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Caffe2 Quick Start Guide
Caffe2 Quick Start Guide

Caffe2 Quick Start Guide: Modular and scalable deep learning made easy

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Caffe2 Quick Start Guide

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

Operators

In Caffe2, a neural network can be thought of as a directed graph, where the nodes are operators and the edges represent the flow of data between operators. Operators are the basic units of computation in a Caffe2 network. Every operator is defined with a certain number of inputs and a certain number of outputs. When the operator is executed, it reads its inputs, performs the computation it is associated with, and writes the results to its outputs.

To obtain the best possible performance, Caffe2 operators are typically implemented in C++ for execution on CPUs and implemented in CUDA for execution on GPUs. All operators in Caffe2 are derived from a common interface. You can see this common interface defined in the caffe2/proto/caffe2.proto file in the Caffe2 source code.

The following is the Caffe2 operator interface found in my caffe2.proto file:

// Operator Definition...

Difference between layers and operators

Older deep learning frameworks, such as Caffe, did not have operators. Instead, their basic units of computation were called layers. These older frameworks chose the name layer inspired by the layers in neural networks.

However, contemporary frameworks, such as Caffe2, TensorFlow, and PyTorch, prefer to use the term operator for their basic units of computation. There is a subtle difference between operators and layers. A layer in older frameworks, such as Caffe, was composed of both the computation function of that layer and the trained parameters of that layer. In contrast to this, an operator in Caffe2 only holds the computation function. Both the trained parameters and the inputs are external to the operator and need to be fed to it explicitly.

...

Building a computation graph

In this section, we will learn how to build a network in Caffe2 using model_helper. (model_helper was introduced earlier in this chapter.) To maintain the simplicity of this example, we use mathematical operators that require no trained parameters. So, our network is a computation graph rather than a neural network because it has no trained parameters that were learned from training data. The network we will build is illustrated by the graph shown in Figure 2.5:

Figure 2.5: Our simple computation graph with three operators

As you can see, we provide two inputs to the network: a matrix, A, and a vector, B. A MatMul operator is applied to A and B and its result is fed to a Sigmoid function, designated by σ in Figure 2.5. The result of the Sigmoid function is fed to a SoftMax function. (We will learn a bit more about the Sigmoid and SoftMax operators...

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...

Summary

In this chapter, we learned about Caffe2 operators and how they differ from layers used in older deep learning frameworks. We built a simple computation graph by composing several operators. We then tackled the MNIST machine learning problem and built an MLP network using Brew helper functions. We loaded pretrained weights into this network and used it for inference on a batch of input images. We also introduced several common layers, such as matrix multiplication, fully connected, Sigmoid, SoftMax, and ReLU.

We learned about performing inference on our networks in this chapter. In the next chapter, we will learn about training and how to train a network to solve the MNIST problem.

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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.

Who is this book for?

Data scientists and machine learning engineers who wish to create fast and scalable deep learning models in Caffe2 will find this book to be very useful. Some understanding of the basic machine learning concepts and prior exposure to programming languages like C++ and Python will be useful.

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

Product Details

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Publication date : May 31, 2019
Length: 136 pages
Edition : 1st
Language : English
ISBN-13 : 9781789138269
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Product Details

Publication date : May 31, 2019
Length: 136 pages
Edition : 1st
Language : English
ISBN-13 : 9781789138269
Category :
Languages :
Concepts :
Tools :

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Table of Contents

8 Chapters
Introduction and Installation Chevron down icon Chevron up icon
Composing Networks Chevron down icon Chevron up icon
Training Networks Chevron down icon Chevron up icon
Working with Caffe Chevron down icon Chevron up icon
Working with Other Frameworks Chevron down icon Chevron up icon
Deploying Models to Accelerators for Inference Chevron down icon Chevron up icon
Caffe2 at the Edge and in the cloud Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Merrill Aug 27, 2019
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Well written , highly informational .
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Shruti Aug 27, 2019
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Great book for a beginner like me :)
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