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Composing Networks

In this chapter, we will learn about Caffe2 operators and how we can compose networks using these operators. To learn how to use operators, we will start off by building a simple computation graph from scratch. After that, we will solve a real computer vision problem called MNIST (by building a genuine neural network with trained parameters) and use it for inference.

This chapter covers the following topics:

Introduction to Caffe2 operators
The difference between operators and layers
How to use operators to compose a network
Introduction to the MNIST problem
Composing a network for the MNIST problem
Inference through a Caffe2 network

Key benefits

Migrate models trained with other deep learning frameworks to Caffe2

Integrate Caffe2 with Android or iOS, and implement deep learning models for mobile devices

Leverage the distributed capabilities of Caffe2 to build models that scale easily

Description

Caffe2 is a popular deep learning library used for fast and scalable training, and inference of deep learning models on different platforms. This book introduces you to the Caffe2 framework and demonstrates how you can leverage its power to build, train, and deploy efficient neural network models at scale. The Caffe 2 Quick Start Guide will help you in installing Caffe2, composing networks using its operators, training models, and deploying models to different architectures. The book will also guide you on how to import models from Caffe and other frameworks using the ONNX interchange format. You will then cover deep learning accelerators such as CPU and GPU and learn how to deploy Caffe2 models for inference on accelerators using inference engines. Finally, you'll understand how to deploy Caffe2 to a diverse set of hardware, using containers on the cloud and resource-constrained hardware such as Raspberry Pi. By the end of this book, you will not only be able to compose and train popular neural network models with Caffe2, but also deploy them on accelerators, to the cloud and on resource-constrained platforms such as mobile and embedded hardware.

What you will learn

Build and install Caffe2

Compose neural networks

Import deep learning models from other frameworks

Train neural networks on a CPU or GPU

Deploy models at the edge and in the cloud

Import a neural network from Caffe

Deploy models on CPU or GPU accelerators using inference engines

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