mini_from_mini80_ssl_sl_1shot_5way.log

{'dataset': 'MiniImageNet',
 'gamma': 0.5,
 'gpu': '0',
 'init_weights': './saves/initialization/miniimagenet/mini_imagenet_amdim_ndf192_rkhs1536_rd8_cpt.pth',
 'lr': 0.0001,
 'max_epoch': 100,
 'model_type': 'AmdimNet',
 'nd': 8,
 'ndf': 192,
 'query': 15,
 'rkhs': 1536,
 'shot': 1,
 'step_size': 10,
 'temperature': 128.0,
 'way': 5}
using gpu: 0
Using a 128x128 encoder
epoch 1, train 1/100, loss=1.9023 acc=0.3733
epoch 1, train 2/100, loss=1.4098 acc=0.5200
epoch 1, train 3/100, loss=1.6199 acc=0.4533
epoch 1, train 4/100, loss=0.9387 acc=0.6933
epoch 1, train 5/100, loss=1.6370 acc=0.4667
epoch 1, train 6/100, loss=0.8001 acc=0.7467
epoch 1, train 7/100, loss=2.5595 acc=0.3600
epoch 1, train 8/100, loss=1.9584 acc=0.4933
epoch 1, train 9/100, loss=1.1044 acc=0.7467
epoch 1, train 10/100, loss=1.8556 acc=0.4800
epoch 1, train 11/100, loss=2.6648 acc=0.4133
epoch 1, train 12/100, loss=1.2956 acc=0.5733
epoch 1, train 13/100, loss=2.4823 acc=0.4000
epoch 1, train 14/100, loss=1.5357 acc=0.5733
epoch 1, train 15/100, loss=1.9452 acc=0.5067
epoch 1, train 16/100, loss=2.4739 acc=0.3467
epoch 1, train 17/100, loss=2.9666 acc=0.2800
epoch 1, train 18/100, loss=1.3881 acc=0.5200
epoch 1, train 19/100, loss=1.5735 acc=0.5867
epoch 1, train 20/100, loss=1.5086 acc=0.5600
epoch 1, train 21/100, loss=1.8745 acc=0.3733
epoch 1, train 22/100, loss=1.4836 acc=0.5200
epoch 1, train 23/100, loss=2.2092 acc=0.4400
epoch 1, train 24/100, loss=1.1700 acc=0.6667
epoch 1, train 25/100, loss=1.5363 acc=0.5467
epoch 1, train 26/100, loss=1.6967 acc=0.5200
epoch 1, train 27/100, loss=2.0946 acc=0.5200
epoch 1, train 28/100, loss=1.7395 acc=0.4267
epoch 1, train 29/100, loss=2.1568 acc=0.4133
epoch 1, train 30/100, loss=1.4641 acc=0.5067
epoch 1, train 31/100, loss=1.7844 acc=0.4933
epoch 1, train 32/100, loss=1.3183 acc=0.5867
epoch 1, train 33/100, loss=2.0663 acc=0.4667
epoch 1, train 34/100, loss=1.2210 acc=0.6133
epoch 1, train 35/100, loss=1.7036 acc=0.5467
epoch 1, train 36/100, loss=1.2273 acc=0.6000
epoch 1, train 37/100, loss=1.7469 acc=0.4133
epoch 1, train 38/100, loss=1.3192 acc=0.6800
epoch 1, train 39/100, loss=1.1733 acc=0.6000
epoch 1, train 40/100, loss=0.8714 acc=0.6800
epoch 1, train 41/100, loss=2.2058 acc=0.5600
epoch 1, train 42/100, loss=1.3029 acc=0.6267
epoch 1, train 43/100, loss=1.1498 acc=0.6133
epoch 1, train 44/100, loss=0.4506 acc=0.8400
epoch 1, train 45/100, loss=0.9091 acc=0.6933
epoch 1, train 46/100, loss=0.9238 acc=0.7200
epoch 1, train 47/100, loss=1.1415 acc=0.5600
epoch 1, train 48/100, loss=1.3507 acc=0.5333
epoch 1, train 49/100, loss=1.9061 acc=0.5733
epoch 1, train 50/100, loss=1.6750 acc=0.4533
epoch 1, train 51/100, loss=1.1637 acc=0.6267
epoch 1, train 52/100, loss=1.3034 acc=0.6133
epoch 1, train 53/100, loss=1.2331 acc=0.6267
epoch 1, train 54/100, loss=2.2395 acc=0.4400
epoch 1, train 55/100, loss=1.1708 acc=0.5867
epoch 1, train 56/100, loss=1.6965 acc=0.5333
epoch 1, train 57/100, loss=0.7722 acc=0.7333
epoch 1, train 58/100, loss=1.5280 acc=0.4800
epoch 1, train 59/100, loss=1.1725 acc=0.6000
epoch 1, train 60/100, loss=1.6547 acc=0.4800
epoch 1, train 61/100, loss=1.8181 acc=0.5067
epoch 1, train 62/100, loss=1.6181 acc=0.4800
epoch 1, train 63/100, loss=1.3524 acc=0.5867
epoch 1, train 64/100, loss=0.8763 acc=0.7067
epoch 1, train 65/100, loss=1.0406 acc=0.7333
epoch 1, train 66/100, loss=1.3228 acc=0.6267
epoch 1, train 67/100, loss=1.5146 acc=0.5200
epoch 1, train 68/100, loss=1.2768 acc=0.5733
epoch 1, train 69/100, loss=1.7888 acc=0.5333
epoch 1, train 70/100, loss=1.1558 acc=0.6267
epoch 1, train 71/100, loss=2.3126 acc=0.4000
epoch 1, train 72/100, loss=1.3299 acc=0.6133
epoch 1, train 73/100, loss=1.3845 acc=0.5333
epoch 1, train 74/100, loss=1.4222 acc=0.6000
epoch 1, train 75/100, loss=1.0219 acc=0.6267
epoch 1, train 76/100, loss=1.2777 acc=0.6933
epoch 1, train 77/100, loss=1.2907 acc=0.6400
epoch 1, train 78/100, loss=1.4662 acc=0.7067
epoch 1, train 79/100, loss=1.1857 acc=0.6133
epoch 1, train 80/100, loss=0.8520 acc=0.6933
epoch 1, train 81/100, loss=1.5041 acc=0.5600
epoch 1, train 82/100, loss=1.1943 acc=0.6667
epoch 1, train 83/100, loss=0.5101 acc=0.8400
epoch 1, train 84/100, loss=1.1924 acc=0.5867
epoch 1, train 85/100, loss=0.7064 acc=0.7733
epoch 1, train 86/100, loss=1.2435 acc=0.6267
epoch 1, train 87/100, loss=1.7834 acc=0.4933
epoch 1, train 88/100, loss=0.8434 acc=0.7467
epoch 1, train 89/100, loss=1.5870 acc=0.5600
epoch 1, train 90/100, loss=1.9675 acc=0.4400
epoch 1, train 91/100, loss=0.9229 acc=0.6933
epoch 1, train 92/100, loss=3.0856 acc=0.4267
epoch 1, train 93/100, loss=1.1760 acc=0.6267
epoch 1, train 94/100, loss=1.2302 acc=0.6533
epoch 1, train 95/100, loss=1.5430 acc=0.5200
epoch 1, train 96/100, loss=2.0650 acc=0.4267
epoch 1, train 97/100, loss=1.2968 acc=0.5467
epoch 1, train 98/100, loss=1.0294 acc=0.6267
epoch 1, train 99/100, loss=1.2498 acc=0.6800
epoch 1, train 100/100, loss=1.1605 acc=0.6533
best epoch 0, best val acc=0.0000
epoch 1, val, loss=1.8174 acc=0.5497
ETA:4m/6.8h
epoch 2, train 1/100, loss=1.1624 acc=0.6533
epoch 2, train 2/100, loss=0.8396 acc=0.7200
epoch 2, train 3/100, loss=1.8442 acc=0.5467
epoch 2, train 4/100, loss=1.6161 acc=0.5200
epoch 2, train 5/100, loss=2.2154 acc=0.4800
epoch 2, train 6/100, loss=1.4698 acc=0.4933
epoch 2, train 7/100, loss=1.3571 acc=0.6133
epoch 2, train 8/100, loss=1.7582 acc=0.5333
epoch 2, train 9/100, loss=1.2095 acc=0.6400
epoch 2, train 10/100, loss=0.5315 acc=0.8400
epoch 2, train 11/100, loss=1.2638 acc=0.6800
epoch 2, train 12/100, loss=0.9402 acc=0.6800
epoch 2, train 13/100, loss=0.9233 acc=0.6267
epoch 2, train 14/100, loss=0.8952 acc=0.8000
epoch 2, train 15/100, loss=0.7039 acc=0.8000
epoch 2, train 16/100, loss=1.9781 acc=0.3200
epoch 2, train 17/100, loss=0.9059 acc=0.7733
epoch 2, train 18/100, loss=0.9236 acc=0.7200
epoch 2, train 19/100, loss=1.3299 acc=0.6533
epoch 2, train 20/100, loss=1.0642 acc=0.6667
epoch 2, train 21/100, loss=1.2301 acc=0.6400
epoch 2, train 22/100, loss=1.3243 acc=0.5867
epoch 2, train 23/100, loss=1.0401 acc=0.6533
epoch 2, train 24/100, loss=1.1871 acc=0.6533
epoch 2, train 25/100, loss=0.6902 acc=0.8667
epoch 2, train 26/100, loss=1.2294 acc=0.6533
epoch 2, train 27/100, loss=1.8395 acc=0.4533
epoch 2, train 28/100, loss=1.5305 acc=0.5467
epoch 2, train 29/100, loss=1.7264 acc=0.5200
epoch 2, train 30/100, loss=0.9013 acc=0.7333
epoch 2, train 31/100, loss=1.7358 acc=0.6400
epoch 2, train 32/100, loss=1.0798 acc=0.6533
epoch 2, train 33/100, loss=1.0904 acc=0.7600
epoch 2, train 34/100, loss=0.9698 acc=0.6933
epoch 2, train 35/100, loss=0.8193 acc=0.6800
epoch 2, train 36/100, loss=1.3338 acc=0.5467
epoch 2, train 37/100, loss=1.7668 acc=0.5867
epoch 2, train 38/100, loss=1.2358 acc=0.6533
epoch 2, train 39/100, loss=1.0661 acc=0.6667
epoch 2, train 40/100, loss=0.4881 acc=0.8533
epoch 2, train 41/100, loss=0.5381 acc=0.8533
epoch 2, train 42/100, loss=1.3889 acc=0.5467
epoch 2, train 43/100, loss=0.9849 acc=0.7200
epoch 2, train 44/100, loss=1.4830 acc=0.4933
epoch 2, train 45/100, loss=0.9532 acc=0.7333
epoch 2, train 46/100, loss=0.9699 acc=0.7067
epoch 2, train 47/100, loss=2.5361 acc=0.3467
epoch 2, train 48/100, loss=0.9985 acc=0.6533
epoch 2, train 49/100, loss=1.2371 acc=0.6267
epoch 2, train 50/100, loss=1.5492 acc=0.5600
epoch 2, train 51/100, loss=2.0454 acc=0.4800
epoch 2, train 52/100, loss=1.2212 acc=0.6267
epoch 2, train 53/100, loss=0.9885 acc=0.7333
epoch 2, train 54/100, loss=1.2975 acc=0.6533
epoch 2, train 55/100, loss=1.0386 acc=0.6400
epoch 2, train 56/100, loss=1.6118 acc=0.5867
epoch 2, train 57/100, loss=1.3236 acc=0.6533
epoch 2, train 58/100, loss=2.5606 acc=0.3333
epoch 2, train 59/100, loss=1.4187 acc=0.6267
epoch 2, train 60/100, loss=1.8653 acc=0.5200
epoch 2, train 61/100, loss=1.0212 acc=0.6800
epoch 2, train 62/100, loss=1.4063 acc=0.6133
epoch 2, train 63/100, loss=0.7771 acc=0.7200
epoch 2, train 64/100, loss=2.1215 acc=0.4400
epoch 2, train 65/100, loss=1.6878 acc=0.5333
epoch 2, train 66/100, loss=0.6844 acc=0.7333
epoch 2, train 67/100, loss=1.0193 acc=0.6800
epoch 2, train 68/100, loss=0.8187 acc=0.7467
epoch 2, train 69/100, loss=1.1119 acc=0.6667
epoch 2, train 70/100, loss=0.8679 acc=0.7333
epoch 2, train 71/100, loss=1.9961 acc=0.4667
epoch 2, train 72/100, loss=1.3780 acc=0.5333
epoch 2, train 73/100, loss=1.5761 acc=0.6533
epoch 2, train 74/100, loss=1.4566 acc=0.5600
epoch 2, train 75/100, loss=0.9883 acc=0.6800
epoch 2, train 76/100, loss=0.9583 acc=0.7067
epoch 2, train 77/100, loss=0.3464 acc=0.8933
epoch 2, train 78/100, loss=0.8328 acc=0.7600
epoch 2, train 79/100, loss=0.6406 acc=0.8000
epoch 2, train 80/100, loss=0.6546 acc=0.7600
epoch 2, train 81/100, loss=1.1235 acc=0.6667
epoch 2, train 82/100, loss=1.3074 acc=0.6133
epoch 2, train 83/100, loss=0.9125 acc=0.6933
epoch 2, train 84/100, loss=0.6546 acc=0.7733
epoch 2, train 85/100, loss=0.6759 acc=0.8133
epoch 2, train 86/100, loss=1.0048 acc=0.7200
epoch 2, train 87/100, loss=1.7328 acc=0.5467
epoch 2, train 88/100, loss=1.4789 acc=0.5600
epoch 2, train 89/100, loss=1.0378 acc=0.7200
epoch 2, train 90/100, loss=1.3068 acc=0.6133
epoch 2, train 91/100, loss=1.5869 acc=0.5600
epoch 2, train 92/100, loss=1.8932 acc=0.4400
epoch 2, train 93/100, loss=0.4778 acc=0.8533
epoch 2, train 94/100, loss=1.2959 acc=0.6267
epoch 2, train 95/100, loss=1.3336 acc=0.6667
epoch 2, train 96/100, loss=2.3936 acc=0.3867
epoch 2, train 97/100, loss=1.7424 acc=0.5467
epoch 2, train 98/100, loss=1.2260 acc=0.6800
epoch 2, train 99/100, loss=1.2363 acc=0.6400
epoch 2, train 100/100, loss=0.9492 acc=0.7333
best epoch 1, best val acc=0.5497
epoch 2, val, loss=1.4791 acc=0.6045
ETA:8m/6.6h
epoch 3, train 1/100, loss=2.0808 acc=0.4133
epoch 3, train 2/100, loss=1.1882 acc=0.6533
epoch 3, train 3/100, loss=1.3148 acc=0.7067
epoch 3, train 4/100, loss=1.1454 acc=0.6800
epoch 3, train 5/100, loss=1.0904 acc=0.6267
epoch 3, train 6/100, loss=1.0093 acc=0.6667
epoch 3, train 7/100, loss=1.2056 acc=0.6267
epoch 3, train 8/100, loss=0.9757 acc=0.7200
epoch 3, train 9/100, loss=0.5459 acc=0.7733
epoch 3, train 10/100, loss=1.3677 acc=0.5200
epoch 3, train 11/100, loss=1.8659 acc=0.4533
epoch 3, train 12/100, loss=1.2141 acc=0.6533
epoch 3, train 13/100, loss=1.0072 acc=0.6667
epoch 3, train 14/100, loss=0.6076 acc=0.7867
epoch 3, train 15/100, loss=0.8173 acc=0.7333
epoch 3, train 16/100, loss=0.5769 acc=0.7600
epoch 3, train 17/100, loss=1.0907 acc=0.6400
epoch 3, train 18/100, loss=0.6842 acc=0.7867
epoch 3, train 19/100, loss=1.0473 acc=0.7600
epoch 3, train 20/100, loss=0.6288 acc=0.8667
epoch 3, train 21/100, loss=1.2130 acc=0.6533
epoch 3, train 22/100, loss=1.5339 acc=0.6800
epoch 3, train 23/100, loss=1.2208 acc=0.6400
epoch 3, train 24/100, loss=0.9342 acc=0.6800
epoch 3, train 25/100, loss=0.8582 acc=0.7467
epoch 3, train 26/100, loss=1.3053 acc=0.6400
epoch 3, train 27/100, loss=0.6544 acc=0.8133
epoch 3, train 28/100, loss=0.6554 acc=0.7733
epoch 3, train 29/100, loss=0.8527 acc=0.7600
epoch 3, train 30/100, loss=1.0383 acc=0.6933
epoch 3, train 31/100, loss=0.9777 acc=0.6933
epoch 3, train 32/100, loss=2.1343 acc=0.4533
epoch 3, train 33/100, loss=1.6112 acc=0.5600
epoch 3, train 34/100, loss=1.2297 acc=0.6800
epoch 3, train 35/100, loss=1.2946 acc=0.6667
epoch 3, train 36/100, loss=0.5260 acc=0.7867
epoch 3, train 37/100, loss=0.3933 acc=0.8267
epoch 3, train 38/100, loss=1.0099 acc=0.7333
epoch 3, train 39/100, loss=0.4633 acc=0.8533
epoch 3, train 40/100, loss=1.0953 acc=0.7200
epoch 3, train 41/100, loss=0.4912 acc=0.8267
epoch 3, train 42/100, loss=0.5086 acc=0.8133
epoch 3, train 43/100, loss=0.7447 acc=0.7733
epoch 3, train 44/100, loss=1.4937 acc=0.6267
epoch 3, train 45/100, loss=0.8024 acc=0.7600
epoch 3, train 46/100, loss=0.4851 acc=0.8800
epoch 3, train 47/100, loss=2.5690 acc=0.4000
epoch 3, train 48/100, loss=1.0270 acc=0.7467
epoch 3, train 49/100, loss=0.7602 acc=0.8000
epoch 3, train 50/100, loss=1.5565 acc=0.5200
epoch 3, train 51/100, loss=1.1483 acc=0.6533
epoch 3, train 52/100, loss=0.9526 acc=0.6667
epoch 3, train 53/100, loss=0.8829 acc=0.6933
epoch 3, train 54/100, loss=1.1528 acc=0.6800
epoch 3, train 55/100, loss=0.5119 acc=0.8533
epoch 3, train 56/100, loss=0.8004 acc=0.7733
epoch 3, train 57/100, loss=1.9664 acc=0.5333
epoch 3, train 58/100, loss=0.9149 acc=0.7067
epoch 3, train 59/100, loss=0.7142 acc=0.7600
epoch 3, train 60/100, loss=0.8451 acc=0.6800
epoch 3, train 61/100, loss=1.4812 acc=0.4800
epoch 3, train 62/100, loss=0.3574 acc=0.8667
epoch 3, train 63/100, loss=1.5528 acc=0.6133
epoch 3, train 64/100, loss=0.7625 acc=0.8133
epoch 3, train 65/100, loss=1.0353 acc=0.6267
epoch 3, train 66/100, loss=1.6674 acc=0.5600
epoch 3, train 67/100, loss=1.3752 acc=0.6400
epoch 3, train 68/100, loss=1.4380 acc=0.6000
epoch 3, train 69/100, loss=0.5723 acc=0.8267
epoch 3, train 70/100, loss=0.9579 acc=0.7600
epoch 3, train 71/100, loss=0.6544 acc=0.7867
epoch 3, train 72/100, loss=1.2452 acc=0.6933
epoch 3, train 73/100, loss=1.3417 acc=0.6400
epoch 3, train 74/100, loss=0.7905 acc=0.7200
epoch 3, train 75/100, loss=0.8846 acc=0.7333
epoch 3, train 76/100, loss=1.2935 acc=0.4933
epoch 3, train 77/100, loss=1.6094 acc=0.5733
epoch 3, train 78/100, loss=2.0832 acc=0.4000
epoch 3, train 79/100, loss=0.9648 acc=0.7867
epoch 3, train 80/100, loss=0.5238 acc=0.8267
epoch 3, train 81/100, loss=1.4250 acc=0.5733
epoch 3, train 82/100, loss=0.9066 acc=0.6933
epoch 3, train 83/100, loss=0.8412 acc=0.7733
epoch 3, train 84/100, loss=1.3900 acc=0.6533
epoch 3, train 85/100, loss=0.5356 acc=0.8400
epoch 3, train 86/100, loss=1.2601 acc=0.5600
epoch 3, train 87/100, loss=1.0843 acc=0.6667
epoch 3, train 88/100, loss=0.9879 acc=0.7067
epoch 3, train 89/100, loss=1.4603 acc=0.5733
epoch 3, train 90/100, loss=1.0163 acc=0.7200
epoch 3, train 91/100, loss=0.8839 acc=0.8000
epoch 3, train 92/100, loss=0.3634 acc=0.8667
epoch 3, train 93/100, loss=0.4786 acc=0.8400
epoch 3, train 94/100, loss=0.5538 acc=0.7733
epoch 3, train 95/100, loss=1.2856 acc=0.6533
epoch 3, train 96/100, loss=1.3649 acc=0.6000
epoch 3, train 97/100, loss=0.6735 acc=0.7600
epoch 3, train 98/100, loss=1.9632 acc=0.4933
epoch 3, train 99/100, loss=1.1315 acc=0.6933
epoch 3, train 100/100, loss=1.7777 acc=0.6000
best epoch 2, best val acc=0.6045
epoch 3, val, loss=1.2459 acc=0.6205
ETA:12m/6.6h
epoch 4, train 1/100, loss=1.0261 acc=0.6667
epoch 4, train 2/100, loss=3.7523 acc=0.2400
epoch 4, train 3/100, loss=0.1994 acc=0.9067
epoch 4, train 4/100, loss=0.7658 acc=0.7333
epoch 4, train 5/100, loss=1.5908 acc=0.5467
epoch 4, train 6/100, loss=0.4747 acc=0.8533
epoch 4, train 7/100, loss=1.1036 acc=0.7200
epoch 4, train 8/100, loss=0.9782 acc=0.6933
epoch 4, train 9/100, loss=0.7126 acc=0.8000
epoch 4, train 10/100, loss=1.2706 acc=0.6800
epoch 4, train 11/100, loss=2.2218 acc=0.3733
epoch 4, train 12/100, loss=1.8825 acc=0.5067
epoch 4, train 13/100, loss=1.4182 acc=0.5467
epoch 4, train 14/100, loss=0.4662 acc=0.8267
epoch 4, train 15/100, loss=1.1154 acc=0.6400
epoch 4, train 16/100, loss=0.7587 acc=0.7600
epoch 4, train 17/100, loss=0.7463 acc=0.7600
epoch 4, train 18/100, loss=1.8608 acc=0.4667
epoch 4, train 19/100, loss=1.8109 acc=0.5333
epoch 4, train 20/100, loss=1.0021 acc=0.6000
epoch 4, train 21/100, loss=1.1862 acc=0.6133
epoch 4, train 22/100, loss=1.0330 acc=0.6533
epoch 4, train 23/100, loss=1.2939 acc=0.7067
epoch 4, train 24/100, loss=0.7552 acc=0.7733
epoch 4, train 25/100, loss=0.4680 acc=0.8267
epoch 4, train 26/100, loss=0.5995 acc=0.8667
epoch 4, train 27/100, loss=0.2657 acc=0.8933
epoch 4, train 28/100, loss=0.2846 acc=0.9200
epoch 4, train 29/100, loss=0.7857 acc=0.7733
epoch 4, train 30/100, loss=1.1575 acc=0.5867
epoch 4, train 31/100, loss=1.2732 acc=0.6000
epoch 4, train 32/100, loss=1.1364 acc=0.6533
epoch 4, train 33/100, loss=1.5756 acc=0.5333
epoch 4, train 34/100, loss=1.1088 acc=0.6000
epoch 4, train 35/100, loss=1.0227 acc=0.7200
epoch 4, train 36/100, loss=1.3109 acc=0.6800
epoch 4, train 37/100, loss=2.2696 acc=0.4133
epoch 4, train 38/100, loss=1.1110 acc=0.6667
epoch 4, train 39/100, loss=0.5656 acc=0.8667
epoch 4, train 40/100, loss=1.0567 acc=0.6800
epoch 4, train 41/100, loss=2.8437 acc=0.4800
epoch 4, train 42/100, loss=0.4263 acc=0.8133
epoch 4, train 43/100, loss=1.6428 acc=0.6000
epoch 4, train 44/100, loss=1.1962 acc=0.6667
epoch 4, train 45/100, loss=0.7303 acc=0.7467
epoch 4, train 46/100, loss=0.6202 acc=0.7867
epoch 4, train 47/100, loss=0.6535 acc=0.7733
epoch 4, train 48/100, loss=1.3309 acc=0.6667
epoch 4, train 49/100, loss=1.2282 acc=0.6933
epoch 4, train 50/100, loss=0.7980 acc=0.7600
epoch 4, train 51/100, loss=0.2954 acc=0.8933
epoch 4, train 52/100, loss=0.8769 acc=0.6800
epoch 4, train 53/100, loss=1.6455 acc=0.5600
epoch 4, train 54/100, loss=0.7645 acc=0.7600
epoch 4, train 55/100, loss=0.6697 acc=0.8133
epoch 4, train 56/100, loss=0.7320 acc=0.7200
epoch 4, train 57/100, loss=0.8559 acc=0.7067
epoch 4, train 58/100, loss=0.7013 acc=0.7333
epoch 4, train 59/100, loss=1.1102 acc=0.7067
