神经网络训练集最少可以是多少个?
(mnist 0 ,2)---81*30*2---(1,0)(0,1)
用81*30*2的網絡分類mnist的0和2。讓訓練集的數量n分別等于5000,4500,4000,3500,3000,2500,2000,1500,1000,500,400,300,200,100,50,40,30,20,10,5,4,3,2,共22個值。看看訓練集的大小對分類結果到底有什么影響。
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讓收斂標準δ等于0.5到1e-5的25個值,每個值收斂199次,取平均值。因此共收斂了25*199*22次,首先比較迭代次數
| ? | 5000 | 4500 | 4000 | 3500 | 3000 | 2500 | 2000 | 1500 | 1000 | 500 | 400 | 300 | 200 | 100 | 50 | 40 | 30 | 20 | 10 | 5 | 4 | 3 | 2 |
| δ | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n | 迭代次數n |
| 0.5 | 8.5025126 | 8.6834171 | 9.5778894 | 9.4371859 | 7.8341709 | 9.6934673 | 9.8140704 | 8.8341709 | 9.7537688 | 10.095477 | 10.065327 | 9.4974874 | 9.0150754 | 8.4572864 | 10.482412 | 9.0251256 | 9.3417085 | 8.321608 | 8.6030151 | 9.5778894 | 8.6633166 | 8.1758794 | 7.9447236 |
| 0.4 | 211.40201 | 210.37688 | 212.00503 | 210.46231 | 212.63317 | 213.46231 | 211.74874 | 213.21106 | 213.30653 | 210.75377 | 211.64322 | 211.88442 | 212.9397 | 213.55276 | 227.33166 | 235.95477 | 211.8794 | 195.67337 | 174.76382 | 171.79397 | 150.35176 | 129.59296 | 118.9799 |
| 0.3 | 271.01508 | 271.75377 | 272.50251 | 269.72362 | 269.22613 | 268.26131 | 267.67839 | 268.72864 | 268.68844 | 270.77889 | 269.22111 | 267.52764 | 268.20603 | 281.98492 | 295.76382 | 294.37186 | 272.61809 | 249.99497 | 225.26131 | 217.75377 | 195.09045 | 167.88442 | 157.98492 |
| 0.2 | 325.13568 | 325.26131 | 325.52261 | 321.67839 | 324.00503 | 325.17085 | 325.0804 | 325.42211 | 325.43216 | 325.38693 | 325.78392 | 325.87437 | 328.44724 | 322.22111 | 349.41206 | 340.33166 | 324.15578 | 298.72362 | 271.43216 | 261.93467 | 233.53266 | 204.02513 | 195.34673 |
| 0.1 | 410.8593 | 410.97487 | 409.00503 | 410.27638 | 411.20101 | 411.34673 | 409.16583 | 411.29146 | 409.62814 | 410.60302 | 410.75879 | 410.36181 | 408.59296 | 397.97487 | 418.71357 | 420.41206 | 396.0804 | 366.27136 | 335.72362 | 330.03518 | 296.15578 | 262.67839 | 262.76382 |
| 0.01 | 688.45226 | 695.28643 | 693.63317 | 689.74372 | 688.12563 | 688.92462 | 686.01508 | 692.93467 | 688.88945 | 687.72362 | 685.58291 | 745.15578 | 651.65829 | 720.39196 | 790.16583 | 785.43719 | 799.26633 | 691.66834 | 670.41709 | 785.43216 | 724.11558 | 688.52764 | 890.45729 |
| 0.001 | 1435.1357 | 1435.3719 | 1435.201 | 1440.8643 | 1441.4623 | 1434.4824 | 1440.0101 | 1432.7688 | 1441.0553 | 1372.9447 | 1359.8392 | 1413.9849 | 1443.7588 | 1736.0101 | 1960.397 | 1815.5025 | 2019.0402 | 1686.5829 | 1910.7889 | 3384.2462 | 3165.5729 | 3392.8693 | 5421.1558 |
| 9.00E-04 | 1459.3266 | 1450.7739 | 1468.0905 | 1458.8442 | 1457.2563 | 1470.3518 | 1455.9296 | 1445.6884 | 1454.2211 | 1430.8844 | 1389.608 | 1450.3116 | 1468.3719 | 1820.4874 | 2014.6432 | 1913.0905 | 2106.3719 | 1744.2513 | 2038.8693 | 3644.6784 | 3416.3367 | 3675.3417 | 5947.9045 |
