Hi I have tried angular loss. The results have been reported by loss but I need accuracy so the following code added here
predicy = torch.max(embedded, 1)[1].data.squeeze() acc = (predicy == target).sum().item() / float(target.size(0))
The accuracy was low and it had a descending behavior. I think the accuracy has to compute in another way. How can I do that?
any suggestion would be appreciated.
Train Epoch: 1 [ 0/48600 ( 0%)] train_loss: 28.2820 val_loss: 8.0892 acc:0.0000
Train Epoch: 1 [ 1600/48600 ( 3%)] train_loss: 6.9128 val_loss: 6.9078 acc:0.8333
Train Epoch: 1 [ 3200/48600 ( 7%)] train_loss: 6.9110 val_loss: 6.9078 acc:1.7378
Train Epoch: 1 [ 4800/48600 ( 10%)] train_loss: 6.9079 val_loss: 6.9078 acc:2.1107
Train Epoch: 1 [ 6400/48600 ( 13%)] train_loss: 6.9061 val_loss: 6.9021 acc:2.4691
Train Epoch: 1 [ 8000/48600 ( 16%)] train_loss: 6.8372 val_loss: 6.8911 acc:2.9455
Train Epoch: 1 [ 9600/48600 ( 20%)] train_loss: 6.8924 val_loss: 6.8379 acc:2.9752
Train Epoch: 1 [11200/48600 ( 23%)] train_loss: 6.6970 val_loss: 6.5565 acc:3.0319
Train Epoch: 1 [12800/48600 ( 26%)] train_loss: 6.3550 val_loss: 6.4852 acc:3.0590
Train Epoch: 1 [14400/48600 ( 30%)] train_loss: 6.9291 val_loss: 6.4815 acc:3.0732
Train Epoch: 1 [16000/48600 ( 33%)] train_loss: 6.4415 val_loss: 6.3958 acc:3.1032
Train Epoch: 1 [17600/48600 ( 36%)] train_loss: 6.3201 val_loss: 6.4022 acc:3.1618
Train Epoch: 1 [19200/48600 ( 40%)] train_loss: 7.9138 val_loss: 6.2496 acc:3.1639
Train Epoch: 1 [20800/48600 ( 43%)] train_loss: 6.5684 val_loss: 6.3088 acc:3.1561
Train Epoch: 1 [22400/48600 ( 46%)] train_loss: 6.4354 val_loss: 6.5117 acc:3.1539
Train Epoch: 1 [24000/48600 ( 49%)] train_loss: 6.3263 val_loss: 6.3474 acc:3.1520
Train Epoch: 1 [25600/48600 ( 53%)] train_loss: 6.3033 val_loss: 6.2607 acc:3.1347
Train Epoch: 1 [27200/48600 ( 56%)] train_loss: 7.0122 val_loss: 6.6542 acc:3.0865
Train Epoch: 1 [28800/48600 ( 59%)] train_loss: 6.6477 val_loss: 6.7577 acc:2.9882
Train Epoch: 1 [30400/48600 ( 63%)] train_loss: 6.4013 val_loss: 6.7127 acc:2.9068
Train Epoch: 1 [32000/48600 ( 66%)] train_loss: 6.0408 val_loss: 5.8954 acc:2.8180
Train Epoch: 1 [33600/48600 ( 69%)] train_loss: 6.3112 val_loss: 5.9389 acc:2.7049
Train Epoch: 1 [35200/48600 ( 72%)] train_loss: 6.2653 val_loss: 6.3091 acc:2.6446
Train Epoch: 1 [36800/48600 ( 76%)] train_loss: 6.4967 val_loss: 6.2455 acc:2.6546
Train Epoch: 1 [38400/48600 ( 79%)] train_loss: 7.1699 val_loss: 6.2683 acc:2.6923
Train Epoch: 1 [40000/48600 ( 82%)] train_loss: 6.9097 val_loss: 6.7606 acc:2.7246
Train Epoch: 1 [41600/48600 ( 86%)] train_loss: 6.3366 val_loss: 6.3285 acc:2.6775
Train Epoch: 1 [43200/48600 ( 89%)] train_loss: 6.2499 val_loss: 6.0655 acc:2.6317
Train Epoch: 1 [44800/48600 ( 92%)] train_loss: 7.1336 val_loss: 5.7625 acc:2.5691
Train Epoch: 1 [46400/48600 ( 96%)] train_loss: 5.9713 val_loss: 6.0712 acc:2.5194
Train Epoch: 1 [48000/48600 ( 99%)] train_loss: 5.8089 val_loss: 5.9169 acc:2.4688
Train Epoch: 2 [ 0/48600 ( 0%)] train_loss: 6.2228 val_loss: 6.3985 acc:0.0000
Train Epoch: 2 [ 1600/48600 ( 3%)] train_loss: 6.3038 val_loss: 5.3646 acc:1.1310
Train Epoch: 2 [ 3200/48600 ( 7%)] train_loss: 6.0695 val_loss: 6.5455 acc:1.1890
Train Epoch: 2 [ 4800/48600 ( 10%)] train_loss: 6.3791 val_loss: 6.8738 acc:1.0451
