94cd8e7d5953a7d280b928e10951587220fa4ca6,art/attacks/fast_gradient_unittest.py,TestFastGradientMethod,setUpClass,#Any#,44

Before Change

        cls.mnist = (x_train, y_train), (x_test, y_test)

        // Keras classifier
        cls.classifier_k = cls._cnn_mnist_k([28, 28, 1])
        cls.classifier_k.fit(x_train, y_train, batch_size=BATCH_SIZE, nb_epochs=2)

        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 = cls._cnn_mnist_tf([28, 28, 1])
        cls.classifier_tf.fit(x_train, y_train, nb_epochs=2, batch_size=BATCH_SIZE)

        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]
        logger.info("[TF, MNIST] Accuracy on training set: %.2f%%", (acc * 100))

        scores = get_labels_np_array(cls.classifier_tf.predict(x_test))
        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(y_test, axis=1)) / y_test.shape[0]
        logger.info("[TF, MNIST] Accuracy on test set: %.2f%%", (acc * 100))

        // Create basic PyTorch model
        cls.classifier_py = cls._cnn_mnist_py()
        x_train, x_test = np.swapaxes(x_train, 1, 3), np.swapaxes(x_test, 1, 3)
        cls.classifier_py.fit(x_train, y_train, nb_epochs=2, batch_size=BATCH_SIZE)

        scores = get_labels_np_array(cls.classifier_py.predict(x_train))
        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(y_train, axis=1)) / y_train.shape[0]
        logger.info("[PyTorch, MNIST] Accuracy on training set: %.2f%%", (acc * 100))

After Change

        cls.mnist = (x_train, y_train), (x_test, y_test)

        // Keras classifier
        cls.classifier_k, sess = 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)

Instances