| 8.00E-04 | 1474.7739 | 1495.1457 | 1480.4523 | 1472.9146 | 1512.7688 | 1498.4221 | 1479.7387 | 1487.8392 | 1492.2312 | 1494.603 | 1444.0503 | 1502.7688 | 1522.6834 | 1928.2764 | 2115.6281 | 1992.995 | 2198.5779 | 1875.2965 | 2170.5678 | 3979.9095 | 3711.9045 | 4048.3266 | 6568.804 |
| 7.00E-04 | 1557.2563 | 1570.2211 | 1553.206 | 1561.4271 | 1545.005 | 1577.6583 | 1556 | 1545.8794 | 1556.5226 | 1577.5779 | 1537.0553 | 1560.7186 | 1608.9246 | 2056.5477 | 2240.6834 | 2128.3668 | 2295.397 | 1992.3518 | 2360.2462 | 4397.1508 | 4146.3769 | 4542.598 | 7411.608 |
| 6.00E-04 | 1742.1206 | 1725.6281 | 1732.7035 | 1713.0151 | 1718.4724 | 1696.7337 | 1694.7035 | 1762.8744 | 1697.3668 | 1656.5729 | 1646.2814 | 1646.2513 | 1741.7889 | 2170.3015 | 2367.4724 | 2263.5126 | 2439.3668 | 2148.1206 | 2561.6834 | 5037.7337 | 4643.9849 | 5188.4472 | 8466.9548 |
| 5.00E-04 | 1961.4472 | 2003.9598 | 1919.0955 | 2015.8894 | 1967.5678 | 1989.8693 | 1975.5276 | 1974.1307 | 2019.005 | 1741.9397 | 1780.8643 | 1751.2462 | 1858.5427 | 2375.7035 | 2552.4422 | 2439.3116 | 2615.4673 | 2381.7085 | 2918.7889 | 5764.6281 | 5407.3417 | 5969.9347 | 9980.6784 |
| 4.00E-04 | 2182.2111 | 2193.1256 | 2188.1206 | 2177.2362 | 2185.8995 | 2202.2814 | 2203.3769 | 2175.4874 | 2203.2764 | 1914.0201 | 1870.3819 | 1922.8945 | 1983.4271 | 2638.9598 | 2779.7789 | 2657.8442 | 2848.3417 | 2701.5578 | 3299.7739 | 6930.2412 | 6531.9648 | 7238.2764 | 12146.844 |
| 3.00E-04 | 2349.397 | 2365.7085 | 2347.1055 | 2334.2814 | 2349.1558 | 2342.8844 | 2349.809 | 2358.3518 | 2296.6533 | 2164.8643 | 1993.0452 | 2099.2563 | 2178.7538 | 2920.5578 | 3172.4121 | 2973.4271 | 3210.1106 | 3099.1005 | 3957.5427 | 8742.0452 | 8331.7839 | 9349.6533 | 15832.533 |
| 2.00E-04 | 2731.1457 | 2733.6784 | 2744.7035 | 2708.7035 | 2710.7337 | 2732.3216 | 2728.5126 | 2718.4724 | 2498.7638 | 2442.8342 | 2314.2211 | 2451.2462 | 2522.1407 | 3393.6784 | 3674.1608 | 3478.8442 | 3757.196 | 3898.1055 | 5219.3518 | 12351.271 | 11827.01 | 13288.427 | 22821.206 |
| 1.00E-04 | 3228.6935 | 3250.1809 | 3276.6533 | 3237.2261 | 3259.5779 | 3205.6181 | 3128.9447 | 3242.2814 | 3391.1055 | 3194.1508 | 2912.3216 | 3138.4523 | 3102.8543 | 4219.8392 | 4922.4472 | 4632.4171 | 5125.1809 | 5944.3266 | 8577.8442 | 22394.99 | 21686.98 | 24946.296 | 43436.141 |
| 9.00E-05 | 3358.2915 | 3326.8744 | 3366.3317 | 3375.3367 | 3367.4573 | 3343.4372 | 3330.9347 | 3476.9849 | 3506.593 | 3296.1709 | 3048.7638 | 3277.6683 | 3177.5377 | 4403.1256 | 5123.799 | 4878.5327 | 5354.3367 | 6259.9497 | 9137.0201 | 24672.975 | 23955.583 | 27614.417 | 47741.548 |
