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)

Building a multilayer perceptron neural network

In this section, we introduce the MNIST problem and learn how to build a MultiLayer Perceptron (MLP) network using Caffe2 to solve it. We also learn how to load pretrained parameters into the network and use it for inference.

MNIST problem

The MNIST problem is a classic image classification problem that used to be popular in machine learning. State-of-the-art methods can now achieve greater than 99% accuracy in relation to this problem, so it is no longer relevant. However, it acts as a stepping stone for us to learn how to build a Caffe2 network that solves a real machine learning problem.

The MNIST problem lies in identifying the handwritten digit that is present in a grayscale...