Regular Expressions 101

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  • Substitution
  • A single character of: a, b or c
    [abc]
  • A character except: a, b or c
    [^abc]
  • A character in the range: a-z
    [a-z]
  • A character not in the range: a-z
    [^a-z]
  • A character in the range: a-z or A-Z
    [a-zA-Z]
  • Any single character
    .
  • Alternate - match either a or b
    a|b
  • Any whitespace character
    \s
  • Any non-whitespace character
    \S
  • Any digit
    \d
  • Any non-digit
    \D
  • Any word character
    \w
  • Any non-word character
    \W
  • Non-capturing group
    (?:...)
  • Capturing group
    (...)
  • Zero or one of a
    a?
  • Zero or more of a
    a*
  • One or more of a
    a+
  • Exactly 3 of a
    a{3}
  • 3 or more of a
    a{3,}
  • Between 3 and 6 of a
    a{3,6}
  • Start of string
    ^
  • End of string
    $
  • A word boundary
    \b
  • Non-word boundary
    \B

Regular Expression
No Match

/
/
g

Test String

Code Generator

Language

Generated Code

# coding=utf8 # the above tag defines encoding for this document and is for Python 2.x compatibility import re regex = r"Iteration (?P<iter_num>\d+) (.?)*, loss = ((\d*.\d*)+)\n.*( *)Train net output #0: accuracy_training = ((\d*.\d*)+)" test_str = ("I0312 23:23:16.136683 2676 solver.cpp:273] Solving AlexNet\n" "I0312 23:23:16.136685 2676 solver.cpp:274] Learning Rate Policy: step\n" "I0312 23:23:16.138639 2676 solver.cpp:331] Iteration 0, Testing net (#0)\n" "I0312 23:23:16.161350 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:23:16.401897 2676 blocking_queue.cpp:49] Waiting for data\n" "I0312 23:23:22.186173 2676 solver.cpp:398] Test net output #0: accuracy = 0.545357\n" "I0312 23:23:22.186219 2676 solver.cpp:398] Test net output #1: loss = 1.26114 (* 1 = 1.26114 loss)\n" "I0312 23:23:22.457931 2676 solver.cpp:219] Iteration 0 (-7.07474e-05 iter/s, 6.31881s/20 iters), loss = 1.38639\n" "I0312 23:23:22.460357 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.335938\n" "I0312 23:23:22.460372 2676 solver.cpp:238] Train net output #1: loss = 1.38639 (* 1 = 1.38639 loss)\n" "I0312 23:23:22.460417 2676 sgd_solver.cpp:105] Iteration 0, lr = 0.001\n" "I0312 23:23:33.165230 2676 solver.cpp:219] Iteration 20 (1.86903 iter/s, 10.7008s/20 iters), loss = 1.13092\n" "I0312 23:23:33.165272 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.605469\n" "I0312 23:23:33.165280 2676 solver.cpp:238] Train net output #1: loss = 1.13092 (* 1 = 1.13092 loss)\n" "I0312 23:23:33.165285 2676 sgd_solver.cpp:105] Iteration 20, lr = 0.001\n" "I0312 23:23:36.300024 2676 solver.cpp:331] Iteration 28, Testing net (#0)\n" "I0312 23:23:36.300057 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:23:38.279549 2676 solver.cpp:398] Test net output #0: accuracy = 0.545357\n" "I0312 23:23:38.279577 2676 solver.cpp:398] Test net output #1: loss = 1.16134 (* 1 = 1.16134 loss)\n" "I0312 23:23:41.725931 2676 solver.cpp:219] Iteration 40 (2.33717 iter/s, 8.55736s/20 iters), loss = 1.20381\n" "I0312 23:23:41.738235 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.515625\n" "I0312 23:23:41.738255 2676 solver.cpp:238] Train net output #1: loss = 1.20381 (* 1 = 1.20381 loss)\n" "I0312 23:23:41.738261 2676 sgd_solver.cpp:105] Iteration 40, lr = 0.001\n" "I0312 23:23:45.567281 2676 solver.cpp:331] Iteration 56, Testing net (#0)\n" "I0312 23:23:45.567314 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:23:47.537325 2676 solver.cpp:398] Test net output #0: accuracy = 0.545714\n" "I0312 23:23:47.537364 2676 solver.cpp:398] Test net output #1: loss = 1.14157 (* 1 = 1.14157 