Tao Hu dongzhuoyao

Caffe + Ubuntu 14.04 64bit + CUDA 6.5 配置说明

本步骤能实现用Intel核芯显卡来进行显示，用NVIDIA GPU进行计算。

1. 安装开发所需的依赖包

安装开发所需要的一些基本包

sudo apt-get install build-essential  # basic requirement
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler #required by caffe

Theory of Building Caffe on OS X

Introduction

Our goal is to run python -c "import caffe" without crashing. For anyone who doesn't spend most of their time with build systems, getting to this point can be extremely difficult on OS X. Instead of providing a list of steps to follow, I'll try to epxlain why each step happens.

This page has OS X specific install instructions.

I assume:

	#http://www.cnblogs.com/hhh5460/p/4304628.html

	import math
	import random
	import string

	random.seed(0)

	# 生成区间[a, b)内的随机数
	def rand(a, b):

	"""
	Multiclass SVMs (Crammer-Singer formulation).

	A pure Python re-implementation of:

	Large-scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex.
	Mathieu Blondel, Akinori Fujino, and Naonori Ueda.
	ICPR 2014.
	http://www.mblondel.org/publications/mblondel-icpr2014.pdf
	"""

	# VGG 16-layer network convolutional finetuning
	# Network modified to have smaller receptive field (128 pixels)
	# nand smaller stride (8 pixels) when run in convolutional mode.
	#
	# In this model we also change max pooling size in the first 4 layers
	# from 2 to 3 while retaining stride = 2
	# which makes it easier to exactly align responses at different layers.
	#
	# For alignment to work, we set (we choose 32x so as to be able to evaluate
	# the model for all different subsampling sizes):

	name: "deeplabv2_resnet101_test"

	layer {
	name: "data"
	type: "ImageSegData"
	top: "data"
	top: "label"
	top: "data_dim"
	include {
	phase: TEST

	name: "deeplabv2_resnet101_deploy"

	layers {
	name: "data"
	type: MEMORY_DATA
	top: "data"
	top: "label"
	top: "data_dim"

	memory_data_param {

	name: "deeplabv2_resnet101_train"

	layer {
	name: "data"
	type: "ImageSegData"
	top: "data"
	top: "label"
	#top: "data_dim"
	include {
	phase: TRAIN