In earlier sections, we built the layers of a LeNet network required for inference and added inputs of image pixels and the label corresponding to each image. In this section, we are adding a few layers at the end of the network required to compute the loss function and for backpropagation. These layers are only required during training and can be discarded when using the trained network for inference.
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Caffe2 Quick Start Guide
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Caffe2 Quick Start Guide
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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)
Preface
Free Chapter
Introduction and Installation
Composing Networks
Training Networks
Working with Caffe
Working with Other Frameworks
Deploying Models to Accelerators for Inference
Caffe2 at the Edge and in the cloud
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