Train Epoch: 2 [ 6400/48600 ( 13%)] train_loss: 7.1383 val_loss: 5.7615 acc:0.9877
Train Epoch: 2 [ 8000/48600 ( 16%)] train_loss: 6.0784 val_loss: 5.4069 acc:0.9777
Train Epoch: 2 [ 9600/48600 ( 20%)] train_loss: 5.9274 val_loss: 6.2934 acc:0.9401
Train Epoch: 2 [11200/48600 ( 23%)] train_loss: 6.2468 val_loss: 6.4788 acc:0.8865
Train Epoch: 2 [12800/48600 ( 26%)] train_loss: 5.4620 val_loss: 5.8104 acc:0.8385
Train Epoch: 2 [14400/48600 ( 30%)] train_loss: 5.8782 val_loss: 5.9951 acc:0.7735
Train Epoch: 2 [16000/48600 ( 33%)] train_loss: 5.6917 val_loss: 6.0670 acc:0.7649
Train Epoch: 2 [17600/48600 ( 36%)] train_loss: 6.7636 val_loss: 6.5156 acc:0.7353
Train Epoch: 2 [19200/48600 ( 40%)] train_loss: 6.0429 val_loss: 5.4953 acc:0.7624
Train Epoch: 2 [20800/48600 ( 43%)] train_loss: 5.4111 val_loss: 5.5173 acc:0.7615
Train Epoch: 2 [22400/48600 ( 46%)] train_loss: 5.7747 val_loss: 5.2676 acc:0.7295
Train Epoch: 2 [24000/48600 ( 49%)] train_loss: 5.5873 val_loss: 5.3546 acc:0.6977
Train Epoch: 2 [25600/48600 ( 53%)] train_loss: 5.6621 val_loss: 5.2749 acc:0.6815
Train Epoch: 2 [27200/48600 ( 56%)] train_loss: 5.6667 val_loss: 5.7213 acc:0.6708
Train Epoch: 2 [28800/48600 ( 59%)] train_loss: 5.8191 val_loss: 5.1638 acc:0.6440
Train Epoch: 2 [30400/48600 ( 63%)] train_loss: 5.7820 val_loss: 6.2523 acc:0.6266
Train Epoch: 2 [32000/48600 ( 66%)] train_loss: 5.4359 val_loss: 5.2851 acc:0.6047
Train Epoch: 2 [33600/48600 ( 69%)] train_loss: 5.3940 val_loss: 5.4324 acc:0.5879
Train Epoch: 2 [35200/48600 ( 72%)] train_loss: 5.1442 val_loss: 4.8492 acc:0.5782
Train Epoch: 2 [36800/48600 ( 76%)] train_loss: 4.7314 val_loss: 5.0989 acc:0.5613
Train Epoch: 2 [38400/48600 ( 79%)] train_loss: 4.7219 val_loss: 4.7842 acc:0.5509
Train Epoch: 2 [40000/48600 ( 82%)] train_loss: 4.6455 val_loss: 5.1125 acc:0.5314
Train Epoch: 2 [41600/48600 ( 86%)] train_loss: 4.6511 val_loss: 5.7798 acc:0.5134
Train Epoch: 2 [43200/48600 ( 89%)] train_loss: 5.1393 val_loss: 4.8760 acc:0.5083
Train Epoch: 2 [44800/48600 ( 92%)] train_loss: 6.5540 val_loss: 5.3094 acc:0.5058
Train Epoch: 2 [46400/48600 ( 96%)] train_loss: 4.7982 val_loss: 6.1943 acc:0.5077
Train Epoch: 2 [48000/48600 ( 99%)] train_loss: 4.1264 val_loss: 5.1458 acc:0.4950
Train Epoch: 3 [ 0/48600 ( 0%)] train_loss: 4.8764 val_loss: 5.4665 acc:0.0000
Train Epoch: 3 [ 1600/48600 ( 3%)] train_loss: 4.5176 val_loss: 6.2836 acc:0.1786
Train Epoch: 3 [ 3200/48600 ( 7%)] train_loss: 10.4286 val_loss: 4.8917 acc:0.2134
Train Epoch: 3 [ 4800/48600 ( 10%)] train_loss: 5.2087 val_loss: 5.5892 acc:0.1844
Train Epoch: 3 [ 6400/48600 ( 13%)] train_loss: 4.4848 val_loss: 3.3409 acc:0.2469
Train Epoch: 3 [ 8000/48600 ( 16%)] train_loss: 4.4374 val_loss: 4.4239 acc:0.2351
Train Epoch: 3 [ 9600/48600 ( 20%)] train_loss: 5.6288 val_loss: 3.5758 acc:0.2273
Train Epoch: 3 [11200/48600 ( 23%)] train_loss: 3.2552 val_loss: 5.1751 acc:0.2216
Train Epoch: 3 [12800/48600 ( 26%)] train_loss: 4.5074 val_loss: 3.0077 acc:0.1941
Train Epoch: 3 [14400/48600 ( 30%)] train_loss: 3.7271 val_loss: 2.9183 acc:0.2072
Train Epoch: 3 [16000/48600 ( 33%)] train_loss: 3.5051 val_loss: 3.6776 acc:0.2177
Train Epoch: 3 [17600/48600 ( 36%)] train_loss: 4.3313 val_loss: 5.3244 acc:0.2262
