bug:刷机卡Logo界面，adb shell 报- exec '/system/bin/sh' failed: No such file or directory (2) -

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bug:卡刷，- exec '/system/bin/sh' failed: No such file or directory (2) - 卡刷遇到此问题，可能得做些修改，用adb sideload 完全可以成功，但放到/sdcard 下，这种方式卡在logo界面了，，， - exec '/system/bin/sh' failed: No such file or directory (2) - ，，，待解决，有事，，，， adb push mksh /system/bin failed to copy 'm... 阅读详情

可能的问题：

1.update.zip 问题

2.recovery 问题

 

 

换！

 

 

 

有可能是不该取消cache.img的生成，不科学啊，，，那我换了img可能就可以。但为什么我换成adb sideload方式就可以的呢？到底有什么区别，是什么导致的sdcard方式卡在第一界面？

 

有可能是recovery 有问题，

symlink /sbin/busybox /system/bin/sh

原因分析：

有可能是recovery的原因，因为每次进入recovery都发现 /system 没有 mount ,

mount之后重启还是进不去。

 

munger:~/project/nexus4/B2G/out/target/product/mako/fota/fullimg$ adb shell
~ # cd cache/
lost+found/  recovery/
~ # cd cache/recovery/
/cache/recovery # ls
last_install  last_log.1    last_log.3    last_log.6    last_log.9
last_kmsg     last_log.10   last_log.4    last_log.7    log
last_log      last_log.2    last_log.5    last_log.8
/cache/recovery #

 

adb sideload 进去的不用在rc下选mount /system也同样可以进去系统，郁闷。。。

排除zip有问题的可能性。

发生了什么。

 

看cache 下的Log发现：

cache里面的log:

刷分区的时候：

minzip: zlib inflate call failed (zerr=-3)

解压失败是这个意思？为什么？

 

有说是文件不完整，，，

zlib压缩库解压失败的问题

 

cache log：minzip: zlib inflate call failed (zerr=-3)

Flashing partition /boot
Flashing partition /data
minzip: zlib inflate call failed (zerr=-3)
Flashing partition /system
minzip: zlib inflate call failed (zerr=-5)
minzip: Can't extract entry to file.
script succeeded: result was [1.0]
Install from sdcard complete.

 

不对，，，应该是zip包的问题，之前用-full卡刷的 update.zip可以正常刷成功啊，那肯定是update.zip的问题了，醉了，绕半天，

用-fullimg就不行。

也不对，fullimg 的update.zip 用sideload 可以正常升级，包到底是有问题还是没问题，，矛盾，，，

 

 

-full 的log:

 

 

-fullimg 的卡刷Log

 

sizeload 的log:

root@mako:/cache/recovery # cat log                                            
I:using /data/media for /sdcard.
No files found.


Sideload started ...
Now send the package you want to apply
to the device with "adb sideload <filename>"...

sideload_service invoked
adbd exiting after successful sideload
E:listen on USB
E:userid is 2000
E:Event loop starting
I:sideload process finished
I:sideload thread finished
Restarting adbd...
Finding update package...
I:Update location: /tmp/update.zip
Opening update package...
Installing update...
Starting B2G FOTA: fullimg
Checking device
Device is compatible
Flashing partition /boot
Flashing partition /data
minzip: Size mismatch on inflated file (1894776832 vs -1)
minzip: Can't extract entry to file.
Flashing partition /system
script succeeded: result was [1.0]Rebooting...
I:using /data/media for /sdcard/0/clockworkmod/.recovery_version.
mkdir: can't create directory '/sdcard/0/': I/O error
root@mako:/cache/recovery #

 

卡刷 Log:

/cache/recovery # cat log 
__bionic_open_tzdata: couldn't find any tzdata when looking for localtime!
__bionic_open_tzdata: couldn't find any tzdata when looking for GMT!
__bionic_open_tzdata: couldn't find any tzdata when looking for posixrules!
Starting recovery on Fri Apr 26 09:03:23 2019

framebuffer: fd 4 (768 x 1280)
ClockworkMod Recovery v6.0.4.7
recovery filesystem table
=========================
  0 /system ext4 /dev/block/platform/msm_sdcc.1/by-name/system 0
  1 /cache ext4 /dev/block/platform/msm_sdcc.1/by-name/cache 0
  2 /data ext4 /dev/block/platform/msm_sdcc.1/by-name/userdata 0
  3 /persist ext4 /dev/block/platform/msm_sdcc.1/by-name/persist 0
  4 /firmware vfat /dev/block/platform/msm_sdcc.1/by-name/modem 0
  5 /boot emmc /dev/block/platform/msm_sdcc.1/by-name/boot 0
  6 /recovery emmc /dev/block/platform/msm_sdcc.1/by-name/recovery 0
  7 /misc emmc /dev/block/platform/msm_sdcc.1/by-name/misc 0
  8 /radio emmc /dev/block/platform/msm_sdcc.1/by-name/modem 0
  9 /sbl1 emmc /dev/block/platform/msm_sdcc.1/by-name/sbl1 0
  10 /sbl2 emmc /dev/block/platform/msm_sdcc.1/by-name/sbl2 0
  11 /sbl3 emmc /dev/block/platform/msm_sdcc.1/by-name/sbl3 0
  12 /tz emmc /dev/block/platform/msm_sdcc.1/by-name/tz 0
  13 /rpm emmc /dev/block/platform/msm_sdcc.1/by-name/rpm 0
  14 /aboot emmc /dev/block/platform/msm_sdcc.1/by-name/aboot 0
  15 /tmp ramdisk ramdisk 0

W:Unable to get recovery.fstab info for /datadata during fstab generation!
W:Unable to get recovery.fstab info for /emmc during fstab generation!
W:Unable to get recovery.fstab info for /sdcard during fstab generation!
W:Unable to get recovery.fstab info for /sd-ext during fstab generation!
W:Unable to get recovery.fstab info for /external_sd during fstab generation!
I:Completed outputting fstab.
I:Connected to Vold..
I:200 0 Volumes listed.
I:Processing arguments.
I:Checking arguments.
I:device_recovery_start()
Command: "/sbin/recovery"

af.resampler.quality=4
ro.cm.device=mako
ro.cm.display.version=11-20140125-UNOFFICIAL-mako
ro.cm.version=11-20140125-UNOFFICIAL-mako
ro.qc.sensors.wl_dis=true
ro.sf.lc
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解决“/usr/bin/ No such file or directory“错误的方法 当遇到"/usr/bin/ No such file or directory"错误时，首先确保指定的文件或目录存在，并且路径设置正确。然后，检查文件或目录的权限以及软链接的有效性。当在编程过程中遇到"/usr/bin/ No such file or directory"错误时，这表明系统无法找到指定的文件或目录。在这种情况下，你需要找到正确的路径或恢复文件。确保"/usr/bin/"路径在环境变量中正确地列出。解决"/usr/bin/ No such file or directory"错误的方法。 阅读详情

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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. 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]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")
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