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| id,url,city,city_region,city_subregion,street,source,term,house_number,korpus,floor,is_last_floor,is_owner,building_year,building_floors,building_type,kap_remont_year,price,update_date,created_at,updated_at,rooms_total,is_room,rooms_separate,longitude,latitude | |
| 1,https://realt.by/vitebsk-region/rent/flat-for-long/object/1137119/,Витебск,Железнодорожный район,Центр,,realt,длительный,,,16,1,1,,16,,,46.0,2020-01-19 03:00:00,2020-01-26 01:53:53,2020-01-26 15:27:42,2,1,,30.190732,55.19471 | |
| 2,https://realt.by/grodno-region/rent/flat-for-long/object/1625135/,Гродно,,Вишневец,,realt,длительный,,,9,1,1,2005,9,панельный,,50.0,2019-12-29 03:00:00,2020-01-26 01:53:53,2020-01-26 15:27:42,3,1,,23.845851,53.646472 | |
| 3,https://realt.by/gomel-region/rent/flat-for-long/object/1637301/,Гомель,Центральный район,Волотова,,realt,длительный,,,3,0,1,,15,панельный,,50.0,2020-01-21 03:00:00,2020-01-26 01:53:53,2020-01-26 15:27:42,3,1,,31.028744,52.471726 | |
| 4,https://realt.by/gomel-region/rent/flat-for-long/object/1637303/,Гомель,Центральный |
stas-sl
/ freshness_detector.ipynb
Last active
June 30, 2018 07:24
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/ pandas_image.ipynb
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January 3, 2022 17:19
Displaying inline images in pandas DataFrame
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stas-sl
/ gridstack-grid.js
Last active
June 27, 2016 06:33
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| import Ember from 'ember'; | |
| export default Ember.Component.extend({ | |
| classNames: ['grid-stack'], | |
| didInsertElement() { | |
| var options = { | |
| width: this.get('width'), | |
| cellHeight: this.get('cellHeight'), | |
| verticalMargin: this.get('verticalMargin'), |
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| I0623 14:13:34.760556 10365 solver.cpp:280] Solving mixed_lstm | |
| I0623 14:13:34.760573 10365 solver.cpp:281] Learning Rate Policy: fixed | |
| I0623 14:13:34.771200 10365 solver.cpp:338] Iteration 0, Testing net (#0) | |
| I0623 14:14:33.060055 10365 solver.cpp:393] Test loss: 4.63185 | |
| I0623 14:14:33.060313 10365 solver.cpp:406] Test net output #0: loss1/accuracy = 0.483626 | |
| I0623 14:14:33.060333 10365 solver.cpp:406] Test net output #1: loss1/accuracy01 = 0.92 | |
| I0623 14:14:33.060346 10365 solver.cpp:406] Test net output #2: loss1/accuracy02 = 0.726 | |
| I0623 14:14:33.060359 10365 solver.cpp:406] Test net output #3: loss1/accuracy03 = 0.492 | |
| I0623 14:14:33.060370 10365 solver.cpp:406] Test net output #4: loss1/accuracy04 = 0.391 | |
| I0623 14:14:33.060381 10365 solver.cpp:406] Test net output #5: loss1/accuracy05 = 0.353 |
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| I0612 14:48:17.543987 6181 solver.cpp:280] Solving mixed_lstm | |
| I0612 14:48:17.543999 6181 solver.cpp:281] Learning Rate Policy: fixed | |
| I0612 14:48:17.554961 6181 solver.cpp:338] Iteration 0, Testing net (#0) | |
| I0612 14:49:17.784380 6181 solver.cpp:393] Test loss: 2.74935 | |
| I0612 14:49:17.784834 6181 solver.cpp:406] Test net output #0: loss1/accuracy = 0.62328 | |
| I0612 14:49:17.784857 6181 solver.cpp:406] Test net output #1: loss1/accuracy01 = 0.772 | |
| I0612 14:49:17.784870 6181 solver.cpp:406] Test net output #2: loss1/accuracy02 = 0.65 | |
| I0612 14:49:17.784883 6181 solver.cpp:406] Test net output #3: loss1/accuracy03 = 0.535 | |
| I0612 14:49:17.784894 6181 solver.cpp:406] Test net output #4: loss1/accuracy04 = 0.498 | |
| I0612 14:49:17.784906 6181 solver.cpp:406] Test net output #5: loss1/accuracy05 = 0.509 |
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| I0607 01:01:32.950039 32403 solver.cpp:280] Solving mixed_lstm | |
| I0607 01:01:32.950052 32403 solver.cpp:281] Learning Rate Policy: fixed | |
| I0607 01:01:32.960855 32403 solver.cpp:338] Iteration 0, Testing net (#0) | |
| I0607 01:02:33.209952 32403 solver.cpp:393] Test loss: 2.53488 | |
| I0607 01:02:33.210446 32403 solver.cpp:406] Test net output #0: loss1/accuracy = 0.638494 | |
| I0607 01:02:33.210469 32403 solver.cpp:406] Test net output #1: loss1/accuracy01 = 0.796 | |
| I0607 01:02:33.210481 32403 solver.cpp:406] Test net output #2: loss1/accuracy02 = 0.667 | |
| I0607 01:02:33.210494 32403 solver.cpp:406] Test net output #3: loss1/accuracy03 = 0.569 | |
| I0607 01:02:33.210505 32403 solver.cpp:406] Test net output #4: loss1/accuracy04 = 0.476 | |
| I0607 01:02:33.210517 32403 solver.cpp:406] Test net output #5: loss1/accuracy05 = 0.534 |
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| I0525 00:09:25.737826 5272 solver.cpp:280] Solving mixed_lstm | |
| I0525 00:09:25.737840 5272 solver.cpp:281] Learning Rate Policy: fixed | |
| I0525 00:09:26.273970 5272 solver.cpp:229] Iteration 0, loss = 27.9836 | |
| I0525 00:09:26.274058 5272 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0525 00:09:26.274077 5272 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0525 00:09:26.274091 5272 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0525 00:09:26.274102 5272 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0525 00:09:26.274114 5272 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0 | |
| I0525 00:09:26.274126 5272 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.125 | |
| I0525 00:09:26.274138 5272 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0 |
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| I0525 00:09:25.737826 5272 solver.cpp:280] Solving mixed_lstm | |
| I0525 00:09:25.737840 5272 solver.cpp:281] Learning Rate Policy: fixed | |
| I0525 00:09:26.273970 5272 solver.cpp:229] Iteration 0, loss = 27.9836 | |
| I0525 00:09:26.274058 5272 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0525 00:09:26.274077 5272 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0525 00:09:26.274091 5272 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0525 00:09:26.274102 5272 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0525 00:09:26.274114 5272 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0 | |
| I0525 00:09:26.274126 5272 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.125 | |
| I0525 00:09:26.274138 5272 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0 |
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| I0510 15:24:57.106338 10926 solver.cpp:280] Solving mixed_lstm | |
| I0510 15:24:57.106350 10926 solver.cpp:281] Learning Rate Policy: fixed | |
| I0510 15:24:57.414443 10926 solver.cpp:229] Iteration 0, loss = 28.4505 | |
| I0510 15:24:57.414511 10926 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0510 15:24:57.414530 10926 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0 | |
| I0510 15:24:57.414542 10926 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0 | |
| I0510 15:24:57.414554 10926 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0 | |
| I0510 15:24:57.414566 10926 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0 | |
| I0510 15:24:57.414578 10926 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0 | |
| I0510 15:24:57.414590 10926 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0 |
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