真正决定分辨准确率的是图片重叠的区域
只保留圖片重疊的區域來用來訓練神經網絡,網絡的分辨準確率是應該上升還是下降?
比如訓練一個分類mnist0和5的二分類網絡
| ? | ? | ? | 0 | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | 5 | ? | ? | ? | ? | ? |
| ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? |
| 0 | 0 | 0 | 0.01 | 0.02 | 0 | 0 | 0 | 0 | ? | ? | 0 | 0 | 0 | 0 | 0 | 0 | 0.11 | 0 | 0 |
| 0 | 0 | 0 | 0 | 0 | 0.03 | 0.09 | 0.07 | 0 | ? | ? | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.13 | 0 |
| 0 | 0 | 0 | 0.86 | 0.55 | 0.36 | 1 | 0.04 | 0 | ? | ? | 0 | 0 | 0 | 0 | 0 | 0 | 0.33 | 0.59 | 0 |
| 0.02 | 0.01 | 0 | 0.72 | 0.05 | 0 | 0.12 | 0.94 | 0 | ? | ? | 0.05 | 0.02 | 0.07 | 0.05 | 0.83 | 0.89 | 0.01 | 1 | 0 |
| 0 | 0.05 | 0.73 | 0 | 0.04 | 0.02 | 0 | 1 | 0 | ? | ? | 0.02 | 0 | 0.05 | 0.96 | 0.94 | 0.87 | 0.04 | 1 | 0 |
| 0 | 0.01 | 0.76 | 0.96 | 0 | 0.05 | 0.05 | 0.83 | 0 | ? | ? | 0.01 | 0.06 | 0.95 | 0.66 | 0.05 | 0.95 | 0.1 | 1 | 0 |
| 0.04 | 0.04 | 0.03 | 1 | 0.91 | 0.06 | 0.09 | 0.93 | 0 | ? | ? | 0.02 | 0.04 | 1 | 0.11 | 0.1 | 0.66 | 1 | 0.68 | 0 |
| 0.07 | 0 | 0 | 0.91 | 0.39 | 0.15 | 0.89 | 0.93 | 0 | ? | ? | 0.02 | 0.05 | 0.67 | 0 | 0 | 0.02 | 0.07 | 0.01 | 0 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ? | ? | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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將圖片從28*28處理成9*9,是每3個點取1個點。然后只保留兩張圖片重疊的區域
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| ? | ? | ? | 0 | 處理后 | ? | ? | ? | ? | ? | ? | ? | ? | 5 | 處理后 | ? | ? | ? | ||
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ? | ? | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.07 | 0 | ? | ? | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.13 | 0 |
| 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.04 | 0 | ? | ? | 0 | 0 | 0 | 0 | 0 | 0 | 0.33 | 0.59 | 0 |
| 0.02 | 0.01 | 0 | 0.72 | 0.05 | 0 | 0.12 | 0.94 | 0 | ? | ? | 0.05 | 0.02 | 0 | 0.05 | 0.83 | 0 | 0.01 | 1 | 0 |
| 0 | 0 | 0.73 | 0 | 0.04 | 0.02 | 0 | 1 | 0 | ? | ? | 0 | 0 | 0.05 | 0 | 0.94 | 0.87 | 0.04 | 1 | 0 |
| 0 | 0.01 | 0.76 | 0.96 | 0 | 0.05 | 0.05 | 0.83 | 0 | ? | ? | 0 | 0.06 | 0.95 | 0.66 | 0 | 0.95 | 0.1 | 1 | 0 |
| 0.04 | 0.04 | 0.03 | 1 | 0.91 | 0.06 | 0.09 | 0.93 | 0 | ? | ? | 0.02 | 0.04 | 1 | 0.11 | 0.1 | 0.66 | 1 | 0.68 | 0 |
| 0.07 | 0 | 0 | 0 | 0 | 0.15 | 0.89 | 0.93 | 0 | ? | ? | 0.02 | 0 | 0 | 0 | 0 | 0.02 | 0.07 | 0.01 | 0 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ? | ? | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
