在本教程中,你成功训练了一个神经网络,以大约 92% 的准确率对 MNIST 数据集进行分类,并用你自己的图像对其进行了测试。当前最先进的研究在这个相同的问题上实现了大约 99% 的准确率,它们使用了更复杂的网络架构,其中包含卷积层。这些卷积层利用图像的 2D 结构来更好地表示内容,这与我们之前将所有像素展平为 784 个单元的单个向量的方法不同。你可以在 TensorFlow 网站上阅读更多关于这个主题的信息,并在 MNIST 网站上查看详细说明最准确结果的研究论文。

既然你已经学会了如何构建和训练神经网络,你可以尝试将这个实现应用于你自己的数据,或者在其他流行的数据集上进行测试,例如 Google StreetView House Numbers,或用于更通用图像识别的 CIFAR-10 数据集


In this tutorial you successfully trained a neural network to classify the
MNIST dataset with around 92% accuracy and tested it on an image of
your own. Current state-of-the-art research achieves around 99% on this
same problem, using more complex network architectures involving
convolutional layers. These use the 2D structure of the image to better
represent the contents, unlike our method which flattened all the pixels
into one vector of 784 units. You can read more about this topic on the
TensorFlow website, and see the research papers detailing the most
accurate results on the MNIST website.
Now that you know how to build and train a neural network, you can
try and use this implementation on your own data, or test it on other
popular datasets such as the Google StreetView House Numbers, or the
CIFAR-10 dataset for more general image recognition.

Last modified: Wednesday, 25 June 2025, 12:06 PM