| 8.00E-05 | 3521.9698 | 3551.1759 | 3578.4121 | 3543.4774 | 3531.3065 | 3544.1206 | 3512.6834 | 3647.005 | 3581.0452 | 3399.598 | 3149.5176 | 3389.196 | 3287.4673 | 4554.3618 | 5447.5678 | 5158.5025 | 5615.3467 | 6859.7638 | 10056.377 | 27109.884 | 26445.668 | 30678.457 | 53467.683 |
| 7.00E-05 | 3729.4673 | 3740.1106 | 3751.6884 | 3720.4422 | 3728.593 | 3650.9246 | 3736.804 | 3816.392 | 3720.9548 | 3598.4623 | 3305.4975 | 3528.3216 | 3436.3116 | 4761.8894 | 5758.6985 | 5453.1558 | 6047.2513 | 7309.8191 | 10892.879 | 30726.698 | 29946.859 | 34728.467 | 60645.221 |
| 6.00E-05 | 3914.7538 | 3968.6432 | 3917.2563 | 3929.3668 | 3919.6884 | 3932.6834 | 3901.6884 | 4179.6482 | 3994.2211 | 3802.9146 | 3488.3317 | 3693.6985 | 3584.4925 | 4960.7337 | 6122.407 | 5816.1859 | 6527.1558 | 8279.3467 | 12253.643 | 35125.136 | 34565.025 | 40030.739 | 69679.583 |
| 5.00E-05 | 4198.8342 | 4177.9698 | 4215.9196 | 4147.9598 | 4163.2261 | 4167.0452 | 4220.8643 | 4471.206 | 4295.4271 | 4119.5578 | 3688.5226 | 3944.8744 | 3733.9196 | 5270.7437 | 6528.407 | 6223.6985 | 7145.8492 | 9307.0653 | 14100.136 | 41277.849 | 41131.583 | 48023.04 | 82621.377 |
| 4.00E-05 | 4530.1407 | 4611.2362 | 4602.402 | 4561.7186 | 4567.1357 | 4505.3467 | 4659.2965 | 5006.3719 | 4719.8291 | 4415.7789 | 3869.608 | 4218.4221 | 3981.8191 | 5719.8995 | 7258.2312 | 6933.598 | 8020.7739 | 10533.543 | 16602.588 | 49964.261 | 49754.824 | 57922.136 | 101854.19 |
| 3.00E-05 | 5094.4824 | 5060.1106 | 5054.5025 | 5015.5578 | 5106.3216 | 4995.9698 | 5403.407 | 5719.2462 | 5226.7236 | 4860.6332 | 4238.7035 | 4659.7889 | 4302.7136 | 6179.5779 | 8160.0302 | 7968.0101 | 9089.201 | 13021.302 | 20963.653 | 64935.221 | 63982.342 | 76814.055 | 133678.95 |
| 2.00E-05 | 6474.4322 | 6472.8643 | 6431.8894 | 6435.7889 | 6231.4271 | 5973.5678 | 6456.4623 | 7055.2261 | 6338.1206 | 5585.0653 | 4811.0653 | 5317.5176 | 4768.8844 | 7156.6432 | 10168.864 | 9704.9799 | 11167.347 | 17203.809 | 28678.688 | 94762.523 | 94579.935 | 112784.95 | 196678.68 |
| 1.00E-05 | 8638.3719 | 8853.1457 | 8095.7387 | 8804.1809 | 8577.8995 | 8696.2312 | 9096.5427 | 11118.04 | 8461.6583 | 7061.0151 | 5876.6633 | 6564.2613 | 5674.8141 | 9151.5678 | 14010.166 | 13193.216 | 16357.859 | 27801.749 | 49539.965 | 175852.4 | 180663.83 | 215775.98 | 381101.72 |
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迭代次數的最大值出現在訓練集n的數量等于2的時候,而迭代次數最小的值出現在約n=200的位置。
訓練集n=5000到n=200的圖。也就是迭代次數隨著訓練集n的數量的減小,是先減小后增大的。
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再比較分類準確率的平均值pave
| ? | 5000 | 4500 | 4000 | 3500 | 3000 | 2500 | 2000 | 1500 | 1000 | 500 | 400 | 300 | 200 | 100 | 50 | 40 | 30 | 20 | 10 | 5 | 4 | 3 | 2 |
| δ | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave | 平均準確率p-ave |