Train Epoch: 3 [19200/48600 ( 40%)] train_loss: 3.5998 val_loss: 4.9520 acc:0.2127
Train Epoch: 3 [20800/48600 ( 43%)] train_loss: 4.8491 val_loss: 3.7798 acc:0.2203
Train Epoch: 3 [22400/48600 ( 46%)] train_loss: 4.6884 val_loss: 3.9837 acc:0.2046
Train Epoch: 3 [24000/48600 ( 49%)] train_loss: 3.8375 val_loss: 4.6442 acc:0.1993
Train Epoch: 3 [25600/48600 ( 53%)] train_loss: 5.0202 val_loss: 4.2715 acc:0.2103
Train Epoch: 3 [27200/48600 ( 56%)] train_loss: 4.1756 val_loss: 3.3878 acc:0.2126
Train Epoch: 3 [28800/48600 ( 59%)] train_loss: 2.9999 val_loss: 4.4034 acc:0.2181
Train Epoch: 3 [30400/48600 ( 63%)] train_loss: 3.9675 val_loss: 3.5241 acc:0.2297
Train Epoch: 3 [32000/48600 ( 66%)] train_loss: 4.3625 val_loss: 4.5549 acc:0.2213
Train Epoch: 3 [33600/48600 ( 69%)] train_loss: 3.7151 val_loss: 2.5271 acc:0.2108
Train Epoch: 3 [35200/48600 ( 72%)] train_loss: 2.9919 val_loss: 2.8000 acc:0.2041
Train Epoch: 3 [36800/48600 ( 76%)] train_loss: 3.0007 val_loss: 4.2227 acc:0.2034
Train Epoch: 3 [38400/48600 ( 79%)] train_loss: 4.5245 val_loss: 5.1717 acc:0.2027
Train Epoch: 3 [40000/48600 ( 82%)] train_loss: 4.7978 val_loss: 2.6621 acc:0.1971
Train Epoch: 3 [41600/48600 ( 86%)] train_loss: 2.5702 val_loss: 6.0276 acc:0.1919
Train Epoch: 3 [43200/48600 ( 89%)] train_loss: 6.6297 val_loss: 2.6658 acc:0.1941
Train Epoch: 3 [44800/48600 ( 92%)] train_loss: 6.1077 val_loss: 5.1710 acc:0.1939
Train Epoch: 3 [46400/48600 ( 96%)] train_loss: 2.8425 val_loss: 3.3412 acc:0.1915
Train Epoch: 3 [48000/48600 ( 99%)] train_loss: 5.5447 val_loss: 3.2437 acc:0.1872
Train Epoch: 4 [ 0/48600 ( 0%)] train_loss: 4.1451 val_loss: 2.6004 acc:0.0000
Train Epoch: 4 [ 1600/48600 ( 3%)] train_loss: 3.4725 val_loss: 4.0200 acc:0.1786
Train Epoch: 4 [ 3200/48600 ( 7%)] train_loss: 2.9762 val_loss: 4.0769 acc:0.1829
Train Epoch: 4 [ 4800/48600 ( 10%)] train_loss: 3.0323 val_loss: 4.2024 acc:0.1844
Train Epoch: 4 [ 6400/48600 ( 13%)] train_loss: 2.6579 val_loss: 3.0648 acc:0.2006
Train Epoch: 4 [ 8000/48600 ( 16%)] train_loss: 2.5428 val_loss: 4.2719 acc:0.2228
Train Epoch: 4 [ 9600/48600 ( 20%)] train_loss: 2.8139 val_loss: 2.3869 acc:0.2169
Train Epoch: 4 [11200/48600 ( 23%)] train_loss: 2.8816 val_loss: 3.6125 acc:0.2039
Train Epoch: 4 [12800/48600 ( 26%)] train_loss: 3.4528 val_loss: 4.4378 acc:0.1941
Train Epoch: 4 [14400/48600 ( 30%)] train_loss: 2.8270 val_loss: 2.6190 acc:0.1865
Train Epoch: 4 [16000/48600 ( 33%)] train_loss: 3.2068 val_loss: 2.4297 acc:0.1803
Train Epoch: 4 [17600/48600 ( 36%)] train_loss: 4.2910 val_loss: 1.9477 acc:0.2036
Train Epoch: 4 [19200/48600 ( 40%)] train_loss: 2.8218 val_loss: 2.9456 acc:0.2230
Train Epoch: 4 [20800/48600 ( 43%)] train_loss: 2.1940 val_loss: 2.7161 acc:0.2203
Train Epoch: 4 [22400/48600 ( 46%)] train_loss: 6.1431 val_loss: 2.2810 acc:0.2135
Train Epoch: 4 [24000/48600 ( 49%)] train_loss: 3.4211 val_loss: 1.5608 acc:0.2076
Train Epoch: 4 [25600/48600 ( 53%)] train_loss: 2.5813 val_loss: 1.6128 acc:0.1947
Train Epoch: 4 [27200/48600 ( 56%)] train_loss: 4.0675 val_loss: 2.5155 acc:0.1906
Train Epoch: 4 [28800/48600 ( 59%)] train_loss: 3.6483 val_loss: 1.7550 acc:0.1870
Train Epoch: 4 [30400/48600 ( 63%)] train_loss: 3.9263 val_loss: 2.0115 acc:0.1870
