Caffe2 简明教程

Caffe2 - Defining Complex Networks

在前一课中，你学习了如何创建一个平凡的网络，并学习了如何执行它和检查其输出。创建复杂网络的过程与上面描述的过程类似。Caffe2 提供了一大组运算符，用于创建复杂架构。建议你查看 Caffe2 文档，了解运算符列表。在研究了各种运算符的用途后，你将能够创建复杂网络并对其进行训练。对于训练网络，Caffe2 提供了多个 predefined computation units - 即运算符。你将需要为训练网络选择合适的运算符，以解决你尝试解决的问题类型。

In the previous lesson, you learned to create a trivial network and learned how to execute it and examine its output. The process for creating complex networks is similar to the process described above. Caffe2 provides a huge set of operators for creating complex architectures. You are encouraged to examine the Caffe2 documentation for a list of operators. After studying the purpose of various operators, you would be in a position to create complex networks and train them. For training the network, Caffe2 provides several predefined computation units - that is the operators. You will need to select the appropriate operators for training your network for the kind of problem that you are trying to solve.

一旦网络经过你的满意训练，你就可以将其存储在模型文件中，类似于你之前使用的预训练模型文件。这些经过训练的模型可能会贡献给 Caffe2 存储库，以造福其他用户。或者，你也可以简单地将经过训练的模型用于自己的私有生产。

Once a network is trained to your satisfaction, you can store it in a model file similar to the pre-trained model files you used earlier. These trained models may be contributed to Caffe2 repository for the benefits of other users. Or you may simply put the trained model for your own private production use.

Summary

Caffe2 是一个深度学习框架，它允许你尝试多种神经网络来预测数据。Caffe2 网站提供了许多预训练模型。你学会了使用其中一个预训练模型对给定图像中的对象进行分类。你还学会了定义你选择的任意神经网络架构。这种自定义网络可以使用 Caffe 中的许多预定义运算符进行训练。经过训练的模型会存储在一个文件中，该文件可以带到生产环境中。

Caffe2, which is a deep learning framework allows you to experiment with several kinds of neural networks for predicting your data. Caffe2 site provides many pre-trained models. You learned to use one of the pre-trained models for classifying objects in a given image. You also learned to define a neural network architecture of your choice. Such custom networks can be trained using many predefined operators in Caffe. A trained model is stored in a file which can be taken into a production environment.