| 0.5 | 0.5192888 | 0.5285348 | 0.5225431 | 0.5110218 | 0.5205626 | 0.5269464 | 0.5150129 | 0.529359 | 0.5204577 | 0.5326708 | 0.5165015 | 0.5238219 | 0.5275857 | 0.5167088 | 0.5261022 | 0.5116312 | 0.5162068 | 0.5160669 | 0.5263744 | 0.5172308 | 0.5183722 | 0.5139565 | 0.5109294 |
| 0.4 | 0.874694 | 0.8783655 | 0.873665 | 0.8743843 | 0.8749063 | 0.8618365 | 0.8783905 | 0.8645414 | 0.8673237 | 0.8849141 | 0.8682703 | 0.8716045 | 0.8712724 | 0.8914178 | 0.8951492 | 0.9167932 | 0.8807532 | 0.8607401 | 0.7406915 | 0.7705426 | 0.7312931 | 0.7919943 | 0.7101337 |
| 0.3 | 0.9513447 | 0.9512398 | 0.9501034 | 0.9513872 | 0.9521639 | 0.9489121 | 0.9501334 | 0.9506204 | 0.9490269 | 0.9503881 | 0.951075 | 0.9491044 | 0.9506654 | 0.9516344 | 0.9533503 | 0.9506504 | 0.9350855 | 0.9299554 | 0.8990229 | 0.8930487 | 0.8706655 | 0.8753434 | 0.778902 |
| 0.2 | 0.9366539 | 0.9373757 | 0.9373757 | 0.9366739 | 0.9378428 | 0.9383048 | 0.9406626 | 0.9376505 | 0.9378028 | 0.9374906 | 0.9389592 | 0.9386545 | 0.9394862 | 0.9592096 | 0.9579233 | 0.9530705 | 0.9394862 | 0.9416191 | 0.9253549 | 0.9018777 | 0.8931437 | 0.8569063 | 0.7845065 |
| 0.1 | 0.9586551 | 0.958218 | 0.9571516 | 0.9574188 | 0.9584103 | 0.9579733 | 0.9575936 | 0.958283 | 0.9574338 | 0.9584378 | 0.9579533 | 0.9580931 | 0.9514521 | 0.9609429 | 0.9647092 | 0.9584628 | 0.9415817 | 0.9471388 | 0.9354301 | 0.9012708 | 0.8983586 | 0.8439838 | 0.7785049 |
| 0.01 | 0.9359122 | 0.939656 | 0.9367139 | 0.940765 | 0.9369062 | 0.9365665 | 0.9397459 | 0.9374557 | 0.9365316 | 0.9390866 | 0.9408449 | 0.9758359 | 0.9614924 | 0.9696145 | 0.9737954 | 0.9642122 | 0.9532853 | 0.9521115 | 0.942281 | 0.901508 | 0.8995574 | 0.835025 | 0.7750132 |
| 0.001 | 0.9765053 | 0.9765153 | 0.9765228 | 0.9766127 | 0.9766027 | 0.9764928 | 0.9764329 | 0.97676 | 0.9766751 | 0.9783485 | 0.9784759 | 0.9770273 | 0.9771896 | 0.9752515 | 0.9702663 | 0.9666149 | 0.9532953 | 0.9535351 | 0.9444789 | 0.9020001 | 0.8999171 | 0.8353922 | 0.7743139 |
| 9.00E-04 | 0.9767725 | 0.9766526 | 0.9767101 | 0.9766576 | 0.9766526 | 0.9767476 | 0.9767625 | 0.9766626 | 0.9766177 | 0.9779064 | 0.9785058 | 0.9782486 | 0.9769998 | 0.9751841 | 0.9700241 | 0.9670295 | 0.9531554 | 0.9534751 | 0.9445263 | 0.9022573 | 0.8997497 | 0.8354771 | 0.7748459 |
| 8.00E-04 | 0.9767476 | 0.9767001 | 0.976775 | 0.9765028 | 0.976795 | 0.976835 | 0.9766826 | 0.9766402 | 0.976765 | 0.9780913 | 0.9785009 | 0.9784459 | 0.9772021 | 0.9749343 | 0.9700241 | 0.9673142 | 0.9529581 | 0.9534976 | 0.9445388 | 0.9028792 | 0.9001643 | 0.8352523 | 0.7750182 |
| 7.00E-04 | 0.9769798 | 0.9767675 | 0.9768799 | 0.9769948 | 0.9769349 | 0.9769574 | 0.9769623 | 0.9768749 | 0.9769798 | 0.9780962 | 0.9782186 | 0.9779814 | 0.9770997 | 0.974732 | 0.9698193 | 0.967529 | 0.9525435 | 0.9534052 | 0.9446986 | 0.9023272 | 0.8996623 | 0.8352823 | 0.7756651 |