loss)\n" "I0312 23:23:48.830137 2676 solver.cpp:219] Iteration 60 (2.7988 iter/s, 7.14593s/20 iters), loss = 1.18281\n" "I0312 23:23:48.842432 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.523438\n" "I0312 23:23:48.842471 2676 solver.cpp:238] Train net output #1: loss = 1.18281 (* 1 = 1.18281 loss)\n" "I0312 23:23:48.842490 2676 sgd_solver.cpp:105] Iteration 60, lr = 0.001\n" "I0312 23:23:54.134619 2676 solver.cpp:219] Iteration 80 (3.7488 iter/s, 5.33504s/20 iters), loss = 1.20185\n" "I0312 23:23:54.146620 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.539062\n" "I0312 23:23:54.146651 2676 solver.cpp:238] Train net output #1: loss = 1.20185 (* 1 = 1.20185 loss)\n" "I0312 23:23:54.146656 2676 sgd_solver.cpp:105] Iteration 80, lr = 0.001\n" "I0312 23:23:54.778365 2676 solver.cpp:331] Iteration 84, Testing net (#0)\n" "I0312 23:23:54.778399 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:23:56.768332 2676 solver.cpp:398] Test net output #0: accuracy = 0.545357\n" "I0312 23:23:56.768355 2676 solver.cpp:398] Test net output #1: loss = 1.10908 (* 1 = 1.10908 loss)\n" "I0312 23:24:01.276773 2676 solver.cpp:219] Iteration 100 (2.79421 iter/s, 7.15766s/20 iters), loss = 1.10751\n" "I0312 23:24:01.288919 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.550781\n" "I0312 23:24:01.288950 2676 solver.cpp:238] Train net output #1: loss = 1.10751 (* 1 = 1.10751 loss)\n" "I0312 23:24:01.288956 2676 sgd_solver.cpp:105] Iteration 100, lr = 0.001\n" "I0312 23:24:04.057387 2676 solver.cpp:331] Iteration 112, Testing net (#0)\n" "I0312 23:24:04.057422 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:24:06.030336 2676 solver.cpp:398] Test net output #0: accuracy = 0.548214\n" "I0312 23:24:06.030366 2676 solver.cpp:398] Test net output #1: loss = 1.05868 (* 1 = 1.05868 loss)\n" "I0312 23:24:08.410758 2676 solver.cpp:219] Iteration 120 (2.8032 iter/s, 7.1347s/20 iters), loss = 1.07986\n" "I0312 23:24:08.422947 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.542969\n" "I0312 23:24:08.422967 2676 solver.cpp:238] Train net output #1: loss = 1.07986 (* 1 = 1.07986 loss)\n" "I0312 23:24:08.422972 2676 sgd_solver.cpp:105] Iteration 120, lr = 0.001\n" "I0312 23:24:13.330821 2676 solver.cpp:331] Iteration 140, Testing net (#0)\n" "I0312 23:24:13.330871 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:24:15.309830 2676 solver.cpp:398] Test net output #0: accuracy = 0.571786\n" "I0312 23:24:15.309869 2676 solver.cpp:398] Test net output #1: loss = 1.00978 (* 1 = 1.00978 loss)\n" "I0312 23:24:15.570574 2676 solver.cpp:219] Iteration 140 (2.7953 iter/s, 7.15487s/20 iters), loss = 1.1149\n" "I0312 23:24:15.573030 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.503906\n" "I0312 23:24:15.573060 2676 solver.cpp:238] Train net output #1: loss = 1.1149 (* 1 = 1.1149 loss)\n" "I0312 23:24:15.573066 2676 sgd_solver.cpp:105] Iteration 140, lr = 0.001\n" "I0312 23:24:20.904796 2676 solver.cpp:219] Iteration 160 (3.74837 iter/s, 5.33566s/20 iters), loss = 1.15146\n" "I0312 23:24:20.916966 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.488281\n" "I0312 23:24:20.916983 2676 solver.cpp:238] Train net output #1: loss = 1.15146 (* 1 = 1.15146 loss)\n" "I0312 23:24:20.916988 2676 sgd_solver.cpp:105] Iteration 160, lr = 0.001\n" "I0312 23:24:22.625000 2676 solver.cpp:331] Iteration 168, Testing net (#0)\n" "I0312 23:24:22.625020 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:24:24.634449 2676 solver.cpp:398] Test net output #0: accuracy = 0.5775\n" "I0312 23:24:24.634475 2676 solver.cpp:398] Test net output #1: loss = 0.970126 (* 1 = 0.970126 loss)\n" "I0312 