Book Image

Caffe2 Quick Start Guide

By : Ashwin Nanjappa
Book Image

Caffe2 Quick Start Guide

By: Ashwin Nanjappa

Overview of this book

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.
Table of Contents (9 chapters)

LeNet network

In Chapter 2, Composing Networks, we built an MLP network that was composed of multiple pairs of fully connected layers and activation layers. In this chapter, we will build and train a convolutional neural network (CNN). This type of network is so named because it primarily uses convolution layers (introduced in the next section). For computer vision problems, CNNs have been shown to deliver better results with fewer numbers of parameters compared to MLPs. One of the first successful CNNs was used to solve the MNIST problem that we looked at earlier. This network, named LeNet-5, was created by Yann LeCun and his colleagues:

Figure 3.4: Structure of our LeNet model

We will construct a network similar in spirit to the LeNet. We will refer to this as the LeNet model in the remainder of this book. From Figure 3.4, we can see that our LeNet network has eight layers...