epoch 4, train 60/100, loss=0.7309 acc=0.7333
epoch 4, train 61/100, loss=0.3647 acc=0.9200
epoch 4, train 62/100, loss=0.2662 acc=0.9200
epoch 4, train 63/100, loss=1.1398 acc=0.6533
epoch 4, train 64/100, loss=0.8348 acc=0.7333
epoch 4, train 65/100, loss=1.7200 acc=0.4267
epoch 4, train 66/100, loss=0.5076 acc=0.8133
epoch 4, train 67/100, loss=0.8397 acc=0.6533
epoch 4, train 68/100, loss=0.5846 acc=0.7733
epoch 4, train 69/100, loss=1.0323 acc=0.7733
epoch 4, train 70/100, loss=1.3506 acc=0.6400
epoch 4, train 71/100, loss=0.3544 acc=0.8800
epoch 4, train 72/100, loss=0.4104 acc=0.8800
epoch 4, train 73/100, loss=0.9110 acc=0.6933
epoch 4, train 74/100, loss=0.3344 acc=0.8533
epoch 4, train 75/100, loss=1.0864 acc=0.6133
epoch 4, train 76/100, loss=1.5249 acc=0.6267
epoch 4, train 77/100, loss=1.2672 acc=0.6267
epoch 4, train 78/100, loss=1.7147 acc=0.5333
epoch 4, train 79/100, loss=0.4414 acc=0.8667
epoch 4, train 80/100, loss=0.7631 acc=0.6667
epoch 4, train 81/100, loss=1.0375 acc=0.6800
epoch 4, train 82/100, loss=0.9881 acc=0.7200
epoch 4, train 83/100, loss=0.6401 acc=0.8400
epoch 4, train 84/100, loss=0.7009 acc=0.7600
epoch 4, train 85/100, loss=0.8984 acc=0.7067
epoch 4, train 86/100, loss=1.7091 acc=0.4533
epoch 4, train 87/100, loss=0.5060 acc=0.8133
epoch 4, train 88/100, loss=1.5929 acc=0.6133
epoch 4, train 89/100, loss=2.1363 acc=0.4800
epoch 4, train 90/100, loss=1.4528 acc=0.5600
epoch 4, train 91/100, loss=0.6198 acc=0.7867
epoch 4, train 92/100, loss=1.5661 acc=0.6000
epoch 4, train 93/100, loss=1.3231 acc=0.5333
epoch 4, train 94/100, loss=1.0067 acc=0.6800
epoch 4, train 95/100, loss=0.9385 acc=0.7200
epoch 4, train 96/100, loss=0.5719 acc=0.8133
epoch 4, train 97/100, loss=0.3424 acc=0.9067
epoch 4, train 98/100, loss=1.1561 acc=0.6533
epoch 4, train 99/100, loss=0.6161 acc=0.8000
epoch 4, train 100/100, loss=0.6680 acc=0.8133
best epoch 3, best val acc=0.6205
epoch 4, val, loss=1.2914 acc=0.6314
ETA:16m/6.5h
epoch 5, train 1/100, loss=0.5476 acc=0.7733
epoch 5, train 2/100, loss=0.4180 acc=0.8933
epoch 5, train 3/100, loss=1.7256 acc=0.5600
epoch 5, train 4/100, loss=0.8814 acc=0.7600
epoch 5, train 5/100, loss=1.1392 acc=0.6533
epoch 5, train 6/100, loss=0.4389 acc=0.8800
epoch 5, train 7/100, loss=1.3702 acc=0.6400
epoch 5, train 8/100, loss=0.7588 acc=0.7867
epoch 5, train 9/100, loss=0.4646 acc=0.8000
epoch 5, train 10/100, loss=0.6861 acc=0.7467
epoch 5, train 11/100, loss=0.2985 acc=0.8933
epoch 5, train 12/100, loss=1.5332 acc=0.5067
epoch 5, train 13/100, loss=0.3815 acc=0.8667
epoch 5, train 14/100, loss=0.8297 acc=0.8000
epoch 5, train 15/100, loss=0.2193 acc=0.9333
epoch 5, train 16/100, loss=0.7039 acc=0.8000
epoch 5, train 17/100, loss=1.2478 acc=0.7067
epoch 5, train 18/100, loss=1.4933 acc=0.5733
epoch 5, train 19/100, loss=0.5340 acc=0.8400
epoch 5, train 20/100, loss=0.4309 acc=0.8800
epoch 5, train 21/100, loss=0.3688 acc=0.8533
epoch 5, train 22/100, loss=0.5189 acc=0.8533
epoch 5, train 23/100, loss=1.1147 acc=0.6000
epoch 5, train 24/100, loss=0.7228 acc=0.7467
epoch 5, train 25/100, loss=1.0165 acc=0.6667
epoch 5, train 26/100, loss=0.6002 acc=0.7867
epoch 5, train 27/100, loss=1.1226 acc=0.6933
epoch 5, train 28/100, loss=0.4398 acc=0.8533
epoch 5, train 29/100, loss=0.4051 acc=0.8667
epoch 5, train 30/100, loss=0.5442 acc=0.8000
epoch 5, train 31/100, loss=2.0043 acc=0.5333
epoch 5, train 32/100, loss=1.1878 acc=0.6800
epoch 5, train 33/100, loss=0.7717 acc=0.7467
epoch 5, train 34/100, loss=0.6019 acc=0.7733
epoch 5, train 35/100, loss=0.5022 acc=0.7867
epoch 5, train 36/100, loss=0.9507 acc=0.6800
epoch 5, train 37/100, loss=1.1416 acc=0.6800
epoch 5, train 38/100, loss=0.7561 acc=0.7200
epoch 5, train 39/100, loss=1.1247 acc=0.5600
epoch 5, train 40/100, loss=0.8472 acc=0.7600
epoch 5, train 41/100, loss=0.5512 acc=0.7733
epoch 5, train 42/100, loss=1.3611 acc=0.6000
epoch 5, train 43/100, loss=0.7746 acc=0.7467
epoch 5, train 44/100, loss=0.5291 acc=0.8267
epoch 5, train 45/100, loss=2.0633 acc=0.4400
epoch 5, train 46/100, loss=0.8580 acc=0.7067
epoch 5, train 47/100, loss=0.8837 acc=0.7600
epoch 5, train 48/100, loss=0.4036 acc=0.8533
epoch 5, train 49/100, loss=0.5435 acc=0.8267
epoch 5, train 50/100, loss=0.9867 acc=0.7200
epoch 5, train 51/100, loss=0.0863 acc=0.9733
epoch 5, train 52/100, loss=0.5510 acc=0.7867
epoch 5, train 53/100, loss=1.1966 acc=0.6400
epoch 5, train 54/100, loss=1.1021 acc=0.7600
epoch 5, train 55/100, loss=0.5299 acc=0.8133
epoch 5, train 56/100, loss=0.9253 acc=0.7467
epoch 5, train 57/100, loss=0.9176 acc=0.7067
epoch 5, train 58/100, loss=0.1178 acc=0.9467
epoch 5, train 59/100, loss=1.4966 acc=0.6400
epoch 5, train 60/100, loss=1.5760 acc=0.5333
epoch 5, train 61/100, loss=0.8726 acc=0.7733
epoch 5, train 62/100, loss=0.9742 acc=0.6933
epoch 5, train 63/100, loss=1.5112 acc=0.4667
epoch 5, train 64/100, loss=0.8549 acc=0.7333
epoch 5, train 65/100, loss=2.0019 acc=0.5200
epoch 5, train 66/100, loss=1.0923 acc=0.7467
epoch 5, train 67/100, loss=0.6666 acc=0.7867
epoch 5, train 68/100, loss=0.7446 acc=0.7067
epoch 5, train 69/100, loss=0.6770 acc=0.8000
epoch 5, train 70/100, loss=0.9379 acc=0.6533
epoch 5, train 71/100, loss=1.1769 acc=0.6133
epoch 5, train 72/100, loss=0.8103 acc=0.7200
epoch 5, train 73/100, loss=2.1981 acc=0.4667
epoch 5, train 74/100, loss=0.9305 acc=0.6667
epoch 5, train 75/100, loss=0.7599 acc=0.7733
epoch 5, train 76/100, loss=1.0832 acc=0.7067
epoch 5, train 77/100, loss=0.8746 acc=0.7733
epoch 5, train 78/100, loss=0.7144 acc=0.7467
epoch 5, train 79/100, loss=0.9973 acc=0.7733
epoch 5, train 80/100, loss=0.7313 acc=0.8267
epoch 5, train 81/100, loss=1.0247 acc=0.7067
epoch 5, train 82/100, loss=0.9067 acc=0.6800
epoch 5, train 83/100, loss=1.8353 acc=0.5067
epoch 5, train 84/100, loss=0.6946 acc=0.7467
epoch 5, train 85/100, loss=0.6887 acc=0.7467
epoch 5, train 86/100, loss=1.4253 acc=0.5867
epoch 5, train 87/100, loss=1.1328 acc=0.6133
epoch 5, train 88/100, loss=0.7281 acc=0.7467
epoch 5, train 89/100, loss=0.9414 acc=0.7333
epoch 5, train 90/100, loss=1.1340 acc=0.6667
epoch 5, train 91/100, loss=0.7838 acc=0.7733
epoch 5, train 92/100, loss=0.7041 acc=0.7867
epoch 5, train 93/100, loss=1.1054 acc=0.7200
epoch 5, train 94/100, loss=1.7541 acc=0.5467
epoch 5, train 95/100, loss=0.3119 acc=0.9200
epoch 5, train 96/100, loss=1.4456 acc=0.5867
epoch 5, train 97/100, loss=0.5046 acc=0.8400
epoch 5, train 98/100, loss=1.5965 acc=0.5200
epoch 5, train 99/100, loss=1.1170 acc=0.6667
epoch 5, train 100/100, loss=0.8087 acc=0.7600
best epoch 4, best val acc=0.6314
epoch 5, val, loss=1.2069 acc=0.6584
ETA:20m/6.5h
epoch 6, train 1/100, loss=1.6970 acc=0.5467
epoch 6, train 2/100, loss=0.9447 acc=0.6800
epoch 6, train 3/100, loss=0.7483 acc=0.7600
epoch 6, train 4/100, loss=0.7432 acc=0.7333
epoch 6, train 5/100, loss=0.6378 acc=0.8133
epoch 6, train 6/100, loss=0.4641 acc=0.8533
epoch 6, train 7/100, loss=0.8957 acc=0.6800
epoch 6, train 8/100, loss=0.6220 acc=0.7733
epoch 6, train 9/100, loss=1.0987 acc=0.6133
epoch 6, train 10/100, loss=0.4102 acc=0.8400
epoch 6, train 11/100, loss=0.4623 acc=0.8533
epoch 6, train 12/100, loss=0.4654 acc=0.7733
epoch 6, train 13/100, loss=0.4368 acc=0.8533
epoch 6, train 14/100, loss=1.1612 acc=0.6400
epoch 6, train 15/100, loss=0.8579 acc=0.7600
epoch 6, train 16/100, loss=0.4931 acc=0.8000
epoch 6, train 17/100, loss=0.4770 acc=0.9067
epoch 6, train 18/100, loss=1.5427 acc=0.6000
epoch 6, train 19/100, loss=1.2610 acc=0.6533
epoch 6, train 20/100, loss=1.2152 acc=0.7333
epoch 6, train 21/100, loss=1.2698 acc=0.6267
epoch 6, train 22/100, loss=0.5698 acc=0.8000
epoch 6, train 23/100, loss=0.7256 acc=0.7333
epoch 6, train 24/100, loss=1.1242 acc=0.6133
epoch 6, train 25/100, loss=0.7507 acc=0.7600
epoch 6, train 26/100, loss=0.6019 acc=0.8400
epoch 6, train 27/100, loss=0.5091 acc=0.8400
epoch 6, train 28/100, loss=0.6106 acc=0.7333
epoch 6, train 29/100, loss=1.2884 acc=0.5733
epoch 6, train 30/100, loss=0.8978 acc=0.6133
epoch 6, train 31/100, loss=0.9640 acc=0.6933
epoch 6, train 32/100, loss=0.8121 acc=0.7467
epoch 6, train 33/100, loss=0.7079 acc=0.7333
epoch 6, train 34/100, loss=1.5605 acc=0.5867
epoch 6, train 35/100, loss=0.9803 acc=0.6667
epoch 6, train 36/100, loss=1.9968 acc=0.5200
epoch 6, train 37/100, loss=1.0039 acc=0.6933
epoch 6, train 38/100, loss=0.3117 acc=0.9200
epoch 6, train 39/100, loss=1.1175 acc=0.6800
epoch 6, train 40/100, loss=1.2272 acc=0.5867
epoch 6, train 41/100, loss=1.2785 acc=0.6000
epoch 6, train 42/100, loss=0.3985 acc=0.8933
epoch 6, train 43/100, loss=1.2894 acc=0.6933
epoch 6, train 44/100, loss=1.1329 acc=0.6533
epoch 6, train 45/100, loss=1.8551 acc=0.5067
epoch 6, train 46/100, loss=1.1846 acc=0.7333
epoch 6, train 47/100, loss=0.6901 acc=0.7333
epoch 6, train 48/100, loss=0.4240 acc=0.8933
epoch 6, train 49/100, loss=0.5783 acc=0.8133
epoch 6, train 50/100, loss=0.8803 acc=0.6533
epoch 6, train 51/100, loss=0.4837 acc=0.8533
epoch 6, train 52/100, loss=0.7836 acc=0.7600
epoch 6, train 53/100, loss=0.2895 acc=0.8933
epoch 6, train 54/100, loss=0.5205 acc=0.8533
epoch 6, train 55/100, loss=1.9792 acc=0.5067
epoch 6, train 56/100, loss=0.6581 acc=0.7467
epoch 6, train 57/100, loss=0.5670 acc=0.7867
epoch 6, train 58/100, loss=0.9665 acc=0.6933
epoch 6, train 59/100, loss=1.3638 acc=0.5467
epoch 6, train 60/100, loss=0.8163 acc=0.7467
epoch 6, train 61/100, loss=0.8224 acc=0.7600
epoch 6, train 62/100, loss=0.3267 acc=0.8800
epoch 6, train 63/100, loss=0.8040 acc=0.7467
epoch 6, train 64/100, loss=0.1717 acc=0.9333
epoch 6, train 65/100, loss=1.0254 acc=0.7067
epoch 6, train 66/100, loss=0.8132 acc=0.7467
epoch 6, train 67/100, loss=0.4782 acc=0.8133
epoch 6, train 68/100, loss=1.1183 acc=0.6667
epoch 6, train 69/100, loss=0.5456 acc=0.8400
epoch 6, train 70/100, loss=0.8297 acc=0.7467
epoch 6, train 71/100, loss=1.7746 acc=0.5067
epoch 6, train 72/100, loss=0.7353 acc=0.7600
epoch 6, train 73/100, loss=1.1106 acc=0.6533
epoch 6, train 74/100, loss=0.4863 acc=0.7867
epoch 6, train 75/100, loss=0.9599 acc=0.7733
epoch 6, train 76/100, loss=1.0698 acc=0.7600
epoch 6, train 77/100, loss=1.7973 acc=0.5067
epoch 6, train 78/100, loss=0.9913 acc=0.6933
epoch 6, train 79/100, loss=1.0383 acc=0.7200
epoch 6, train 80/100, loss=0.6213 acc=0.8133
epoch 6, train 81/100, loss=1.4319 acc=0.6800
epoch 6, train 82/100, loss=1.2972 acc=0.6400
epoch 6, train 83/100, loss=1.3767 acc=0.6400
epoch 6, train 84/100, loss=1.3007 acc=0.6533
epoch 6, train 85/100, loss=1.4951 acc=0.6000
epoch 6, train 86/100, loss=1.2031 acc=0.6800
epoch 6, train 87/100, loss=1.0932 acc=0.6667
epoch 6, train 88/100, loss=0.5374 acc=0.8400
epoch 6, train 89/100, loss=0.8740 acc=0.7733
epoch 6, train 90/100, loss=0.6605 acc=0.8400
epoch 6, train 91/100, loss=0.5293 acc=0.8267
epoch 6, train 92/100, loss=0.6250 acc=0.7733
epoch 6, train 93/100, loss=1.0023 acc=0.7067
epoch 6, train 94/100, loss=0.4629 acc=0.8400
epoch 6, train 95/100, loss=1.2353 acc=0.6267
epoch 6, train 96/100, loss=1.5803 acc=0.5733
epoch 6, train 97/100, loss=0.7178 acc=0.7467
epoch 6, train 98/100, loss=0.8129 acc=0.7067
epoch 6, train 99/100, loss=0.2894 acc=0.8800
epoch 6, train 100/100, loss=1.0795 acc=0.6133
best epoch 5, best val acc=0.6584
epoch 6, val, loss=1.1864 acc=0.6527
ETA:23m/6.5h
epoch 7, train 1/100, loss=0.7731 acc=0.8000
epoch 7, train 2/100, loss=0.6653 acc=0.7733
epoch 7, train 3/100, loss=1.3610 acc=0.6533
epoch 7, train 4/100, loss=1.4515 acc=0.4800
epoch 7, train 5/100, loss=1.7026 acc=0.4267
epoch 7, train 6/100, loss=1.9315 acc=0.5067
epoch 7, train 7/100, loss=1.0549 acc=0.6800
epoch 7, train 8/100, loss=1.9123 acc=0.6000
epoch 7, train 9/100, loss=1.0380 acc=0.6933
epoch 7, train 10/100, loss=0.6044 acc=0.7733
epoch 7, train 11/100, loss=0.6489 acc=0.8000
epoch 7, train 12/100, loss=0.5365 acc=0.8533
epoch 7, train 13/100, loss=1.2289 acc=0.6133
epoch 7, train 14/100, loss=0.4790 acc=0.8667
epoch 7, train 15/100, loss=1.0789 acc=0.6800
epoch 7, train 16/100, loss=0.7738 acc=0.7733
epoch 7, train 17/100, loss=1.0495 acc=0.6800
epoch 7, train 18/100, loss=0.4132 acc=0.8667
epoch 7, train 19/100, loss=0.7149 acc=0.7467
epoch 7, train 20/100, loss=2.1129 acc=0.4000
epoch 7, train 21/100, loss=0.6521 acc=0.7733
epoch 7, train 22/100, loss=0.9886 acc=0.6533
epoch 7, train 23/100, loss=1.7244 acc=0.4533
epoch 7, train 24/100, loss=0.9850 acc=0.6933
epoch 7, train 25/100, loss=0.5711 acc=0.7733
epoch 7, train 26/100, loss=0.5784 acc=0.8267
epoch 7, train 27/100, loss=0.4894 acc=0.8800
epoch 7, train 28/100, loss=0.4176 acc=0.8533
epoch 7, train 29/100, loss=0.4038 acc=0.9067
epoch 7, train 30/100, loss=0.9682 acc=0.7333
epoch 7, train 31/100, loss=0.9292 acc=0.6800
epoch 7, train 32/100, loss=1.6202 acc=0.5733
epoch 7, train 33/100, loss=1.4477 acc=0.6267
epoch 7, train 34/100, loss=1.0929 acc=0.6267
epoch 7, train 35/100, loss=0.8057 acc=0.7067
epoch 7, train 36/100, loss=1.2373 acc=0.7200
epoch 7, train 37/100, loss=0.7527 acc=0.7333
epoch 7, train 38/100, loss=1.0768 acc=0.6133
epoch 7, train 39/100, loss=0.8773 acc=0.7733
epoch 7, train 40/100, loss=0.8212 acc=0.7200
epoch 7, train 41/100, loss=0.7788 acc=0.7600
epoch 7, train 42/100, loss=1.8694 acc=0.4267
epoch 7, train 43/100, loss=0.7016 acc=0.8000
epoch 7, train 44/100, loss=0.5465 acc=0.8267
epoch 7, train 45/100, loss=0.2440 acc=0.9200
epoch 7, train 46/100, loss=1.1199 acc=0.6400
epoch 7, train 47/100, loss=1.3052 acc=0.6800
epoch 7, train 48/100, loss=1.0307 acc=0.6533
epoch 7, train 49/100, loss=0.9592 acc=0.6400
epoch 7, train 50/100, loss=0.1629 acc=0.9467
epoch 7, train 51/100, loss=1.0408 acc=0.7333
epoch 7, train 52/100, loss=1.0230 acc=0.6400
epoch 7, train 53/100, loss=0.2544 acc=0.9200
epoch 7, train 54/100, loss=1.4439 acc=0.6533
epoch 7, train 55/100, loss=0.6462 acc=0.8000
epoch 7, train 56/100, loss=0.8275 acc=0.6800
epoch 7, train 57/100, loss=0.4578 acc=0.8800
epoch 7, train 58/100, loss=0.7115 acc=0.8000
epoch 7, train 59/100, loss=2.6830 acc=0.3467
epoch 7, train 60/100, loss=0.8891 acc=0.6267
epoch 7, train 61/100, loss=0.6840 acc=0.7200
epoch 7, train 62/100, loss=0.5651 acc=0.7733
epoch 7, train 63/100, loss=0.9782 acc=0.7067
epoch 7, train 64/100, loss=0.3036 acc=0.8933
epoch 7, train 65/100, loss=0.2990 acc=0.9333
epoch 7, train 66/100, loss=0.8037 acc=0.7467
epoch 7, train 67/100, loss=0.3031 acc=0.8933
epoch 7, train 68/100, loss=1.4293 acc=0.6133
epoch 7, train 69/100, loss=1.0403 acc=0.7867
epoch 7, train 70/100, loss=1.1611 acc=0.7067
epoch 7, train 71/100, loss=0.7177 acc=0.7600
epoch 7, train 72/100, loss=0.9132 acc=0.7200
epoch 7, train 73/100, loss=0.7950 acc=0.8000
epoch 7, train 74/100, loss=0.4399 acc=0.8933
epoch 7, train 75/100, loss=1.0416 acc=0.7333
epoch 7, train 76/100, loss=1.1446 acc=0.6400
epoch 7, train 77/100, loss=0.1768 acc=0.9200
epoch 7, train 78/100, loss=0.6726 acc=0.7467
epoch 7, train 79/100, loss=0.6478 acc=0.7333
epoch 7, train 80/100, loss=0.6436 acc=0.8000
epoch 7, train 81/100, loss=1.2942 acc=0.5600
epoch 7, train 82/100, loss=1.2456 acc=0.6267
epoch 7, train 83/100, loss=0.9212 acc=0.7200
epoch 7, train 84/100, loss=0.7213 acc=0.8000
epoch 7, train 85/100, loss=0.8491 acc=0.6667
epoch 7, train 86/100, loss=0.7627 acc=0.7733
epoch 7, train 87/100, loss=0.7806 acc=0.7467
epoch 7, train 88/100, loss=0.7587 acc=0.7467
epoch 7, train 89/100, loss=0.9962 acc=0.7067
epoch 7, train 90/100, loss=0.6683 acc=0.7467
epoch 7, train 91/100, loss=0.4256 acc=0.8533
epoch 7, train 92/100, loss=1.0954 acc=0.5867
epoch 7, train 93/100, loss=0.6407 acc=0.7867
epoch 7, train 94/100, loss=0.9982 acc=0.6267
epoch 7, train 95/100, loss=0.1970 acc=0.9200
epoch 7, train 96/100, loss=0.9458 acc=0.7333
epoch 7, train 97/100, loss=0.8168 acc=0.7467
epoch 7, train 98/100, loss=0.8365 acc=0.7600
epoch 7, train 99/100, loss=0.6467 acc=0.7600
epoch 7, train 100/100, loss=1.2780 acc=0.6800
best epoch 5, best val acc=0.6584
epoch 7, val, loss=1.2725 acc=0.6380
ETA:27m/6.5h
epoch 8, train 1/100, loss=1.2378 acc=0.6667
epoch 8, train 2/100, loss=0.6020 acc=0.8000
epoch 8, train 3/100, loss=0.6121 acc=0.7867
epoch 8, train 4/100, loss=0.6527 acc=0.7867
epoch 8, train 5/100, loss=0.5986 acc=0.8000
epoch 8, train 6/100, loss=1.1827 acc=0.6667
epoch 8, train 7/100, loss=0.6009 acc=0.8267
epoch 8, train 8/100, loss=0.9287 acc=0.6667
epoch 8, train 9/100, loss=0.5247 acc=0.8267
epoch 8, train 10/100, loss=0.8833 acc=0.8000
epoch 8, train 11/100, loss=0.4106 acc=0.8400
epoch 8, train 12/100, loss=0.5519 acc=0.8267
epoch 8, train 13/100, loss=0.5554 acc=0.8133
epoch 8, train 14/100, loss=0.9185 acc=0.6800
epoch 8, train 15/100, loss=0.2633 acc=0.9067
epoch 8, train 16/100, loss=0.7902 acc=0.7600
epoch 8, train 17/100, loss=1.4298 acc=0.5733
epoch 8, train 18/100, loss=0.3711 acc=0.8533
epoch 8, train 19/100, loss=0.6591 acc=0.7867
epoch 8, train 20/100, loss=1.4536 acc=0.6400
epoch 8, train 21/100, loss=0.3657 acc=0.8400
epoch 8, train 22/100, loss=0.1150 acc=0.9467
epoch 8, train 23/100, loss=0.4939 acc=0.8133
epoch 8, train 24/100, loss=1.7293 acc=0.5067
