Caffe2 - Defining Complex Networks

In the previous chapter, you learned to create a trivial network and learned how to execute it and examine its output. The process for creating complex networks is same 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 different 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 resolve.

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.


Caffe2, which is a deep learning framework permits 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 picture. You also learned to define a neural network architecture of your choice. Such custom networks can be trained utilizing many predefined operators in Caffe. A trained model is stored in a file which can be taken into a production environment.

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