正则表达式及其在python上的应用
今天学习了一早上正则表达式。如下内容部分转载自《读懂正则表达式就这么简单》 一、什么是正则表达式正则表达式是一种特殊的字符串模式,用于匹配一组字符串,就好比用模具做产品,而正则就是这个模具,定义一种规则去匹配符合规则的字符。 1.2 常用的正则匹配工具 在线匹配工具: 正则匹配软件 McTracer 用过几个之后还是觉得这个是最好用的,支持将正则导成对应的语言如java C# js等还帮你转义了,Copy直接用就行了很方便,另外支持把正则表达式用法解释,如哪一段是捕获分组,哪段是贪婪匹配等等,总之用起来 So Happy . 二 正则字符简单介绍关于这部分建议跳到: 《读懂正则表达式就这么简单》 另外关于python的正则表达式,主要使用re模块。 我们以任务为导向介绍python正则表达式的用法。 I1113 23:35:50.763059 4460 solver.cpp:218] Iteration 400 (27.3075 iter/s,0.7324s/20 iters),loss = 0.0202583
I1113 23:35:50.763141 4460 solver.cpp:237] Train net output #0: rpn_cls_loss = 0.00101873 (* 1 = 0.00101873 loss)
I1113 23:35:50.763165 4460 solver.cpp:237] Train net output #1: rpn_loss_bbox = 0.0192396 (* 1 = 0.0192396 loss)
I1113 23:35:50.763175 4460 sgd_solver.cpp:105] Iteration 400,lr = 0.001
I1113 23:35:51.751206 4460 solver.cpp:218] Iteration 420 (20.2456 iter/s,0.987868s/20 iters),loss = 0.00228514
I1113 23:35:51.751341 4460 solver.cpp:237] Train net output #0: rpn_cls_loss = 0.00140554 (* 1 = 0.00140554 loss)
I1113 23:35:51.751379 4460 solver.cpp:237] Train net output #1: rpn_loss_bbox = 0.000879596 (* 1 = 0.000879596 loss)
I1113 23:35:51.751410 4460 sgd_solver.cpp:105] Iteration 420,lr = 0.001
I1113 23:35:52.523890 4460 solver.cpp:218] Iteration 440 (25.8933 iter/s,0.772401s/20 iters),loss = 0.0132958
I1113 23:35:52.523974 4460 solver.cpp:237] Train net output #0: rpn_cls_loss = 0.00312161 (* 1 = 0.00312161 loss)
I1113 23:35:52.523988 4460 solver.cpp:237] Train net output #1: rpn_loss_bbox = 0.0101742 (* 1 = 0.0101742 loss)
I1113 23:35:52.523998 4460 sgd_solver.cpp:105] Iteration 440,lr = 0.001
I1113 23:35:53.461998 4460 solver.cpp:218] Iteration 460 (21.3325 iter/s,0.937539s/20 iters),loss = 0.0154897
I1113 23:35:53.462057 4460 solver.cpp:237] Train net output #0: rpn_cls_loss = 0.00780452 (* 1 = 0.00780452 loss)
I1113 23:35:53.462069 4460 solver.cpp:237] Train net output #1: rpn_loss_bbox = 0.00768522 (* 1 = 0.00768522 loss)
I1113 23:35:53.462082 4460 sgd_solver.cpp:105] Iteration 460,lr = 0.001
I1113 23:35:54.356657 4460 solver.cpp:218] Iteration 480 (22.3584 iter/s,0.894517s/20 iters),loss = 0.00275768
I1113 23:35:54.356729 4460 solver.cpp:237] Train net output #0: rpn_cls_loss = 0.00107937 (* 1 = 0.00107937 loss)
I1113 23:35:54.356739 4460 solver.cpp:237] Train net output #1: rpn_loss_bbox = 0.00167831 (* 1 = 0.00167831 loss)
I1113 23:35:54.356748 4460 sgd_solver.cpp:105] Iteration 480,lr = 0.001
I1113 23:35:55.153437 4460 solver.cpp:218] Iteration 500 (25.1734 iter/s,0.79449s/20 iters),loss = 0.0230187
I1113 23:35:55.153519 4460 solver.cpp:237] Train net output #0: rpn_cls_loss = 0.0105348 (* 1 = 0.0105348 loss)
I1113 23:35:55.153530 4460 solver.cpp:237] Train net output #1: rpn_loss_bbox = 0.0124839 (* 1 = 0.0124839 loss)
I1113 23:35:55.153542 4460 sgd_solver.cpp:105] Iteration 500,lr = 0.001
I1113 23:35:56.104395 4460 solver.cpp:218] Iteration 520 (21.0352 iter/s,0.950785s/20 iters),loss = 0.0144106
I1113 23:35:56.104485 4460 solver.cpp:237] Train net output #0: rpn_cls_loss = 0.00135394 (* 1 = 0.00135394 loss)