epoch 8, train 25/100, loss=0.3331 acc=0.8667
epoch 8, train 26/100, loss=1.3645 acc=0.6133
epoch 8, train 27/100, loss=0.4250 acc=0.8400
epoch 8, train 28/100, loss=0.6443 acc=0.8267
epoch 8, train 29/100, loss=0.2214 acc=0.9200
epoch 8, train 30/100, loss=0.7654 acc=0.7333
epoch 8, train 31/100, loss=1.0205 acc=0.7200
epoch 8, train 32/100, loss=0.4986 acc=0.8267
epoch 8, train 33/100, loss=1.5555 acc=0.6267
epoch 8, train 34/100, loss=0.5147 acc=0.8133
epoch 8, train 35/100, loss=0.6600 acc=0.8400
epoch 8, train 36/100, loss=0.8669 acc=0.6800
epoch 8, train 37/100, loss=1.3488 acc=0.6000
epoch 8, train 38/100, loss=0.9826 acc=0.7200
epoch 8, train 39/100, loss=0.7637 acc=0.8133
epoch 8, train 40/100, loss=0.3946 acc=0.8933
epoch 8, train 41/100, loss=0.7666 acc=0.6933
epoch 8, train 42/100, loss=1.0316 acc=0.7733
epoch 8, train 43/100, loss=1.3971 acc=0.7067
epoch 8, train 44/100, loss=0.9571 acc=0.6667
epoch 8, train 45/100, loss=0.5189 acc=0.8267
epoch 8, train 46/100, loss=0.4887 acc=0.8133
epoch 8, train 47/100, loss=0.2814 acc=0.9067
epoch 8, train 48/100, loss=1.2486 acc=0.6800
epoch 8, train 49/100, loss=1.2924 acc=0.5867
epoch 8, train 50/100, loss=0.3465 acc=0.9200
epoch 8, train 51/100, loss=0.3203 acc=0.8800
epoch 8, train 52/100, loss=1.7901 acc=0.5867
epoch 8, train 53/100, loss=0.4882 acc=0.8267
epoch 8, train 54/100, loss=1.3337 acc=0.6933
epoch 8, train 55/100, loss=0.7012 acc=0.8000
epoch 8, train 56/100, loss=0.4896 acc=0.8533
epoch 8, train 57/100, loss=1.3907 acc=0.6667
epoch 8, train 58/100, loss=0.2676 acc=0.8800
epoch 8, train 59/100, loss=1.2866 acc=0.5867
epoch 8, train 60/100, loss=0.4060 acc=0.8133
epoch 8, train 61/100, loss=1.2817 acc=0.6933
epoch 8, train 62/100, loss=1.9482 acc=0.5333
epoch 8, train 63/100, loss=0.4587 acc=0.8533
epoch 8, train 64/100, loss=0.8675 acc=0.7333
epoch 8, train 65/100, loss=1.4053 acc=0.6267
epoch 8, train 66/100, loss=0.5979 acc=0.7333
epoch 8, train 67/100, loss=0.3249 acc=0.8533
epoch 8, train 68/100, loss=1.4491 acc=0.5733
epoch 8, train 69/100, loss=0.4788 acc=0.8667
epoch 8, train 70/100, loss=1.2040 acc=0.6533
epoch 8, train 71/100, loss=1.0689 acc=0.6800
epoch 8, train 72/100, loss=0.6406 acc=0.7733
epoch 8, train 73/100, loss=0.6454 acc=0.7067
epoch 8, train 74/100, loss=0.4300 acc=0.8667
epoch 8, train 75/100, loss=1.4952 acc=0.4800
epoch 8, train 76/100, loss=0.9279 acc=0.7333
epoch 8, train 77/100, loss=0.7517 acc=0.7467
epoch 8, train 78/100, loss=1.2406 acc=0.7067
epoch 8, train 79/100, loss=0.7157 acc=0.8133
epoch 8, train 80/100, loss=0.6808 acc=0.8400
epoch 8, train 81/100, loss=1.3418 acc=0.5600
epoch 8, train 82/100, loss=0.8269 acc=0.7333
epoch 8, train 83/100, loss=0.8771 acc=0.6933
epoch 8, train 84/100, loss=1.4154 acc=0.6000
epoch 8, train 85/100, loss=0.7004 acc=0.7600
epoch 8, train 86/100, loss=0.3725 acc=0.8533
epoch 8, train 87/100, loss=0.8648 acc=0.7067
epoch 8, train 88/100, loss=1.0910 acc=0.6133
epoch 8, train 89/100, loss=0.7666 acc=0.7467
epoch 8, train 90/100, loss=0.6126 acc=0.8133
epoch 8, train 91/100, loss=1.4118 acc=0.4933
epoch 8, train 92/100, loss=0.8908 acc=0.7200
epoch 8, train 93/100, loss=0.4795 acc=0.8000
epoch 8, train 94/100, loss=0.6786 acc=0.7467
epoch 8, train 95/100, loss=1.0152 acc=0.7067
epoch 8, train 96/100, loss=0.0908 acc=0.9733
epoch 8, train 97/100, loss=0.8434 acc=0.7067
epoch 8, train 98/100, loss=0.7054 acc=0.7467
epoch 8, train 99/100, loss=0.8035 acc=0.7200
epoch 8, train 100/100, loss=0.9033 acc=0.7200
best epoch 5, best val acc=0.6584
epoch 8, val, loss=1.2918 acc=0.6397
ETA:31m/6.5h
epoch 9, train 1/100, loss=0.3853 acc=0.8933
epoch 9, train 2/100, loss=0.4513 acc=0.8533
epoch 9, train 3/100, loss=1.0490 acc=0.7067
epoch 9, train 4/100, loss=1.4715 acc=0.6000
epoch 9, train 5/100, loss=1.1143 acc=0.5733
epoch 9, train 6/100, loss=0.9494 acc=0.7600
epoch 9, train 7/100, loss=0.9696 acc=0.7067
epoch 9, train 8/100, loss=1.1512 acc=0.7200
epoch 9, train 9/100, loss=0.2356 acc=0.8933
epoch 9, train 10/100, loss=0.6624 acc=0.7733
epoch 9, train 11/100, loss=1.2105 acc=0.6267
epoch 9, train 12/100, loss=0.2732 acc=0.9200
epoch 9, train 13/100, loss=0.5210 acc=0.8533
epoch 9, train 14/100, loss=0.2076 acc=0.8933
epoch 9, train 15/100, loss=2.4184 acc=0.4400
epoch 9, train 16/100, loss=0.4769 acc=0.8400
epoch 9, train 17/100, loss=0.3934 acc=0.8133
epoch 9, train 18/100, loss=0.7147 acc=0.7867
epoch 9, train 19/100, loss=0.2861 acc=0.8800
epoch 9, train 20/100, loss=1.0778 acc=0.6800
epoch 9, train 21/100, loss=1.5906 acc=0.6267
epoch 9, train 22/100, loss=0.3300 acc=0.9200
epoch 9, train 23/100, loss=0.8867 acc=0.7200
epoch 9, train 24/100, loss=0.4689 acc=0.8267
epoch 9, train 25/100, loss=0.6117 acc=0.7200
epoch 9, train 26/100, loss=1.1094 acc=0.6933
epoch 9, train 27/100, loss=0.6210 acc=0.8400
epoch 9, train 28/100, loss=1.0411 acc=0.6400
epoch 9, train 29/100, loss=0.6095 acc=0.7733
epoch 9, train 30/100, loss=0.7130 acc=0.7333
epoch 9, train 31/100, loss=0.6354 acc=0.8000
epoch 9, train 32/100, loss=1.0462 acc=0.6800
epoch 9, train 33/100, loss=1.1234 acc=0.5600
epoch 9, train 34/100, loss=0.6760 acc=0.8133
epoch 9, train 35/100, loss=0.6401 acc=0.8000
epoch 9, train 36/100, loss=2.1889 acc=0.4133
epoch 9, train 37/100, loss=0.7593 acc=0.7867
epoch 9, train 38/100, loss=0.7068 acc=0.8000
epoch 9, train 39/100, loss=0.9451 acc=0.6933
epoch 9, train 40/100, loss=0.9414 acc=0.7333
epoch 9, train 41/100, loss=0.5944 acc=0.8267
epoch 9, train 42/100, loss=2.2802 acc=0.5200
epoch 9, train 43/100, loss=0.9045 acc=0.6667
epoch 9, train 44/100, loss=1.4633 acc=0.5467
epoch 9, train 45/100, loss=0.8485 acc=0.7467
epoch 9, train 46/100, loss=0.7642 acc=0.7733
epoch 9, train 47/100, loss=0.8084 acc=0.7067
epoch 9, train 48/100, loss=0.9185 acc=0.7333
epoch 9, train 49/100, loss=0.4767 acc=0.8133
epoch 9, train 50/100, loss=1.5810 acc=0.5333
epoch 9, train 51/100, loss=0.2671 acc=0.8800
epoch 9, train 52/100, loss=1.5451 acc=0.6000
epoch 9, train 53/100, loss=0.9195 acc=0.7467
epoch 9, train 54/100, loss=0.7205 acc=0.8400
epoch 9, train 55/100, loss=0.5338 acc=0.8267
epoch 9, train 56/100, loss=0.7453 acc=0.7733
epoch 9, train 57/100, loss=0.5992 acc=0.7867
epoch 9, train 58/100, loss=1.3098 acc=0.6133
epoch 9, train 59/100, loss=0.3087 acc=0.9067
epoch 9, train 60/100, loss=0.7236 acc=0.7067
epoch 9, train 61/100, loss=1.2238 acc=0.6000
epoch 9, train 62/100, loss=0.5730 acc=0.7867
epoch 9, train 63/100, loss=0.8075 acc=0.7333
epoch 9, train 64/100, loss=1.5254 acc=0.6267
epoch 9, train 65/100, loss=0.3988 acc=0.8133
epoch 9, train 66/100, loss=0.4059 acc=0.9067
epoch 9, train 67/100, loss=0.4219 acc=0.8533
epoch 9, train 68/100, loss=0.6052 acc=0.8133
epoch 9, train 69/100, loss=0.5264 acc=0.8933
epoch 9, train 70/100, loss=0.2373 acc=0.8933
epoch 9, train 71/100, loss=0.6838 acc=0.7867
epoch 9, train 72/100, loss=0.8905 acc=0.7067
epoch 9, train 73/100, loss=0.5137 acc=0.8533
epoch 9, train 74/100, loss=0.7231 acc=0.7333
epoch 9, train 75/100, loss=1.2046 acc=0.7067
epoch 9, train 76/100, loss=0.6070 acc=0.7467
epoch 9, train 77/100, loss=0.7064 acc=0.7200
epoch 9, train 78/100, loss=0.6583 acc=0.7733
epoch 9, train 79/100, loss=0.6386 acc=0.8133
epoch 9, train 80/100, loss=0.3316 acc=0.8933
epoch 9, train 81/100, loss=0.2779 acc=0.9333
epoch 9, train 82/100, loss=0.3874 acc=0.8400
epoch 9, train 83/100, loss=0.8173 acc=0.7333
epoch 9, train 84/100, loss=0.0900 acc=0.9600
epoch 9, train 85/100, loss=0.6052 acc=0.8000
epoch 9, train 86/100, loss=0.4691 acc=0.8267
epoch 9, train 87/100, loss=0.4590 acc=0.8667
epoch 9, train 88/100, loss=0.2820 acc=0.9200
epoch 9, train 89/100, loss=0.6461 acc=0.7333
epoch 9, train 90/100, loss=0.5072 acc=0.8533
epoch 9, train 91/100, loss=0.5221 acc=0.8000
epoch 9, train 92/100, loss=1.0680 acc=0.6533
epoch 9, train 93/100, loss=1.0203 acc=0.5200
epoch 9, train 94/100, loss=0.4948 acc=0.8400
epoch 9, train 95/100, loss=0.8782 acc=0.7600
epoch 9, train 96/100, loss=0.5795 acc=0.7733
epoch 9, train 97/100, loss=0.7918 acc=0.6933
epoch 9, train 98/100, loss=1.1644 acc=0.6000
epoch 9, train 99/100, loss=0.7332 acc=0.7333
epoch 9, train 100/100, loss=0.5690 acc=0.8133
best epoch 5, best val acc=0.6584
epoch 9, val, loss=1.1031 acc=0.6794
ETA:35m/6.5h
epoch 10, train 1/100, loss=0.5944 acc=0.8267
epoch 10, train 2/100, loss=0.8793 acc=0.7600
epoch 10, train 3/100, loss=0.2606 acc=0.9200
epoch 10, train 4/100, loss=0.6234 acc=0.8000
epoch 10, train 5/100, loss=1.1146 acc=0.7067
epoch 10, train 6/100, loss=0.5753 acc=0.8000
epoch 10, train 7/100, loss=0.7267 acc=0.7600
epoch 10, train 8/100, loss=0.9640 acc=0.6667
epoch 10, train 9/100, loss=1.3442 acc=0.6133
epoch 10, train 10/100, loss=0.5540 acc=0.8000
epoch 10, train 11/100, loss=1.3410 acc=0.5600
epoch 10, train 12/100, loss=0.9474 acc=0.6933
epoch 10, train 13/100, loss=0.4416 acc=0.8800
epoch 10, train 14/100, loss=0.6295 acc=0.8267
epoch 10, train 15/100, loss=0.6898 acc=0.7600
epoch 10, train 16/100, loss=1.6013 acc=0.5467
epoch 10, train 17/100, loss=0.9382 acc=0.6400
epoch 10, train 18/100, loss=0.7546 acc=0.7467
epoch 10, train 19/100, loss=0.5979 acc=0.8133
epoch 10, train 20/100, loss=0.6551 acc=0.8000
epoch 10, train 21/100, loss=1.0969 acc=0.6533
epoch 10, train 22/100, loss=1.2748 acc=0.6000
epoch 10, train 23/100, loss=0.5545 acc=0.7867
epoch 10, train 24/100, loss=0.6522 acc=0.7600
epoch 10, train 25/100, loss=1.6140 acc=0.5333
epoch 10, train 26/100, loss=0.6922 acc=0.7867
epoch 10, train 27/100, loss=0.9111 acc=0.7867
epoch 10, train 28/100, loss=0.8804 acc=0.7600
epoch 10, train 29/100, loss=0.8556 acc=0.7600
epoch 10, train 30/100, loss=1.7400 acc=0.6133
epoch 10, train 31/100, loss=0.8327 acc=0.7467
epoch 10, train 32/100, loss=0.7267 acc=0.7867
epoch 10, train 33/100, loss=0.2473 acc=0.9067
epoch 10, train 34/100, loss=0.8663 acc=0.7600
epoch 10, train 35/100, loss=0.7368 acc=0.7733
epoch 10, train 36/100, loss=0.3751 acc=0.8800
epoch 10, train 37/100, loss=0.5410 acc=0.8400
epoch 10, train 38/100, loss=1.3361 acc=0.6533
epoch 10, train 39/100, loss=0.5859 acc=0.8533
epoch 10, train 40/100, loss=0.6790 acc=0.8400
epoch 10, train 41/100, loss=0.8030 acc=0.8267
epoch 10, train 42/100, loss=0.3159 acc=0.9200
epoch 10, train 43/100, loss=0.5071 acc=0.8800
epoch 10, train 44/100, loss=0.8862 acc=0.7600
epoch 10, train 45/100, loss=0.8613 acc=0.7200
epoch 10, train 46/100, loss=0.5038 acc=0.8800
epoch 10, train 47/100, loss=1.6577 acc=0.4400
epoch 10, train 48/100, loss=1.8192 acc=0.5200
epoch 10, train 49/100, loss=0.5296 acc=0.8267
epoch 10, train 50/100, loss=0.8660 acc=0.6667
epoch 10, train 51/100, loss=1.1321 acc=0.6667
epoch 10, train 52/100, loss=0.1561 acc=0.9467
epoch 10, train 53/100, loss=0.8750 acc=0.7333
epoch 10, train 54/100, loss=0.6753 acc=0.7600
epoch 10, train 55/100, loss=0.5531 acc=0.8133
epoch 10, train 56/100, loss=0.3265 acc=0.8533
epoch 10, train 57/100, loss=0.7620 acc=0.7333
epoch 10, train 58/100, loss=0.8466 acc=0.6933
epoch 10, train 59/100, loss=0.2466 acc=0.9200
epoch 10, train 60/100, loss=0.5347 acc=0.7333
epoch 10, train 61/100, loss=0.6958 acc=0.8000
epoch 10, train 62/100, loss=0.7661 acc=0.7200
epoch 10, train 63/100, loss=0.7631 acc=0.8000
epoch 10, train 64/100, loss=0.5404 acc=0.8667
epoch 10, train 65/100, loss=0.7769 acc=0.7467
epoch 10, train 66/100, loss=0.7065 acc=0.8133
epoch 10, train 67/100, loss=0.7046 acc=0.8000
epoch 10, train 68/100, loss=1.2438 acc=0.6400
epoch 10, train 69/100, loss=1.2401 acc=0.6267
epoch 10, train 70/100, loss=2.3819 acc=0.4267
epoch 10, train 71/100, loss=0.6678 acc=0.8667
epoch 10, train 72/100, loss=0.6911 acc=0.7867
epoch 10, train 73/100, loss=0.5891 acc=0.8533
epoch 10, train 74/100, loss=0.9984 acc=0.6267
epoch 10, train 75/100, loss=0.5856 acc=0.8267
epoch 10, train 76/100, loss=0.5418 acc=0.8133
epoch 10, train 77/100, loss=0.5543 acc=0.8267
epoch 10, train 78/100, loss=1.2158 acc=0.6667
epoch 10, train 79/100, loss=0.3005 acc=0.8933
epoch 10, train 80/100, loss=1.0577 acc=0.7200
epoch 10, train 81/100, loss=0.5932 acc=0.7733
epoch 10, train 82/100, loss=0.4857 acc=0.8400
epoch 10, train 83/100, loss=0.1966 acc=0.9467
epoch 10, train 84/100, loss=1.0968 acc=0.5600
epoch 10, train 85/100, loss=0.7887 acc=0.7467
epoch 10, train 86/100, loss=0.4259 acc=0.8267
epoch 10, train 87/100, loss=0.3625 acc=0.8400
epoch 10, train 88/100, loss=1.0122 acc=0.7200
epoch 10, train 89/100, loss=0.4844 acc=0.8400
epoch 10, train 90/100, loss=0.6319 acc=0.8267
epoch 10, train 91/100, loss=0.2881 acc=0.8667
epoch 10, train 92/100, loss=0.5004 acc=0.8533
epoch 10, train 93/100, loss=1.2046 acc=0.6933
epoch 10, train 94/100, loss=1.0468 acc=0.5600
epoch 10, train 95/100, loss=0.6707 acc=0.7867
epoch 10, train 96/100, loss=0.5098 acc=0.8267
epoch 10, train 97/100, loss=0.1590 acc=0.9333
epoch 10, train 98/100, loss=0.6965 acc=0.8400
epoch 10, train 99/100, loss=0.7090 acc=0.8267
epoch 10, train 100/100, loss=0.1512 acc=0.9467
best epoch 9, best val acc=0.6794
epoch 10, val, loss=1.0978 acc=0.6710
ETA:39m/6.5h
epoch 11, train 1/100, loss=0.7579 acc=0.8133
epoch 11, train 2/100, loss=1.5959 acc=0.6667
epoch 11, train 3/100, loss=0.5810 acc=0.7867
epoch 11, train 4/100, loss=0.4665 acc=0.8667
epoch 11, train 5/100, loss=0.4449 acc=0.8400
epoch 11, train 6/100, loss=0.2177 acc=0.9333
epoch 11, train 7/100, loss=0.3053 acc=0.8533
epoch 11, train 8/100, loss=0.5303 acc=0.8133
epoch 11, train 9/100, loss=0.7277 acc=0.7867
epoch 11, train 10/100, loss=1.9073 acc=0.4933
epoch 11, train 11/100, loss=0.4283 acc=0.8133
epoch 11, train 12/100, loss=0.3063 acc=0.9067
epoch 11, train 13/100, loss=0.8266 acc=0.7600
epoch 11, train 14/100, loss=0.9052 acc=0.6667
epoch 11, train 15/100, loss=0.2752 acc=0.9333
epoch 11, train 16/100, loss=1.3194 acc=0.5600
epoch 11, train 17/100, loss=0.8517 acc=0.7333
epoch 11, train 18/100, loss=0.4059 acc=0.8800
epoch 11, train 19/100, loss=1.0155 acc=0.6400
epoch 11, train 20/100, loss=0.7631 acc=0.7333
epoch 11, train 21/100, loss=0.7343 acc=0.7733
epoch 11, train 22/100, loss=1.0409 acc=0.6400
epoch 11, train 23/100, loss=0.6489 acc=0.8133
epoch 11, train 24/100, loss=0.6243 acc=0.7600
epoch 11, train 25/100, loss=0.8601 acc=0.7200
epoch 11, train 26/100, loss=0.4705 acc=0.8000
epoch 11, train 27/100, loss=0.4558 acc=0.8400
epoch 11, train 28/100, loss=0.6205 acc=0.8133
epoch 11, train 29/100, loss=0.9068 acc=0.7067
epoch 11, train 30/100, loss=1.0519 acc=0.6133
epoch 11, train 31/100, loss=0.7568 acc=0.6800
epoch 11, train 32/100, loss=0.5613 acc=0.8267
epoch 11, train 33/100, loss=1.0472 acc=0.7867
epoch 11, train 34/100, loss=1.0012 acc=0.7333
epoch 11, train 35/100, loss=0.5036 acc=0.8133
epoch 11, train 36/100, loss=0.4097 acc=0.8667
epoch 11, train 37/100, loss=0.3150 acc=0.8933
epoch 11, train 38/100, loss=0.9761 acc=0.7067
epoch 11, train 39/100, loss=0.3556 acc=0.8667
epoch 11, train 40/100, loss=1.4416 acc=0.5733
epoch 11, train 41/100, loss=0.8206 acc=0.7067
epoch 11, train 42/100, loss=1.3830 acc=0.5867
epoch 11, train 43/100, loss=0.3794 acc=0.8533
epoch 11, train 44/100, loss=0.5209 acc=0.8000
epoch 11, train 45/100, loss=0.9785 acc=0.7600
epoch 11, train 46/100, loss=0.6309 acc=0.8400
epoch 11, train 47/100, loss=0.2945 acc=0.8667
epoch 11, train 48/100, loss=0.7740 acc=0.7467
epoch 11, train 49/100, loss=1.2429 acc=0.6800
epoch 11, train 50/100, loss=0.5422 acc=0.7600
epoch 11, train 51/100, loss=0.7986 acc=0.7200
epoch 11, train 52/100, loss=0.4985 acc=0.8533
epoch 11, train 53/100, loss=0.6446 acc=0.7733
epoch 11, train 54/100, loss=0.8050 acc=0.6933
epoch 11, train 55/100, loss=0.1653 acc=0.9733
epoch 11, train 56/100, loss=0.6711 acc=0.7333
epoch 11, train 57/100, loss=0.5311 acc=0.8400
epoch 11, train 58/100, loss=0.4550 acc=0.8400
epoch 11, train 59/100, loss=1.4690 acc=0.5067
epoch 11, train 60/100, loss=0.9373 acc=0.7333
epoch 11, train 61/100, loss=0.5523 acc=0.8000
epoch 11, train 62/100, loss=0.5104 acc=0.8267
epoch 11, train 63/100, loss=0.6568 acc=0.8000
epoch 11, train 64/100, loss=0.4172 acc=0.8800
epoch 11, train 65/100, loss=0.7097 acc=0.7333
epoch 11, train 66/100, loss=0.4579 acc=0.8400
epoch 11, train 67/100, loss=1.7720 acc=0.5200
epoch 11, train 68/100, loss=0.7057 acc=0.7200
epoch 11, train 69/100, loss=0.5769 acc=0.8000
epoch 11, train 70/100, loss=1.1829 acc=0.6933
epoch 11, train 71/100, loss=1.4320 acc=0.5867
epoch 11, train 72/100, loss=0.9534 acc=0.6800
epoch 11, train 73/100, loss=0.2787 acc=0.9067
epoch 11, train 74/100, loss=0.8254 acc=0.7733
epoch 11, train 75/100, loss=0.4711 acc=0.8400
epoch 11, train 76/100, loss=0.3023 acc=0.9067
epoch 11, train 77/100, loss=0.4591 acc=0.8533
epoch 11, train 78/100, loss=0.6880 acc=0.7200
epoch 11, train 79/100, loss=0.2963 acc=0.8933
epoch 11, train 80/100, loss=0.4427 acc=0.8533
epoch 11, train 81/100, loss=0.5104 acc=0.7600
epoch 11, train 82/100, loss=0.3208 acc=0.9200
epoch 11, train 83/100, loss=0.5402 acc=0.8267
epoch 11, train 84/100, loss=0.3268 acc=0.8667
epoch 11, train 85/100, loss=0.6702 acc=0.8267
epoch 11, train 86/100, loss=0.6615 acc=0.7867
epoch 11, train 87/100, loss=0.8580 acc=0.7733
epoch 11, train 88/100, loss=0.5962 acc=0.8000