Train Epoch: 4 [32000/48600 ( 66%)] train_loss: 2.7900 val_loss: 2.4709 acc:0.1808
Train Epoch: 4 [33600/48600 ( 69%)] train_loss: 2.4542 val_loss: 3.4334 acc:0.1752
Train Epoch: 4 [35200/48600 ( 72%)] train_loss: 3.8415 val_loss: 2.0339 acc:0.1701
Train Epoch: 4 [36800/48600 ( 76%)] train_loss: 2.6147 val_loss: 3.0280 acc:0.1708
Train Epoch: 4 [38400/48600 ( 79%)] train_loss: 2.9265 val_loss: 3.4645 acc:0.1767
Train Epoch: 4 [40000/48600 ( 82%)] train_loss: 4.2819 val_loss: 3.5770 acc:0.1697
Train Epoch: 4 [41600/48600 ( 86%)] train_loss: 2.9299 val_loss: 3.5644 acc:0.1655
Train Epoch: 4 [43200/48600 ( 89%)] train_loss: 3.7723 val_loss: 3.9711 acc:0.1594
Train Epoch: 4 [44800/48600 ( 92%)] train_loss: 2.3379 val_loss: 1.9857 acc:0.1582
Train Epoch: 4 [46400/48600 ( 96%)] train_loss: 1.9125 val_loss: 2.5104 acc:0.1549
Train Epoch: 4 [48000/48600 ( 99%)] train_loss: 2.7565 val_loss: 1.8923 acc:0.1498
Train Epoch: 5 [ 0/48600 ( 0%)] train_loss: 3.1658 val_loss: 3.4583 acc:0.0000
Train Epoch: 5 [ 1600/48600 ( 3%)] train_loss: 1.7627 val_loss: 1.9335 acc:0.0000
Train Epoch: 5 [ 3200/48600 ( 7%)] train_loss: 3.0281 val_loss: 2.2129 acc:0.0000
Train Epoch: 5 [ 4800/48600 ( 10%)] train_loss: 3.0675 val_loss: 2.8893 acc:0.0410
Train Epoch: 5 [ 6400/48600 ( 13%)] train_loss: 3.3126 val_loss: 1.0205 acc:0.0463
Train Epoch: 5 [ 8000/48600 ( 16%)] train_loss: 5.3068 val_loss: 1.5672 acc:0.1114
Train Epoch: 5 [ 9600/48600 ( 20%)] train_loss: 2.2021 val_loss: 3.3186 acc:0.1446
Train Epoch: 5 [11200/48600 ( 23%)] train_loss: 0.9840 val_loss: 1.4443 acc:0.1330
Train Epoch: 5 [12800/48600 ( 26%)] train_loss: 3.4536 val_loss: 1.9790 acc:0.1320
Train Epoch: 5 [14400/48600 ( 30%)] train_loss: 1.9458 val_loss: 1.9623 acc:0.1243
Train Epoch: 5 [16000/48600 ( 33%)] train_loss: 3.2405 val_loss: 1.6737 acc:0.1182
Train Epoch: 5 [17600/48600 ( 36%)] train_loss: 1.9205 val_loss: 1.2705 acc:0.1075
Train Epoch: 5 [19200/48600 ( 40%)] train_loss: 1.6301 val_loss: 4.0377 acc:0.0985
Train Epoch: 5 [20800/48600 ( 43%)] train_loss: 1.8229 val_loss: 1.1486 acc:0.1006
Train Epoch: 5 [22400/48600 ( 46%)] train_loss: 2.2251 val_loss: 2.9438 acc:0.0934
Train Epoch: 5 [24000/48600 ( 49%)] train_loss: 3.0371 val_loss: 2.4870 acc:0.0872
Train Epoch: 5 [25600/48600 ( 53%)] train_loss: 1.6742 val_loss: 1.4461 acc:0.0935
Train Epoch: 5 [27200/48600 ( 56%)] train_loss: 4.4680 val_loss: 2.3280 acc:0.0916
Train Epoch: 5 [28800/48600 ( 59%)] train_loss: 4.5051 val_loss: 0.8891 acc:0.0900
Train Epoch: 5 [30400/48600 ( 63%)] train_loss: 1.9852 val_loss: 3.2037 acc:0.0951
Train Epoch: 5 [32000/48600 ( 66%)] train_loss: 1.2374 val_loss: 3.0852 acc:0.0904
Train Epoch: 5 [33600/48600 ( 69%)] train_loss: 1.3662 val_loss: 1.4447 acc:0.0891
Train Epoch: 5 [35200/48600 ( 72%)] train_loss: 2.8708 val_loss: 4.2809 acc:0.0879
Train Epoch: 5 [36800/48600 ( 76%)] train_loss: 2.5946 val_loss: 2.4300 acc:0.0895
Train Epoch: 5 [38400/48600 ( 79%)] train_loss: 2.4010 val_loss: 1.4934 acc:0.0858
Train Epoch: 5 [40000/48600 ( 82%)] train_loss: 2.0134 val_loss: 1.8889 acc:0.0923
Train Epoch: 5 [41600/48600 ( 86%)] train_loss: 1.7741 val_loss: 3.0926 acc:0.0912
Train Epoch: 5 [43200/48600 ( 89%)] train_loss: 2.5331 val_loss: 1.5891 acc:0.0878
Train Epoch: 5 [44800/48600 ( 92%)] train_loss: 2.1189 val_loss: 1.8439 acc:0.0847
