Caffe2 Quick Start Guide Modular and Scalable Deep Learning Made Easy
Caffe2 by Facebook is a popular and relatively lightweight deep learning framework. Caffe2 is known for speed, accuracy and high efficiency in training neural networks. Caffe2 is widely used in mobile apps. This book is a fast paced guide that will teach you how to train and deploy deep learning mod...
Uloženo v:
| Hlavní autor: | |
|---|---|
| Médium: | E-kniha |
| Jazyk: | angličtina |
| Vydáno: |
Birmingham
Packt Publishing, Limited
2019
Packt Publishing Limited Packt Publishing |
| Vydání: | 1 |
| Témata: | |
| ISBN: | 9781789137750, 1789137756 |
| On-line přístup: | Získat plný text |
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Obsah:
- 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 -- Chapter 5: 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 -- Chapter 6: 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 -- Chapter 7: 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 -- Other Books You May Enjoy -- Index
- Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Chapter 1: 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 -- Chapter 2: 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 -- Chapter 3: 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 -- Summary -- Chapter 4: Working with Caffe -- The relationship between Caffe and Caffe2 -- Introduction to AlexNet -- Building and installing Caffe -- Installing Caffe prerequisites -- Building Caffe
- Caffe2 Quick Start Guide: Modular and scalable deep learning made easy