epoch 11, train 89/100, loss=0.6734 acc=0.8267
epoch 11, train 90/100, loss=1.1782 acc=0.6933
epoch 11, train 91/100, loss=0.3782 acc=0.8800
epoch 11, train 92/100, loss=0.9239 acc=0.7600
epoch 11, train 93/100, loss=0.1523 acc=0.9333
epoch 11, train 94/100, loss=0.4373 acc=0.8400
epoch 11, train 95/100, loss=0.7449 acc=0.7467
epoch 11, train 96/100, loss=0.8378 acc=0.7733
epoch 11, train 97/100, loss=0.5083 acc=0.8133
epoch 11, train 98/100, loss=0.4769 acc=0.7867
epoch 11, train 99/100, loss=0.7544 acc=0.7067
epoch 11, train 100/100, loss=0.6460 acc=0.7600
best epoch 9, best val acc=0.6794
epoch 11, val, loss=1.0947 acc=0.6831
ETA:43m/6.5h
epoch 12, train 1/100, loss=0.4981 acc=0.8267
epoch 12, train 2/100, loss=0.5356 acc=0.8133
epoch 12, train 3/100, loss=0.7812 acc=0.7867
epoch 12, train 4/100, loss=1.0912 acc=0.6800
epoch 12, train 5/100, loss=0.3873 acc=0.8800
epoch 12, train 6/100, loss=0.6088 acc=0.7600
epoch 12, train 7/100, loss=0.4052 acc=0.8267
epoch 12, train 8/100, loss=0.3708 acc=0.8667
epoch 12, train 9/100, loss=1.6850 acc=0.4933
epoch 12, train 10/100, loss=0.7679 acc=0.7467
epoch 12, train 11/100, loss=1.0808 acc=0.6667
epoch 12, train 12/100, loss=0.4721 acc=0.8933
epoch 12, train 13/100, loss=0.6172 acc=0.8000
epoch 12, train 14/100, loss=0.1591 acc=0.9467
epoch 12, train 15/100, loss=0.3361 acc=0.8933
epoch 12, train 16/100, loss=1.4022 acc=0.5733
epoch 12, train 17/100, loss=0.4229 acc=0.8933
epoch 12, train 18/100, loss=0.4836 acc=0.8800
epoch 12, train 19/100, loss=0.5969 acc=0.8267
epoch 12, train 20/100, loss=0.5889 acc=0.8000
epoch 12, train 21/100, loss=0.8676 acc=0.7733
epoch 12, train 22/100, loss=0.7269 acc=0.8000
epoch 12, train 23/100, loss=1.0864 acc=0.6267
epoch 12, train 24/100, loss=0.4822 acc=0.8400
epoch 12, train 25/100, loss=0.0879 acc=0.9600
epoch 12, train 26/100, loss=1.3080 acc=0.7200
epoch 12, train 27/100, loss=0.4736 acc=0.8400
epoch 12, train 28/100, loss=0.2944 acc=0.9067
epoch 12, train 29/100, loss=0.3395 acc=0.8667
epoch 12, train 30/100, loss=0.6576 acc=0.7467
epoch 12, train 31/100, loss=0.4264 acc=0.8400
epoch 12, train 32/100, loss=0.9565 acc=0.7067
epoch 12, train 33/100, loss=0.6264 acc=0.7867
epoch 12, train 34/100, loss=0.7469 acc=0.8000
epoch 12, train 35/100, loss=0.2150 acc=0.9600
epoch 12, train 36/100, loss=0.6801 acc=0.7600
epoch 12, train 37/100, loss=0.5558 acc=0.7867
epoch 12, train 38/100, loss=0.8608 acc=0.6533
epoch 12, train 39/100, loss=1.0563 acc=0.6933
epoch 12, train 40/100, loss=1.1811 acc=0.6533
epoch 12, train 41/100, loss=0.1661 acc=0.9333
epoch 12, train 42/100, loss=0.2681 acc=0.9067
epoch 12, train 43/100, loss=0.2872 acc=0.9067
epoch 12, train 44/100, loss=0.2852 acc=0.8533
epoch 12, train 45/100, loss=0.3862 acc=0.8800
epoch 12, train 46/100, loss=0.7728 acc=0.7600
epoch 12, train 47/100, loss=0.4973 acc=0.8267
epoch 12, train 48/100, loss=0.7969 acc=0.6933
epoch 12, train 49/100, loss=1.6002 acc=0.6000
epoch 12, train 50/100, loss=0.2301 acc=0.9067
epoch 12, train 51/100, loss=1.1455 acc=0.6800
epoch 12, train 52/100, loss=0.4593 acc=0.8400
epoch 12, train 53/100, loss=0.3493 acc=0.8933
epoch 12, train 54/100, loss=0.4757 acc=0.8267
epoch 12, train 55/100, loss=0.6916 acc=0.7467
epoch 12, train 56/100, loss=1.1386 acc=0.5867
epoch 12, train 57/100, loss=0.5875 acc=0.8800
epoch 12, train 58/100, loss=1.1057 acc=0.6400
epoch 12, train 59/100, loss=1.0054 acc=0.6667
epoch 12, train 60/100, loss=0.3669 acc=0.8533
epoch 12, train 61/100, loss=0.8592 acc=0.6933
epoch 12, train 62/100, loss=0.5689 acc=0.8400
epoch 12, train 63/100, loss=0.5799 acc=0.8133
epoch 12, train 64/100, loss=0.4597 acc=0.7867
epoch 12, train 65/100, loss=0.5969 acc=0.7867
epoch 12, train 66/100, loss=0.4707 acc=0.8267
epoch 12, train 67/100, loss=1.0751 acc=0.6933
epoch 12, train 68/100, loss=0.6543 acc=0.7467
epoch 12, train 69/100, loss=0.9373 acc=0.6667
epoch 12, train 70/100, loss=0.2686 acc=0.9200
epoch 12, train 71/100, loss=1.3759 acc=0.6400
epoch 12, train 72/100, loss=0.5822 acc=0.7867
epoch 12, train 73/100, loss=0.3856 acc=0.8800
epoch 12, train 74/100, loss=0.7428 acc=0.7333
epoch 12, train 75/100, loss=0.9860 acc=0.7067
epoch 12, train 76/100, loss=0.3830 acc=0.8800
epoch 12, train 77/100, loss=0.4355 acc=0.8667
epoch 12, train 78/100, loss=0.7477 acc=0.7467
epoch 12, train 79/100, loss=0.3312 acc=0.9067
epoch 12, train 80/100, loss=0.1708 acc=0.9467
epoch 12, train 81/100, loss=0.5372 acc=0.8000
epoch 12, train 82/100, loss=0.2534 acc=0.9200
epoch 12, train 83/100, loss=0.4023 acc=0.8533
epoch 12, train 84/100, loss=0.2335 acc=0.9200
epoch 12, train 85/100, loss=0.8231 acc=0.7333
epoch 12, train 86/100, loss=1.0590 acc=0.6400
epoch 12, train 87/100, loss=0.1661 acc=0.9333
epoch 12, train 88/100, loss=0.3310 acc=0.9600
epoch 12, train 89/100, loss=0.7782 acc=0.7867
epoch 12, train 90/100, loss=0.1390 acc=0.9200
epoch 12, train 91/100, loss=1.5866 acc=0.5600
epoch 12, train 92/100, loss=0.6524 acc=0.7867
epoch 12, train 93/100, loss=0.2398 acc=0.9067
epoch 12, train 94/100, loss=0.9960 acc=0.6933
epoch 12, train 95/100, loss=0.5947 acc=0.8400
epoch 12, train 96/100, loss=0.4849 acc=0.8267
epoch 12, train 97/100, loss=0.8060 acc=0.8133
epoch 12, train 98/100, loss=0.7372 acc=0.8267
epoch 12, train 99/100, loss=1.0500 acc=0.6933
epoch 12, train 100/100, loss=0.8941 acc=0.7733
best epoch 11, best val acc=0.6831
epoch 12, val, loss=1.0938 acc=0.6753
ETA:46m/6.5h
epoch 13, train 1/100, loss=0.4640 acc=0.8800
epoch 13, train 2/100, loss=1.3745 acc=0.5733
epoch 13, train 3/100, loss=0.8827 acc=0.6800
epoch 13, train 4/100, loss=0.8836 acc=0.7333
epoch 13, train 5/100, loss=0.5293 acc=0.8800
epoch 13, train 6/100, loss=0.7913 acc=0.7867
epoch 13, train 7/100, loss=0.5268 acc=0.7867
epoch 13, train 8/100, loss=0.6222 acc=0.7600
epoch 13, train 9/100, loss=1.0779 acc=0.5600
epoch 13, train 10/100, loss=0.3204 acc=0.9333
epoch 13, train 11/100, loss=0.5273 acc=0.8533
epoch 13, train 12/100, loss=0.3347 acc=0.8667
epoch 13, train 13/100, loss=0.2704 acc=0.8800
epoch 13, train 14/100, loss=0.3686 acc=0.8667
epoch 13, train 15/100, loss=1.2429 acc=0.5600
epoch 13, train 16/100, loss=0.5588 acc=0.8800
epoch 13, train 17/100, loss=0.4613 acc=0.7867
epoch 13, train 18/100, loss=0.4268 acc=0.8667
epoch 13, train 19/100, loss=0.6426 acc=0.8267
epoch 13, train 20/100, loss=1.2518 acc=0.6000
epoch 13, train 21/100, loss=0.9825 acc=0.6800
epoch 13, train 22/100, loss=0.8278 acc=0.7067
epoch 13, train 23/100, loss=0.4094 acc=0.9067
epoch 13, train 24/100, loss=0.5718 acc=0.8800
epoch 13, train 25/100, loss=0.5768 acc=0.7067
epoch 13, train 26/100, loss=0.7828 acc=0.7733
epoch 13, train 27/100, loss=0.5078 acc=0.7733
epoch 13, train 28/100, loss=0.5519 acc=0.8400
epoch 13, train 29/100, loss=1.0381 acc=0.7200
epoch 13, train 30/100, loss=1.3465 acc=0.5067
epoch 13, train 31/100, loss=0.6025 acc=0.7867
epoch 13, train 32/100, loss=0.7613 acc=0.7867
epoch 13, train 33/100, loss=1.0681 acc=0.6133
epoch 13, train 34/100, loss=0.6283 acc=0.8000
epoch 13, train 35/100, loss=0.3654 acc=0.8267
epoch 13, train 36/100, loss=0.1631 acc=0.9200
epoch 13, train 37/100, loss=0.8580 acc=0.7067
epoch 13, train 38/100, loss=0.4825 acc=0.8000
epoch 13, train 39/100, loss=0.4891 acc=0.8667
epoch 13, train 40/100, loss=0.6469 acc=0.8000
epoch 13, train 41/100, loss=0.4755 acc=0.8667
epoch 13, train 42/100, loss=0.8117 acc=0.7733
epoch 13, train 43/100, loss=0.2212 acc=0.9333
epoch 13, train 44/100, loss=0.7172 acc=0.8267
epoch 13, train 45/100, loss=0.2950 acc=0.9067
epoch 13, train 46/100, loss=1.1548 acc=0.6133
epoch 13, train 47/100, loss=0.5648 acc=0.7867
epoch 13, train 48/100, loss=0.6312 acc=0.8267
epoch 13, train 49/100, loss=0.3336 acc=0.8800
epoch 13, train 50/100, loss=0.1952 acc=0.9200
epoch 13, train 51/100, loss=0.2517 acc=0.8800
epoch 13, train 52/100, loss=1.0608 acc=0.6667
epoch 13, train 53/100, loss=0.8789 acc=0.7600
epoch 13, train 54/100, loss=0.6509 acc=0.7733
epoch 13, train 55/100, loss=0.3669 acc=0.8267
epoch 13, train 56/100, loss=0.4575 acc=0.8267
epoch 13, train 57/100, loss=0.1976 acc=0.9600
epoch 13, train 58/100, loss=0.5697 acc=0.8533
epoch 13, train 59/100, loss=0.4411 acc=0.8533
epoch 13, train 60/100, loss=0.3470 acc=0.8933
epoch 13, train 61/100, loss=0.4401 acc=0.8533
epoch 13, train 62/100, loss=0.9154 acc=0.6800
epoch 13, train 63/100, loss=0.4238 acc=0.8800
epoch 13, train 64/100, loss=0.5547 acc=0.8267
epoch 13, train 65/100, loss=1.4720 acc=0.4933
epoch 13, train 66/100, loss=0.7266 acc=0.7200
epoch 13, train 67/100, loss=0.6724 acc=0.7200
epoch 13, train 68/100, loss=0.7207 acc=0.7467
epoch 13, train 69/100, loss=0.4015 acc=0.8667
epoch 13, train 70/100, loss=0.8099 acc=0.7600
epoch 13, train 71/100, loss=0.2998 acc=0.8933
epoch 13, train 72/100, loss=0.3980 acc=0.8533
epoch 13, train 73/100, loss=0.6374 acc=0.7867
epoch 13, train 74/100, loss=0.3954 acc=0.8667
epoch 13, train 75/100, loss=0.5954 acc=0.8267
epoch 13, train 76/100, loss=0.5591 acc=0.8267
epoch 13, train 77/100, loss=0.2093 acc=0.9200
epoch 13, train 78/100, loss=0.6358 acc=0.7600
epoch 13, train 79/100, loss=0.5434 acc=0.8533
epoch 13, train 80/100, loss=0.8693 acc=0.7067
epoch 13, train 81/100, loss=0.6089 acc=0.7733
epoch 13, train 82/100, loss=0.4537 acc=0.8533
epoch 13, train 83/100, loss=1.1393 acc=0.6800
epoch 13, train 84/100, loss=0.6247 acc=0.8533
epoch 13, train 85/100, loss=0.3479 acc=0.8533
epoch 13, train 86/100, loss=0.6465 acc=0.7733
epoch 13, train 87/100, loss=0.9152 acc=0.7200
epoch 13, train 88/100, loss=0.7340 acc=0.7333
epoch 13, train 89/100, loss=0.9016 acc=0.7333
epoch 13, train 90/100, loss=1.2260 acc=0.6533
epoch 13, train 91/100, loss=0.5640 acc=0.8000
epoch 13, train 92/100, loss=0.9604 acc=0.7067
epoch 13, train 93/100, loss=1.1593 acc=0.5600
epoch 13, train 94/100, loss=1.1310 acc=0.6533
epoch 13, train 95/100, loss=0.6871 acc=0.7867
epoch 13, train 96/100, loss=1.2784 acc=0.6000
epoch 13, train 97/100, loss=0.3505 acc=0.8400
epoch 13, train 98/100, loss=0.1728 acc=0.9333
epoch 13, train 99/100, loss=0.7061 acc=0.7867
epoch 13, train 100/100, loss=0.4904 acc=0.8533
best epoch 11, best val acc=0.6831
epoch 13, val, loss=1.0651 acc=0.6791
ETA:50m/6.4h
epoch 14, train 1/100, loss=0.4455 acc=0.8800
epoch 14, train 2/100, loss=1.0105 acc=0.6667
epoch 14, train 3/100, loss=0.4720 acc=0.8000
epoch 14, train 4/100, loss=0.5356 acc=0.8667
epoch 14, train 5/100, loss=1.2389 acc=0.6267
epoch 14, train 6/100, loss=1.1594 acc=0.6800
epoch 14, train 7/100, loss=0.9664 acc=0.7067
epoch 14, train 8/100, loss=0.1328 acc=0.9600
epoch 14, train 9/100, loss=0.8742 acc=0.7867
epoch 14, train 10/100, loss=0.6237 acc=0.7467
epoch 14, train 11/100, loss=0.2377 acc=0.9200
epoch 14, train 12/100, loss=0.4999 acc=0.8400
epoch 14, train 13/100, loss=0.8060 acc=0.7333
epoch 14, train 14/100, loss=0.7215 acc=0.7200
epoch 14, train 15/100, loss=0.3315 acc=0.8800
epoch 14, train 16/100, loss=0.7606 acc=0.7733
epoch 14, train 17/100, loss=0.8101 acc=0.7467
epoch 14, train 18/100, loss=0.8438 acc=0.7067
epoch 14, train 19/100, loss=0.2799 acc=0.9200
epoch 14, train 20/100, loss=0.5778 acc=0.7733
epoch 14, train 21/100, loss=0.3251 acc=0.8933
epoch 14, train 22/100, loss=1.4034 acc=0.5067
epoch 14, train 23/100, loss=0.1464 acc=0.9333
epoch 14, train 24/100, loss=0.5187 acc=0.8000
epoch 14, train 25/100, loss=1.0831 acc=0.6267
epoch 14, train 26/100, loss=1.0633 acc=0.6400
epoch 14, train 27/100, loss=1.3873 acc=0.5333
epoch 14, train 28/100, loss=1.4128 acc=0.6000
epoch 14, train 29/100, loss=0.7107 acc=0.6933
epoch 14, train 30/100, loss=0.4703 acc=0.8267
epoch 14, train 31/100, loss=0.4740 acc=0.8133
epoch 14, train 32/100, loss=0.8599 acc=0.7200
epoch 14, train 33/100, loss=0.7939 acc=0.6933
epoch 14, train 34/100, loss=0.6266 acc=0.8000
epoch 14, train 35/100, loss=0.6737 acc=0.7467
epoch 14, train 36/100, loss=0.2612 acc=0.9067
epoch 14, train 37/100, loss=0.3080 acc=0.8800
epoch 14, train 38/100, loss=0.8527 acc=0.7467
epoch 14, train 39/100, loss=0.6654 acc=0.7733
epoch 14, train 40/100, loss=1.0561 acc=0.6933
epoch 14, train 41/100, loss=0.5042 acc=0.7867
epoch 14, train 42/100, loss=1.2527 acc=0.6000
epoch 14, train 43/100, loss=0.8130 acc=0.8267
epoch 14, train 44/100, loss=1.1027 acc=0.6533
epoch 14, train 45/100, loss=1.5594 acc=0.4800
epoch 14, train 46/100, loss=1.2086 acc=0.6533
epoch 14, train 47/100, loss=1.5680 acc=0.5200
epoch 14, train 48/100, loss=0.7437 acc=0.7200
epoch 14, train 49/100, loss=1.0297 acc=0.6667
epoch 14, train 50/100, loss=0.9734 acc=0.6933
epoch 14, train 51/100, loss=0.3139 acc=0.8800
epoch 14, train 52/100, loss=0.6199 acc=0.8267
epoch 14, train 53/100, loss=0.2061 acc=0.9333
epoch 14, train 54/100, loss=0.5564 acc=0.8400
epoch 14, train 55/100, loss=0.6036 acc=0.7867
epoch 14, train 56/100, loss=1.0868 acc=0.6933
epoch 14, train 57/100, loss=0.3622 acc=0.8933
epoch 14, train 58/100, loss=0.4815 acc=0.8267
epoch 14, train 59/100, loss=0.2413 acc=0.9333
epoch 14, train 60/100, loss=0.7929 acc=0.7867
epoch 14, train 61/100, loss=0.6924 acc=0.8000
epoch 14, train 62/100, loss=1.1747 acc=0.6133
epoch 14, train 63/100, loss=0.7468 acc=0.7600
epoch 14, train 64/100, loss=0.5949 acc=0.8133
epoch 14, train 65/100, loss=1.2693 acc=0.6267
epoch 14, train 66/100, loss=0.5410 acc=0.7467
epoch 14, train 67/100, loss=0.8610 acc=0.7333
epoch 14, train 68/100, loss=0.9437 acc=0.7200
epoch 14, train 69/100, loss=0.6033 acc=0.7867
epoch 14, train 70/100, loss=0.3337 acc=0.8933
epoch 14, train 71/100, loss=0.4615 acc=0.8133
epoch 14, train 72/100, loss=0.5326 acc=0.8000
epoch 14, train 73/100, loss=0.6888 acc=0.8133
epoch 14, train 74/100, loss=0.7310 acc=0.7867
epoch 14, train 75/100, loss=0.7363 acc=0.7200
epoch 14, train 76/100, loss=0.9285 acc=0.7067
epoch 14, train 77/100, loss=0.3508 acc=0.8533
epoch 14, train 78/100, loss=0.8633 acc=0.8400
epoch 14, train 79/100, loss=1.0679 acc=0.6000
epoch 14, train 80/100, loss=0.7225 acc=0.7467
epoch 14, train 81/100, loss=0.1570 acc=0.9467
epoch 14, train 82/100, loss=1.0127 acc=0.7467
epoch 14, train 83/100, loss=0.8867 acc=0.7333
epoch 14, train 84/100, loss=0.1839 acc=0.9333
epoch 14, train 85/100, loss=0.2269 acc=0.8933
epoch 14, train 86/100, loss=0.6045 acc=0.7600
epoch 14, train 87/100, loss=0.4279 acc=0.8800
epoch 14, train 88/100, loss=0.1817 acc=0.9333
epoch 14, train 89/100, loss=0.6257 acc=0.7333
epoch 14, train 90/100, loss=0.1227 acc=0.9467
epoch 14, train 91/100, loss=0.2592 acc=0.9467
epoch 14, train 92/100, loss=0.2305 acc=0.9333
epoch 14, train 93/100, loss=0.4953 acc=0.8133
epoch 14, train 94/100, loss=0.9777 acc=0.7067
epoch 14, train 95/100, loss=0.4747 acc=0.8133
epoch 14, train 96/100, loss=0.6884 acc=0.7200
epoch 14, train 97/100, loss=0.2654 acc=0.9467
epoch 14, train 98/100, loss=0.2848 acc=0.9067
epoch 14, train 99/100, loss=0.5710 acc=0.8533
epoch 14, train 100/100, loss=0.3397 acc=0.9200
best epoch 11, best val acc=0.6831
epoch 14, val, loss=1.0357 acc=0.6909
ETA:54m/6.4h
epoch 15, train 1/100, loss=0.3265 acc=0.9200
epoch 15, train 2/100, loss=0.5560 acc=0.8800
epoch 15, train 3/100, loss=0.6346 acc=0.7600
epoch 15, train 4/100, loss=0.5190 acc=0.8400
epoch 15, train 5/100, loss=0.3401 acc=0.9067
epoch 15, train 6/100, loss=0.4798 acc=0.8400
epoch 15, train 7/100, loss=0.2526 acc=0.9333
epoch 15, train 8/100, loss=0.7559 acc=0.7867
epoch 15, train 9/100, loss=0.8671 acc=0.7867
epoch 15, train 10/100, loss=0.9531 acc=0.7333
epoch 15, train 11/100, loss=0.7061 acc=0.8000
epoch 15, train 12/100, loss=0.7830 acc=0.7600
epoch 15, train 13/100, loss=0.4717 acc=0.8267
epoch 15, train 14/100, loss=0.9856 acc=0.7467
epoch 15, train 15/100, loss=0.6537 acc=0.7067
epoch 15, train 16/100, loss=0.4232 acc=0.8400
epoch 15, train 17/100, loss=0.5437 acc=0.8400
epoch 15, train 18/100, loss=0.4368 acc=0.8267
epoch 15, train 19/100, loss=0.2427 acc=0.8933
epoch 15, train 20/100, loss=1.0450 acc=0.6800
epoch 15, train 21/100, loss=0.6150 acc=0.8133
epoch 15, train 22/100, loss=0.8585 acc=0.6667
epoch 15, train 23/100, loss=1.6260 acc=0.6000
epoch 15, train 24/100, loss=0.2284 acc=0.9067
epoch 15, train 25/100, loss=0.2700 acc=0.9200
epoch 15, train 26/100, loss=0.4602 acc=0.8667
epoch 15, train 27/100, loss=0.3413 acc=0.9467
epoch 15, train 28/100, loss=0.5003 acc=0.8533
epoch 15, train 29/100, loss=0.7930 acc=0.7467
epoch 15, train 30/100, loss=0.1716 acc=0.9467
epoch 15, train 31/100, loss=0.4093 acc=0.8533
epoch 15, train 32/100, loss=0.2972 acc=0.8933
epoch 15, train 33/100, loss=0.3302 acc=0.9333
epoch 15, train 34/100, loss=0.5101 acc=0.8000
epoch 15, train 35/100, loss=1.0718 acc=0.6933
epoch 15, train 36/100, loss=0.5415 acc=0.7733
epoch 15, train 37/100, loss=0.6067 acc=0.7733
epoch 15, train 38/100, loss=0.1585 acc=0.9733
epoch 15, train 39/100, loss=0.5299 acc=0.8533
epoch 15, train 40/100, loss=0.3904 acc=0.8133
epoch 15, train 41/100, loss=0.3716 acc=0.8800
epoch 15, train 42/100, loss=0.4307 acc=0.8667
epoch 15, train 43/100, loss=0.6773 acc=0.7467
epoch 15, train 44/100, loss=1.6603 acc=0.5467
epoch 15, train 45/100, loss=0.3537 acc=0.8933