Train Epoch: 5 [46400/48600 ( 96%)] train_loss: 1.8148 val_loss: 2.7419 acc:0.0861
Train Epoch: 5 [48000/48600 ( 99%)] train_loss: 3.9189 val_loss: 1.6547 acc:0.0894
Train Epoch: 6 [ 0/48600 ( 0%)] train_loss: 2.1578 val_loss: 3.6621 acc:0.0000
Train Epoch: 6 [ 1600/48600 ( 3%)] train_loss: 0.9891 val_loss: 2.1597 acc:0.1190
Train Epoch: 6 [ 3200/48600 ( 7%)] train_loss: 1.9248 val_loss: 1.6877 acc:0.1220
Train Epoch: 6 [ 4800/48600 ( 10%)] train_loss: 4.8497 val_loss: 1.7575 acc:0.1230
Train Epoch: 6 [ 6400/48600 ( 13%)] train_loss: 1.5550 val_loss: 2.3763 acc:0.0926
Train Epoch: 6 [ 8000/48600 ( 16%)] train_loss: 2.9609 val_loss: 2.3509 acc:0.0866
Train Epoch: 6 [ 9600/48600 ( 20%)] train_loss: 2.7966 val_loss: 2.4106 acc:0.0723
Train Epoch: 6 [11200/48600 ( 23%)] train_loss: 2.6849 val_loss: 1.3554 acc:0.0709
Train Epoch: 6 [12800/48600 ( 26%)] train_loss: 2.4549 val_loss: 1.6572 acc:0.0699
Train Epoch: 6 [14400/48600 ( 30%)] train_loss: 1.7165 val_loss: 2.6332 acc:0.0622
Train Epoch: 6 [16000/48600 ( 33%)] train_loss: 2.1179 val_loss: 3.0956 acc:0.0622
Train Epoch: 6 [17600/48600 ( 36%)] train_loss: 2.9120 val_loss: 2.2677 acc:0.0792
Train Epoch: 6 [19200/48600 ( 40%)] train_loss: 1.4499 val_loss: 1.7480 acc:0.0726
Train Epoch: 6 [20800/48600 ( 43%)] train_loss: 2.7923 val_loss: 1.0949 acc:0.0670
Train Epoch: 6 [22400/48600 ( 46%)] train_loss: 1.8943 val_loss: 2.4960 acc:0.0623
Train Epoch: 6 [24000/48600 ( 49%)] train_loss: 3.1129 val_loss: 2.2752 acc:0.0664
Train Epoch: 6 [25600/48600 ( 53%)] train_loss: 2.2632 val_loss: 1.1593 acc:0.0662
Train Epoch: 6 [27200/48600 ( 56%)] train_loss: 3.5387 val_loss: 1.1749 acc:0.0660
Train Epoch: 6 [28800/48600 ( 59%)] train_loss: 2.7328 val_loss: 2.7489 acc:0.0658
Train Epoch: 6 [30400/48600 ( 63%)] train_loss: 4.1653 val_loss: 1.8294 acc:0.0656
Train Epoch: 6 [32000/48600 ( 66%)] train_loss: 1.1152 val_loss: 1.5345 acc:0.0686
Train Epoch: 6 [33600/48600 ( 69%)] train_loss: 3.2282 val_loss: 1.1130 acc:0.0683
Train Epoch: 6 [35200/48600 ( 72%)] train_loss: 2.9923 val_loss: 1.3599 acc:0.0680
Train Epoch: 6 [36800/48600 ( 76%)] train_loss: 2.0414 val_loss: 2.7477 acc:0.0651
Train Epoch: 6 [38400/48600 ( 79%)] train_loss: 1.7603 val_loss: 1.3875 acc:0.0676
Train Epoch: 6 [40000/48600 ( 82%)] train_loss: 4.2912 val_loss: 2.0376 acc:0.0649
Train Epoch: 6 [41600/48600 ( 86%)] train_loss: 3.7480 val_loss: 1.9521 acc:0.0696
Train Epoch: 6 [43200/48600 ( 89%)] train_loss: 2.5395 val_loss: 2.3025 acc:0.0670
Train Epoch: 6 [44800/48600 ( 92%)] train_loss: 2.3830 val_loss: 3.2211 acc:0.0646
Train Epoch: 6 [46400/48600 ( 96%)] train_loss: 2.0125 val_loss: 2.4834 acc:0.0624
Train Epoch: 6 [48000/48600 ( 99%)] train_loss: 1.6565 val_loss: 2.0749 acc:0.0666
Train Epoch: 7 [ 0/48600 ( 0%)] train_loss: 4.3678 val_loss: 2.5146 acc:0.0000
Train Epoch: 7 [ 1600/48600 ( 3%)] train_loss: 3.4234 val_loss: 2.7618 acc:0.1190
Train Epoch: 7 [ 3200/48600 ( 7%)] train_loss: 2.0455 val_loss: 1.2993 acc:0.0610
Train Epoch: 7 [ 4800/48600 ( 10%)] train_loss: 2.1082 val_loss: 0.8334 acc:0.1434
Train Epoch: 7 [ 6400/48600 ( 13%)] train_loss: 1.8545 val_loss: 1.2720 acc:0.1852
Train Epoch: 7 [ 8000/48600 ( 16%)] train_loss: 3.3667 val_loss: 1.6813 acc:0.1856