處理后的效果如圖。
剩余的4999組圖片用同樣的辦法處理。用處理后的數據去訓練網絡,但測試集不變。
比較方法同樣是用固定收斂標準多次測量取平均值的辦法。
網絡結構是
(mnist 0,mnist5)-81*30*2-(1,0)(0,1)
網絡沒有用卷積核,每個收斂標準計算199次。
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比較正常輸入訓練的結果和經過重疊處理的結果
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| ? | 正常 | 重疊 | ? | 正常 | 重疊 | ? | 正常 | 重疊 | ? | 正常 | 重疊 | ? |
| ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? |
| δ | 迭代次數n | 迭代次數n | ? | 平均準確率p-ave | 平均準確率p-ave | 耗時 min/199 | 耗時 min/199 | 最大值p-max | 最大值p-max | |||
| 0.5 | 10.37688 | 8.954773869 | 0.862954 | 0.524822 | 0.513655 | 0.978722 | 0.05835 | 0.05905 | 1.011997 | 0.842415 | 0.747329 | 0.887127 |
| 0.4 | 260.804 | 500.9447236 | 1.920771 | 0.809037 | 0.802219 | 0.991572 | 0.0718 | 0.085817 | 1.195218 | 0.9375 | 0.956197 | 1.019943 |
| 0.3 | 342.5025 | 632.6633166 | 1.847179 | 0.903154 | 0.927125 | 1.026542 | 0.08035 | 0.096383 | 1.199544 | 0.94391 | 0.959936 | 1.016978 |
| 0.2 | 446.2211 | 731.8291457 | 1.640059 | 0.938246 | 0.950291 | 1.012837 | 0.08215 | 0.107383 | 1.307162 | 0.953526 | 0.966346 | 1.013445 |
| 0.1 | 545.2814 | 928.321608 | 1.702463 | 0.962159 | 0.955109 | 0.992674 | 0.0899 | 0.11475 | 1.276418 | 0.96688 | 0.963675 | 0.996685 |
| 0.01 | 858.794 | 1554.60804 | 1.810222 | 0.965648 | 0.963613 | 0.997893 | 0.116267 | 0.151117 | 1.299742 | 0.969551 | 0.96688 | 0.997245 |
| 0.001 | 1503.749 | 2944 | 1.957774 | 0.972279 | 0.972907 | 1.000646 | 0.1503 | 0.238017 | 1.583611 | 0.97703 | 0.974893 | 0.997813 |
| 9.00E-04 | 1570.111 | 2944 | 1.875027 | 0.972939 | 0.972695 | 0.999749 | 0.149817 | 0.232317 | 1.550673 | 0.978098 | 0.974359 | 0.996177 |
| 8.00E-04 | 1651.859 | 2944 | 1.782234 | 0.97324 | 0.972818 | 0.999567 | 0.158333 | 0.23075 | 1.457368 | 0.978098 | 0.974359 | 0.996177 |
| 7.00E-04 | 1721.397 | 2949.346734 | 1.713345 | 0.972977 | 0.972797 | 0.999815 | 0.16385 | 0.2321 | 1.41654 | 0.977564 | 0.974359 | 0.996721 |
| 6.00E-04 | 1791.035 | 2971.286432 | 1.658977 | 0.972252 | 0.972713 | 1.000475 | 0.168033 | 0.234433 | 1.39516 | 0.97703 | 0.974359 | 0.997266 |
| 5.00E-04 | 1852.794 | 3022.080402 | 1.631094 | 0.971575 | 0.972407 | 1.000856 | 0.1692 | 0.234 | 1.382979 | 0.97703 | 0.974893 | 0.997813 |
| 4.00E-04 | 2008.945 | 3306.170854 | 1.645725 | 0.971989 | 0.970907 | 0.998887 | 0.184333 | 0.2555 | 1.386076 | 0.978632 | 0.975427 | 0.996725 |