23:24:28.095824 2676 solver.cpp:219] Iteration 180 (2.78425 iter/s, 7.18325s/20 iters), loss = 1.03344\n" "I0312 23:24:28.108048 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.5625\n" "I0312 23:24:28.108081 2676 solver.cpp:238] Train net output #1: loss = 1.03344 (* 1 = 1.03344 loss)\n" "I0312 23:24:28.108086 2676 sgd_solver.cpp:105] Iteration 180, lr = 0.001\n" "I0312 23:24:31.963044 2676 solver.cpp:331] Iteration 196, Testing net (#0)\n" "I0312 23:24:31.963080 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:24:33.957167 2676 solver.cpp:398] Test net output #0: accuracy = 0.576429\n" "I0312 23:24:33.957206 2676 solver.cpp:398] Test net output #1: loss = 0.958613 (* 1 = 0.958613 loss)\n" "I0312 23:24:35.273638 2676 solver.cpp:219] Iteration 200 (2.78957 iter/s, 7.16956s/20 iters), loss = 0.960444\n" "I0312 23:24:35.285887 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.5625\n" "I0312 23:24:35.285918 2676 solver.cpp:238] Train net output #1: loss = 0.960444 (* 1 = 0.960444 loss)\n" "I0312 23:24:35.285924 2676 sgd_solver.cpp:105] Iteration 200, lr = 0.001\n" "I0312 23:24:40.657598 2676 solver.cpp:219] Iteration 220 (3.72122 iter/s, 5.37458s/20 iters), loss = 0.904809\n" "I0312 23:24:40.669728 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.578125\n" "I0312 23:24:40.669744 2676 solver.cpp:238] Train net output #1: loss = 0.904809 (* 1 = 0.904809 loss)\n" "I0312 23:24:40.669764 2676 sgd_solver.cpp:105] Iteration 220, lr = 0.001\n" "I0312 23:24:41.304330 2676 solver.cpp:331] Iteration 224, Testing net (#0)\n" "I0312 23:24:41.304366 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:24:43.297623 2676 solver.cpp:398] Test net output #0: accuracy = 0.595\n" "I0312 23:24:43.297662 2676 solver.cpp:398] Test net output #1: loss = 0.935005 (* 1 = 0.935005 loss)\n" "I0312 23:24:47.847645 2676 solver.cpp:219] Iteration 240 (2.78486 iter/s, 7.18169s/20 iters), loss = 0.964164\n" "I0312 23:24:47.859764 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.582031\n" "I0312 23:24:47.859781 2676 solver.cpp:238] Train net output #1: loss = 0.964164 (* 1 = 0.964164 loss)\n" "I0312 23:24:47.859800 2676 sgd_solver.cpp:105] Iteration 240, lr = 0.001\n" "I0312 23:24:50.647053 2676 solver.cpp:331] Iteration 252, Testing net (#0)\n" "I0312 23:24:50.647089 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:24:52.633581 2676 solver.cpp:398] Test net output #0: accuracy = 0.599286\n" "I0312 23:24:52.633618 2676 solver.cpp:398] Test net output #1: loss = 0.980615 (* 1 = 0.980615 loss)\n" "I0312 23:24:55.028817 2676 solver.cpp:219] Iteration 260 (2.78831 iter/s, 7.17279s/20 iters), loss = 0.988547\n" "I0312 23:24:55.041051 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.59375\n" "I0312 23:24:55.041085 2676 solver.cpp:238] Train net output #1: loss = 0.988547 (* 1 = 0.988547 loss)\n" "I0312 23:24:55.041091 2676 sgd_solver.cpp:105] Iteration 260, lr = 0.001\n" "I0312 23:24:59.975488 2676 solver.cpp:331] Iteration 280, Testing net (#0)\n" "I0312 23:24:59.975523 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:25:01.941673 2676 solver.cpp:398] Test net output #0: accuracy = 0.6\n" "I0312 23:25:01.941711 2676 solver.cpp:398] Test net output #1: loss = 0.919044 (* 1 = 0.919044 loss)\n" "I0312 23:25:02.204524 2676 solver.cpp:219] Iteration 280 (2.79049 iter/s, 7.1672s/20 iters), loss = 1.0459\n" "I0312 23:25:02.206957 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.585938\n" "I0312 23:25:02.206985 2676 solver.cpp:238] Train net output #1: loss = 1.0459 (* 1 = 1.0459 loss)\n" "I0312 23:25:02.207006 2676 sgd_solver.cpp:105] Iteration 280, lr = 0.001\n" "I0312 