Train Epoch: 7 [ 9600/48600 ( 20%)] train_loss: 2.0930 val_loss: 3.2544 acc:0.2169
Train Epoch: 7 [11200/48600 ( 23%)] train_loss: 5.3971 val_loss: 0.6087 acc:0.2128
Train Epoch: 7 [12800/48600 ( 26%)] train_loss: 1.9346 val_loss: 1.6443 acc:0.2562
Train Epoch: 7 [14400/48600 ( 30%)] train_loss: 3.0240 val_loss: 2.7684 acc:0.2486
Train Epoch: 7 [16000/48600 ( 33%)] train_loss: 1.4758 val_loss: 3.2663 acc:0.2736
Train Epoch: 7 [17600/48600 ( 36%)] train_loss: 2.5216 val_loss: 2.1520 acc:0.2771
Train Epoch: 7 [19200/48600 ( 40%)] train_loss: 3.1282 val_loss: 1.4846 acc:0.2541
Train Epoch: 7 [20800/48600 ( 43%)] train_loss: 1.7439 val_loss: 3.0154 acc:0.2490
Train Epoch: 7 [22400/48600 ( 46%)] train_loss: 1.5755 val_loss: 0.8257 acc:0.2536
Train Epoch: 7 [24000/48600 ( 49%)] train_loss: 0.5203 val_loss: 1.8853 acc:0.2450
Train Epoch: 7 [25600/48600 ( 53%)] train_loss: 1.1313 val_loss: 1.4520 acc:0.2375
Train Epoch: 7 [27200/48600 ( 56%)] train_loss: 1.5652 val_loss: 1.0145 acc:0.2419
Train Epoch: 7 [28800/48600 ( 59%)] train_loss: 0.9993 val_loss: 0.3366 acc:0.2597
Train Epoch: 7 [30400/48600 ( 63%)] train_loss: 10.0994 val_loss: 0.3272 acc:0.2526
Train Epoch: 7 [32000/48600 ( 66%)] train_loss: 1.5385 val_loss: 1.8713 acc:0.2494
Train Epoch: 7 [33600/48600 ( 69%)] train_loss: 3.9916 val_loss: 0.4924 acc:0.2435
Train Epoch: 7 [35200/48600 ( 72%)] train_loss: 1.9014 val_loss: 4.9956 acc:0.2353
Train Epoch: 7 [36800/48600 ( 76%)] train_loss: 0.8910 val_loss: 2.5894 acc:0.2278
Train Epoch: 7 [38400/48600 ( 79%)] train_loss: 2.0577 val_loss: 0.7675 acc:0.2261
Train Epoch: 7 [40000/48600 ( 82%)] train_loss: 2.4401 val_loss: 2.3511 acc:0.2171
Train Epoch: 7 [41600/48600 ( 86%)] train_loss: 0.8720 val_loss: 0.7571 acc:0.2111
Train Epoch: 7 [43200/48600 ( 89%)] train_loss: 1.8235 val_loss: 1.4891 acc:0.2033
Train Epoch: 7 [44800/48600 ( 92%)] train_loss: 0.7328 val_loss: 0.8030 acc:0.2005
Train Epoch: 7 [46400/48600 ( 96%)] train_loss: 0.4907 val_loss: 0.5466 acc:0.1958
Train Epoch: 7 [48000/48600 ( 99%)] train_loss: 0.8253 val_loss: 0.9916 acc:0.1913
Train Epoch: 8 [ 0/48600 ( 0%)] train_loss: 2.1975 val_loss: 3.1143 acc:0.0000
Train Epoch: 8 [ 1600/48600 ( 3%)] train_loss: 1.2722 val_loss: 2.1082 acc:0.3571
Train Epoch: 8 [ 3200/48600 ( 7%)] train_loss: 2.1132 val_loss: 0.9495 acc:0.2439
Train Epoch: 8 [ 4800/48600 ( 10%)] train_loss: 3.8457 val_loss: 1.1225 acc:0.2254
Train Epoch: 8 [ 6400/48600 ( 13%)] train_loss: 1.0284 val_loss: 1.9752 acc:0.2006
Train Epoch: 8 [ 8000/48600 ( 16%)] train_loss: 1.6263 val_loss: 1.8020 acc:0.1609
Train Epoch: 8 [ 9600/48600 ( 20%)] train_loss: 4.3424 val_loss: 0.6326 acc:0.1756
Train Epoch: 8 [11200/48600 ( 23%)] train_loss: 0.7334 val_loss: 1.1563 acc:0.1684
Train Epoch: 8 [12800/48600 ( 26%)] train_loss: 1.8218 val_loss: 0.4827 acc:0.1863
Train Epoch: 8 [14400/48600 ( 30%)] train_loss: 1.6978 val_loss: 1.0881 acc:0.1796
Train Epoch: 8 [16000/48600 ( 33%)] train_loss: 4.1991 val_loss: 2.0204 acc:0.1866
Train Epoch: 8 [17600/48600 ( 36%)] train_loss: 1.6649 val_loss: 3.1394 acc:0.1867
Train Epoch: 8 [19200/48600 ( 40%)] train_loss: 0.7311 val_loss: 0.9547 acc:0.1763
Train Epoch: 8 [20800/48600 ( 43%)] train_loss: 1.0196 val_loss: 2.2460 acc:0.1820
Train Epoch: 8 [22400/48600 ( 46%)] train_loss: 1.2451 val_loss: 4.5765 acc:0.1735