| 6.00E-04 | 0.9775418 | 0.9777166 | 0.9776792 | 0.9775992 | 0.9778065 | 0.9777666 | 0.9774694 | 0.9779089 | 0.9781712 | 0.978366 | 0.9783035 | 0.9781287 | 0.9770098 | 0.9748369 | 0.9696719 | 0.9676489 | 0.9518942 | 0.9535476 | 0.944806 | 0.9026494 | 0.8998571 | 0.8348927 | 0.7756102 |
| 5.00E-04 | 0.977809 | 0.9776092 | 0.9779913 | 0.9779264 | 0.9775768 | 0.9777641 | 0.978361 | 0.9778215 | 0.9798296 | 0.9785158 | 0.9786632 | 0.9786157 | 0.9776692 | 0.9749343 | 0.9697294 | 0.9676089 | 0.9514296 | 0.9534676 | 0.9448235 | 0.9024446 | 0.899977 | 0.8349476 | 0.7769264 |
| 4.00E-04 | 0.9756261 | 0.9753639 | 0.9753964 | 0.9754963 | 0.9752915 | 0.9761381 | 0.9755962 | 0.9753864 | 0.9798271 | 0.9784484 | 0.9788455 | 0.9786457 | 0.977814 | 0.9746321 | 0.9700515 | 0.967564 | 0.9507403 | 0.9533952 | 0.9450233 | 0.9023422 | 0.8998796 | 0.8352049 | 0.7751231 |
| 3.00E-04 | 0.9738104 | 0.9743149 | 0.9742075 | 0.9739877 | 0.9735232 | 0.9740427 | 0.9738853 | 0.9743049 | 0.9783435 | 0.9784534 | 0.9778815 | 0.9771821 | 0.9771472 | 0.9739053 | 0.9698393 | 0.9676988 | 0.9500734 | 0.953048 | 0.9454304 | 0.9028867 | 0.8997697 | 0.8353048 | 0.7744263 |
| 2.00E-04 | 0.9798296 | 0.9802467 | 0.9797996 | 0.9794424 | 0.979405 | 0.9797821 | 0.9798346 | 0.9793375 | 0.9789804 | 0.9770772 | 0.9783535 | 0.9765502 | 0.9760482 | 0.9735407 | 0.969567 | 0.9675415 | 0.950051 | 0.9528108 | 0.9456702 | 0.9028492 | 0.9001868 | 0.8352298 | 0.7748584 |
| 1.00E-04 | 0.9809535 | 0.9808111 | 0.9808885 | 0.9809984 | 0.980916 | 0.980936 | 0.9812082 | 0.980931 | 0.9789229 | 0.9767476 | 0.9756911 | 0.9735207 | 0.9745971 | 0.9730636 | 0.9686055 | 0.9668746 | 0.9501933 | 0.9524936 | 0.9461622 | 0.9037184 | 0.9007887 | 0.8344831 | 0.7744013 |
| 9.00E-05 | 0.9807462 | 0.9807712 | 0.9805089 | 0.9807412 | 0.9807162 | 0.9807362 | 0.9806962 | 0.9807262 | 0.9787706 | 0.9763929 | 0.9753914 | 0.9730062 | 0.9745647 | 0.9728214 | 0.968523 | 0.9666998 | 0.9500085 | 0.9524511 | 0.9463021 | 0.9032114 | 0.9007013 | 0.8351 | 0.774219 |
| 8.00E-05 | 0.9807237 | 0.9804465 | 0.9805688 | 0.9805214 | 0.9805639 | 0.9806613 | 0.9803016 | 0.9805564 | 0.978858 | 0.9759034 | 0.9755687 | 0.9725616 | 0.9744598 | 0.9727839 | 0.9684631 | 0.9665924 | 0.9499635 | 0.9523812 | 0.946402 | 0.9032838 | 0.9003392 | 0.8343682 | 0.7755552 |
| 7.00E-05 | 0.9807437 | 0.9806563 | 0.9806663 | 0.9808161 | 0.9807761 | 0.9805788 | 0.980409 | 0.9804764 | 0.9791228 | 0.9757285 | 0.9762181 | 0.9722145 | 0.974275 | 0.9725766 | 0.9682958 | 0.9664475 | 0.9499186 | 0.9525435 | 0.9464669 | 0.9039956 | 0.9007188 | 0.8348402 | 0.7744538 |