epoch 15, train 46/100, loss=0.3948 acc=0.8533
epoch 15, train 47/100, loss=0.2133 acc=0.9467
epoch 15, train 48/100, loss=0.8055 acc=0.7200
epoch 15, train 49/100, loss=0.9748 acc=0.7200
epoch 15, train 50/100, loss=0.3982 acc=0.8533
epoch 15, train 51/100, loss=0.5907 acc=0.8133
epoch 15, train 52/100, loss=0.0891 acc=0.9600
epoch 15, train 53/100, loss=0.6904 acc=0.8000
epoch 15, train 54/100, loss=0.6323 acc=0.8267
epoch 15, train 55/100, loss=1.1064 acc=0.7200
epoch 15, train 56/100, loss=0.5399 acc=0.7867
epoch 15, train 57/100, loss=0.4527 acc=0.8667
epoch 15, train 58/100, loss=0.1586 acc=0.9467
epoch 15, train 59/100, loss=0.7950 acc=0.6933
epoch 15, train 60/100, loss=0.5858 acc=0.8000
epoch 15, train 61/100, loss=0.3123 acc=0.8933
epoch 15, train 62/100, loss=0.6806 acc=0.7867
epoch 15, train 63/100, loss=0.2816 acc=0.8800
epoch 15, train 64/100, loss=0.3997 acc=0.8400
epoch 15, train 65/100, loss=0.6740 acc=0.7867
epoch 15, train 66/100, loss=0.3917 acc=0.8400
epoch 15, train 67/100, loss=0.7462 acc=0.7733
epoch 15, train 68/100, loss=0.6112 acc=0.8133
epoch 15, train 69/100, loss=0.5019 acc=0.8133
epoch 15, train 70/100, loss=0.4523 acc=0.8400
epoch 15, train 71/100, loss=0.4847 acc=0.8133
epoch 15, train 72/100, loss=0.9852 acc=0.6800
epoch 15, train 73/100, loss=0.8166 acc=0.7467
epoch 15, train 74/100, loss=0.1554 acc=0.9467
epoch 15, train 75/100, loss=0.5706 acc=0.7867
epoch 15, train 76/100, loss=0.4523 acc=0.9200
epoch 15, train 77/100, loss=0.5460 acc=0.8533
epoch 15, train 78/100, loss=0.4799 acc=0.8400
epoch 15, train 79/100, loss=0.3702 acc=0.8667
epoch 15, train 80/100, loss=0.5631 acc=0.8267
epoch 15, train 81/100, loss=0.2169 acc=0.9200
epoch 15, train 82/100, loss=0.1803 acc=0.9333
epoch 15, train 83/100, loss=0.5269 acc=0.8000
epoch 15, train 84/100, loss=0.5986 acc=0.7600
epoch 15, train 85/100, loss=0.4973 acc=0.8533
epoch 15, train 86/100, loss=1.2963 acc=0.6000
epoch 15, train 87/100, loss=0.5778 acc=0.8267
epoch 15, train 88/100, loss=0.4678 acc=0.7733
epoch 15, train 89/100, loss=0.1691 acc=0.9333
epoch 15, train 90/100, loss=1.0078 acc=0.6933
epoch 15, train 91/100, loss=0.5721 acc=0.7600
epoch 15, train 92/100, loss=0.7267 acc=0.7067
epoch 15, train 93/100, loss=0.4610 acc=0.8000
epoch 15, train 94/100, loss=0.8099 acc=0.7600
epoch 15, train 95/100, loss=0.4168 acc=0.8800
epoch 15, train 96/100, loss=0.3596 acc=0.8000
epoch 15, train 97/100, loss=0.5397 acc=0.8267
epoch 15, train 98/100, loss=0.4017 acc=0.8800
epoch 15, train 99/100, loss=0.4183 acc=0.8800
epoch 15, train 100/100, loss=1.1032 acc=0.6400
best epoch 14, best val acc=0.6909
epoch 15, val, loss=1.0448 acc=0.6845
ETA:58m/6.4h
epoch 16, train 1/100, loss=0.5854 acc=0.8133
epoch 16, train 2/100, loss=0.5899 acc=0.8133
epoch 16, train 3/100, loss=0.4196 acc=0.9067
epoch 16, train 4/100, loss=0.1994 acc=0.9467
epoch 16, train 5/100, loss=0.6181 acc=0.8533
epoch 16, train 6/100, loss=0.2795 acc=0.9067
epoch 16, train 7/100, loss=1.3032 acc=0.6800
epoch 16, train 8/100, loss=0.1050 acc=0.9600
epoch 16, train 9/100, loss=0.6571 acc=0.8267
epoch 16, train 10/100, loss=0.1367 acc=0.9200
epoch 16, train 11/100, loss=1.0203 acc=0.6400
epoch 16, train 12/100, loss=0.7443 acc=0.7333
epoch 16, train 13/100, loss=0.8333 acc=0.7600
epoch 16, train 14/100, loss=0.3080 acc=0.9067
epoch 16, train 15/100, loss=1.5397 acc=0.4933
epoch 16, train 16/100, loss=0.5868 acc=0.8000
epoch 16, train 17/100, loss=0.4706 acc=0.8933
epoch 16, train 18/100, loss=0.2836 acc=0.9467
epoch 16, train 19/100, loss=0.1461 acc=0.9467
epoch 16, train 20/100, loss=0.0438 acc=0.9733
epoch 16, train 21/100, loss=1.0402 acc=0.7333
epoch 16, train 22/100, loss=2.0314 acc=0.4667
epoch 16, train 23/100, loss=1.0147 acc=0.6533
epoch 16, train 24/100, loss=0.3309 acc=0.9333
epoch 16, train 25/100, loss=0.3199 acc=0.8667
epoch 16, train 26/100, loss=0.3490 acc=0.8667
epoch 16, train 27/100, loss=1.3456 acc=0.6133
epoch 16, train 28/100, loss=0.6330 acc=0.8000
epoch 16, train 29/100, loss=0.4926 acc=0.7867
epoch 16, train 30/100, loss=0.3248 acc=0.8933
epoch 16, train 31/100, loss=0.4939 acc=0.8400
epoch 16, train 32/100, loss=0.4738 acc=0.8267
epoch 16, train 33/100, loss=0.1950 acc=0.9200
epoch 16, train 34/100, loss=0.3702 acc=0.8800
epoch 16, train 35/100, loss=0.6425 acc=0.7333
epoch 16, train 36/100, loss=1.3278 acc=0.6667
epoch 16, train 37/100, loss=1.6544 acc=0.5867
epoch 16, train 38/100, loss=1.1673 acc=0.5867
epoch 16, train 39/100, loss=0.4962 acc=0.8133
epoch 16, train 40/100, loss=0.8659 acc=0.7733
epoch 16, train 41/100, loss=0.2702 acc=0.9600
epoch 16, train 42/100, loss=0.3112 acc=0.8933
epoch 16, train 43/100, loss=0.4535 acc=0.8667
epoch 16, train 44/100, loss=0.8314 acc=0.7467
epoch 16, train 45/100, loss=0.2973 acc=0.9200
epoch 16, train 46/100, loss=0.1770 acc=0.9467
epoch 16, train 47/100, loss=1.8415 acc=0.5867
epoch 16, train 48/100, loss=0.2334 acc=0.9333
epoch 16, train 49/100, loss=0.2112 acc=0.9467
epoch 16, train 50/100, loss=0.7954 acc=0.7600
epoch 16, train 51/100, loss=0.7116 acc=0.7200
epoch 16, train 52/100, loss=0.5006 acc=0.8133
epoch 16, train 53/100, loss=0.4164 acc=0.8267
epoch 16, train 54/100, loss=0.5696 acc=0.8133
epoch 16, train 55/100, loss=0.3950 acc=0.8533
epoch 16, train 56/100, loss=0.1797 acc=0.9333
epoch 16, train 57/100, loss=0.7736 acc=0.7733
epoch 16, train 58/100, loss=0.2610 acc=0.8800
epoch 16, train 59/100, loss=0.2453 acc=0.9467
epoch 16, train 60/100, loss=0.2450 acc=0.9067
epoch 16, train 61/100, loss=1.0083 acc=0.6667
epoch 16, train 62/100, loss=1.9455 acc=0.5600
epoch 16, train 63/100, loss=0.2440 acc=0.9200
epoch 16, train 64/100, loss=0.1064 acc=0.9733
epoch 16, train 65/100, loss=1.2767 acc=0.6133
epoch 16, train 66/100, loss=1.3987 acc=0.6267
epoch 16, train 67/100, loss=0.3253 acc=0.8800
epoch 16, train 68/100, loss=0.1981 acc=0.9467
epoch 16, train 69/100, loss=0.4287 acc=0.8667
epoch 16, train 70/100, loss=0.8556 acc=0.7467
epoch 16, train 71/100, loss=0.5302 acc=0.8000
epoch 16, train 72/100, loss=0.3452 acc=0.8800
epoch 16, train 73/100, loss=0.7961 acc=0.7733
epoch 16, train 74/100, loss=0.0723 acc=0.9867
epoch 16, train 75/100, loss=0.7148 acc=0.7867
epoch 16, train 76/100, loss=1.3946 acc=0.6267
epoch 16, train 77/100, loss=0.8731 acc=0.7600
epoch 16, train 78/100, loss=0.8294 acc=0.7067
epoch 16, train 79/100, loss=0.5521 acc=0.8267
epoch 16, train 80/100, loss=1.2431 acc=0.6800
epoch 16, train 81/100, loss=0.4256 acc=0.8800
epoch 16, train 82/100, loss=1.1102 acc=0.6000
epoch 16, train 83/100, loss=0.6077 acc=0.7867
epoch 16, train 84/100, loss=0.3678 acc=0.8667
epoch 16, train 85/100, loss=0.3283 acc=0.8667
epoch 16, train 86/100, loss=0.6370 acc=0.8400
epoch 16, train 87/100, loss=0.3364 acc=0.8667
epoch 16, train 88/100, loss=0.7955 acc=0.7333
epoch 16, train 89/100, loss=0.2697 acc=0.9200
epoch 16, train 90/100, loss=0.6495 acc=0.7333
epoch 16, train 91/100, loss=0.4463 acc=0.8533
epoch 16, train 92/100, loss=0.6708 acc=0.7600
epoch 16, train 93/100, loss=0.4579 acc=0.8400
epoch 16, train 94/100, loss=0.4718 acc=0.8267
epoch 16, train 95/100, loss=0.4788 acc=0.8267
epoch 16, train 96/100, loss=1.4969 acc=0.5600
epoch 16, train 97/100, loss=0.4912 acc=0.7867
epoch 16, train 98/100, loss=0.2524 acc=0.9467
epoch 16, train 99/100, loss=0.6312 acc=0.8267
epoch 16, train 100/100, loss=0.2934 acc=0.9067
best epoch 14, best val acc=0.6909
epoch 16, val, loss=0.9859 acc=0.6953
ETA:1.0h/6.4h
epoch 17, train 1/100, loss=1.0943 acc=0.6667
epoch 17, train 2/100, loss=0.3550 acc=0.8667
epoch 17, train 3/100, loss=0.6518 acc=0.7867
epoch 17, train 4/100, loss=0.3596 acc=0.8800
epoch 17, train 5/100, loss=0.5665 acc=0.7867
epoch 17, train 6/100, loss=0.3908 acc=0.8667
epoch 17, train 7/100, loss=0.5861 acc=0.8133
epoch 17, train 8/100, loss=0.5892 acc=0.7867
epoch 17, train 9/100, loss=0.7610 acc=0.7333
epoch 17, train 10/100, loss=0.5222 acc=0.8133
epoch 17, train 11/100, loss=0.1696 acc=0.9333
epoch 17, train 12/100, loss=0.2813 acc=0.9067
epoch 17, train 13/100, loss=0.4907 acc=0.8533
epoch 17, train 14/100, loss=1.4475 acc=0.5467
epoch 17, train 15/100, loss=0.8043 acc=0.7333
epoch 17, train 16/100, loss=1.1362 acc=0.6800
epoch 17, train 17/100, loss=1.0274 acc=0.7067
epoch 17, train 18/100, loss=0.2430 acc=0.9333
epoch 17, train 19/100, loss=1.3544 acc=0.6267
epoch 17, train 20/100, loss=0.3916 acc=0.8667
epoch 17, train 21/100, loss=0.4696 acc=0.8800
epoch 17, train 22/100, loss=0.6744 acc=0.8000
epoch 17, train 23/100, loss=1.1604 acc=0.6933
epoch 17, train 24/100, loss=0.6689 acc=0.7467
epoch 17, train 25/100, loss=0.3509 acc=0.8800
epoch 17, train 26/100, loss=0.3573 acc=0.9200
epoch 17, train 27/100, loss=0.1205 acc=0.9600
epoch 17, train 28/100, loss=1.1721 acc=0.6000
epoch 17, train 29/100, loss=0.2796 acc=0.9067
epoch 17, train 30/100, loss=0.8023 acc=0.6800
epoch 17, train 31/100, loss=0.3478 acc=0.9200
epoch 17, train 32/100, loss=0.2392 acc=0.9200
epoch 17, train 33/100, loss=0.5161 acc=0.8533
epoch 17, train 34/100, loss=0.4109 acc=0.8800
epoch 17, train 35/100, loss=0.9658 acc=0.6667
epoch 17, train 36/100, loss=0.4993 acc=0.8400
epoch 17, train 37/100, loss=1.1774 acc=0.6133
epoch 17, train 38/100, loss=0.7689 acc=0.7867
epoch 17, train 39/100, loss=1.0588 acc=0.6400
epoch 17, train 40/100, loss=0.8497 acc=0.7333
epoch 17, train 41/100, loss=1.0356 acc=0.7333
epoch 17, train 42/100, loss=0.1680 acc=0.9467
epoch 17, train 43/100, loss=0.1444 acc=0.9467
epoch 17, train 44/100, loss=0.3175 acc=0.8933
epoch 17, train 45/100, loss=0.1727 acc=0.9333
epoch 17, train 46/100, loss=1.1973 acc=0.6000
epoch 17, train 47/100, loss=0.3882 acc=0.8400
epoch 17, train 48/100, loss=0.6797 acc=0.7733
epoch 17, train 49/100, loss=0.3597 acc=0.8667
epoch 17, train 50/100, loss=1.7056 acc=0.5867
epoch 17, train 51/100, loss=0.2067 acc=0.9200
epoch 17, train 52/100, loss=0.4055 acc=0.8133
epoch 17, train 53/100, loss=1.3722 acc=0.5867
epoch 17, train 54/100, loss=1.3398 acc=0.5600
epoch 17, train 55/100, loss=0.8320 acc=0.6800
epoch 17, train 56/100, loss=0.3130 acc=0.9067
epoch 17, train 57/100, loss=1.0608 acc=0.6400
epoch 17, train 58/100, loss=0.4832 acc=0.8533
epoch 17, train 59/100, loss=0.3270 acc=0.8533
epoch 17, train 60/100, loss=0.9227 acc=0.7067
epoch 17, train 61/100, loss=0.7118 acc=0.7733
epoch 17, train 62/100, loss=0.4959 acc=0.8800
epoch 17, train 63/100, loss=0.3342 acc=0.8933
epoch 17, train 64/100, loss=1.2510 acc=0.5600
epoch 17, train 65/100, loss=1.2370 acc=0.6267
epoch 17, train 66/100, loss=0.9348 acc=0.7600
epoch 17, train 67/100, loss=0.8800 acc=0.7467
epoch 17, train 68/100, loss=0.4115 acc=0.8533
epoch 17, train 69/100, loss=0.3528 acc=0.8667
epoch 17, train 70/100, loss=0.2569 acc=0.9467
epoch 17, train 71/100, loss=0.6294 acc=0.8133
epoch 17, train 72/100, loss=0.7295 acc=0.8000
epoch 17, train 73/100, loss=1.4245 acc=0.6000
epoch 17, train 74/100, loss=0.2501 acc=0.8933
epoch 17, train 75/100, loss=1.4199 acc=0.5733
epoch 17, train 76/100, loss=0.2719 acc=0.8800
epoch 17, train 77/100, loss=1.4089 acc=0.6267
epoch 17, train 78/100, loss=0.5103 acc=0.8667
epoch 17, train 79/100, loss=0.8686 acc=0.7600
epoch 17, train 80/100, loss=0.3327 acc=0.8933
epoch 17, train 81/100, loss=0.8797 acc=0.8000
epoch 17, train 82/100, loss=0.9438 acc=0.7200
epoch 17, train 83/100, loss=0.2334 acc=0.9200
epoch 17, train 84/100, loss=0.5731 acc=0.8133
epoch 17, train 85/100, loss=1.1159 acc=0.6000
epoch 17, train 86/100, loss=0.0654 acc=0.9867
epoch 17, train 87/100, loss=0.4054 acc=0.8933
epoch 17, train 88/100, loss=0.2492 acc=0.8800
epoch 17, train 89/100, loss=1.2168 acc=0.5733
epoch 17, train 90/100, loss=1.1834 acc=0.6133
epoch 17, train 91/100, loss=0.1024 acc=0.9600
epoch 17, train 92/100, loss=0.3957 acc=0.8933
epoch 17, train 93/100, loss=0.6882 acc=0.7333
epoch 17, train 94/100, loss=1.0329 acc=0.6800
epoch 17, train 95/100, loss=0.5338 acc=0.8000
epoch 17, train 96/100, loss=0.0841 acc=0.9733
epoch 17, train 97/100, loss=0.5887 acc=0.8267
epoch 17, train 98/100, loss=0.4537 acc=0.8400
epoch 17, train 99/100, loss=0.3977 acc=0.8267
epoch 17, train 100/100, loss=0.4730 acc=0.8133
best epoch 16, best val acc=0.6953
epoch 17, val, loss=1.0319 acc=0.6853
ETA:1.1h/6.4h
epoch 18, train 1/100, loss=0.3935 acc=0.8933
epoch 18, train 2/100, loss=0.6422 acc=0.8000
epoch 18, train 3/100, loss=0.4043 acc=0.8533
epoch 18, train 4/100, loss=0.8562 acc=0.7067
epoch 18, train 5/100, loss=0.3635 acc=0.8533
epoch 18, train 6/100, loss=0.8493 acc=0.7200
epoch 18, train 7/100, loss=0.5412 acc=0.8000
epoch 18, train 8/100, loss=0.7048 acc=0.8133
epoch 18, train 9/100, loss=1.5986 acc=0.5067
epoch 18, train 10/100, loss=0.7108 acc=0.7200
epoch 18, train 11/100, loss=0.6129 acc=0.7333
epoch 18, train 12/100, loss=1.3601 acc=0.6133
epoch 18, train 13/100, loss=0.1998 acc=0.9200
epoch 18, train 14/100, loss=0.7195 acc=0.7600
epoch 18, train 15/100, loss=0.4760 acc=0.8933
epoch 18, train 16/100, loss=0.1958 acc=0.9200
epoch 18, train 17/100, loss=0.3939 acc=0.8267
epoch 18, train 18/100, loss=0.6462 acc=0.7733
epoch 18, train 19/100, loss=0.4349 acc=0.8533
epoch 18, train 20/100, loss=0.5882 acc=0.8000
epoch 18, train 21/100, loss=0.5585 acc=0.7867
epoch 18, train 22/100, loss=0.4836 acc=0.8533
epoch 18, train 23/100, loss=0.9201 acc=0.7200
epoch 18, train 24/100, loss=0.4274 acc=0.8400
epoch 18, train 25/100, loss=1.1403 acc=0.7733
epoch 18, train 26/100, loss=0.6247 acc=0.8400
epoch 18, train 27/100, loss=0.9231 acc=0.6800
epoch 18, train 28/100, loss=1.2904 acc=0.7333
epoch 18, train 29/100, loss=1.0090 acc=0.6667
epoch 18, train 30/100, loss=0.9851 acc=0.7200
epoch 18, train 31/100, loss=0.6408 acc=0.8400
epoch 18, train 32/100, loss=0.6483 acc=0.8267
epoch 18, train 33/100, loss=0.3513 acc=0.8667
epoch 18, train 34/100, loss=0.6634 acc=0.7733
epoch 18, train 35/100, loss=0.9822 acc=0.6667
epoch 18, train 36/100, loss=1.2140 acc=0.6667
epoch 18, train 37/100, loss=0.6097 acc=0.7200
epoch 18, train 38/100, loss=0.5791 acc=0.7600
epoch 18, train 39/100, loss=1.0441 acc=0.6533
epoch 18, train 40/100, loss=0.7570 acc=0.7733
epoch 18, train 41/100, loss=0.4469 acc=0.8667
epoch 18, train 42/100, loss=0.6572 acc=0.8000
epoch 18, train 43/100, loss=0.5328 acc=0.8400
epoch 18, train 44/100, loss=1.3573 acc=0.5867
epoch 18, train 45/100, loss=0.4046 acc=0.8133
epoch 18, train 46/100, loss=0.2783 acc=0.9067
epoch 18, train 47/100, loss=0.3182 acc=0.8800
epoch 18, train 48/100, loss=0.5852 acc=0.8000
epoch 18, train 49/100, loss=0.8011 acc=0.7067
epoch 18, train 50/100, loss=1.2470 acc=0.5867
epoch 18, train 51/100, loss=0.8967 acc=0.7867
epoch 18, train 52/100, loss=0.2453 acc=0.8667
epoch 18, train 53/100, loss=0.4283 acc=0.9067
epoch 18, train 54/100, loss=0.2186 acc=0.9067
epoch 18, train 55/100, loss=0.4667 acc=0.8667
epoch 18, train 56/100, loss=0.5169 acc=0.8667
epoch 18, train 57/100, loss=1.2929 acc=0.6533
epoch 18, train 58/100, loss=0.5869 acc=0.8000
epoch 18, train 59/100, loss=0.5801 acc=0.8000
epoch 18, train 60/100, loss=1.9722 acc=0.4267
epoch 18, train 61/100, loss=1.9383 acc=0.4533
epoch 18, train 62/100, loss=0.4551 acc=0.8133
epoch 18, train 63/100, loss=0.8063 acc=0.7867
epoch 18, train 64/100, loss=0.8246 acc=0.6933
epoch 18, train 65/100, loss=0.2821 acc=0.8800
epoch 18, train 66/100, loss=0.3902 acc=0.8667
epoch 18, train 67/100, loss=0.4579 acc=0.8400
epoch 18, train 68/100, loss=0.7538 acc=0.7200
epoch 18, train 69/100, loss=0.9211 acc=0.6933
epoch 18, train 70/100, loss=0.4649 acc=0.8800
epoch 18, train 71/100, loss=0.3751 acc=0.8933
epoch 18, train 72/100, loss=0.4730 acc=0.8533
epoch 18, train 73/100, loss=0.5225 acc=0.8133
epoch 18, train 74/100, loss=0.8795 acc=0.7733
epoch 18, train 75/100, loss=0.6813 acc=0.7467
epoch 18, train 76/100, loss=1.3582 acc=0.5600
epoch 18, train 77/100, loss=0.8531 acc=0.7867
epoch 18, train 78/100, loss=0.0536 acc=0.9867
epoch 18, train 79/100, loss=0.5552 acc=0.8667
epoch 18, train 80/100, loss=0.5012 acc=0.8133
epoch 18, train 81/100, loss=0.3619 acc=0.8800
epoch 18, train 82/100, loss=0.4075 acc=0.8267
epoch 18, train 83/100, loss=0.5000 acc=0.8000
epoch 18, train 84/100, loss=0.2906 acc=0.9200
epoch 18, train 85/100, loss=1.2773 acc=0.6533
epoch 18, train 86/100, loss=0.7684 acc=0.7067
epoch 18, train 87/100, loss=1.0487 acc=0.6933
epoch 18, train 88/100, loss=0.8128 acc=0.7200
epoch 18, train 89/100, loss=1.3738 acc=0.5467
epoch 18, train 90/100, loss=0.8512 acc=0.7333
epoch 18, train 91/100, loss=0.9009 acc=0.7467
epoch 18, train 92/100, loss=1.1453 acc=0.6533
epoch 18, train 93/100, loss=0.4863 acc=0.8667
epoch 18, train 94/100, loss=0.1622 acc=0.9733
epoch 18, train 95/100, loss=0.5904 acc=0.8133
epoch 18, train 96/100, loss=0.2768 acc=0.9200
epoch 18, train 97/100, loss=0.7108 acc=0.7200
epoch 18, train 98/100, loss=0.7368 acc=0.7600
epoch 18, train 99/100, loss=0.2683 acc=0.8933
epoch 18, train 100/100, loss=0.4124 acc=0.8267
best epoch 16, best val acc=0.6953
epoch 18, val, loss=1.0152 acc=0.6894
ETA:1.2h/6.4h
epoch 19, train 1/100, loss=0.7459 acc=0.7733
epoch 19, train 2/100, loss=0.8580 acc=0.7600
epoch 19, train 3/100, loss=0.1337 acc=0.9467
