Ubuntu 7.10(Gutsy Gibbon) 配置

Ubuntu最小精简版 ubuntu-7.10-jeos-i386 ubuntu-7.10-jeos-i386最小的精简版系统,针对虚拟机进行优化,只有151M大小,完整一个包下载,不是分卷 立即下载
Ubuntu 7.10(Gutsy Gibbon) 配置

A. 启动设置简介

1. GRUB配置文件/boot/grub/menu.lst, 修改前备份.

2. 一些内核参数
Parameter
Function
splash
boot with a startup image
quiet
keep boot message from displaying
vga=xxxsetup console frame buffer, detail in section B.
vga=normal, keep normal frame buffer
vga=ask, ask you which mode to choose when boot.
*This argument is not supported by default setup of Ubuntu 7.10.



B. 设置控制台缓存

1. 在/etc/initramfs-tools/modules中添加fbcon和vesafb以获系统对显示缓存设置的支持,Ubuntu7.10中默认不支持.

$ echo "vesafb" | sudo tee -a /etc/initramfs-tools/modules
$ echo "fbcon" | sudo tee -a /etc/initramfs-tools/modules

2. 在黑名单/etc/modprobe.d/blacklist-framebuffer中删掉或注释掉模块vesafb.

#blacklist vesafb

3. 在/boot/grub/menu.lst中添加参数,参见下表:

# defoptions=quiet splash vga=791

Colour depth

640x480

800x600

1024x768

1280x1024

1400x1050

1600x1200

8 (256)

769

771

773

775



15 (32K)

784

787

790

793



16 (65K)

785

788

791

794

834

884

24 (16M)

786

789

792

795




4. 更新GRUB设置

$ sudo update-grub

5. 如果Usplash(启动画面)的分辨率高于缓存设置,修改/etc/usplash.conf设置与缓存设置保持一致.

xres=1024
yres=768


6. 更新initramfs,重建启动画面.

$ sudo update-initramfs -u


C. 中文支持

1. 安装中文语言包

$ sudo apt-get install language-pack-zh language-support-zh language-pack-gnome-zh

2. 安装Windows字体, 例如, 宋体(Simsun), Tahoma, Verdana, Courier New...

$ sudo mkdir /usr/share/fonts/winfont
$ sudo cp (Windows Fonts) /usr/share/fonts/winfont
$ cd /usr/share/fonts/winfont
$ sudo mkfontscale
$ sudo mkfontdir
$ sudo fc-cache

3. 安装X11字体支持(这一部不确定是否必需)

apt-get install xfonts-intl-chinese xfonts-intl-chinese-big

4. 设置字体选择顺序, 修改/etc/fonts/language-selector.conf
下边是我的设置,主要是删除了系统自带的字体,使用宋体,宋体之前的两个字体是用来显示其它语言的.

    <alias>
        <family>serif</family>
        <prefer>
            <family>Bitstream Vera Serif</family>
            <family>DejaVu Serif</family>
            <family>SimSun</family>
        </prefer>
    </alias>
    <alias>
        <family>sans-serif</family>
        <prefer>
            <family>Bitstream Vera Sans</family>
            <family>DejaVu Sans</family>
            <family>SimSun</family>
        </prefer>
    </alias>
    <alias>
        <family>monospace</family>
        <prefer>
            <family>Bitstream Vera Sans Mono</family>
            <family>DejaVu Sans Mono</family>
            <family>SimSun</family>
        </prefer>
    </alias>


5. 设置GNOME的字体,System -> Preference -> Appearence -> Fonts.

Application/Document/Desktop font: Tahoma(10)
Window Title font: Tahoma Bold(10)
Fixed Width font: Courier New(10)

Rendering: Monochrome

6. 按'Ctrl + Alt + Backspace'重新启动Xwindow使配置生效.


D. 为GNOME安装一个紧凑的主题

在GNOME默认安装的主题,控件普遍过大,会占据很大的空间,使得工作空间很小.

感谢Martin Ankerl的大作!


E. 安装并配置防火墙GuardDog

1. 安装GuardDog

$ sudo apt-get install guarddog

2. 打开
GuardDog,第一次打开会警告文件丢失,忽略即可.
 
$ gksudo guarddog

3. GuardDog默认不设置任何规则,你必须知道每一个规则是用来干什么的,简单说一下.

打开Protocol页, 选择Internet Zone, 我们最常用的协议有: 用来上网的DNS, HTTP, HTTPS, FTP; 用来收发邮件的POP3, SMTP; 同步时间用的NTP; 安全登录用的SSH; 聊天用的Jabber, MSN; 等等...
打开Advanced页, 选择Enable DHCP on Interfaces.

4. 自定义QQ协议

打开Advanced页, 建立一个新的协议, Name: QQ, Type: UDP, Ports: 4000-8000.
Open tab Protocol, enable QQ protocol.