| 3.00E-04 | 2421.176 | 4384.321608 | 1.810823 | 0.974523 | 0.970818 | 0.996199 | 0.212933 | 0.341933 | 1.605823 | 0.980235 | 0.983974 | 1.003815 |
| 2.00E-04 | 2884.171 | 10441.28643 | 3.620204 | 0.978678 | 0.975862 | 0.997123 | 0.237933 | 0.697983 | 2.933525 | 0.981838 | 0.986645 | 1.004897 |
| 1.00E-04 | 3701.739 | 18356.21106 | 4.958808 | 0.978772 | 0.984849 | 1.006209 | 0.28295 | 1.208383 | 4.27066 | 0.980769 | 0.986645 | 1.005991 |
| 9.00E-05 | 3804.09 | 18591.38191 | 4.887208 | 0.978769 | 0.984519 | 1.005875 | 0.2906 | 1.384117 | 4.762962 | 0.980769 | 0.987179 | 1.006536 |
| 8.00E-05 | 4102.754 | 19038.83417 | 4.640501 | 0.978514 | 0.984286 | 1.005898 | 0.309067 | 1.4335 | 4.638158 | 0.981303 | 0.987714 | 1.006532 |
| 7.00E-05 | 4245.648 | 19696.58291 | 4.63924 | 0.978772 | 0.984353 | 1.005702 | 0.3175 | 1.44435 | 4.549134 | 0.981303 | 0.987714 | 1.006532 |
| 6.00E-05 | 4405.829 | 20854.70854 | 4.733436 | 0.978224 | 0.98453 | 1.006446 | -1.84253 | 1.528483 | -0.82956 | 0.981838 | 0.987714 | 1.005985 |
| 5.00E-05 | 4542.523 | 22543.76382 | 4.962829 | 0.976547 | 0.984514 | 1.008159 | 0.346 | 1.621517 | 4.686464 | 0.981838 | 0.988248 | 1.006529 |
| 4.00E-05 | 4640.01 | 25131.43216 | 5.416245 | 0.974501 | 0.985435 | 1.011219 | 0.331 | 1.7829 | 5.386405 | 0.981303 | 0.988248 | 1.007077 |
| 3.00E-05 | 4650 | 29251.83417 | 6.290717 | 0.974077 | 0.985861 | 1.012098 | 0.331617 | 2.065017 | 6.22712 | 0.975962 | 0.988248 | 1.012589 |
| 2.00E-05 | 4692.864 | 35816.31156 | 7.632079 | 0.974281 | 0.985386 | 1.011398 | 0.333417 | 2.42425 | 7.270932 | 0.979167 | 0.989316 | 1.010366 |
| 1.00E-05 | 5420.995 | 50387.91457 | 9.294957 | 0.97594 | 0.98526 | 1.00955 | 0.377167 | 3.283667 | 8.706142 | 0.981303 | 0.990385 | 1.009254 |
| ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? |
| ? | ? | ? | 5.745602 | ? | ? | 1.008255 | ? | ? | 4.966842 | ? | ? | 1.007739 |
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對比1e-4>=δ>=1e-5的區間
平均準確率上升0.8%,代價是耗時是原來的約5倍。因為將不重疊的區域去掉后分類性能是上升的,因此這個實驗證明圖片中重疊的區域才是決定分類性能的關鍵,而不重疊的區域事實上只是干擾。
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| 重疊 | 05 | 無核 | ? | ? | ? | ? | ? | ? |
| ? | ? | ? | ? | ? | ? | ? | ? | ? |
| f2[0] | f2[1] | 迭代次數n | 平均準確率p-ave | δ | 耗時ms/次 | 耗時ms/199次 | 耗時 min/199 | 最大值p-max |
| 0.500595 | 0.500735 | 8.954774 | 0.513655 | 0.5 | 17.79899 | 3543 | 0.05905 | 0.747329 |
| 0.539992 | 0.460466 | 500.9447 | 0.802219 | 0.4 | 25.86935 | 5149 | 0.085817 | 0.956197 |
