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Result.txt
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Because of the performance restriction of numpy and my laptop
this nerual network based on numpy runs very slowly,
and it's impossible for me to finish even one epoch(it's estimated to take 10,000 seconds)
So I run the training program for some time. The results are below.
Batch means the number of data has been trained. So it shows training
2000 data takes 343s which is 3.3% of all the 60,000 dataset.
But the loss shows that this model does work, the overall trend of the loss does decrease,
while it maybe unstable because of the restricted number of training data.
Epoch:0 Batch:1 ( 0.001667% ) Loss:2.861995 Runtime:3.24s
Epoch:0 Batch:21 ( 0.035000% ) Loss:2.312338 Runtime:6.46s
Epoch:0 Batch:41 ( 0.068333% ) Loss:2.293660 Runtime:9.67s
Epoch:0 Batch:61 ( 0.101667% ) Loss:2.367850 Runtime:12.89s
Epoch:0 Batch:81 ( 0.135000% ) Loss:2.239376 Runtime:16.11s
Epoch:0 Batch:101 ( 0.168333% ) Loss:2.024684 Runtime:19.33s
Epoch:0 Batch:121 ( 0.201667% ) Loss:2.239738 Runtime:22.54s
Epoch:0 Batch:141 ( 0.235000% ) Loss:2.179049 Runtime:25.77s
Epoch:0 Batch:161 ( 0.268333% ) Loss:2.210369 Runtime:28.98s
Epoch:0 Batch:181 ( 0.301667% ) Loss:2.287664 Runtime:32.20s
Epoch:0 Batch:201 ( 0.335000% ) Loss:2.122423 Runtime:35.41s
Epoch:0 Batch:221 ( 0.368333% ) Loss:2.254846 Runtime:38.63s
Epoch:0 Batch:241 ( 0.401667% ) Loss:2.088890 Runtime:41.84s
Epoch:0 Batch:261 ( 0.435000% ) Loss:2.109991 Runtime:45.06s
Epoch:0 Batch:281 ( 0.468333% ) Loss:1.609916 Runtime:48.29s
Epoch:0 Batch:301 ( 0.501667% ) Loss:1.991831 Runtime:51.62s
Epoch:0 Batch:321 ( 0.535000% ) Loss:2.308771 Runtime:55.11s
Epoch:0 Batch:341 ( 0.568333% ) Loss:2.173141 Runtime:58.37s
Epoch:0 Batch:361 ( 0.601667% ) Loss:1.727534 Runtime:61.64s
Epoch:0 Batch:381 ( 0.635000% ) Loss:1.511966 Runtime:64.85s
Epoch:0 Batch:401 ( 0.668333% ) Loss:2.067997 Runtime:68.05s
Epoch:0 Batch:421 ( 0.701667% ) Loss:2.277882 Runtime:71.27s
Epoch:0 Batch:441 ( 0.735000% ) Loss:2.341856 Runtime:74.59s
Epoch:0 Batch:461 ( 0.768333% ) Loss:2.285234 Runtime:77.81s
Epoch:0 Batch:481 ( 0.801667% ) Loss:2.311207 Runtime:81.02s
Epoch:0 Batch:501 ( 0.835000% ) Loss:2.224984 Runtime:84.28s
Epoch:0 Batch:521 ( 0.868333% ) Loss:2.207295 Runtime:88.20s
Epoch:0 Batch:541 ( 0.901667% ) Loss:2.209761 Runtime:91.75s
Epoch:0 Batch:561 ( 0.935000% ) Loss:2.774883 Runtime:95.73s
Epoch:0 Batch:581 ( 0.968333% ) Loss:2.301742 Runtime:99.00s
Epoch:0 Batch:601 ( 1.001667% ) Loss:2.172821 Runtime:102.23s
Epoch:0 Batch:621 ( 1.035000% ) Loss:2.349057 Runtime:105.44s
Epoch:0 Batch:641 ( 1.068333% ) Loss:2.353453 Runtime:108.88s
Epoch:0 Batch:661 ( 1.101667% ) Loss:2.173092 Runtime:112.44s
Epoch:0 Batch:681 ( 1.135000% ) Loss:2.350878 Runtime:116.25s
Epoch:0 Batch:701 ( 1.168333% ) Loss:2.165628 Runtime:120.06s
Epoch:0 Batch:721 ( 1.201667% ) Loss:2.181503 Runtime:123.31s
Epoch:0 Batch:741 ( 1.235000% ) Loss:2.226172 Runtime:126.60s
Epoch:0 Batch:761 ( 1.268333% ) Loss:2.100867 Runtime:129.82s
Epoch:0 Batch:781 ( 1.301667% ) Loss:2.215880 Runtime:133.05s
Epoch:0 Batch:801 ( 1.335000% ) Loss:2.106936 Runtime:136.47s
Epoch:0 Batch:821 ( 1.368333% ) Loss:1.693797 Runtime:139.80s
Epoch:0 Batch:841 ( 1.401667% ) Loss:1.995721 Runtime:143.29s
Epoch:0 Batch:861 ( 1.435000% ) Loss:2.034301 Runtime:146.97s
Epoch:0 Batch:881 ( 1.468333% ) Loss:1.895898 Runtime:150.39s
Epoch:0 Batch:901 ( 1.501667% ) Loss:1.763651 Runtime:153.63s
