Caffe2 Quick Start Guide电子书

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Preface

Who this book is for

What this book covers

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Download the example code files

Conventions used

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Introduction and Installation

Introduction to deep learning

AI

ML

Deep learning

Introduction to Caffe2

Caffe2 and PyTorch

Hardware requirements

Software requirements

Building and installing Caffe2

Installing dependencies

Installing acceleration libraries

Building Caffe2

Installing Caffe2

Testing the Caffe2 Python API

Testing the Caffe2 C++ API

Summary

Composing Networks

Operators

Example – the MatMul operator

Difference between layers and operators

Example – a fully connected operator

Building a computation graph

Initializing Caffe2

Composing the model network

Sigmoid operator

Softmax operator

Adding input blobs to the workspace

Running the network

Building a multilayer perceptron neural network

MNIST problem

Building a MNIST MLP network

Initializing global constants

Composing network layers

ReLU layer

Set weights of network layers

Running the network

Summary

Training Networks

Introduction to training

Components of a neural network

Structure of a neural network

Weights of a neural network

Training process

Gradient descent variants

LeNet network

Convolution layer

Pooling layer

Training data

Building LeNet

Layer 1 – Convolution

Layer 2 – Max-pooling

Layers 3 and 4 – Convolution and max-pooling

Layers 5 and 6 – Fully connected and ReLU

Layer 7 and 8 – Fully connected and Softmax

Training layers

Loss layer

Optimization layers

Accuracy layer

Training and monitoring

Summary

Working with Caffe

The relationship between Caffe and Caffe2

Introduction to AlexNet

Building and installing Caffe

Installing Caffe prerequisites

Building Caffe

Caffe model file formats

Prototxt file

Caffemodel file

Downloading Caffe model files

Caffe2 model file formats

predict_net file

init_net file

Converting a Caffe model to Caffe2

Converting a Caffe2 model to Caffe

Summary

Working with Other Frameworks

Open Neural Network Exchange

Installing ONNX

ONNX format

ONNX IR

ONNX operators

ONNX in Caffe2

Exporting the Caffe2 model to ONNX

Using the ONNX model in Caffe2

Visualizing the ONNX model

Summary

Deploying Models to Accelerators for Inference

Inference engines

NVIDIA TensorRT

Installing TensorRT

Using TensorRT

Importing a pre-trained network or creating a network

Building an optimized engine from the network

Inference using execution context of an engine

TensorRT API and usage

Intel OpenVINO

Installing OpenVINO

Model conversion

Model inference

Summary

Caffe2 at the Edge and in the cloud

Caffe2 at the edge on Raspberry Pi

Raspberry Pi

Installing Raspbian

Building Caffe2 on Raspbian

Caffe2 in the cloud using containers

Installing Docker

Installing nvidia-docker

Running Caffe2 containers

Caffe2 model visualization

Visualization using Caffe2 net_drawer

Visualization using Netron

Summary

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