| 0.581046 | 0.419118 | 632.6633 | 0.927125 | 0.3 | 28.9799 | 5783 | 0.096383 | 0.959936 |
| 0.479588 | 0.521407 | 731.8291 | 0.950291 | 0.2 | 32.36683 | 6443 | 0.107383 | 0.966346 |
| 0.796647 | 0.203773 | 928.3216 | 0.955109 | 0.1 | 34.57286 | 6885 | 0.11475 | 0.963675 |
| 0.013561 | 0.986429 | 1554.608 | 0.963613 | 0.01 | 45.54774 | 9067 | 0.151117 | 0.96688 |
| 4.68E-04 | 0.999533 | 2944 | 0.972907 | 0.001 | 71.74372 | 14281 | 0.238017 | 0.974893 |
| 4.65E-04 | 0.999536 | 2944 | 0.972695 | 9.00E-04 | 70.01005 | 13939 | 0.232317 | 0.974359 |
| 4.57E-04 | 0.999544 | 2944 | 0.972818 | 8.00E-04 | 69.50251 | 13845 | 0.23075 | 0.974359 |
| 4.64E-04 | 0.999537 | 2949.347 | 0.972797 | 7.00E-04 | 69.92462 | 13926 | 0.2321 | 0.974359 |
| 4.52E-04 | 0.999549 | 2971.286 | 0.972713 | 6.00E-04 | 70.63819 | 14066 | 0.234433 | 0.974359 |
| 4.11E-04 | 0.999589 | 3022.08 | 0.972407 | 5.00E-04 | 70.50754 | 14040 | 0.234 | 0.974893 |
| 3.07E-04 | 0.999694 | 3306.171 | 0.970907 | 4.00E-04 | 77.00503 | 15330 | 0.2555 | 0.975427 |
| 0.020341 | 0.979659 | 4384.322 | 0.970818 | 3.00E-04 | 103.0704 | 20516 | 0.341933 | 0.983974 |
| 0.241295 | 0.758705 | 10441.29 | 0.975862 | 2.00E-04 | 210.4322 | 41879 | 0.697983 | 0.986645 |
| 0.20105 | 0.79895 | 18356.21 | 0.984849 | 1.00E-04 | 364.2563 | 72503 | 1.208383 | 0.986645 |
| 0.226167 | 0.773833 | 18591.38 | 0.984519 | 9.00E-05 | 417.3116 | 83047 | 1.384117 | 0.987179 |
| 0.271384 | 0.728616 | 19038.83 | 0.984286 | 8.00E-05 | 432.206 | 86010 | 1.4335 | 0.987714 |
| 0.150792 | 0.849208 | 19696.58 | 0.984353 | 7.00E-05 | 435.4422 | 86661 | 1.44435 | 0.987714 |
| 0.15581 | 0.84419 | 20854.71 | 0.98453 | 6.00E-05 | 460.8291 | 91709 | 1.528483 | 0.987714 |
| 0.296499 | 0.703501 | 22543.76 | 0.984514 | 5.00E-05 | 488.8995 | 97291 | 1.621517 | 0.988248 |
| 0.527639 | 0.472361 | 25131.43 | 0.985435 | 4.00E-05 | 537.5578 | 106974 | 1.7829 | 0.988248 |
| 0.728633 | 0.271367 | 29251.83 | 0.985861 | 3.00E-05 | 622.5377 | 123901 | 2.065017 | 0.988248 |
| 0.834161 | 0.165839 | 35816.31 | 0.985386 | 2.00E-05 | 730.8894 | 145455 | 2.42425 | 0.989316 |
| 0.899491 | 0.100509 | 50387.91 | 0.98526 | 1.00E-05 | 990.0101 | 197020 | 3.283667 | 0.990385 |
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| 正常 | 05 | 無核 | ? | ? | ? | ? | ? | ? |
| ? | ? | ? | ? | ? | ? | ? | ? | ? |
| f2[0] | f2[1] | 迭代次數n | 平均準確率p-ave | δ | 耗時ms/次 | 耗時ms/199次 | 耗時 min/199 | 最大值p-max |