Train Epoch: 8 [24000/48600 ( 49%)] train_loss: 0.3058 val_loss: 0.2852 acc:0.1786
Train Epoch: 8 [25600/48600 ( 53%)] train_loss: 1.5444 val_loss: 0.3506 acc:0.1830
Train Epoch: 8 [27200/48600 ( 56%)] train_loss: 0.2598 val_loss: 0.6633 acc:0.1833
Train Epoch: 8 [28800/48600 ( 59%)] train_loss: 0.8047 val_loss: 0.6396 acc:0.1801
Train Epoch: 8 [30400/48600 ( 63%)] train_loss: 3.1570 val_loss: 5.5352 acc:0.1739
Train Epoch: 8 [32000/48600 ( 66%)] train_loss: 1.7883 val_loss: 1.5653 acc:0.1777
Train Epoch: 8 [33600/48600 ( 69%)] train_loss: 3.0879 val_loss: 2.5457 acc:0.1692
Train Epoch: 8 [35200/48600 ( 72%)] train_loss: 1.5755 val_loss: 1.1518 acc:0.1701
Train Epoch: 8 [36800/48600 ( 76%)] train_loss: 1.3925 val_loss: 1.7460 acc:0.1627
Train Epoch: 8 [38400/48600 ( 79%)] train_loss: 1.4959 val_loss: 0.5023 acc:0.1559
Train Epoch: 8 [40000/48600 ( 82%)] train_loss: 2.5972 val_loss: 2.2564 acc:0.1497
Train Epoch: 8 [41600/48600 ( 86%)] train_loss: 1.0541 val_loss: 1.4504 acc:0.1464
Train Epoch: 8 [43200/48600 ( 89%)] train_loss: 2.7753 val_loss: 2.5657 acc:0.1456
Train Epoch: 8 [44800/48600 ( 92%)] train_loss: 0.5221 val_loss: 0.8378 acc:0.1471
Train Epoch: 8 [46400/48600 ( 96%)] train_loss: 1.8296 val_loss: 0.1730 acc:0.1463
Train Epoch: 8 [48000/48600 ( 99%)] train_loss: 2.7760 val_loss: 0.2322 acc:0.1435
Train Epoch: 9 [ 0/48600 ( 0%)] train_loss: 1.7369 val_loss: 0.5453 acc:0.0000
Train Epoch: 9 [ 1600/48600 ( 3%)] train_loss: 1.8183 val_loss: 3.5716 acc:0.0000
Train Epoch: 9 [ 3200/48600 ( 7%)] train_loss: 0.5386 val_loss: 0.6056 acc:0.0610
Train Epoch: 9 [ 4800/48600 ( 10%)] train_loss: 1.9499 val_loss: 1.5724 acc:0.0615
Train Epoch: 9 [ 6400/48600 ( 13%)] train_loss: 0.1948 val_loss: 0.1295 acc:0.0463
Train Epoch: 9 [ 8000/48600 ( 16%)] train_loss: 0.5698 val_loss: 1.3827 acc:0.0619
Train Epoch: 9 [ 9600/48600 ( 20%)] train_loss: 6.7942 val_loss: 1.7945 acc:0.0723
Train Epoch: 9 [11200/48600 ( 23%)] train_loss: 2.0678 val_loss: 0.2870 acc:0.0975
Train Epoch: 9 [12800/48600 ( 26%)] train_loss: 0.3828 val_loss: 1.1246 acc:0.1009
Train Epoch: 9 [14400/48600 ( 30%)] train_loss: 0.4578 val_loss: 5.6668 acc:0.1036
Train Epoch: 9 [16000/48600 ( 33%)] train_loss: 0.1977 val_loss: 0.8671 acc:0.0933
Train Epoch: 9 [17600/48600 ( 36%)] train_loss: 0.9688 val_loss: 0.1571 acc:0.0905
Train Epoch: 9 [19200/48600 ( 40%)] train_loss: 0.2019 val_loss: 0.9592 acc:0.0882
Train Epoch: 9 [20800/48600 ( 43%)] train_loss: 0.9002 val_loss: 1.0291 acc:0.0862
Train Epoch: 9 [22400/48600 ( 46%)] train_loss: 0.2466 val_loss: 2.1738 acc:0.0845
Train Epoch: 9 [24000/48600 ( 49%)] train_loss: 0.2533 val_loss: 0.3966 acc:0.0872
Train Epoch: 9 [25600/48600 ( 53%)] train_loss: 1.2943 val_loss: 0.2307 acc:0.0974
Train Epoch: 9 [27200/48600 ( 56%)] train_loss: 0.2004 val_loss: 0.5857 acc:0.0990
Train Epoch: 9 [28800/48600 ( 59%)] train_loss: 0.5910 val_loss: 1.4511 acc:0.0970
Train Epoch: 9 [30400/48600 ( 63%)] train_loss: 1.5112 val_loss: 1.1903 acc:0.0919
Train Epoch: 9 [32000/48600 ( 66%)] train_loss: 0.2350 val_loss: 1.2722 acc:0.0935
Train Epoch: 9 [33600/48600 ( 69%)] train_loss: 1.0798 val_loss: 0.7553 acc:0.0920
Train Epoch: 9 [35200/48600 ( 72%)] train_loss: 1.0279 val_loss: 1.0756 acc:0.0907