5. Press apply to take effect


F. 安装输入法 - SCIM

1. 在系统菜单中添加语言支持


System -> Administration -> Language Support


2. 如果需要输入法支持QT,需要安装scim-qtimm,注意只安装scim,realplayer无法启动


$ sudo apt-get install scim-qtimm


找到以下语句:

GTK_IM_MODULE=”scim”
QT_IM_MODULE=”scim”

替换为:

GTK_IM_MODULE=”xim”
QT_IM_MODULE=”xim”


3. 设置scim为默认输入法


$ im-switch -s scim

4. 重启计算机或按Ctrl + Alt + Backspace重启Xwindow生效


G. 安装Real player

1. 下载安装包并安装

$ wget http://www.debian-multimedia.org/pool/main/r/realplay/realplayer_10.0.9-0.1_i386.deb
$ sudo dpkg -i realplayer_10.0.9-0.1_i386.deb
$ sudo dpkg -r realplayer


2. 如果在播放rm时非常卡,需要安装并且配置alsa-oss

$ sudo apt-get install alsa-oss

如果安装失败,需要在恢复模式下安装(recovery mode).这时可能需要运行dhclient来获得IP地址.

$ sudo gedit /usr/bin/realplay  or  $ sudo gedit /usr/bin/realplayer  or  $ sudo gedit /opt/RealPlayer

找到以下语句:
if [ -n "$LD_PRELOAD" ]; then
echo "Warning: LD_PRELOAD=/"$LD_PRELOAD/""
fi

在其后添加:

LD_PRELOAD="$LDPRELOAD:/usr/lib/libaoss.so"
export LD_PRELOAD

H. 用LiveCD必定Windows启动设置


从LiveCD启动并打开一个终端(terminal).你需要运行几条命令,比如sudo -i,来获得root权限.小心任何一个输入错误,这在root下可能带来不可挽回的结果.

我们需要找到安装Ubuntu的分区.用fdisk -l命令得到分区信息,例如:

$ sudo fdisk -l 

Disk /dev/hda: 120.0 GB, 120034123776 bytes
255 heads, 63 sectors/track, 14593 cylinders
Units = cylinders of 16065 * 512 = 8225280 bytes

Device Boot Start End Blocks Id System
/dev/hda1 1 8 64228+ 83 Linux
/dev/hda2 9 1224 9767520 83 Linux
/dev/hda3 * 1225 2440 9767520 a5 FreeBSD
/dev/hda4 2441 14593 97618972+ 5 Extended
/dev/hda5 14532 14593 498015 82 Linux swap / Solaris
/dev/hda6 2441 14530 97112862 83 Linux

Partition table entries are not in disk order

Here I have three Linux partitions. /dev/hda2 is my root partition, /dev/hda1 is my /boot partition and /dev/hda6 is my /home partitoins. If you only have one, obviously this is the one your Ubuntu system is installed on. If you have more than one and you don't know which one your Ubuntu is installed on, we'll look for it later. First, create a mountpoint for your partition, for example :


$ mkdir /mnt/root


Then mount your partition in it. If you don't know which one it is, then mount any of them, we'll se if it's the correct one.


$ mount -t ext3 /dev/hda2 /mnt/root


Of course, replace /dev/hda2 with the correct name of your partition. You can check if it's the correct one by running ls /mnt/root, which should output something like this :

bin    dev      home        lib    mnt   root     srv  usr 
boot etc initrd lib64 opt sbin sys var
cdrom initrd.img media proc selinux tmp vmlinuz

If what you have looks not at all like this, you didn't mount the correct partition. Do umount /mnt/root to unmount it and try another one. You also need to mount your /boot partition if you made one, like this :


$ mount -t ext3 /dev/hda1 /mnt/root/boot


To make sure it was the correct one, run ls /mnt/root/boot, which sould output something like this :

config-2.6.18-3-686      initrd.img-2.6.18-3-686.bak  System.map-2.6.18-3-686 
grub lost+found vmlinuz-2.6.18-3-686
initrd.img-2.6.18-3-686 memtest86+.bin

Once again, if what you have doesn't fit, unmount it and try another partition.

Now that everything is mounted, we just need to reinstall GRUB :


$ grub-install --root-directory=/mnt/root /dev/hda


If you got BIOS warnings try:


$ grub-install --root-directory=/mnt/root /dev/hda --recheck


Of course, replace /dev/hda with the location you want to install GRUB on. If all went well, you should see something like this :


Installation finished. No error reported.

This is the contents of the device map /boot/grub/device.map.

Check if this is correct or not. If any of the lines is incorrect,

fix it and re-run the script `grub-install'.


(hd0) /dev/hda

Now you can reboot and the GRUB menu should appear. If you see a warning message regarding XFS filesystem, you can ignore it. 
 
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Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); 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