epoch 19, train 4/100, loss=0.9134 acc=0.7067
epoch 19, train 5/100, loss=0.6077 acc=0.7733
epoch 19, train 6/100, loss=0.4436 acc=0.8533
epoch 19, train 7/100, loss=0.2984 acc=0.9067
epoch 19, train 8/100, loss=1.0117 acc=0.6800
epoch 19, train 9/100, loss=1.0345 acc=0.6267
epoch 19, train 10/100, loss=0.5026 acc=0.8267
epoch 19, train 11/100, loss=0.6451 acc=0.7600
epoch 19, train 12/100, loss=0.3265 acc=0.8800
epoch 19, train 13/100, loss=0.5883 acc=0.8133
epoch 19, train 14/100, loss=0.8012 acc=0.7200
epoch 19, train 15/100, loss=0.7287 acc=0.7867
epoch 19, train 16/100, loss=0.5060 acc=0.8533
epoch 19, train 17/100, loss=1.3272 acc=0.5600
epoch 19, train 18/100, loss=1.4076 acc=0.6000
epoch 19, train 19/100, loss=0.2822 acc=0.8667
epoch 19, train 20/100, loss=0.5405 acc=0.8933
epoch 19, train 21/100, loss=0.2243 acc=0.8933
epoch 19, train 22/100, loss=0.6894 acc=0.7600
epoch 19, train 23/100, loss=0.3411 acc=0.8533
epoch 19, train 24/100, loss=0.5836 acc=0.7867
epoch 19, train 25/100, loss=1.0847 acc=0.6400
epoch 19, train 26/100, loss=1.1417 acc=0.6267
epoch 19, train 27/100, loss=0.2682 acc=0.8800
epoch 19, train 28/100, loss=0.4761 acc=0.8267
epoch 19, train 29/100, loss=0.4285 acc=0.8267
epoch 19, train 30/100, loss=0.2935 acc=0.8800
epoch 19, train 31/100, loss=0.5631 acc=0.8267
epoch 19, train 32/100, loss=0.1525 acc=0.9467
epoch 19, train 33/100, loss=0.4092 acc=0.8400
epoch 19, train 34/100, loss=0.7894 acc=0.7200
epoch 19, train 35/100, loss=1.0654 acc=0.7333
epoch 19, train 36/100, loss=0.8939 acc=0.6933
epoch 19, train 37/100, loss=1.0557 acc=0.6933
epoch 19, train 38/100, loss=0.6011 acc=0.8133
epoch 19, train 39/100, loss=1.0652 acc=0.7067
epoch 19, train 40/100, loss=0.7774 acc=0.7200
epoch 19, train 41/100, loss=0.3985 acc=0.8533
epoch 19, train 42/100, loss=0.1667 acc=0.9467
epoch 19, train 43/100, loss=0.5406 acc=0.7733
epoch 19, train 44/100, loss=0.0350 acc=0.9867
epoch 19, train 45/100, loss=0.3060 acc=0.8667
epoch 19, train 46/100, loss=0.9322 acc=0.6667
epoch 19, train 47/100, loss=0.4929 acc=0.8133
epoch 19, train 48/100, loss=0.3489 acc=0.9067
epoch 19, train 49/100, loss=0.5538 acc=0.7733
epoch 19, train 50/100, loss=0.7040 acc=0.8267
epoch 19, train 51/100, loss=1.2884 acc=0.6267
epoch 19, train 52/100, loss=0.6255 acc=0.8533
epoch 19, train 53/100, loss=0.9257 acc=0.6667
epoch 19, train 54/100, loss=0.2262 acc=0.9333
epoch 19, train 55/100, loss=1.1741 acc=0.6533
epoch 19, train 56/100, loss=1.1905 acc=0.6533
epoch 19, train 57/100, loss=0.4149 acc=0.8933
epoch 19, train 58/100, loss=1.3580 acc=0.5467
epoch 19, train 59/100, loss=0.1080 acc=0.9600
epoch 19, train 60/100, loss=0.4793 acc=0.8667
epoch 19, train 61/100, loss=0.5552 acc=0.8533
epoch 19, train 62/100, loss=0.5897 acc=0.7733
epoch 19, train 63/100, loss=0.0805 acc=0.9867
epoch 19, train 64/100, loss=0.2100 acc=0.9467
epoch 19, train 65/100, loss=0.4401 acc=0.8267
epoch 19, train 66/100, loss=0.7093 acc=0.7200
epoch 19, train 67/100, loss=0.3057 acc=0.8800
epoch 19, train 68/100, loss=0.2823 acc=0.8933
epoch 19, train 69/100, loss=1.1602 acc=0.6400
epoch 19, train 70/100, loss=0.4501 acc=0.8667
epoch 19, train 71/100, loss=1.2767 acc=0.6667
epoch 19, train 72/100, loss=0.2617 acc=0.8933
epoch 19, train 73/100, loss=0.9171 acc=0.7733
epoch 19, train 74/100, loss=0.7225 acc=0.7733
epoch 19, train 75/100, loss=0.5325 acc=0.8000
epoch 19, train 76/100, loss=0.4180 acc=0.8667
epoch 19, train 77/100, loss=1.6217 acc=0.5867
epoch 19, train 78/100, loss=0.7519 acc=0.7733
epoch 19, train 79/100, loss=1.0808 acc=0.6400
epoch 19, train 80/100, loss=0.9898 acc=0.7600
epoch 19, train 81/100, loss=1.3116 acc=0.6667
epoch 19, train 82/100, loss=0.7171 acc=0.7733
epoch 19, train 83/100, loss=1.2662 acc=0.5733
epoch 19, train 84/100, loss=0.9394 acc=0.7333
epoch 19, train 85/100, loss=0.5471 acc=0.8933
epoch 19, train 86/100, loss=0.2427 acc=0.9067
epoch 19, train 87/100, loss=0.1565 acc=0.9333
epoch 19, train 88/100, loss=0.1427 acc=0.9467
epoch 19, train 89/100, loss=0.9676 acc=0.6800
epoch 19, train 90/100, loss=1.1040 acc=0.6667
epoch 19, train 91/100, loss=0.6636 acc=0.8267
epoch 19, train 92/100, loss=0.2587 acc=0.9200
epoch 19, train 93/100, loss=1.5184 acc=0.6267
epoch 19, train 94/100, loss=0.5787 acc=0.8267
epoch 19, train 95/100, loss=1.0343 acc=0.7067
epoch 19, train 96/100, loss=0.7337 acc=0.7467
epoch 19, train 97/100, loss=1.0804 acc=0.6800
epoch 19, train 98/100, loss=0.7709 acc=0.7067
epoch 19, train 99/100, loss=1.2144 acc=0.6267
epoch 19, train 100/100, loss=0.1353 acc=0.9333
best epoch 16, best val acc=0.6953
epoch 19, val, loss=1.0031 acc=0.6926
ETA:1.2h/6.4h
epoch 20, train 1/100, loss=0.8640 acc=0.7867
epoch 20, train 2/100, loss=0.1698 acc=0.9200
epoch 20, train 3/100, loss=1.7104 acc=0.5467
epoch 20, train 4/100, loss=0.5969 acc=0.7600
epoch 20, train 5/100, loss=2.2826 acc=0.3867
epoch 20, train 6/100, loss=0.3257 acc=0.8933
epoch 20, train 7/100, loss=0.8542 acc=0.7467
epoch 20, train 8/100, loss=0.9914 acc=0.6667
epoch 20, train 9/100, loss=1.9299 acc=0.4000
epoch 20, train 10/100, loss=0.9254 acc=0.7067
epoch 20, train 11/100, loss=0.5706 acc=0.8000
epoch 20, train 12/100, loss=0.2503 acc=0.9200
epoch 20, train 13/100, loss=0.3278 acc=0.9200
epoch 20, train 14/100, loss=0.2720 acc=0.9333
epoch 20, train 15/100, loss=0.5496 acc=0.8667
epoch 20, train 16/100, loss=0.5828 acc=0.7733
epoch 20, train 17/100, loss=0.7298 acc=0.7867
epoch 20, train 18/100, loss=0.2507 acc=0.9067
epoch 20, train 19/100, loss=1.0660 acc=0.7067
epoch 20, train 20/100, loss=0.5763 acc=0.7867
epoch 20, train 21/100, loss=0.8116 acc=0.6667
epoch 20, train 22/100, loss=0.1666 acc=0.9333
epoch 20, train 23/100, loss=0.4976 acc=0.8267
epoch 20, train 24/100, loss=0.6218 acc=0.8133
epoch 20, train 25/100, loss=0.6934 acc=0.8000
epoch 20, train 26/100, loss=0.3215 acc=0.8667
epoch 20, train 27/100, loss=0.6309 acc=0.8400
epoch 20, train 28/100, loss=0.5218 acc=0.8133
epoch 20, train 29/100, loss=0.5402 acc=0.8000
epoch 20, train 30/100, loss=0.2471 acc=0.9200
epoch 20, train 31/100, loss=0.5396 acc=0.8000
epoch 20, train 32/100, loss=0.8207 acc=0.7467
epoch 20, train 33/100, loss=0.2643 acc=0.9333
epoch 20, train 34/100, loss=0.6216 acc=0.8000
epoch 20, train 35/100, loss=0.3229 acc=0.8667
epoch 20, train 36/100, loss=0.2713 acc=0.8800
epoch 20, train 37/100, loss=0.3509 acc=0.9067
epoch 20, train 38/100, loss=0.9313 acc=0.6667
epoch 20, train 39/100, loss=1.0849 acc=0.7333
epoch 20, train 40/100, loss=0.3545 acc=0.8800
epoch 20, train 41/100, loss=0.6880 acc=0.8267
epoch 20, train 42/100, loss=0.1107 acc=0.9733
epoch 20, train 43/100, loss=0.1818 acc=0.9600
epoch 20, train 44/100, loss=1.9695 acc=0.4533
epoch 20, train 45/100, loss=1.2025 acc=0.6533
epoch 20, train 46/100, loss=0.6262 acc=0.8133
epoch 20, train 47/100, loss=0.5523 acc=0.8133
epoch 20, train 48/100, loss=0.5136 acc=0.8933
epoch 20, train 49/100, loss=0.7986 acc=0.7333
epoch 20, train 50/100, loss=0.6295 acc=0.8267
epoch 20, train 51/100, loss=0.7914 acc=0.7867
epoch 20, train 52/100, loss=1.2661 acc=0.6267
epoch 20, train 53/100, loss=0.3906 acc=0.8933
epoch 20, train 54/100, loss=0.4638 acc=0.8133
epoch 20, train 55/100, loss=1.5784 acc=0.4800
epoch 20, train 56/100, loss=0.1641 acc=0.9467
epoch 20, train 57/100, loss=0.3569 acc=0.8800
epoch 20, train 58/100, loss=0.7822 acc=0.7200
epoch 20, train 59/100, loss=0.5571 acc=0.8267
epoch 20, train 60/100, loss=0.4876 acc=0.8400
epoch 20, train 61/100, loss=0.9591 acc=0.7600
epoch 20, train 62/100, loss=0.4222 acc=0.8667
epoch 20, train 63/100, loss=0.6252 acc=0.8133
epoch 20, train 64/100, loss=0.9467 acc=0.6933
epoch 20, train 65/100, loss=0.3416 acc=0.8667
epoch 20, train 66/100, loss=0.4579 acc=0.8400
epoch 20, train 67/100, loss=0.3481 acc=0.8667
epoch 20, train 68/100, loss=0.3966 acc=0.8667
epoch 20, train 69/100, loss=1.0059 acc=0.6133
epoch 20, train 70/100, loss=0.5277 acc=0.8667
epoch 20, train 71/100, loss=1.4230 acc=0.5867
epoch 20, train 72/100, loss=0.7076 acc=0.8533
epoch 20, train 73/100, loss=1.4423 acc=0.6267
epoch 20, train 74/100, loss=0.2558 acc=0.9333
epoch 20, train 75/100, loss=0.4005 acc=0.8933
epoch 20, train 76/100, loss=0.7160 acc=0.8267
epoch 20, train 77/100, loss=0.7405 acc=0.7867
epoch 20, train 78/100, loss=0.5156 acc=0.8400
epoch 20, train 79/100, loss=0.2095 acc=0.9200
epoch 20, train 80/100, loss=0.2474 acc=0.8933
epoch 20, train 81/100, loss=0.1806 acc=0.9333
epoch 20, train 82/100, loss=0.6873 acc=0.8400
epoch 20, train 83/100, loss=0.3181 acc=0.9200
epoch 20, train 84/100, loss=0.7611 acc=0.7600
epoch 20, train 85/100, loss=0.4655 acc=0.8133
epoch 20, train 86/100, loss=0.6810 acc=0.8267
epoch 20, train 87/100, loss=0.6717 acc=0.7467
epoch 20, train 88/100, loss=1.1424 acc=0.6533
epoch 20, train 89/100, loss=0.8829 acc=0.7067
epoch 20, train 90/100, loss=0.4809 acc=0.8800
epoch 20, train 91/100, loss=0.5990 acc=0.8400
epoch 20, train 92/100, loss=0.6708 acc=0.8133
epoch 20, train 93/100, loss=0.4872 acc=0.8400
epoch 20, train 94/100, loss=0.6529 acc=0.8267
epoch 20, train 95/100, loss=0.3589 acc=0.8400
epoch 20, train 96/100, loss=0.2345 acc=0.9200
epoch 20, train 97/100, loss=1.0104 acc=0.6533
epoch 20, train 98/100, loss=0.5048 acc=0.8533
epoch 20, train 99/100, loss=1.4866 acc=0.5600
epoch 20, train 100/100, loss=0.3926 acc=0.8533
best epoch 16, best val acc=0.6953
epoch 20, val, loss=1.0286 acc=0.6890
ETA:1.3h/6.4h
epoch 21, train 1/100, loss=0.9788 acc=0.7867
epoch 21, train 2/100, loss=0.2200 acc=0.8933
epoch 21, train 3/100, loss=0.5965 acc=0.8000
epoch 21, train 4/100, loss=0.6551 acc=0.7867
epoch 21, train 5/100, loss=1.4207 acc=0.6133
epoch 21, train 6/100, loss=0.3143 acc=0.8800
epoch 21, train 7/100, loss=0.4646 acc=0.8267
epoch 21, train 8/100, loss=0.9170 acc=0.6533
epoch 21, train 9/100, loss=1.3382 acc=0.5867
epoch 21, train 10/100, loss=0.4136 acc=0.8400
epoch 21, train 11/100, loss=0.4555 acc=0.8667
epoch 21, train 12/100, loss=0.6642 acc=0.7333
epoch 21, train 13/100, loss=1.2670 acc=0.6267
epoch 21, train 14/100, loss=1.1379 acc=0.6533
epoch 21, train 15/100, loss=0.6299 acc=0.7600
epoch 21, train 16/100, loss=0.2589 acc=0.8933
epoch 21, train 17/100, loss=0.1753 acc=0.9600
epoch 21, train 18/100, loss=1.0747 acc=0.6267
epoch 21, train 19/100, loss=1.2430 acc=0.5733
epoch 21, train 20/100, loss=0.6961 acc=0.7200
epoch 21, train 21/100, loss=0.2355 acc=0.9200
epoch 21, train 22/100, loss=0.3065 acc=0.9067
epoch 21, train 23/100, loss=0.4251 acc=0.8400
epoch 21, train 24/100, loss=0.4012 acc=0.8533
epoch 21, train 25/100, loss=0.7685 acc=0.6933
epoch 21, train 26/100, loss=0.3515 acc=0.8800
epoch 21, train 27/100, loss=0.1404 acc=0.9467
epoch 21, train 28/100, loss=0.2990 acc=0.9067
epoch 21, train 29/100, loss=0.5482 acc=0.8267
epoch 21, train 30/100, loss=0.3925 acc=0.8933
epoch 21, train 31/100, loss=1.5798 acc=0.6533
epoch 21, train 32/100, loss=0.2916 acc=0.9067
epoch 21, train 33/100, loss=0.7291 acc=0.7733
epoch 21, train 34/100, loss=1.1214 acc=0.6000
epoch 21, train 35/100, loss=0.9514 acc=0.6533
epoch 21, train 36/100, loss=0.4208 acc=0.9067
epoch 21, train 37/100, loss=0.3577 acc=0.8933
epoch 21, train 38/100, loss=1.3125 acc=0.5733
epoch 21, train 39/100, loss=0.8973 acc=0.6933
epoch 21, train 40/100, loss=0.8308 acc=0.7733
epoch 21, train 41/100, loss=0.1844 acc=0.9200
epoch 21, train 42/100, loss=0.6081 acc=0.8267
epoch 21, train 43/100, loss=0.4692 acc=0.8133
epoch 21, train 44/100, loss=0.3748 acc=0.8667
epoch 21, train 45/100, loss=0.7670 acc=0.7467
epoch 21, train 46/100, loss=0.1501 acc=0.9467
epoch 21, train 47/100, loss=0.4716 acc=0.8400
epoch 21, train 48/100, loss=0.4639 acc=0.8000
epoch 21, train 49/100, loss=1.1806 acc=0.6400
epoch 21, train 50/100, loss=0.7613 acc=0.8000
epoch 21, train 51/100, loss=0.3067 acc=0.9200
epoch 21, train 52/100, loss=0.4007 acc=0.8667
epoch 21, train 53/100, loss=0.7100 acc=0.8000
epoch 21, train 54/100, loss=1.6930 acc=0.4933
epoch 21, train 55/100, loss=0.8527 acc=0.7067
epoch 21, train 56/100, loss=0.9425 acc=0.7333
epoch 21, train 57/100, loss=0.5289 acc=0.8133
epoch 21, train 58/100, loss=0.6731 acc=0.7867
epoch 21, train 59/100, loss=0.5184 acc=0.8267
epoch 21, train 60/100, loss=0.8278 acc=0.7333
epoch 21, train 61/100, loss=0.1027 acc=0.9600
epoch 21, train 62/100, loss=0.2794 acc=0.9067
epoch 21, train 63/100, loss=0.5762 acc=0.8800
epoch 21, train 64/100, loss=0.1799 acc=0.9333
epoch 21, train 65/100, loss=0.8821 acc=0.7200
epoch 21, train 66/100, loss=0.4655 acc=0.7867
epoch 21, train 67/100, loss=0.1628 acc=0.9200
epoch 21, train 68/100, loss=1.0559 acc=0.6133
epoch 21, train 69/100, loss=0.5107 acc=0.8400
epoch 21, train 70/100, loss=1.4452 acc=0.5467
epoch 21, train 71/100, loss=0.7867 acc=0.7333
epoch 21, train 72/100, loss=0.5185 acc=0.8133
epoch 21, train 73/100, loss=0.3596 acc=0.9200
epoch 21, train 74/100, loss=0.1201 acc=0.9600
epoch 21, train 75/100, loss=0.4829 acc=0.8667
epoch 21, train 76/100, loss=0.7962 acc=0.7333
epoch 21, train 77/100, loss=0.2307 acc=0.9067
epoch 21, train 78/100, loss=0.3099 acc=0.8933
epoch 21, train 79/100, loss=1.9480 acc=0.5200
epoch 21, train 80/100, loss=1.1262 acc=0.5200
epoch 21, train 81/100, loss=0.9503 acc=0.6933
epoch 21, train 82/100, loss=2.1162 acc=0.4667
epoch 21, train 83/100, loss=0.3350 acc=0.8533
epoch 21, train 84/100, loss=1.0578 acc=0.7067
epoch 21, train 85/100, loss=0.2113 acc=0.9067
epoch 21, train 86/100, loss=0.1981 acc=0.9600
epoch 21, train 87/100, loss=0.3518 acc=0.9067
epoch 21, train 88/100, loss=0.6187 acc=0.8267
epoch 21, train 89/100, loss=0.3720 acc=0.8667
epoch 21, train 90/100, loss=0.8414 acc=0.7333
epoch 21, train 91/100, loss=0.4219 acc=0.8667
epoch 21, train 92/100, loss=0.4987 acc=0.8800
epoch 21, train 93/100, loss=0.2111 acc=0.9333
epoch 21, train 94/100, loss=0.4334 acc=0.8533
epoch 21, train 95/100, loss=0.3623 acc=0.8533
epoch 21, train 96/100, loss=0.4066 acc=0.8533
epoch 21, train 97/100, loss=0.8164 acc=0.6400
epoch 21, train 98/100, loss=1.6576 acc=0.5467
epoch 21, train 99/100, loss=0.7027 acc=0.8133
epoch 21, train 100/100, loss=0.3484 acc=0.8933
best epoch 16, best val acc=0.6953
epoch 21, val, loss=1.0161 acc=0.6942
ETA:1.4h/6.4h
epoch 22, train 1/100, loss=1.1630 acc=0.6267
epoch 22, train 2/100, loss=0.5178 acc=0.8000
epoch 22, train 3/100, loss=0.3949 acc=0.8533
epoch 22, train 4/100, loss=1.1362 acc=0.6933
epoch 22, train 5/100, loss=0.7227 acc=0.8000
epoch 22, train 6/100, loss=1.5035 acc=0.5467
epoch 22, train 7/100, loss=0.7465 acc=0.7467
epoch 22, train 8/100, loss=0.8705 acc=0.7600
epoch 22, train 9/100, loss=0.2518 acc=0.9333
epoch 22, train 10/100, loss=0.3533 acc=0.8667
epoch 22, train 11/100, loss=0.4467 acc=0.8267
epoch 22, train 12/100, loss=0.9176 acc=0.7867
epoch 22, train 13/100, loss=2.9172 acc=0.2933
epoch 22, train 14/100, loss=0.9125 acc=0.7867
epoch 22, train 15/100, loss=0.6031 acc=0.7600
epoch 22, train 16/100, loss=0.2449 acc=0.8933
epoch 22, train 17/100, loss=0.6876 acc=0.8400
epoch 22, train 18/100, loss=1.4470 acc=0.6400
epoch 22, train 19/100, loss=0.9560 acc=0.6933
epoch 22, train 20/100, loss=0.4273 acc=0.8267
epoch 22, train 21/100, loss=0.4937 acc=0.8133
epoch 22, train 22/100, loss=0.9978 acc=0.6800
epoch 22, train 23/100, loss=0.5343 acc=0.7600
epoch 22, train 24/100, loss=0.2165 acc=0.9067
epoch 22, train 25/100, loss=1.1052 acc=0.6933
epoch 22, train 26/100, loss=0.5636 acc=0.8000
epoch 22, train 27/100, loss=0.6855 acc=0.7867
epoch 22, train 28/100, loss=0.8634 acc=0.7467
epoch 22, train 29/100, loss=0.5843 acc=0.7733
epoch 22, train 30/100, loss=0.5846 acc=0.7867
epoch 22, train 31/100, loss=0.1964 acc=0.9333
epoch 22, train 32/100, loss=0.4397 acc=0.8400
epoch 22, train 33/100, loss=0.0737 acc=0.9600
epoch 22, train 34/100, loss=1.2131 acc=0.6933
epoch 22, train 35/100, loss=0.2971 acc=0.9200
epoch 22, train 36/100, loss=0.3365 acc=0.9067
epoch 22, train 37/100, loss=0.2938 acc=0.9333
epoch 22, train 38/100, loss=0.8560 acc=0.7467
epoch 22, train 39/100, loss=0.2893 acc=0.9333
epoch 22, train 40/100, loss=0.9297 acc=0.7467
epoch 22, train 41/100, loss=1.8581 acc=0.5200
epoch 22, train 42/100, loss=1.4043 acc=0.5333
epoch 22, train 43/100, loss=0.2471 acc=0.9067
epoch 22, train 44/100, loss=0.5180 acc=0.8267
epoch 22, train 45/100, loss=1.1048 acc=0.6533
epoch 22, train 46/100, loss=0.8411 acc=0.7333
epoch 22, train 47/100, loss=1.4975 acc=0.7467
epoch 22, train 48/100, loss=0.4992 acc=0.8267
epoch 22, train 49/100, loss=1.1902 acc=0.7067
epoch 22, train 50/100, loss=1.5009 acc=0.5200
epoch 22, train 51/100, loss=0.4296 acc=0.8133
epoch 22, train 52/100, loss=0.1578 acc=0.9333
epoch 22, train 53/100, loss=0.1321 acc=0.9467
epoch 22, train 54/100, loss=1.0867 acc=0.7200
epoch 22, train 55/100, loss=0.2386 acc=0.9067
epoch 22, train 56/100, loss=1.4414 acc=0.6000
epoch 22, train 57/100, loss=0.0751 acc=0.9733
epoch 22, train 58/100, loss=0.8011 acc=0.8000
epoch 22, train 59/100, loss=0.1596 acc=0.9333
epoch 22, train 60/100, loss=1.3830 acc=0.6000
epoch 22, train 61/100, loss=0.6507 acc=0.8133
epoch 22, train 62/100, loss=0.4950 acc=0.8133