I1113 23:35:56.104504 4460 solver.cpp:237] Train net output #1: rpn_loss_bbox = 0.0130567 (* 1 = 0.0130567 loss)
I1113 23:35:56.104521 4460 sgd_solver.cpp:105] Iteration 520,lr = 0.001
I1113 23:35:56.854631 4460 solver.cpp:218] Iteration 540 (26.6699 iter/s,0.749909s/20 iters),loss = 0.0167331
I1113 23:35:56.854696 4460 solver.cpp:237] Train net output #0: rpn_cls_loss = 0.00285695 (* 1 = 0.00285695 loss)
I1113 23:35:56.854710 4460 solver.cpp:237] Train net output #1: rpn_loss_bbox = 0.0138762 (* 1 = 0.0138762 loss)
I1113 23:35:56.854720 4460 sgd_solver.cpp:105] Iteration 540,lr = 0.001
I1113 23:35:57.824692 4460 solver.cpp:218] Iteration 560 (20.6206 iter/s,0.969902s/20 iters),loss = 0.00817935
I1113 23:35:57.824774 4460 solver.cpp:237] Train net output #0: rpn_cls_loss = 0.00557839 (* 1 = 0.00557839 loss)
I1113 23:35:57.824791 4460 solver.cpp:237] Train net output #1: rpn_loss_bbox = 0.00260096 (* 1 = 0.00260096 loss)
I1113 23:35:57.824806 4460 sgd_solver.cpp:105] Iteration 560,lr = 0.001
I1113 23:35:58.670575 4460 solver.cpp:218] Iteration 580 (23.6486 iter/s,0.845714s/20 iters),loss = 0.00420315
I1113 23:35:58.670637 4460 solver.cpp:237] Train net output #0: rpn_cls_loss = 0.0020043 (* 1 = 0.0020043 loss)
I1113 23:35:58.670648 4460 solver.cpp:237] Train net output #1: rpn_loss_bbox = 0.00219884 (* 1 = 0.00219884 loss)
I1113 23:35:58.670658 4460 sgd_solver.cpp:105] Iteration 580,lr = 0.001
I1114 00:34:17.348683 4460 sgd_solver.cpp:105] Iteration 79980,lr = 0.0001
speed: 0.044s / iter
Wrote snapshot to: /data1/caiyong.wang/program/py-faster-rcnn/output/faster_rcnn_alt_opt/voc_2007_trainval/zf_rpn_stage1_iter_80000.caffemodel
希望我们解析出 Iteration 500 (25.1734 iter/s,0.79449s/20 iters),loss = 0.0230187
中的Iteration与loss值。 其实这是faster rcnn生成的log文件一部分。 我们通过上面的语法学习,在MTracer中生成了正则表达式: bIterations(?<Iteration>d+)s(.*).*losss=s(?<loss>d*.*d+)b
注:.*表示除换行符以外的任意字符,*表示0个或多个 那么如何转化成python代码? 正确的代码如下: import re
pattern = re.compile(r'bIterations(?P<Iteration>d+)s(.*).*losss=s(?P<loss>d*.*d+)b')
arr=pattern.search("I1113 23:35:50.763059 4460 solver.cpp:218] Iteration 400 (27.3075 iter/s,0.7324s/20 iters),loss = 0.0202583")
arr.groups()
arr.group()
arr.group("Iteration")
arr.group("loss")
结果为: arr.groups()
Out[147]: ('400','0.0202583')
arr.group()
Out[148]: 'Iteration 400 (27.3075 iter/s,loss = 0.0202583'
arr.group("Iteration")
Out[149]: '400'
arr.group("loss")
Out[150]: '0.0202583'
这里python的命名组与以往的不一样,使用的是 而且compile里面必须加上r。 参考文献:
三 python正则表达式的其他用法。
python多行匹配 r = re.compile("需要的正则表达式",re.M)
匹配到需要的字符,可以获取红括号内的数字 r = re.compile("r([0-9]{5,})")
举个例子: 需要获取20462和24729连个数字 import re
data = """ r24062 line1 hello word !!!! r24729 line2 revision:24181 """
r = re.compile("^r([0-9]{5,})",re.M)
nums = r.findall(data)
print nums
---------------------
output:["24062","24729"]
注:{5,}表示至少重复5次 (?:)
把它包围起来。 分支条件 满足条件A 或者 满足条件B ,这个时候我们就可以使用分支条件了。 分支条件使用的符号为 |
代码示例: 我们突然发现,它把字符串分割成两个部分了
如果我们只要区分dog和cat呢?正则要怎么写?我添加一个括号试试 还是不对,前面的 “I have a ”根本没有匹配 正确的写法是应该使用无捕获分组 参考:正则表达式-python-无捕获分组与分支选择 (编辑:李大同) 【声明】本站内容均来自网络,其相关言论仅代表作者个人观点,不代表本站立场。若无意侵犯到您的权利,请及时与联系站长删除相关内容! |