How to use the art.utils.load_dataset function in art
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IBM / adversarial-robustness-toolbox / tests / attacks / test_hop_skip_jump.py View on Github 
def setUpClass(cls):
# Get MNIST
(x_train, y_train), (x_test, y_test), _, _ = load_dataset('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 setUpClass(cls):
k.set_learning_phase(1)
# Get MNIST
(x_train, y_train), (x_test, y_test), _, _ = load_dataset('mnist')
x_train, y_train, x_test, y_test = x_train[:NB_TRAIN], y_train[:NB_TRAIN], x_test[:NB_TEST], y_test[:NB_TEST]
cls.mnist = (x_train, y_train), (x_test, y_test)
# Keras classifier
cls.classifier_k = get_classifier_kr()
scores = cls.classifier_k._model.evaluate(x_train, y_train)
logger.info('[Keras, MNIST] Accuracy on training set: %.2f%%', (scores[1] * 100))
scores = cls.classifier_k._model.evaluate(x_test, y_test)
logger.info('[Keras, MNIST] Accuracy on test set: %.2f%%', (scores[1] * 100))
# Create basic CNN on MNIST using TensorFlow
cls.classifier_tf, sess = get_classifier_tf()
scores = get_labels_np_array(cls.classifier_tf.predict(x_train))
acc = np.sum(np.argmax(scores, axis=1) == np.argmax(y_train, axis=1)) / y_train.shape[0]def setUpClass(cls):
(x_train, y_train), (x_test, y_test), _, _ = load_dataset('mnist')
cls.x_train = x_train[:NB_TRAIN]
cls.y_train = y_train[:NB_TRAIN]
cls.x_test = x_test[:NB_TEST]
cls.y_test = y_test[:NB_TEST]def setUpClass(cls):
np.random.seed(1234)
(x_train, y_train), (x_test, y_test), _, _ = load_dataset('iris')
cls.x_train = x_train
cls.y_train = y_train
cls.x_test = x_test
cls.y_test = y_testdef setUpClass(cls):
# Get Iris
(x_train, y_train), (x_test, y_test), _, _ = load_dataset('iris')
cls.iris = (x_train, y_train), (x_test, y_test)def setUpClass(cls):
(x_train, y_train), (_, _), _, _ = load_dataset('iris')
cls.x_train = x_train
cls.y_train = y_traindef setUpClass(cls):
(x_train, y_train), (x_test, y_test), _, _ = load_dataset('mnist')
cls.x_train = x_train[:NB_TRAIN]
cls.y_train = y_train[:NB_TRAIN]
cls.x_test = x_test[:NB_TEST]
cls.y_test = y_test[:NB_TEST]def setUpClass(cls):
# Get MNIST
(x_train, y_train), (x_test, y_test), _, _ = load_dataset('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 setUpClass(cls):
k.set_learning_phase(1)
(x_train, y_train), (x_test, y_test), _, _ = load_dataset('mnist')
x_train, y_train, x_test, y_test = x_train[:NB_TRAIN], y_train[:NB_TRAIN], x_test[:NB_TEST], y_test[:NB_TEST]
cls.mnist = (x_train, y_train), (x_test, y_test)
# Keras classifier
cls.classifier_k = get_classifier_kr()def setUpClass(cls):
# Get MNIST
(x_train, y_train), (x_test, y_test), _, _ = load_dataset('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)
Popular art functions
- art._baseclass.ArtBaseClass
- art.art.artError
- art.attacks.attack.Attack
- art.attacks.fast_gradient.FastGradientMethod
- art.attacks.FastGradientMethod
- art.classifiers.classifier.Classifier
- art.classifiers.classifier.ClassifierGradients
- art.classifiers.KerasClassifier
- art.DATA_PATH
- art.NUMPY_DTYPE
- art.tprint
- art.utils.get_labels_np_array
- art.utils.load_dataset
- art.utils.load_mnist
- art.utils.master_seed
- art.utils.random_targets
- art.utils_test.get_classifier_kr
- art.utils_test.get_classifier_pt
- art.utils_test.get_classifier_tf
- art.utils_test.get_iris_classifier_kr