epoch 22, train 63/100, loss=0.9280 acc=0.7067
epoch 22, train 64/100, loss=0.4373 acc=0.8400
epoch 22, train 65/100, loss=0.4772 acc=0.7867
epoch 22, train 66/100, loss=0.3684 acc=0.8800
epoch 22, train 67/100, loss=0.6760 acc=0.7867
epoch 22, train 68/100, loss=0.6145 acc=0.8000
epoch 22, train 69/100, loss=0.3674 acc=0.8933
epoch 22, train 70/100, loss=0.1551 acc=0.9467
epoch 22, train 71/100, loss=0.1885 acc=0.9467
epoch 22, train 72/100, loss=0.4552 acc=0.9067
epoch 22, train 73/100, loss=0.4247 acc=0.8800
epoch 22, train 74/100, loss=2.2424 acc=0.4133
epoch 22, train 75/100, loss=0.7897 acc=0.6267
epoch 22, train 76/100, loss=0.2373 acc=0.9067
epoch 22, train 77/100, loss=0.4312 acc=0.8800
epoch 22, train 78/100, loss=1.7029 acc=0.4533
epoch 22, train 79/100, loss=1.2209 acc=0.6667
epoch 22, train 80/100, loss=0.8238 acc=0.7733
epoch 22, train 81/100, loss=0.7806 acc=0.7467
epoch 22, train 82/100, loss=1.6844 acc=0.4667
epoch 22, train 83/100, loss=0.3391 acc=0.9067
epoch 22, train 84/100, loss=1.2461 acc=0.6267
epoch 22, train 85/100, loss=0.3769 acc=0.8800
epoch 22, train 86/100, loss=0.2460 acc=0.9600
epoch 22, train 87/100, loss=1.0174 acc=0.6133
epoch 22, train 88/100, loss=0.3019 acc=0.9333
epoch 22, train 89/100, loss=0.4547 acc=0.8533
epoch 22, train 90/100, loss=0.7334 acc=0.7467
epoch 22, train 91/100, loss=0.2968 acc=0.8933
epoch 22, train 92/100, loss=1.0652 acc=0.6400
epoch 22, train 93/100, loss=1.3080 acc=0.5600
epoch 22, train 94/100, loss=0.3130 acc=0.8933
epoch 22, train 95/100, loss=0.8776 acc=0.7200
epoch 22, train 96/100, loss=0.6759 acc=0.7867
epoch 22, train 97/100, loss=0.2048 acc=0.9200
epoch 22, train 98/100, loss=0.7973 acc=0.7067
epoch 22, train 99/100, loss=0.4551 acc=0.8800
epoch 22, train 100/100, loss=1.4940 acc=0.5733
best epoch 16, best val acc=0.6953
epoch 22, val, loss=1.0309 acc=0.6969
ETA:1.4h/6.4h
epoch 23, train 1/100, loss=0.3010 acc=0.8800
epoch 23, train 2/100, loss=0.3733 acc=0.8400
epoch 23, train 3/100, loss=0.8120 acc=0.7200
epoch 23, train 4/100, loss=0.0869 acc=0.9867
epoch 23, train 5/100, loss=0.3416 acc=0.9200
epoch 23, train 6/100, loss=0.7198 acc=0.7067
epoch 23, train 7/100, loss=0.2621 acc=0.8800
epoch 23, train 8/100, loss=0.4365 acc=0.8800
epoch 23, train 9/100, loss=0.8922 acc=0.6667
epoch 23, train 10/100, loss=0.3952 acc=0.8533
epoch 23, train 11/100, loss=0.3380 acc=0.8933
epoch 23, train 12/100, loss=0.9218 acc=0.7200
epoch 23, train 13/100, loss=0.1235 acc=0.9600
epoch 23, train 14/100, loss=0.4755 acc=0.7867
epoch 23, train 15/100, loss=0.1600 acc=0.9467
epoch 23, train 16/100, loss=0.7109 acc=0.7067
epoch 23, train 17/100, loss=0.6226 acc=0.8133
epoch 23, train 18/100, loss=1.4900 acc=0.6133
epoch 23, train 19/100, loss=0.8612 acc=0.6933
epoch 23, train 20/100, loss=1.4892 acc=0.6000
epoch 23, train 21/100, loss=0.1817 acc=0.9200
epoch 23, train 22/100, loss=0.2054 acc=0.9333
epoch 23, train 23/100, loss=0.1247 acc=0.9467
epoch 23, train 24/100, loss=0.2761 acc=0.8667
epoch 23, train 25/100, loss=0.6997 acc=0.7467
epoch 23, train 26/100, loss=0.2003 acc=0.9467
epoch 23, train 27/100, loss=0.2628 acc=0.9067
epoch 23, train 28/100, loss=0.8043 acc=0.6533
epoch 23, train 29/100, loss=1.3087 acc=0.6267
epoch 23, train 30/100, loss=0.6740 acc=0.7067
epoch 23, train 31/100, loss=0.1967 acc=0.9333
epoch 23, train 32/100, loss=0.2915 acc=0.9067
epoch 23, train 33/100, loss=0.9404 acc=0.6933
epoch 23, train 34/100, loss=0.3078 acc=0.8533
epoch 23, train 35/100, loss=0.5856 acc=0.7600
epoch 23, train 36/100, loss=0.5214 acc=0.8667
epoch 23, train 37/100, loss=0.2680 acc=0.9333
epoch 23, train 38/100, loss=0.5854 acc=0.7733
epoch 23, train 39/100, loss=0.7425 acc=0.8133
epoch 23, train 40/100, loss=0.4742 acc=0.8533
epoch 23, train 41/100, loss=1.1996 acc=0.6267
epoch 23, train 42/100, loss=0.1427 acc=0.9733
epoch 23, train 43/100, loss=0.4280 acc=0.8400
epoch 23, train 44/100, loss=1.0355 acc=0.6800
epoch 23, train 45/100, loss=0.4632 acc=0.8400
epoch 23, train 46/100, loss=0.1292 acc=0.9467
epoch 23, train 47/100, loss=0.4252 acc=0.8267
epoch 23, train 48/100, loss=0.4873 acc=0.9200
epoch 23, train 49/100, loss=0.2018 acc=0.9467
epoch 23, train 50/100, loss=0.4922 acc=0.8533
epoch 23, train 51/100, loss=0.3463 acc=0.9200
epoch 23, train 52/100, loss=0.2752 acc=0.9067
epoch 23, train 53/100, loss=0.9510 acc=0.7200
epoch 23, train 54/100, loss=0.1179 acc=0.9200
epoch 23, train 55/100, loss=0.5650 acc=0.8533
epoch 23, train 56/100, loss=0.6090 acc=0.8000
epoch 23, train 57/100, loss=1.6694 acc=0.4800
epoch 23, train 58/100, loss=0.1410 acc=0.9333
epoch 23, train 59/100, loss=1.2744 acc=0.6000
epoch 23, train 60/100, loss=1.1994 acc=0.7200
epoch 23, train 61/100, loss=0.5455 acc=0.8667
epoch 23, train 62/100, loss=0.4739 acc=0.8400
epoch 23, train 63/100, loss=0.3380 acc=0.9067
epoch 23, train 64/100, loss=1.4786 acc=0.5867
epoch 23, train 65/100, loss=0.3109 acc=0.8400
epoch 23, train 66/100, loss=0.1962 acc=0.9200
epoch 23, train 67/100, loss=0.1779 acc=0.9333
epoch 23, train 68/100, loss=0.5117 acc=0.7867
epoch 23, train 69/100, loss=0.5510 acc=0.8400
epoch 23, train 70/100, loss=0.3537 acc=0.9333
epoch 23, train 71/100, loss=0.1973 acc=0.8933
epoch 23, train 72/100, loss=0.6570 acc=0.7867
epoch 23, train 73/100, loss=0.4978 acc=0.7867
epoch 23, train 74/100, loss=0.4022 acc=0.9333
epoch 23, train 75/100, loss=0.6492 acc=0.8133
epoch 23, train 76/100, loss=0.4790 acc=0.8133
epoch 23, train 77/100, loss=0.2399 acc=0.8933
epoch 23, train 78/100, loss=0.5620 acc=0.7733
epoch 23, train 79/100, loss=0.2929 acc=0.8800
epoch 23, train 80/100, loss=0.1967 acc=0.9467
epoch 23, train 81/100, loss=0.0789 acc=0.9733
epoch 23, train 82/100, loss=0.1577 acc=0.9600
epoch 23, train 83/100, loss=0.3908 acc=0.8267
epoch 23, train 84/100, loss=0.3033 acc=0.8533
epoch 23, train 85/100, loss=0.1726 acc=0.9333
epoch 23, train 86/100, loss=0.3713 acc=0.8667
epoch 23, train 87/100, loss=0.6643 acc=0.8000
epoch 23, train 88/100, loss=0.7548 acc=0.6933
epoch 23, train 89/100, loss=0.8679 acc=0.7733
epoch 23, train 90/100, loss=0.3348 acc=0.8533
epoch 23, train 91/100, loss=0.3770 acc=0.8400
epoch 23, train 92/100, loss=0.9611 acc=0.7333
epoch 23, train 93/100, loss=0.3283 acc=0.8800
epoch 23, train 94/100, loss=0.6623 acc=0.7867
epoch 23, train 95/100, loss=0.4304 acc=0.8533
epoch 23, train 96/100, loss=0.4495 acc=0.8133
epoch 23, train 97/100, loss=0.7308 acc=0.7733
epoch 23, train 98/100, loss=0.2943 acc=0.8933
epoch 23, train 99/100, loss=1.9385 acc=0.4800
epoch 23, train 100/100, loss=0.9797 acc=0.6133
best epoch 22, best val acc=0.6969
epoch 23, val, loss=1.0218 acc=0.7002
ETA:1.5h/6.4h
epoch 24, train 1/100, loss=0.3186 acc=0.8667
epoch 24, train 2/100, loss=0.8801 acc=0.6933
epoch 24, train 3/100, loss=0.5441 acc=0.8400
epoch 24, train 4/100, loss=0.4920 acc=0.8267
epoch 24, train 5/100, loss=0.4036 acc=0.8267
epoch 24, train 6/100, loss=0.3279 acc=0.8800
epoch 24, train 7/100, loss=0.2800 acc=0.8933
epoch 24, train 8/100, loss=0.5512 acc=0.8267
epoch 24, train 9/100, loss=0.6784 acc=0.8000
epoch 24, train 10/100, loss=0.2537 acc=0.9067
epoch 24, train 11/100, loss=0.4359 acc=0.8533
epoch 24, train 12/100, loss=0.4237 acc=0.8000
epoch 24, train 13/100, loss=0.1508 acc=0.9333
epoch 24, train 14/100, loss=0.9135 acc=0.6667
epoch 24, train 15/100, loss=0.7021 acc=0.7733
epoch 24, train 16/100, loss=0.5151 acc=0.8133
epoch 24, train 17/100, loss=0.3326 acc=0.8800
epoch 24, train 18/100, loss=1.4061 acc=0.6400
epoch 24, train 19/100, loss=0.3391 acc=0.8533
epoch 24, train 20/100, loss=0.4514 acc=0.9333
epoch 24, train 21/100, loss=0.2033 acc=0.8933
epoch 24, train 22/100, loss=0.6126 acc=0.7867
epoch 24, train 23/100, loss=0.6165 acc=0.8133
epoch 24, train 24/100, loss=0.2190 acc=0.9067
epoch 24, train 25/100, loss=1.7151 acc=0.5467
epoch 24, train 26/100, loss=0.4464 acc=0.8533
epoch 24, train 27/100, loss=1.1480 acc=0.7200
epoch 24, train 28/100, loss=0.5501 acc=0.8133
epoch 24, train 29/100, loss=0.3940 acc=0.8800
epoch 24, train 30/100, loss=0.9274 acc=0.7333
epoch 24, train 31/100, loss=1.2396 acc=0.6000
epoch 24, train 32/100, loss=0.2919 acc=0.9200
epoch 24, train 33/100, loss=0.4690 acc=0.8800
epoch 24, train 34/100, loss=0.4671 acc=0.8800
epoch 24, train 35/100, loss=0.7769 acc=0.7333
epoch 24, train 36/100, loss=0.3655 acc=0.9067
epoch 24, train 37/100, loss=0.5860 acc=0.7600
epoch 24, train 38/100, loss=0.3547 acc=0.8800
epoch 24, train 39/100, loss=0.6198 acc=0.7867
epoch 24, train 40/100, loss=0.6974 acc=0.7200
epoch 24, train 41/100, loss=0.0986 acc=0.9733
epoch 24, train 42/100, loss=0.7674 acc=0.8133
epoch 24, train 43/100, loss=0.7348 acc=0.7600
epoch 24, train 44/100, loss=0.9601 acc=0.7333
epoch 24, train 45/100, loss=0.4922 acc=0.8133
epoch 24, train 46/100, loss=0.6096 acc=0.8267
epoch 24, train 47/100, loss=0.5833 acc=0.8400
epoch 24, train 48/100, loss=0.7141 acc=0.8533
epoch 24, train 49/100, loss=0.2320 acc=0.9067
epoch 24, train 50/100, loss=0.6684 acc=0.8000
epoch 24, train 51/100, loss=0.6176 acc=0.7867
epoch 24, train 52/100, loss=0.6304 acc=0.8000
epoch 24, train 53/100, loss=0.2944 acc=0.8800
epoch 24, train 54/100, loss=0.6248 acc=0.7600
epoch 24, train 55/100, loss=0.7431 acc=0.8000
epoch 24, train 56/100, loss=0.8463 acc=0.7867
epoch 24, train 57/100, loss=0.8036 acc=0.8267
epoch 24, train 58/100, loss=0.9343 acc=0.6933
epoch 24, train 59/100, loss=0.5101 acc=0.8933
epoch 24, train 60/100, loss=0.3832 acc=0.8533
epoch 24, train 61/100, loss=0.1449 acc=0.9333
epoch 24, train 62/100, loss=0.6452 acc=0.8133
epoch 24, train 63/100, loss=0.7817 acc=0.7200
epoch 24, train 64/100, loss=0.7308 acc=0.7867
epoch 24, train 65/100, loss=0.5255 acc=0.8400
epoch 24, train 66/100, loss=0.2117 acc=0.9067
epoch 24, train 67/100, loss=0.0944 acc=0.9733
epoch 24, train 68/100, loss=0.7711 acc=0.7067
epoch 24, train 69/100, loss=0.3559 acc=0.9067
epoch 24, train 70/100, loss=0.4358 acc=0.8800
epoch 24, train 71/100, loss=1.6285 acc=0.5200
epoch 24, train 72/100, loss=0.9995 acc=0.7200
epoch 24, train 73/100, loss=0.4706 acc=0.8267
epoch 24, train 74/100, loss=1.0936 acc=0.6400
epoch 24, train 75/100, loss=0.9287 acc=0.7733
epoch 24, train 76/100, loss=1.1335 acc=0.6000
epoch 24, train 77/100, loss=0.8695 acc=0.7733
epoch 24, train 78/100, loss=0.5835 acc=0.8000
epoch 24, train 79/100, loss=0.4846 acc=0.8800
epoch 24, train 80/100, loss=0.3160 acc=0.8933
epoch 24, train 81/100, loss=0.6142 acc=0.7200
epoch 24, train 82/100, loss=0.5898 acc=0.7867
epoch 24, train 83/100, loss=0.5398 acc=0.7867
epoch 24, train 84/100, loss=0.7950 acc=0.6667
epoch 24, train 85/100, loss=0.1977 acc=0.9333
epoch 24, train 86/100, loss=0.6098 acc=0.7600
epoch 24, train 87/100, loss=0.5786 acc=0.8000
epoch 24, train 88/100, loss=0.9680 acc=0.7067
epoch 24, train 89/100, loss=1.0119 acc=0.5867
epoch 24, train 90/100, loss=0.3856 acc=0.8800
epoch 24, train 91/100, loss=0.3029 acc=0.9467
epoch 24, train 92/100, loss=1.2884 acc=0.6400
epoch 24, train 93/100, loss=0.2999 acc=0.9200
epoch 24, train 94/100, loss=0.1692 acc=0.9200
epoch 24, train 95/100, loss=0.9629 acc=0.6667
epoch 24, train 96/100, loss=1.4349 acc=0.6533
epoch 24, train 97/100, loss=0.8860 acc=0.6533
epoch 24, train 98/100, loss=0.2771 acc=0.9200
epoch 24, train 99/100, loss=0.1653 acc=0.9467
epoch 24, train 100/100, loss=0.5191 acc=0.8133
best epoch 23, best val acc=0.7002
epoch 24, val, loss=1.0457 acc=0.6891
ETA:1.5h/6.4h
epoch 25, train 1/100, loss=0.5084 acc=0.8933
epoch 25, train 2/100, loss=0.8113 acc=0.7867
epoch 25, train 3/100, loss=0.6462 acc=0.7733
epoch 25, train 4/100, loss=0.4557 acc=0.8267
epoch 25, train 5/100, loss=0.3748 acc=0.8933
epoch 25, train 6/100, loss=0.5382 acc=0.8400
epoch 25, train 7/100, loss=0.7165 acc=0.8000
epoch 25, train 8/100, loss=0.3567 acc=0.8933
epoch 25, train 9/100, loss=0.5824 acc=0.8400
epoch 25, train 10/100, loss=0.8130 acc=0.7200
epoch 25, train 11/100, loss=1.1812 acc=0.6933
epoch 25, train 12/100, loss=0.5505 acc=0.8267
epoch 25, train 13/100, loss=0.0209 acc=1.0000
epoch 25, train 14/100, loss=0.3686 acc=0.8400
epoch 25, train 15/100, loss=0.8601 acc=0.6400
epoch 25, train 16/100, loss=0.0946 acc=0.9600
epoch 25, train 17/100, loss=0.2368 acc=0.8800
epoch 25, train 18/100, loss=0.2200 acc=0.9067
epoch 25, train 19/100, loss=0.1562 acc=0.9333
epoch 25, train 20/100, loss=0.3062 acc=0.9333
epoch 25, train 21/100, loss=0.9699 acc=0.6933
epoch 25, train 22/100, loss=0.4277 acc=0.8667
epoch 25, train 23/100, loss=0.1649 acc=0.9467
epoch 25, train 24/100, loss=1.0723 acc=0.7067
epoch 25, train 25/100, loss=0.3514 acc=0.8533
epoch 25, train 26/100, loss=0.9915 acc=0.6267
epoch 25, train 27/100, loss=0.3363 acc=0.8667
epoch 25, train 28/100, loss=0.6412 acc=0.7733
epoch 25, train 29/100, loss=0.2547 acc=0.9200
epoch 25, train 30/100, loss=0.5809 acc=0.7867
epoch 25, train 31/100, loss=0.5787 acc=0.7733
epoch 25, train 32/100, loss=0.6567 acc=0.8000
epoch 25, train 33/100, loss=0.5950 acc=0.8267
epoch 25, train 34/100, loss=0.1280 acc=0.9600
epoch 25, train 35/100, loss=1.2150 acc=0.6000
epoch 25, train 36/100, loss=0.7924 acc=0.7467
epoch 25, train 37/100, loss=0.0732 acc=0.9867
epoch 25, train 38/100, loss=0.2748 acc=0.9200
epoch 25, train 39/100, loss=0.8896 acc=0.7333
epoch 25, train 40/100, loss=0.2573 acc=0.9333
epoch 25, train 41/100, loss=0.6871 acc=0.8400
epoch 25, train 42/100, loss=0.2913 acc=0.8667
epoch 25, train 43/100, loss=1.1757 acc=0.6133
epoch 25, train 44/100, loss=1.6579 acc=0.6667
epoch 25, train 45/100, loss=0.1909 acc=0.9067
epoch 25, train 46/100, loss=0.1549 acc=0.9467
epoch 25, train 47/100, loss=0.4027 acc=0.8533
epoch 25, train 48/100, loss=0.5761 acc=0.8133
epoch 25, train 49/100, loss=0.5342 acc=0.8400
epoch 25, train 50/100, loss=0.3463 acc=0.8933
epoch 25, train 51/100, loss=0.3758 acc=0.8933
epoch 25, train 52/100, loss=0.2956 acc=0.9200
epoch 25, train 53/100, loss=0.9414 acc=0.7467
epoch 25, train 54/100, loss=0.4560 acc=0.8667
epoch 25, train 55/100, loss=0.5914 acc=0.8000
epoch 25, train 56/100, loss=0.6033 acc=0.8133
epoch 25, train 57/100, loss=0.5736 acc=0.8000
epoch 25, train 58/100, loss=0.3236 acc=0.8800
epoch 25, train 59/100, loss=0.6275 acc=0.8133
epoch 25, train 60/100, loss=0.4468 acc=0.8533
epoch 25, train 61/100, loss=0.2115 acc=0.9200
epoch 25, train 62/100, loss=0.2849 acc=0.8933
epoch 25, train 63/100, loss=0.2475 acc=0.9333
epoch 25, train 64/100, loss=0.7725 acc=0.7467
epoch 25, train 65/100, loss=0.5034 acc=0.8533
epoch 25, train 66/100, loss=0.2791 acc=0.9333
epoch 25, train 67/100, loss=0.1237 acc=0.9333
epoch 25, train 68/100, loss=0.9035 acc=0.6800
epoch 25, train 69/100, loss=0.7329 acc=0.8000
epoch 25, train 70/100, loss=0.5102 acc=0.8267
epoch 25, train 71/100, loss=0.1581 acc=0.9333
epoch 25, train 72/100, loss=0.1991 acc=0.9200
epoch 25, train 73/100, loss=0.0672 acc=0.9733
epoch 25, train 74/100, loss=0.5571 acc=0.8400
epoch 25, train 75/100, loss=0.5107 acc=0.8000
epoch 25, train 76/100, loss=0.8273 acc=0.7200
epoch 25, train 77/100, loss=0.2891 acc=0.8933
epoch 25, train 78/100, loss=1.1115 acc=0.6133
epoch 25, train 79/100, loss=1.2125 acc=0.7067
epoch 25, train 80/100, loss=0.5167 acc=0.8533
epoch 25, train 81/100, loss=0.5356 acc=0.8267
epoch 25, train 82/100, loss=1.1763 acc=0.6933
epoch 25, train 83/100, loss=0.9770 acc=0.6667
epoch 25, train 84/100, loss=0.2605 acc=0.8667
epoch 25, train 85/100, loss=0.5311 acc=0.8400
epoch 25, train 86/100, loss=1.3042 acc=0.6133
epoch 25, train 87/100, loss=1.7748 acc=0.6000
epoch 25, train 88/100, loss=0.8798 acc=0.6533
epoch 25, train 89/100, loss=0.4747 acc=0.8133
epoch 25, train 90/100, loss=1.1212 acc=0.6533
epoch 25, train 91/100, loss=0.1837 acc=0.9067
epoch 25, train 92/100, loss=0.3782 acc=0.8933
epoch 25, train 93/100, loss=0.5337 acc=0.8000
epoch 25, train 94/100, loss=0.3670 acc=0.8533
epoch 25, train 95/100, loss=0.2278 acc=0.9200
epoch 25, train 96/100, loss=0.8297 acc=0.7600
epoch 25, train 97/100, loss=0.5044 acc=0.8800
epoch 25, train 98/100, loss=0.8419 acc=0.7733
epoch 25, train 99/100, loss=0.5644 acc=0.8000
epoch 25, train 100/100, loss=0.4087 acc=0.8533
best epoch 23, best val acc=0.7002
epoch 25, val, loss=0.9466 acc=0.7049
ETA:1.6h/6.4h
epoch 26, train 1/100, loss=0.5045 acc=0.7600
epoch 26, train 2/100, loss=0.8006 acc=0.7067
epoch 26, train 3/100, loss=1.1080 acc=0.6267
epoch 26, train 4/100, loss=0.6491 acc=0.7733
epoch 26, train 5/100, loss=0.2741 acc=0.9200
epoch 26, train 6/100, loss=1.5887 acc=0.5467
epoch 26, train 7/100, loss=0.5992 acc=0.7733
epoch 26, train 8/100, loss=0.4132 acc=0.8667
epoch 26, train 9/100, loss=0.4028 acc=0.8667
epoch 26, train 10/100, loss=0.6739 acc=0.7867
epoch 26, train 11/100, loss=0.4328 acc=0.8667
epoch 26, train 12/100, loss=1.1355 acc=0.6267
epoch 26, train 13/100, loss=0.5643 acc=0.8133
epoch 26, train 14/100, loss=0.5564 acc=0.7867
epoch 26, train 15/100, loss=0.1671 acc=0.9333
epoch 26, train 16/100, loss=1.3516 acc=0.5600
epoch 26, train 17/100, loss=0.2358 acc=0.9200
epoch 26, train 18/100, loss=0.6094 acc=0.7467
epoch 26, train 19/100, loss=0.5307 acc=0.8133
epoch 26, train 20/100, loss=0.3460 acc=0.8667