Train Epoch: 9 [36800/48600 ( 76%)] train_loss: 1.0904 val_loss: 0.4587 acc:0.0976
Train Epoch: 9 [38400/48600 ( 79%)] train_loss: 1.7454 val_loss: 1.8685 acc:0.0988
Train Epoch: 9 [40000/48600 ( 82%)] train_loss: 0.2800 val_loss: 2.7765 acc:0.0948
Train Epoch: 9 [41600/48600 ( 86%)] train_loss: 0.6667 val_loss: 0.8545 acc:0.1008
Train Epoch: 9 [43200/48600 ( 89%)] train_loss: 0.5946 val_loss: 0.6464 acc:0.1086
Train Epoch: 9 [44800/48600 ( 92%)] train_loss: 1.2888 val_loss: 0.3420 acc:0.1047
Train Epoch: 9 [46400/48600 ( 96%)] train_loss: 1.0532 val_loss: 0.6097 acc:0.1076
Train Epoch: 9 [48000/48600 ( 99%)] train_loss: 0.6210 val_loss: 0.7133 acc:0.1061
Train Epoch: 10 [ 0/48600 ( 0%)] train_loss: 0.6377 val_loss: 0.2274 acc:0.0000
Train Epoch: 10 [ 1600/48600 ( 3%)] train_loss: 3.9319 val_loss: 1.6273 acc:0.0595
Train Epoch: 10 [ 3200/48600 ( 7%)] train_loss: 1.4825 val_loss: 0.9609 acc:0.0915
Train Epoch: 10 [ 4800/48600 ( 10%)] train_loss: 1.2792 val_loss: 2.4387 acc:0.1434
Train Epoch: 10 [ 6400/48600 ( 13%)] train_loss: 5.9921 val_loss: 2.7745 acc:0.1235
Train Epoch: 10 [ 8000/48600 ( 16%)] train_loss: 0.4326 val_loss: 0.3611 acc:0.1361
Train Epoch: 10 [ 9600/48600 ( 20%)] train_loss: 0.9879 val_loss: 0.6829 acc:0.1240
Train Epoch: 10 [11200/48600 ( 23%)] train_loss: 0.3936 val_loss: 0.6048 acc:0.1330
Train Epoch: 10 [12800/48600 ( 26%)] train_loss: 2.9969 val_loss: 4.3568 acc:0.1320
Train Epoch: 10 [14400/48600 ( 30%)] train_loss: 0.1183 val_loss: 1.6866 acc:0.1381
Train Epoch: 10 [16000/48600 ( 33%)] train_loss: 0.2589 val_loss: 1.4382 acc:0.1368
Train Epoch: 10 [17600/48600 ( 36%)] train_loss: 0.6312 val_loss: 1.1788 acc:0.1301
Train Epoch: 10 [19200/48600 ( 40%)] train_loss: 0.7317 val_loss: 1.5340 acc:0.1193
Train Epoch: 10 [20800/48600 ( 43%)] train_loss: 1.4202 val_loss: 1.0849 acc:0.1293
Train Epoch: 10 [22400/48600 ( 46%)] train_loss: 0.7380 val_loss: 0.2882 acc:0.1290
Train Epoch: 10 [24000/48600 ( 49%)] train_loss: 2.0038 val_loss: 0.7929 acc:0.1204
Train Epoch: 10 [25600/48600 ( 53%)] train_loss: 0.6806 val_loss: 1.0988 acc:0.1207
Train Epoch: 10 [27200/48600 ( 56%)] train_loss: 1.6568 val_loss: 0.3962 acc:0.1246
Train Epoch: 10 [28800/48600 ( 59%)] train_loss: 0.5131 val_loss: 0.3527 acc:0.1212
Train Epoch: 10 [30400/48600 ( 63%)] train_loss: 0.5177 val_loss: 0.3591 acc:0.1214
Train Epoch: 10 [32000/48600 ( 66%)] train_loss: 0.4515 val_loss: 0.3276 acc:0.1185
Train Epoch: 10 [33600/48600 ( 69%)] train_loss: 1.0951 val_loss: 2.4004 acc:0.1217
Train Epoch: 10 [35200/48600 ( 72%)] train_loss: 0.9232 val_loss: 1.5728 acc:0.1219
Train Epoch: 10 [36800/48600 ( 76%)] train_loss: 0.2229 val_loss: 1.1192 acc:0.1166
Train Epoch: 10 [38400/48600 ( 79%)] train_loss: 2.5061 val_loss: 1.6676 acc:0.1143
Train Epoch: 10 [40000/48600 ( 82%)] train_loss: 0.5621 val_loss: 0.4986 acc:0.1098
Train Epoch: 10 [41600/48600 ( 86%)] train_loss: 0.2997 val_loss: 2.0493 acc:0.1128
Train Epoch: 10 [43200/48600 ( 89%)] train_loss: 1.0935 val_loss: 1.0866 acc:0.1109
Train Epoch: 10 [44800/48600 ( 92%)] train_loss: 0.5907 val_loss: 0.3966 acc:0.1092
Train Epoch: 10 [46400/48600 ( 96%)] train_loss: 0.2979 val_loss: 1.3323 acc:0.1097
Train Epoch: 10 [48000/48600 ( 99%)] train_loss: 0.8537 val_loss: 0.4341 acc:0.1102