23:25:07.572079 2676 solver.cpp:219] Iteration 300 (3.72585 iter/s, 5.36791s/20 iters), loss = 0.892142\n" "I0312 23:25:07.584277 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.605469\n" "I0312 23:25:07.584297 2676 solver.cpp:238] Train net output #1: loss = 0.892142 (* 1 = 0.892142 loss)\n" "I0312 23:25:07.584317 2676 sgd_solver.cpp:105] Iteration 300, lr = 0.001\n" "I0312 23:25:09.297149 2676 solver.cpp:331] Iteration 308, Testing net (#0)\n" "I0312 23:25:09.297185 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:25:11.270056 2676 solver.cpp:398] Test net output #0: accuracy = 0.617857\n" "I0312 23:25:11.270094 2676 solver.cpp:398] Test net output #1: loss = 0.876603 (* 1 = 0.876603 loss)\n" "I0312 23:25:14.751806 2676 solver.cpp:219] Iteration 320 (2.78891 iter/s, 7.17125s/20 iters), loss = 0.935945\n" "I0312 23:25:14.763928 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.601562\n" "I0312 23:25:14.763962 2676 solver.cpp:238] Train net output #1: loss = 0.935945 (* 1 = 0.935945 loss)\n" "I0312 23:25:14.763967 2676 sgd_solver.cpp:105] Iteration 320, lr = 0.001\n" "I0312 23:25:18.636039 2676 solver.cpp:331] Iteration 336, Testing net (#0)\n" "I0312 23:25:18.636075 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:25:20.618952 2676 solver.cpp:398] Test net output #0: accuracy = 0.634643\n" "I0312 23:25:20.618978 2676 solver.cpp:398] Test net output #1: loss = 0.85954 (* 1 = 0.85954 loss)\n" "I0312 23:25:21.936133 2676 solver.cpp:219] Iteration 340 (2.7871 iter/s, 7.17593s/20 iters), loss = 0.869285\n" "I0312 23:25:21.948242 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.636719\n" "I0312 23:25:21.948276 2676 solver.cpp:238] Train net output #1: loss = 0.869285 (* 1 = 0.869285 loss)\n" "I0312 23:25:21.948282 2676 sgd_solver.cpp:105] Iteration 340, lr = 0.001\n" "I0312 23:25:27.332120 2676 solver.cpp:219] Iteration 360 (3.71286 iter/s, 5.38669s/20 iters), loss = 0.899806\n" "I0312 23:25:27.344280 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.613281\n" "I0312 23:25:27.344297 2676 solver.cpp:238] Train net output #1: loss = 0.899806 (* 1 = 0.899806 loss)\n" "I0312 23:25:27.344303 2676 sgd_solver.cpp:105] Iteration 360, lr = 0.001\n" "I0312 23:25:27.983243 2676 solver.cpp:331] Iteration 364, Testing net (#0)\n" "I0312 23:25:27.983280 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:25:30.007056 2676 solver.cpp:398] Test net output #0: accuracy = 0.645357\n" "I0312 23:25:30.007093 2676 solver.cpp:398] Test net output #1: loss = 0.844139 (* 1 = 0.844139 loss)\n" "I0312 23:25:32.783954 2676 blocking_queue.cpp:49] Waiting for data\n" "I0312 23:25:34.568060 2676 solver.cpp:219] Iteration 380 (2.7672 iter/s, 7.22753s/20 iters), loss = 0.840824\n" "I0312 23:25:34.580109 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.664062\n" "I0312 23:25:34.580127 2676 solver.cpp:238] Train net output #1: loss = 0.840824 (* 1 = 0.840824 loss)\n" "I0312 23:25:34.580132 2676 sgd_solver.cpp:105] Iteration 380, lr = 0.001\n" "I0312 23:25:37.373685 2676 solver.cpp:331] Iteration 392, Testing net (#0)\n" "I0312 23:25:37.373728 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:25:39.393951 2676 solver.cpp:398] Test net output #0: accuracy = 0.652143\n" "I0312 23:25:39.393977 2676 solver.cpp:398] Test net output #1: loss = 0.85232 (* 1 = 0.85232 loss)\n" "I0312 23:25:41.798810 2676 solver.cpp:219] Iteration 400 (2.76914 iter/s, 7.22245s/20 iters), loss = 0.829997\n" "I0312 23:25:41.811102 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.65625\n" "I0312 23:25:41.811122 2676 solver.cpp:238] Train net output #1: loss = 0.829997 (* 1 = 0.829997 