred_adv).any())
logits = tf.layers.dense(x, 10) loss = tf.reduce_mean(tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=labels_ph)) optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01) train = optimizer.minimize(loss) sess = tf.Session() sess.run(tf.global_variables_initializer()) #print('success') # Step 3: Create the ART classifier classifier = TFClassifier(clip_values=(min_pixel_value, max_pixel_value), input_ph=input_ph, output=logits, labels_ph=labels_ph, train=train, loss=loss, learning=None, sess=sess) # Step 4: Train the ART classifier classifier.fit(x_train, y_train, batch_size=64, nb_epochs=3) print('successful') # Step 5: Evaluate the ART classifier on benign test examples predictions = classifier.predict(x_test) accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test) print('Accuracy on benign test examples: {}%'.format(accuracy * 100)) # Step 6: Generate adversarial test examples attack = FastGradientMethod(classifier=classifier, eps=0.2) x_test_adv = attack.generate(x=x_test) # Step 7: Evaluate the ART classifier on adversarial test examples predictions = classifier.predict(x_test_adv) accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test) print('Accuracy on adversarial test examples: {}%'.format(accuracy * 100))
class TestTFClassifier(unittest.TestCase): """ This class tests the functionalities of the Tensorflow-based classifier. """ @classmethod def setUpClass(cls): # Get MNIST (x_train, y_train), (x_test, y_test), _, _ = load_mnist() x_train, y_train = x_train[:NB_TRAIN], y_train[:NB_TRAIN] x_test, y_test = x_test[:NB_TEST], y_test[:NB_TEST] cls.mnist = (x_train, y_train), (x_test, y_test) def setUp(self): # Define input and output placeholders input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1]) output_ph = tf.placeholder(tf.int32, shape=[None, 10]) # Define the tensorflow graph conv = tf.layers.conv2d(input_ph, 16, 5, activation=tf.nn.relu) conv = tf.layers.max_pooling2d(conv, 2, 2) fc = tf.contrib.layers.flatten(conv) # Logits layer logits = tf.layers.dense(fc, 10) # Train operator loss = tf.reduce_mean( tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=output_ph)) optimizer = tf.train.AdamOptimizer(learning_rate=0.01) train = optimizer.minimize(loss) # Tensorflow session and initialization self.sess = tf.Session() self.sess.run(tf.global_variables_initializer()) # Create classifier self.classifier = TFClassifier((0, 1), input_ph, logits, output_ph, train, loss, None, self.sess) def tearDown(self): self.sess.close() def test_fit_predict(self): # Get MNIST (x_train, y_train), (x_test, y_test) = self.mnist # Test fit and predict self.classifier.fit(x_train, y_train, batch_size=100, nb_epochs=1) preds = self.classifier.predict(x_test) preds_class = np.argmax(preds, axis=1) trues_class = np.argmax(y_test, axis=1) acc = np.sum(preds_class == trues_class) / len(trues_class) logger.info('Accuracy after fitting: %.2f%%', (acc * 100)) self.assertGreater(acc, 0.1) tf.reset_default_graph() def test_nb_classes(self): # Start to test self.assertTrue(self.classifier.nb_classes == 10) tf.reset_default_graph() def test_input_shape(self): # Start to test self.assertTrue( np.array(self.classifier.input_shape == (28, 28, 1)).all()) tf.reset_default_graph() def test_class_gradient(self): # Get MNIST (_, _), (x_test, y_test) = self.mnist # Test gradient grads = self.classifier.class_gradient(x_test) self.assertTrue( np.array(grads.shape == (NB_TEST, 10, 28, 28, 1)).all()) self.assertTrue(np.sum(grads) != 0) tf.reset_default_graph() def test_loss_gradient(self): # Get MNIST (_, _), (x_test, y_test) = self.mnist # Test gradient grads = self.classifier.loss_gradient(x_test, y_test) self.assertTrue(np.array(grads.shape == (NB_TEST, 28, 28, 1)).all()) self.assertTrue(np.sum(grads) != 0) tf.reset_default_graph() def test_layers(self): # Get MNIST (_, _), (x_test, y_test) = self.mnist # Test and get layers layer_names = self.classifier.layer_names logger.debug(layer_names) self.assertTrue(layer_names == [ 'conv2d/Relu:0', 'max_pooling2d/MaxPool:0', 'Flatten/flatten/Reshape:0', 'dense/BiasAdd:0' ]) for i, name in enumerate(layer_names): act_i = self.classifier.get_activations(x_test, i) act_name = self.classifier.get_activations(x_test, name) self.assertAlmostEqual(np.sum(act_name - act_i), 0) self.assertTrue( self.classifier.get_activations(x_test, 0).shape == (20, 24, 24, 16)) self.assertTrue( self.classifier.get_activations(x_test, 1).shape == (20, 12, 12, 16)) self.assertTrue( self.classifier.get_activations(x_test, 2).shape == (20, 2304)) self.assertTrue( self.classifier.get_activations(x_test, 3).shape == (20, 10)) tf.reset_default_graph()
def test_tfclassifier(self): """ First test with the TFClassifier. :return: """ # Build a TFClassifier # Define input and output placeholders input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1]) output_ph = tf.placeholder(tf.int32, shape=[None, 10]) # Define the tensorflow graph conv = tf.layers.conv2d(input_ph, 4, 5, activation=tf.nn.relu) conv = tf.layers.max_pooling2d(conv, 2, 2) fc = tf.contrib.layers.flatten(conv) # Logits layer logits = tf.layers.dense(fc, 10) # Train operator loss = tf.reduce_mean( tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=output_ph)) optimizer = tf.train.AdamOptimizer(learning_rate=0.01) train = optimizer.minimize(loss) # Tensorflow session and initialization sess = tf.Session() sess.run(tf.global_variables_initializer()) # Get MNIST (x_train, y_train), (x_test, _) = self.mnist # Train the classifier tfc = TFClassifier((0, 1), input_ph, logits, output_ph, train, loss, None, sess) tfc.fit(x_train, y_train, batch_size=BATCH_SIZE, nb_epochs=2) # Attack # import time nf = NewtonFool(tfc, max_iter=5) # print("Test Tensorflow....") # starttime = time.clock() # x_test_adv = nf.generate(x_test, batch_size=1) # self.assertFalse((x_test == x_test_adv).all()) # endtime = time.clock() # print(1, endtime - starttime) # starttime = time.clock() # x_test_adv = nf.generate(x_test, batch_size=10) # endtime = time.clock() # print(10, endtime - starttime) # starttime = time.clock() x_test_adv = nf.generate(x_test, batch_size=100) # endtime = time.clock() # print(100, endtime - starttime) # # starttime = time.clock() # x_test_adv = nf.generate(x_test, batch_size=1000) # endtime = time.clock() # print(1000, endtime - starttime) self.assertFalse((x_test == x_test_adv).all()) y_pred = tfc.predict(x_test) y_pred_adv = tfc.predict(x_test_adv) y_pred_bool = y_pred.max(axis=1, keepdims=1) == y_pred y_pred_max = y_pred.max(axis=1) y_pred_adv_max = y_pred_adv[y_pred_bool] self.assertTrue((y_pred_max >= y_pred_adv_max).all())