def test_pytorch_mnist(self):
    x_train_mnist = np.swapaxes(self.x_train_mnist, 1, 3).astype(np.float32)
    x_test_mnist = np.swapaxes(self.x_test_mnist, 1, 3).astype(np.float32)
    classifier = get_image_classifier_pt()
    scores = get_labels_np_array(classifier.predict(x_train_mnist))
    acc = (np.sum((np.argmax(scores, axis=1) == np.argmax(self.y_train_mnist, axis=1))) / self.y_train_mnist.shape[0])
    logger.info('[PyTorch, MNIST] Accuracy on training set: %.2f%%', (acc * 100))
    scores = get_labels_np_array(classifier.predict(x_test_mnist))
    acc = (np.sum((np.argmax(scores, axis=1) == np.argmax(self.y_test_mnist, axis=1))) / self.y_test_mnist.shape[0])
    logger.info('[PyTorch, MNIST] Accuracy on test set: %.2f%%', (acc * 100))
    self._test_backend_mnist(classifier, x_test_mnist, self.y_test_mnist)
def test_pytorch_mnist(self):
    x_train_mnist = np.swapaxes(self.x_train_mnist, 1, 3).astype(np.float32)
    x_test_mnist = np.swapaxes(self.x_test_mnist, 1, 3).astype(np.float32)
    classifier = get_image_classifier_pt()
    scores = get_labels_np_array(classifier.predict(x_train_mnist))
    acc = (np.sum((np.argmax(scores, axis=1) == np.argmax(self.y_train_mnist, axis=1))) / self.y_train_mnist.shape[0])
    logger.info('[PyTorch, MNIST] Accuracy on training set: %.2f%%', (acc * 100))
    scores = get_labels_np_array(classifier.predict(x_test_mnist))
    acc = (np.sum((np.argmax(scores, axis=1) == np.argmax(self.y_test_mnist, axis=1))) / self.y_test_mnist.shape[0])
    logger.info('[PyTorch, MNIST] Accuracy on test set: %.2f%%', (acc * 100))
    self._test_backend_mnist(classifier, x_test_mnist, self.y_test_mnist)