loss)\n" "I0312 23:25:41.811127 2676 sgd_solver.cpp:105] Iteration 400, lr = 0.001\n" "I0312 23:25:46.767246 2676 solver.cpp:331] Iteration 420, Testing net (#0)\n" "I0312 23:25:46.767431 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:25:48.779335 2676 solver.cpp:398] Test net output #0: accuracy = 0.664286\n" "I0312 23:25:48.779371 2676 solver.cpp:398] Test net output #1: loss = 0.804686 (* 1 = 0.804686 loss)\n" "I0312 23:25:49.040144 2676 solver.cpp:219] Iteration 420 (2.76518 iter/s, 7.2328s/20 iters), loss = 0.937281\n" "I0312 23:25:49.042621 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.597656\n" "I0312 23:25:49.042652 2676 solver.cpp:238] Train net output #1: loss = 0.937281 (* 1 = 0.937281 loss)\n" "I0312 23:25:49.042657 2676 sgd_solver.cpp:105] Iteration 420, lr = 0.001\n" "I0312 23:25:54.423571 2676 solver.cpp:219] Iteration 440 (3.71489 iter/s, 5.38374s/20 iters), loss = 0.970864\n" "I0312 23:25:54.435729 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.597656\n" "I0312 23:25:54.435745 2676 solver.cpp:238] Train net output #1: loss = 0.970864 (* 1 = 0.970864 loss)\n" "I0312 23:25:54.435765 2676 sgd_solver.cpp:105] Iteration 440, lr = 0.001\n" "I0312 23:25:56.153745 2676 solver.cpp:331] Iteration 448, Testing net (#0)\n" "I0312 23:25:56.153781 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:25:58.141585 2676 solver.cpp:398] Test net output #0: accuracy = 0.673214\n" "I0312 23:25:58.141623 2676 solver.cpp:398] Test net output #1: loss = 0.799573 (* 1 = 0.799573 loss)\n" "I0312 23:26:01.631712 2676 solver.cpp:219] Iteration 460 (2.77788 iter/s, 7.19972s/20 iters), loss = 0.886954\n" "I0312 23:26:01.643872 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.613281\n" "I0312 23:26:01.643903 2676 solver.cpp:238] Train net output #1: loss = 0.886954 (* 1 = 0.886954 loss)\n" "I0312 23:26:01.643908 2676 sgd_solver.cpp:105] Iteration 460, lr = 0.001\n" "I0312 23:26:05.524873 2676 solver.cpp:331] Iteration 476, Testing net (#0)\n" "I0312 23:26:05.524893 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:26:07.525322 2676 solver.cpp:398] Test net output #0: accuracy = 0.674643\n" "I0312 23:26:07.525360 2676 solver.cpp:398] Test net output #1: loss = 0.781635 (* 1 = 0.781635 loss)\n" "I0312 23:26:08.875036 2676 solver.cpp:219] Iteration 480 (2.773 iter/s, 7.21241s/20 iters), loss = 0.790123\n" "I0312 23:26:08.887622 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.644531\n" "I0312 23:26:08.887660 2676 solver.cpp:238] Train net output #1: loss = 0.790123 (* 1 = 0.790123 loss)\n" "I0312 23:26:08.887665 2676 sgd_solver.cpp:105] Iteration 480, lr = 0.001\n" "I0312 23:26:14.376476 2676 solver.cpp:219] Iteration 500 (3.70628 iter/s, 5.39625s/20 iters), loss = 0.727805\n" "I0312 23:26:14.388772 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.660156\n" "I0312 23:26:14.388804 2676 solver.cpp:238] Train net output #1: loss = 0.727805 (* 1 = 0.727805 loss)\n" "I0312 23:26:14.388809 2676 sgd_solver.cpp:105] Iteration 500, lr = 0.001\n" "I0312 23:26:15.037009 2676 solver.cpp:331] Iteration 504, Testing net (#0)\n" "I0312 23:26:15.037044 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:26:17.052551 2676 solver.cpp:398] Test net output #0: accuracy = 0.678572\n" "I0312 23:26:17.052588 2676 solver.cpp:398] Test net output #1: loss = 0.757533 (* 1 = 0.757533 loss)\n" "I0312 23:26:21.647141 2676 solver.cpp:219] Iteration 520 (2.77673 iter/s, 7.20271s/20 iters), loss = 0.833957\n" "I0312 23:26:21.659446 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.648438\n" "I0312 23:26:21.659463 2676 solver.cpp:238] Train net output #1: loss = 0.833957 (* 1 = 0.833957 loss)\n" "I0312 23:26:21.659482 2676 sgd_solver.cpp:105] Iteration 520, lr = 0.001\n" "I0312 23:26:24.474599 2676 solver.cpp:331] Iteration 532, Testing net (#0)\n" "I0312 23:26:24.474622 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:26:26.479739 2676 solver.cpp:398] Test net output #0: accuracy = 0.706072\n" "I0312 23:26:26.479763 2676 solver.cpp:398] Test net output #1: loss = 0.735266 (* 1 = 0.735266 loss)\n" "I0312 23:26:28.900995 2676 solver.cpp:219] Iteration 540 (2.77077 iter/s, 7.21822s/20 iters), loss = 0.796514\n" "I0312 23:26:28.913269 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.660156\n" "I0312 23:26:28.913302 2676 solver.cpp:238] Train net output #1: loss = 0.796514 (* 1 = 0.796514 loss)\n" "I0312 23:26:28.913308 2676 sgd_solver.cpp:105] Iteration 540, lr = 0.001\n" "I0312 23:26:33.882889 2676 solver.cpp:331] Iteration 560, Testing net (#0)\n" "I0312 23:26:33.882967 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:26:35.880746 2676 solver.cpp:398] Test net output #0: accuracy = 0.701429\n" "I0312 23:26:35.880770 2676 solver.cpp:398] Test net output #1: loss = 0.724797 (* 1 = 0.724797 loss)\n" "I0312 23:26:36.143487 2676 solver.cpp:219] Iteration 560 (2.77038 iter/s, 7.21922s/20 iters), loss = 0.798369\n" "I0312 23:26:36.145952 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.6875\n" "I0312 23:26:36.145998 2676 solver.cpp:238] Train net output #1: loss = 0.798369 (* 1 = 0.798369 loss)\n" "I0312 23:26:36.146019 2676 sgd_solver.cpp:105] Iteration 560, lr = 0.001\n" "I0312 23:26:41.539515 2676 solver.cpp:219] Iteration 580 (3.71042 iter/s, 5.39022s/20 iters), loss = 0.653225\n" "I0312 23:26:41.551695 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.742188\n" "I0312 23:26:41.551728 2676 solver.cpp:238] Train net output #1: loss = 0.653225 (* 1 = 0.653225 loss)\n" "I0312 23:26:41.551734 2676 sgd_solver.cpp:105] Iteration 580, lr = 0.001\n" "I0312 23:26:43.272722 2676 solver.cpp:331] Iteration 588, Testing net (#0)\n" "I0312 23:26:43.272761 2676 net.cpp:678] Ignoring source layer accuracy_training\n" "I0312 23:26:45.273249 2676 solver.cpp:398] Test net output #0: accuracy = 0.720357\n" "I0312 23:26:45.273273 2676 solver.cpp:398] Test net output #1: loss = 0.69917 (* 1 = 0.69917 loss)\n" "I0312 23:26:48.760844 2676 solver.cpp:219] Iteration 600 (2.77404 iter/s, 7.20969s/20 iters), loss = 0.766637\n" "I0312 23:26:48.773182 2676 solver.cpp:238] Train net output #0: accuracy_training = 0.671875\n" "I0312 23:26:48.773214 2676 solver.cpp:238] Train net output #1: loss = 0.766637 (* 1 = 0.766637 loss)\n" "I0312 23:26:48.773221 2676 sgd_solver.cpp:105] Iteration 600, lr = 0.001\n" "I0312 23:26:52.661382 2676 solver.cpp:331] Iteration 616, Testing net (#0)") matches = re.finditer(regex, test_str) for matchNum, match in enumerate(matches, start=1): print ("Match {matchNum} was found at {start}-{end}: {match}".format(matchNum = matchNum, start = match.start(), end = match.end(), match = match.group())) for groupNum in range(0, len(match.groups())): groupNum = groupNum + 1 print ("Group {groupNum} found at {start}-{end}: {group}".format(groupNum = groupNum, start = match.start(groupNum), end = match.end(groupNum), group = match.group(groupNum))) # Note: for Python 2.7 compatibility, use ur"" to prefix the regex and u"" to prefix the test string and substitution.

Please keep in mind that these code samples are automatically generated and are not guaranteed to work. If you find any syntax errors, feel free to submit a bug report. For a full regex reference for Python, please visit: https://docs.python.org/3/library/re.html