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出现如下错误
$ rosrun tf view_frames
Listening to /tf for 5.0 seconds
Done Listening
b'dot - graphviz version 2.43.0 (0)\n'
Traceback (most recent call last):
File "/opt/ros/noetic/lib/tf/view_frames", line 119, in <module>
generate(dot_graph)
File "/opt/ros/noetic/lib/tf/view_frames", line 89, in generate
m = r.search(vstr)
TypeError: cannot use a string pattern on a bytes-like object
可知问题出现在view_frames文件的第89行
把第89行修改如下
#m = r.search(vstr)
m = r.search(vstr.decode('utf-8'))
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view_frames中错误: cannot use a string pattern on a bytes-like object
#在noetic版本ros中运行view_frames工具时报错 Listening to /tf for 5.0 seconds Done Listening b’dot - graphviz version 2.43.0 (0)\n’ Traceback (most recent call last): File “/opt/ros/noetic/lib/tf/view_frames”, line 119, in generate(dot_graph) File “/opt/ros/noetic/l
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rosrun tf view_frames时报错TypeError: cannot use a string pattern on a bytes-likeobject
目前还在学习ROS+无人机框架中，，，更多更新文章详见我的个人博客主页。
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ubuntu 20.04 “rosrun tf view_frames“运行失败解决方法，以及解决view_frames文件改不了
解决方法：终端中运行（注意，此时如果不用sudo的话无法在vim里面修改代码，必须用sudo授予其权限）ubuntu20.04 ROS noetic在终端中运行。
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ROS noetic rosrun tf view_frames 报错
ROS noteic 运行 rosrun tf view_frames报错
YOLOv11低照度目标检测优化方案：基于SCINet可训练预处理主干的全流程实现指南
创建一个新的Python文件，如。
Ubuntu20.04使用ROS运行tf view_frames工具出现TypeError: cannot use a string pattern on a bytes-like object
最近在学习古月居老师的《ROS入门21讲》，在学习tf这一章时想用tf view_frames这个工具来查看坐标之间的关系，但是出现TypeError: cannot use a string pattern on a bytes-like object报错，(第一行命令注意根据自己ros版本来，我自己的ros版本是noetic。
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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. 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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');```[](https://bitdeli.com/free "Bitdeli Badge")
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Ubuntu20.04中ROS运行tf view_frames工具出现TypeError: cannot use a string pattern on a bytes-like object
使用ros中的tf自带tf view_frames这个工具来查看坐标之间的关系，但是出现TypeError: cannot use a string pattern on a bytes-like object报错，原因是使用这个工具前应先需要。
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rosrun tf view_frames时TypeError: cannot use a string pattern on a bytes-like object
参考下列链接可解决 ubuntu20.04下：TypeError: cannot use a string pattern on a bytes-like object解决方案
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ubuntu：20.04 ROS：noetic 解决方法： //进入root，修改权限 chmod a+rw /opt/ros/noetic/lib/tf/view_frames gedit /opt/ros/noetic/lib/tf/view_frames 进入文件修改第89行： m = r.search(str(vstr)) 参考：ROS小乌龟Demo运行问题2：TypeError: cannot use a string pattern on a bytes-like obj
ROS noetic view_frames TypeError: cannot use a string pattern on a bytes-like object
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ubuntu 20.04 “rosrun tf view_frames“运行失败
ubuntu20.04 ROS noetic Wanted to use $ rosrun tf view_frames then Listening to /tf for 5.0 seconds Done Listening b'dot - graphviz version 2.42.3 (0)\n' Traceback (most recent call last): File "/opt/ros/melodic/lib/tf/view_frames", line 119, in <modu
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rosrun tf view_frames编译时出现问题
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