Limit search to available items
Book Cover
E-book
Author Nanjappa, Ashwin

Title Caffe2 Quick Start Guide : Modular and Scalable Deep Learning Made Easy
Published Birmingham : Packt Publishing, Limited, 2019

Copies

Description 1 online resource (127 pages)
Contents 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 operatorBuilding 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 variantsLeNet 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; Caffe model file formats; Prototxt file; Caffemodel file
Downloading Caffe model filesCaffe2 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 networkBuilding 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
Summary 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 models with Caffe2 on resource constrained platforms
Notes Visualization using Netron
Print version record
Subject Learning.
Learning
Artificial intelligence.
Pattern recognition.
Computer vision.
Neural networks & fuzzy systems.
Computers -- Intelligence (AI) & Semantics.
Computers -- Computer Vision & Pattern Recognition.
Computers -- Neural Networks.
Learning
Form Electronic book
ISBN 1789138264
9781789138269