如何将数据从文件存储到mnist

Question

我正在使用以下来自网络的代码从文件创建自己的数据：

# https://medium.com/@ashok.tankala/build-the-mnist-model-with-your-own-handwritten-digits-using-tensorflow-keras-and-python-f8ec9f871fd3
# To load images to features and labels
def load_images_to_data(image_label, image_directory, features_data, label_data):
    list_of_files = os.listdir(image_directory)
    for file in list_of_files:
        image_file_name = os.path.join(image_directory, file)
        if ".png" in image_file_name:
            img = Image.open(image_file_name).convert("L")
            img = np.resize(img, (28, 28))
            im2arr = np.array(img)
            im2arr = im2arr.reshape(1, 28, 28)
            if features_data.size ==0:
                features_data = im2arr
                label_data = [image_label]
            else:
                features_data = np.append(features_data, im2arr, axis=0)
                label_data = np.append(label_data, [image_label], axis=0)
    return features_data, label_data


print(train_images.shape)
print(train_labels.shape)

这有效：

(7074, 28, 28)
(7074, 3)

BUT process is much slower than:

fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

如何以mnist格式在本地存储这些数据，这样我就不必每次都创建它并且加载速度更快？