| 0.500303 | 0.499717 | 10.37688 | 0.524822 | 0.5 | 17.58794 | 3501 | 0.05835 | 0.842415 |
| 0.579083 | 0.42149 | 260.804 | 0.809037 | 0.4 | 21.57286 | 4308 | 0.0718 | 0.9375 |
| 0.562925 | 0.437137 | 342.5025 | 0.903154 | 0.3 | 24.19095 | 4821 | 0.08035 | 0.94391 |
| 0.716773 | 0.283868 | 446.2211 | 0.938246 | 0.2 | 24.76884 | 4929 | 0.08215 | 0.953526 |
| 0.158165 | 0.842016 | 545.2814 | 0.962159 | 0.1 | 27.09548 | 5394 | 0.0899 | 0.96688 |
| 0.09662 | 0.903382 | 858.794 | 0.965648 | 0.01 | 34.9196 | 6976 | 0.116267 | 0.969551 |
| 8.28E-04 | 0.99917 | 1503.749 | 0.972279 | 0.001 | 45.31658 | 9018 | 0.1503 | 0.97703 |
| 7.50E-04 | 0.99925 | 1570.111 | 0.972939 | 9.00E-04 | 45 | 8989 | 0.149817 | 0.978098 |
| 6.33E-04 | 0.999366 | 1651.859 | 0.97324 | 8.00E-04 | 47.73367 | 9500 | 0.158333 | 0.978098 |
| 5.23E-04 | 0.999478 | 1721.397 | 0.972977 | 7.00E-04 | 49.38191 | 9831 | 0.16385 | 0.977564 |
| 4.21E-04 | 0.999578 | 1791.035 | 0.972252 | 6.00E-04 | 50.66332 | 10082 | 0.168033 | 0.97703 |
| 3.52E-04 | 0.999648 | 1852.794 | 0.971575 | 5.00E-04 | 50.92462 | 10152 | 0.1692 | 0.97703 |
| 3.23E-04 | 0.999677 | 2008.945 | 0.971989 | 4.00E-04 | 55.47739 | 11060 | 0.184333 | 0.978632 |
| 2.59E-04 | 0.99974 | 2421.176 | 0.974523 | 3.00E-04 | 64.18593 | 12776 | 0.212933 | 0.980235 |
| 1.58E-04 | 0.999841 | 2884.171 | 0.978678 | 2.00E-04 | 71.72362 | 14276 | 0.237933 | 0.981838 |
| 8.69E-05 | 0.999913 | 3701.739 | 0.978772 | 1.00E-04 | 85.30653 | 16977 | 0.28295 | 0.980769 |
| 7.88E-05 | 0.999921 | 3804.09 | 0.978769 | 9.00E-05 | 87.58794 | 17436 | 0.2906 | 0.980769 |
| 6.85E-05 | 0.999932 | 4102.754 | 0.978514 | 8.00E-05 | 93.0201 | 18544 | 0.309067 | 0.981303 |
| 5.70E-05 | 0.999943 | 4245.648 | 0.978772 | 7.00E-05 | 95.71859 | 19050 | 0.3175 | 0.981303 |
| 4.35E-05 | 0.999957 | 4405.829 | 0.978224 | 6.00E-05 | -555.543 | -110552 | -1.84253 | 0.981838 |
| 2.58E-05 | 0.999974 | 4542.523 | 0.976547 | 5.00E-05 | 104.3116 | 20760 | 0.346 | 0.981838 |
| 1.27E-05 | 0.999987 | 4640.01 | 0.974501 | 4.00E-05 | 99.76884 | 19860 | 0.331 | 0.981303 |
| 1.05E-05 | 0.999989 | 4650 | 0.974077 | 3.00E-05 | 99.9397 | 19897 | 0.331617 | 0.975962 |
| 1.07E-05 | 0.999989 | 4692.864 | 0.974281 | 2.00E-05 | 100.4171 | 20005 | 0.333417 | 0.979167 |
| 7.21E-06 | 0.999993 | 5420.995 | 0.97594 | 1.00E-05 | 113.6985 | 22630 | 0.377167 | 0.981303 |
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總結
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