Epoch:0 Batch:921 ( 1.535000% ) Loss:1.642075 Runtime:157.25s
Epoch:0 Batch:941 ( 1.568333% ) Loss:1.378071 Runtime:160.49s
Epoch:0 Batch:961 ( 1.601667% ) Loss:1.947471 Runtime:163.72s
Epoch:0 Batch:981 ( 1.635000% ) Loss:2.220888 Runtime:167.05s
Epoch:0 Batch:1001 ( 1.668333% ) Loss:1.851239 Runtime:170.31s
Epoch:0 Batch:1021 ( 1.701667% ) Loss:2.297099 Runtime:174.12s
Epoch:0 Batch:1041 ( 1.735000% ) Loss:1.390147 Runtime:177.36s
Epoch:0 Batch:1061 ( 1.768333% ) Loss:2.182457 Runtime:180.85s
Epoch:0 Batch:1081 ( 1.801667% ) Loss:1.769916 Runtime:184.29s
Epoch:0 Batch:1101 ( 1.835000% ) Loss:2.132022 Runtime:187.65s
Epoch:0 Batch:1121 ( 1.868333% ) Loss:2.237659 Runtime:191.03s
Epoch:0 Batch:1141 ( 1.901667% ) Loss:1.966287 Runtime:194.59s
Epoch:0 Batch:1161 ( 1.935000% ) Loss:1.897626 Runtime:198.13s
Epoch:0 Batch:1181 ( 1.968333% ) Loss:1.962872 Runtime:202.26s
Epoch:0 Batch:1201 ( 2.001667% ) Loss:1.893307 Runtime:205.50s
Epoch:0 Batch:1221 ( 2.035000% ) Loss:1.641718 Runtime:208.73s
Epoch:0 Batch:1241 ( 2.068333% ) Loss:1.456879 Runtime:212.12s
Epoch:0 Batch:1261 ( 2.101667% ) Loss:1.681768 Runtime:215.49s
Epoch:0 Batch:1281 ( 2.135000% ) Loss:1.469746 Runtime:218.84s
Epoch:0 Batch:1301 ( 2.168333% ) Loss:1.792089 Runtime:222.23s
Epoch:0 Batch:1321 ( 2.201667% ) Loss:1.637528 Runtime:226.51s
Epoch:0 Batch:1341 ( 2.235000% ) Loss:2.102146 Runtime:231.32s
Epoch:0 Batch:1361 ( 2.268333% ) Loss:2.145204 Runtime:236.03s
Epoch:0 Batch:1381 ( 2.301667% ) Loss:2.229327 Runtime:239.32s
Epoch:0 Batch:1401 ( 2.335000% ) Loss:2.105697 Runtime:242.75s
Epoch:0 Batch:1421 ( 2.368333% ) Loss:2.107229 Runtime:246.18s
Epoch:0 Batch:1441 ( 2.401667% ) Loss:2.051644 Runtime:249.87s
Epoch:0 Batch:1461 ( 2.435000% ) Loss:2.189533 Runtime:253.18s
Epoch:0 Batch:1481 ( 2.468333% ) Loss:2.029829 Runtime:256.42s
Epoch:0 Batch:1501 ( 2.501667% ) Loss:2.229454 Runtime:259.66s
Epoch:0 Batch:1521 ( 2.535000% ) Loss:2.109516 Runtime:262.90s
Epoch:0 Batch:1541 ( 2.568333% ) Loss:2.086384 Runtime:266.14s
Epoch:0 Batch:1561 ( 2.601667% ) Loss:1.743512 Runtime:269.62s
Epoch:0 Batch:1581 ( 2.635000% ) Loss:2.146559 Runtime:273.23s
Epoch:0 Batch:1601 ( 2.668333% ) Loss:1.877960 Runtime:276.52s
Epoch:0 Batch:1621 ( 2.701667% ) Loss:2.346272 Runtime:279.77s
Epoch:0 Batch:1641 ( 2.735000% ) Loss:1.689536 Runtime:283.16s
Epoch:0 Batch:1661 ( 2.768333% ) Loss:1.739171 Runtime:286.79s
Epoch:0 Batch:1681 ( 2.801667% ) Loss:1.268169 Runtime:290.03s
Epoch:0 Batch:1701 ( 2.835000% ) Loss:1.452602 Runtime:293.36s
Epoch:0 Batch:1721 ( 2.868333% ) Loss:1.090802 Runtime:296.86s
Epoch:0 Batch:1741 ( 2.901667% ) Loss:1.291804 Runtime:300.40s
Epoch:0 Batch:1761 ( 2.935000% ) Loss:0.985096 Runtime:303.91s
Epoch:0 Batch:1781 ( 2.968333% ) Loss:1.853304 Runtime:307.48s
Epoch:0 Batch:1801 ( 3.001667% ) Loss:3.365271 Runtime:310.71s
Epoch:0 Batch:1821 ( 3.035000% ) Loss:2.084713 Runtime:313.92s
Epoch:0 Batch:1841 ( 3.068333% ) Loss:2.092151 Runtime:317.14s
Epoch:0 Batch:1861 ( 3.101667% ) Loss:2.002915 Runtime:320.35s
Epoch:0 Batch:1881 ( 3.135000% ) Loss:1.856525 Runtime:323.77s
Epoch:0 Batch:1901 ( 3.168333% ) Loss:1.779403 Runtime:327.01s
Epoch:0 Batch:1921 ( 3.201667% ) Loss:1.590287 Runtime:330.22s
Epoch:0 Batch:1941 ( 3.235000% ) Loss:1.765717 Runtime:333.45s
Epoch:0 Batch:1961 ( 3.268333% ) Loss:1.707493 Runtime:336.67s
Epoch:0 Batch:1981 ( 3.301667% ) Loss:1.212449 Runtime:339.90s
Epoch:0 Batch:2001 ( 3.335000% ) Loss:1.290035 Runtime:343.12s