| 6.00E-05 | 0.9812232 | 0.981408 | 0.9812232 | 0.9812931 | 0.981443 | 0.9812632 | 0.9807212 | 0.9807487 | 0.9786632 | 0.9754288 | 0.9751416 | 0.9716525 | 0.9741426 | 0.9725591 | 0.9682333 | 0.9662927 | 0.9498187 | 0.9521989 | 0.9464744 | 0.9037434 | 0.9003292 | 0.834558 | 0.7767016 |
| 5.00E-05 | 0.9823172 | 0.9823022 | 0.9821948 | 0.982005 | 0.9821173 | 0.9822322 | 0.9808486 | 0.9808686 | 0.9782336 | 0.9750217 | 0.9737405 | 0.9714677 | 0.9739178 | 0.9725966 | 0.9683257 | 0.9662502 | 0.9495739 | 0.9520041 | 0.9466567 | 0.9037783 | 0.9004166 | 0.8348727 | 0.7755677 |
| 4.00E-05 | 0.9830939 | 0.9831538 | 0.9829091 | 0.9828991 | 0.9828192 | 0.9824245 | 0.9808186 | 0.9809035 | 0.978316 | 0.9746671 | 0.9732984 | 0.9707709 | 0.9736006 | 0.972714 | 0.9681709 | 0.9661428 | 0.9495864 | 0.9520615 | 0.9467292 | 0.9035935 | 0.9006064 | 0.8346179 | 0.7746211 |
| 3.00E-05 | 0.9828616 | 0.9830764 | 0.9830215 | 0.9826293 | 0.9826418 | 0.9817527 | 0.9814755 | 0.9812307 | 0.9791802 | 0.9739628 | 0.9727489 | 0.970064 | 0.9731585 | 0.9729662 | 0.9680785 | 0.9660454 | 0.9494016 | 0.9520465 | 0.946944 | 0.9039282 | 0.9007463 | 0.8347403 | 0.7752405 |
| 2.00E-05 | 0.9834086 | 0.983551 | 0.9835035 | 0.9803541 | 0.9821248 | 0.980921 | 0.9812682 | 0.9813881 | 0.9789155 | 0.9731835 | 0.9722045 | 0.9694247 | 0.9724667 | 0.9732185 | 0.9678936 | 0.965968 | 0.9495315 | 0.9517643 | 0.9470539 | 0.9039756 | 0.9007338 | 0.8342483 | 0.774761 |
| 1.00E-05 | 0.9823072 | 0.9822722 | 0.9812332 | 0.979892 | 0.9826968 | 0.9822322 | 0.9803466 | 0.9804814 | 0.9778015 | 0.970616 | 0.9718323 | 0.9689027 | 0.9713728 | 0.9728813 | 0.9678262 | 0.9659805 | 0.9493292 | 0.9515645 | 0.9473311 | 0.9045501 | 0.9011159 | 0.835025 | 0.7762346 |
隨著訓練集n的減小分類準確率是減小的,
訓練集數量n=5000到n=500的圖像,
| ? | 5000 | 500 | ? |
| 5.00E-05 | 0.982317 | 0.975022 | 0.992573 |
| 4.00E-05 | 0.983094 | 0.974667 | 0.991428 |
| 3.00E-05 | 0.982862 | 0.973963 | 0.990946 |
| 2.00E-05 | 0.983409 | 0.973184 | 0.989602 |
| 1.00E-05 | 0.982307 | 0.970616 | 0.988098 |
| ? | ? | ? | 0.99053 |
比較n=500和n=5000的數據,雖然將訓練集的數量減小到原來的1/10,但分類準確率只下降了約1%。再比較n=5000和n=2500的數據
| ? | 5000 | 2500 | 2500/5000 |
| 5.00E-05 | 0.982317 | 0.982232 | 0.999914 |
| 4.00E-05 | 0.983094 | 0.982425 | 0.999319 |
| 3.00E-05 | 0.982862 | 0.981753 | 0.998872 |
| 2.00E-05 | 0.983409 | 0.980921 | 0.99747 |
| 1.00E-05 | 0.982307 | 0.982232 | 0.999924 |
| ? | ? | ? | 0.9991 |
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訓練集數量下降到一半,分類準確率下降約1‰,也就表明對這個網絡完全可以將訓練集的數量減到一半,分類差異不大。
Mnist的數據集的圖片是從1開始編號,因此訓練集的數量n=2意味著可以用1張圖片實現分類。盡管分類準確率損失比較大。
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因此對這個網絡來說,從實用角度訓練集數量的最小值可以是原來的50%,甚至是10%。但如果僅讓網絡保持基本的分類能力,訓練集數量的最小值是1個。
總結
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