epoch 26, train 21/100, loss=0.6167 acc=0.8000
epoch 26, train 22/100, loss=0.4683 acc=0.8133
epoch 26, train 23/100, loss=0.3595 acc=0.8267
epoch 26, train 24/100, loss=0.1441 acc=0.9600
epoch 26, train 25/100, loss=0.3208 acc=0.8667
epoch 26, train 26/100, loss=0.8089 acc=0.6667
epoch 26, train 27/100, loss=1.2171 acc=0.6533
epoch 26, train 28/100, loss=0.4994 acc=0.7867
epoch 26, train 29/100, loss=0.4022 acc=0.8800
epoch 26, train 30/100, loss=0.2494 acc=0.9333
epoch 26, train 31/100, loss=1.8664 acc=0.4400
epoch 26, train 32/100, loss=1.2774 acc=0.6533
epoch 26, train 33/100, loss=0.6517 acc=0.8267
epoch 26, train 34/100, loss=0.3965 acc=0.9067
epoch 26, train 35/100, loss=0.6743 acc=0.8000
epoch 26, train 36/100, loss=0.4178 acc=0.9200
epoch 26, train 37/100, loss=1.2129 acc=0.6667
epoch 26, train 38/100, loss=1.2145 acc=0.5867
epoch 26, train 39/100, loss=0.3091 acc=0.8933
epoch 26, train 40/100, loss=0.6154 acc=0.8133
epoch 26, train 41/100, loss=0.4514 acc=0.8800
epoch 26, train 42/100, loss=0.1893 acc=0.9600
epoch 26, train 43/100, loss=0.6046 acc=0.7867
epoch 26, train 44/100, loss=0.5764 acc=0.7733
epoch 26, train 45/100, loss=0.6976 acc=0.7867
epoch 26, train 46/100, loss=0.4834 acc=0.8000
epoch 26, train 47/100, loss=0.6717 acc=0.8000
epoch 26, train 48/100, loss=1.1878 acc=0.4933
epoch 26, train 49/100, loss=0.2553 acc=0.8933
epoch 26, train 50/100, loss=0.4385 acc=0.8667
epoch 26, train 51/100, loss=0.2013 acc=0.9200
epoch 26, train 52/100, loss=0.8134 acc=0.7467
epoch 26, train 53/100, loss=1.0156 acc=0.6933
epoch 26, train 54/100, loss=0.8979 acc=0.7467
epoch 26, train 55/100, loss=0.7081 acc=0.7467
epoch 26, train 56/100, loss=0.7942 acc=0.7067
epoch 26, train 57/100, loss=0.6958 acc=0.7067
epoch 26, train 58/100, loss=0.1271 acc=0.9733
epoch 26, train 59/100, loss=0.4188 acc=0.8667
epoch 26, train 60/100, loss=1.1043 acc=0.6800
epoch 26, train 61/100, loss=0.3279 acc=0.9333
epoch 26, train 62/100, loss=0.2700 acc=0.8933
epoch 26, train 63/100, loss=0.5527 acc=0.8400
epoch 26, train 64/100, loss=0.3407 acc=0.8800
epoch 26, train 65/100, loss=0.1964 acc=0.9067
epoch 26, train 66/100, loss=0.6342 acc=0.7733
epoch 26, train 67/100, loss=0.8567 acc=0.7333
epoch 26, train 68/100, loss=0.1907 acc=0.9333
epoch 26, train 69/100, loss=0.5003 acc=0.8533
epoch 26, train 70/100, loss=0.1285 acc=0.9467
epoch 26, train 71/100, loss=1.1285 acc=0.6267
epoch 26, train 72/100, loss=0.6371 acc=0.7867
epoch 26, train 73/100, loss=0.6650 acc=0.7867
epoch 26, train 74/100, loss=0.5593 acc=0.8267
epoch 26, train 75/100, loss=0.8828 acc=0.7200
epoch 26, train 76/100, loss=1.0260 acc=0.6667
epoch 26, train 77/100, loss=0.6598 acc=0.7600
epoch 26, train 78/100, loss=0.5788 acc=0.7733
epoch 26, train 79/100, loss=0.5078 acc=0.8267
epoch 26, train 80/100, loss=1.2068 acc=0.5733
epoch 26, train 81/100, loss=0.9914 acc=0.6667
epoch 26, train 82/100, loss=0.1069 acc=0.9600
epoch 26, train 83/100, loss=1.1003 acc=0.6267
epoch 26, train 84/100, loss=0.5256 acc=0.8000
epoch 26, train 85/100, loss=0.9750 acc=0.7067
epoch 26, train 86/100, loss=0.2452 acc=0.9067
epoch 26, train 87/100, loss=0.3428 acc=0.8533
epoch 26, train 88/100, loss=0.1290 acc=0.9600
epoch 26, train 89/100, loss=0.6363 acc=0.8133
epoch 26, train 90/100, loss=1.5636 acc=0.5467
epoch 26, train 91/100, loss=0.3747 acc=0.8533
epoch 26, train 92/100, loss=0.2447 acc=0.9067
epoch 26, train 93/100, loss=0.3062 acc=0.9067
epoch 26, train 94/100, loss=0.4904 acc=0.9067
epoch 26, train 95/100, loss=1.1345 acc=0.5867
epoch 26, train 96/100, loss=0.6283 acc=0.7867
epoch 26, train 97/100, loss=0.2747 acc=0.8800
epoch 26, train 98/100, loss=0.8937 acc=0.7200
epoch 26, train 99/100, loss=0.7970 acc=0.7467
epoch 26, train 100/100, loss=0.4540 acc=0.9067
best epoch 25, best val acc=0.7049
epoch 26, val, loss=0.9341 acc=0.7125
ETA:1.7h/6.4h
epoch 27, train 1/100, loss=0.6718 acc=0.7867
epoch 27, train 2/100, loss=1.0284 acc=0.6933
epoch 27, train 3/100, loss=0.4608 acc=0.8267
epoch 27, train 4/100, loss=0.6091 acc=0.7333
epoch 27, train 5/100, loss=0.5392 acc=0.7600
epoch 27, train 6/100, loss=0.6278 acc=0.8133
epoch 27, train 7/100, loss=0.4180 acc=0.8667
epoch 27, train 8/100, loss=0.5922 acc=0.7600
epoch 27, train 9/100, loss=0.1330 acc=0.9467
epoch 27, train 10/100, loss=0.7975 acc=0.8000
epoch 27, train 11/100, loss=0.2749 acc=0.8933
epoch 27, train 12/100, loss=0.9586 acc=0.5867
epoch 27, train 13/100, loss=0.3819 acc=0.8400
epoch 27, train 14/100, loss=0.3693 acc=0.8267
epoch 27, train 15/100, loss=0.5313 acc=0.8133
epoch 27, train 16/100, loss=0.3369 acc=0.8800
epoch 27, train 17/100, loss=0.1555 acc=0.9467
epoch 27, train 18/100, loss=0.5675 acc=0.8533
epoch 27, train 19/100, loss=0.3236 acc=0.8800
epoch 27, train 20/100, loss=1.0521 acc=0.7467
epoch 27, train 21/100, loss=0.7127 acc=0.7600
epoch 27, train 22/100, loss=0.7116 acc=0.6933
epoch 27, train 23/100, loss=0.5209 acc=0.7867
epoch 27, train 24/100, loss=1.4992 acc=0.5600
epoch 27, train 25/100, loss=0.3529 acc=0.8400
epoch 27, train 26/100, loss=1.0111 acc=0.6667
epoch 27, train 27/100, loss=0.8536 acc=0.7067
epoch 27, train 28/100, loss=0.3610 acc=0.8000
epoch 27, train 29/100, loss=1.0687 acc=0.6800
epoch 27, train 30/100, loss=0.4010 acc=0.8267
epoch 27, train 31/100, loss=0.2099 acc=0.9200
epoch 27, train 32/100, loss=0.4297 acc=0.8133
epoch 27, train 33/100, loss=0.2672 acc=0.8933
epoch 27, train 34/100, loss=0.2514 acc=0.9333
epoch 27, train 35/100, loss=1.4104 acc=0.6933
epoch 27, train 36/100, loss=0.8329 acc=0.7200
epoch 27, train 37/100, loss=0.2297 acc=0.9200
epoch 27, train 38/100, loss=0.5462 acc=0.8000
epoch 27, train 39/100, loss=0.5118 acc=0.8400
epoch 27, train 40/100, loss=0.2293 acc=0.9467
epoch 27, train 41/100, loss=0.9754 acc=0.7333
epoch 27, train 42/100, loss=0.0886 acc=0.9600
epoch 27, train 43/100, loss=1.9630 acc=0.4800
epoch 27, train 44/100, loss=0.3570 acc=0.8667
epoch 27, train 45/100, loss=0.3282 acc=0.9067
epoch 27, train 46/100, loss=0.2833 acc=0.9333
epoch 27, train 47/100, loss=1.1151 acc=0.6267
epoch 27, train 48/100, loss=0.4384 acc=0.8800
epoch 27, train 49/100, loss=0.5154 acc=0.8000
epoch 27, train 50/100, loss=0.7674 acc=0.6667
epoch 27, train 51/100, loss=0.5827 acc=0.7867
epoch 27, train 52/100, loss=1.3706 acc=0.6133
epoch 27, train 53/100, loss=0.5141 acc=0.7867
epoch 27, train 54/100, loss=1.3738 acc=0.5200
epoch 27, train 55/100, loss=0.4339 acc=0.8800
epoch 27, train 56/100, loss=0.3453 acc=0.8667
epoch 27, train 57/100, loss=0.5551 acc=0.8000
epoch 27, train 58/100, loss=0.7880 acc=0.7333
epoch 27, train 59/100, loss=0.2192 acc=0.9200
epoch 27, train 60/100, loss=0.3937 acc=0.8267
epoch 27, train 61/100, loss=0.6246 acc=0.8400
epoch 27, train 62/100, loss=0.8617 acc=0.7333
epoch 27, train 63/100, loss=0.5362 acc=0.8133
epoch 27, train 64/100, loss=0.0703 acc=0.9600
epoch 27, train 65/100, loss=0.4174 acc=0.8533
epoch 27, train 66/100, loss=0.3610 acc=0.9067
epoch 27, train 67/100, loss=0.5420 acc=0.8133
epoch 27, train 68/100, loss=0.1892 acc=0.9467
epoch 27, train 69/100, loss=0.8454 acc=0.7733
epoch 27, train 70/100, loss=0.3503 acc=0.9200
epoch 27, train 71/100, loss=0.1151 acc=0.9333
epoch 27, train 72/100, loss=0.3078 acc=0.8667
epoch 27, train 73/100, loss=0.3325 acc=0.9333
epoch 27, train 74/100, loss=0.5162 acc=0.7733
epoch 27, train 75/100, loss=0.8012 acc=0.7200
epoch 27, train 76/100, loss=0.3242 acc=0.8800
epoch 27, train 77/100, loss=0.2357 acc=0.9067
epoch 27, train 78/100, loss=0.4645 acc=0.8800
epoch 27, train 79/100, loss=0.2836 acc=0.9333
epoch 27, train 80/100, loss=0.6506 acc=0.7467
epoch 27, train 81/100, loss=0.6989 acc=0.7067
epoch 27, train 82/100, loss=0.8451 acc=0.7200
epoch 27, train 83/100, loss=0.8589 acc=0.6133
epoch 27, train 84/100, loss=0.7803 acc=0.6933
epoch 27, train 85/100, loss=0.1620 acc=0.9467
epoch 27, train 86/100, loss=0.4247 acc=0.8267
epoch 27, train 87/100, loss=0.4001 acc=0.8800
epoch 27, train 88/100, loss=0.3460 acc=0.8400
epoch 27, train 89/100, loss=1.0730 acc=0.6533
epoch 27, train 90/100, loss=0.4498 acc=0.8267
epoch 27, train 91/100, loss=0.4944 acc=0.8800
epoch 27, train 92/100, loss=0.1808 acc=0.9333
epoch 27, train 93/100, loss=0.4379 acc=0.7867
epoch 27, train 94/100, loss=1.3243 acc=0.6000
epoch 27, train 95/100, loss=0.5128 acc=0.8133
epoch 27, train 96/100, loss=0.2480 acc=0.9200
epoch 27, train 97/100, loss=0.5969 acc=0.7333
epoch 27, train 98/100, loss=0.8738 acc=0.8000
epoch 27, train 99/100, loss=0.2746 acc=0.8800
epoch 27, train 100/100, loss=0.0538 acc=0.9733
best epoch 26, best val acc=0.7125
epoch 27, val, loss=0.9721 acc=0.7024
ETA:1.7h/6.4h
epoch 28, train 1/100, loss=0.4613 acc=0.8533
epoch 28, train 2/100, loss=0.4649 acc=0.8400
epoch 28, train 3/100, loss=0.7736 acc=0.7067
epoch 28, train 4/100, loss=0.1958 acc=0.9333
epoch 28, train 5/100, loss=0.7354 acc=0.8000
epoch 28, train 6/100, loss=0.3513 acc=0.8933
epoch 28, train 7/100, loss=0.6521 acc=0.7467
epoch 28, train 8/100, loss=0.2660 acc=0.8933
epoch 28, train 9/100, loss=0.9282 acc=0.6667
epoch 28, train 10/100, loss=0.2755 acc=0.9200
epoch 28, train 11/100, loss=0.1317 acc=0.9600
epoch 28, train 12/100, loss=0.4496 acc=0.8533
epoch 28, train 13/100, loss=0.3992 acc=0.8400
epoch 28, train 14/100, loss=0.6456 acc=0.8400
epoch 28, train 15/100, loss=0.6637 acc=0.8000
epoch 28, train 16/100, loss=0.5069 acc=0.8267
epoch 28, train 17/100, loss=0.8056 acc=0.7467
epoch 28, train 18/100, loss=0.3625 acc=0.8667
epoch 28, train 19/100, loss=0.9318 acc=0.7200
epoch 28, train 20/100, loss=0.5215 acc=0.8133
epoch 28, train 21/100, loss=0.2685 acc=0.9333
epoch 28, train 22/100, loss=0.7796 acc=0.7067
epoch 28, train 23/100, loss=0.5590 acc=0.8000
epoch 28, train 24/100, loss=1.7003 acc=0.5467
epoch 28, train 25/100, loss=0.5848 acc=0.8267
epoch 28, train 26/100, loss=0.3035 acc=0.8400
epoch 28, train 27/100, loss=0.4656 acc=0.8267
epoch 28, train 28/100, loss=0.5385 acc=0.8267
epoch 28, train 29/100, loss=0.3022 acc=0.8800
epoch 28, train 30/100, loss=0.3858 acc=0.8533
epoch 28, train 31/100, loss=0.7366 acc=0.8133
epoch 28, train 32/100, loss=0.2558 acc=0.8800
epoch 28, train 33/100, loss=0.6735 acc=0.7733
epoch 28, train 34/100, loss=0.4335 acc=0.8400
epoch 28, train 35/100, loss=0.4976 acc=0.8400
epoch 28, train 36/100, loss=1.3410 acc=0.5600
epoch 28, train 37/100, loss=0.4511 acc=0.8667
epoch 28, train 38/100, loss=0.6775 acc=0.7733
epoch 28, train 39/100, loss=0.9316 acc=0.6933
epoch 28, train 40/100, loss=0.3759 acc=0.9067
epoch 28, train 41/100, loss=0.7778 acc=0.7467
epoch 28, train 42/100, loss=0.5581 acc=0.8000
epoch 28, train 43/100, loss=0.7548 acc=0.7467
epoch 28, train 44/100, loss=0.6544 acc=0.7867
epoch 28, train 45/100, loss=1.8501 acc=0.4667
epoch 28, train 46/100, loss=0.9695 acc=0.7067
epoch 28, train 47/100, loss=1.1849 acc=0.6400
epoch 28, train 48/100, loss=0.2367 acc=0.9333
epoch 28, train 49/100, loss=0.7765 acc=0.7200
epoch 28, train 50/100, loss=0.1685 acc=0.9200
epoch 28, train 51/100, loss=0.4397 acc=0.8667
epoch 28, train 52/100, loss=0.1735 acc=0.9067
epoch 28, train 53/100, loss=0.5832 acc=0.8533
epoch 28, train 54/100, loss=0.5905 acc=0.8267
epoch 28, train 55/100, loss=0.9776 acc=0.7067
epoch 28, train 56/100, loss=0.2860 acc=0.8933
epoch 28, train 57/100, loss=0.5130 acc=0.8133
epoch 28, train 58/100, loss=0.6878 acc=0.7600
epoch 28, train 59/100, loss=0.3939 acc=0.8667
epoch 28, train 60/100, loss=0.5375 acc=0.8400
epoch 28, train 61/100, loss=0.3986 acc=0.8800
epoch 28, train 62/100, loss=1.1470 acc=0.6267
epoch 28, train 63/100, loss=1.1183 acc=0.6267
epoch 28, train 64/100, loss=0.3041 acc=0.8933
epoch 28, train 65/100, loss=0.7011 acc=0.8267
epoch 28, train 66/100, loss=0.3644 acc=0.8933
epoch 28, train 67/100, loss=0.6135 acc=0.8400
epoch 28, train 68/100, loss=0.3607 acc=0.9067
epoch 28, train 69/100, loss=1.3535 acc=0.5067
epoch 28, train 70/100, loss=0.5179 acc=0.8667
epoch 28, train 71/100, loss=0.7499 acc=0.8000
epoch 28, train 72/100, loss=0.7353 acc=0.7333
epoch 28, train 73/100, loss=0.2339 acc=0.9067
epoch 28, train 74/100, loss=0.3749 acc=0.8533
epoch 28, train 75/100, loss=0.7032 acc=0.6667
epoch 28, train 76/100, loss=0.4033 acc=0.8933
epoch 28, train 77/100, loss=0.5955 acc=0.8133
epoch 28, train 78/100, loss=0.8666 acc=0.6667
epoch 28, train 79/100, loss=0.2143 acc=0.9600
epoch 28, train 80/100, loss=0.1932 acc=0.9333
epoch 28, train 81/100, loss=0.8562 acc=0.7200
epoch 28, train 82/100, loss=0.3937 acc=0.9067
epoch 28, train 83/100, loss=1.4504 acc=0.5733
epoch 28, train 84/100, loss=0.2486 acc=0.9333
epoch 28, train 85/100, loss=1.2299 acc=0.6133
epoch 28, train 86/100, loss=0.7736 acc=0.6933
epoch 28, train 87/100, loss=0.4981 acc=0.8667
epoch 28, train 88/100, loss=0.4940 acc=0.7733
epoch 28, train 89/100, loss=0.4069 acc=0.8667
epoch 28, train 90/100, loss=0.4706 acc=0.8267
epoch 28, train 91/100, loss=0.3082 acc=0.8933
epoch 28, train 92/100, loss=0.4433 acc=0.8267
epoch 28, train 93/100, loss=0.5556 acc=0.7733
epoch 28, train 94/100, loss=0.3189 acc=0.8933
epoch 28, train 95/100, loss=0.1981 acc=0.9200
epoch 28, train 96/100, loss=0.2762 acc=0.8933
epoch 28, train 97/100, loss=0.3066 acc=0.8933
epoch 28, train 98/100, loss=1.4580 acc=0.5200
epoch 28, train 99/100, loss=1.0271 acc=0.6533
epoch 28, train 100/100, loss=0.2287 acc=0.9200
best epoch 26, best val acc=0.7125
epoch 28, val, loss=1.0154 acc=0.6973
ETA:1.8h/6.4h
epoch 29, train 1/100, loss=0.6398 acc=0.7733
epoch 29, train 2/100, loss=1.3122 acc=0.6267
epoch 29, train 3/100, loss=1.3722 acc=0.5733
epoch 29, train 4/100, loss=0.6719 acc=0.8133
epoch 29, train 5/100, loss=0.6433 acc=0.8400
epoch 29, train 6/100, loss=1.2628 acc=0.5600
epoch 29, train 7/100, loss=0.7799 acc=0.7467
epoch 29, train 8/100, loss=0.6191 acc=0.8133
epoch 29, train 9/100, loss=0.4621 acc=0.8133
epoch 29, train 10/100, loss=1.5517 acc=0.5733
epoch 29, train 11/100, loss=0.6522 acc=0.7467
epoch 29, train 12/100, loss=0.7055 acc=0.7600
epoch 29, train 13/100, loss=0.8684 acc=0.7200
epoch 29, train 14/100, loss=1.0821 acc=0.7733
epoch 29, train 15/100, loss=0.4000 acc=0.8800
epoch 29, train 16/100, loss=0.7064 acc=0.8133
epoch 29, train 17/100, loss=0.2646 acc=0.9333
epoch 29, train 18/100, loss=0.3800 acc=0.9067
epoch 29, train 19/100, loss=0.2915 acc=0.9067
epoch 29, train 20/100, loss=1.1137 acc=0.6267
epoch 29, train 21/100, loss=0.6665 acc=0.7600
epoch 29, train 22/100, loss=0.3410 acc=0.8667
epoch 29, train 23/100, loss=0.2346 acc=0.9333
epoch 29, train 24/100, loss=0.4021 acc=0.8800
epoch 29, train 25/100, loss=0.9204 acc=0.7333
epoch 29, train 26/100, loss=1.8450 acc=0.5467
epoch 29, train 27/100, loss=1.2411 acc=0.7067
epoch 29, train 28/100, loss=0.6168 acc=0.6800
epoch 29, train 29/100, loss=0.6978 acc=0.8400
epoch 29, train 30/100, loss=1.0610 acc=0.6267
epoch 29, train 31/100, loss=0.5623 acc=0.8133
epoch 29, train 32/100, loss=0.2652 acc=0.9200
epoch 29, train 33/100, loss=0.9963 acc=0.7467
epoch 29, train 34/100, loss=0.4724 acc=0.8000
epoch 29, train 35/100, loss=0.5528 acc=0.7600
epoch 29, train 36/100, loss=0.2148 acc=0.8933
epoch 29, train 37/100, loss=0.5545 acc=0.8133
epoch 29, train 38/100, loss=0.2135 acc=0.9333
epoch 29, train 39/100, loss=0.5168 acc=0.8400
epoch 29, train 40/100, loss=1.4945 acc=0.6667
epoch 29, train 41/100, loss=1.2558 acc=0.6133
epoch 29, train 42/100, loss=1.7852 acc=0.5467
epoch 29, train 43/100, loss=0.3387 acc=0.9200
epoch 29, train 44/100, loss=0.8905 acc=0.7600
epoch 29, train 45/100, loss=0.4174 acc=0.8800
epoch 29, train 46/100, loss=1.0990 acc=0.6133
epoch 29, train 47/100, loss=1.0213 acc=0.6933
epoch 29, train 48/100, loss=0.6429 acc=0.7733
epoch 29, train 49/100, loss=0.3059 acc=0.8933
epoch 29, train 50/100, loss=0.1746 acc=0.9467
epoch 29, train 51/100, loss=0.3154 acc=0.8400
epoch 29, train 52/100, loss=0.4924 acc=0.8400
epoch 29, train 53/100, loss=0.8067 acc=0.7200
epoch 29, train 54/100, loss=0.3395 acc=0.8800
epoch 29, train 55/100, loss=0.1262 acc=0.9467
epoch 29, train 56/100, loss=0.9167 acc=0.7067
epoch 29, train 57/100, loss=1.5873 acc=0.6000
epoch 29, train 58/100, loss=0.5833 acc=0.8000
epoch 29, train 59/100, loss=0.3427 acc=0.8933
epoch 29, train 60/100, loss=1.1588 acc=0.6800
epoch 29, train 61/100, loss=0.4315 acc=0.8133
epoch 29, train 62/100, loss=0.1151 acc=0.9867
epoch 29, train 63/100, loss=0.5119 acc=0.8133
epoch 29, train 64/100, loss=0.5635 acc=0.8267
epoch 29, train 65/100, loss=1.0961 acc=0.6667
epoch 29, train 66/100, loss=0.8572 acc=0.6533
epoch 29, train 67/100, loss=0.8486 acc=0.6800
epoch 29, train 68/100, loss=0.4731 acc=0.8133
epoch 29, train 69/100, loss=0.9497 acc=0.7200
epoch 29, train 70/100, loss=0.3579 acc=0.9067
epoch 29, train 71/100, loss=0.5537 acc=0.8133
epoch 29, train 72/100, loss=0.8393 acc=0.7200
epoch 29, train 73/100, loss=0.8885 acc=0.7067
epoch 29, train 74/100, loss=0.6293 acc=0.8133
epoch 29, train 75/100, loss=0.1594 acc=0.9200
epoch 29, train 76/100, loss=0.4279 acc=0.8400
epoch 29, train 77/100, loss=0.5408 acc=0.8667
epoch 29, train 78/100, loss=0.3125 acc=0.9200
epoch 29, train 79/100, loss=0.1466 acc=0.9600
epoch 29, train 80/100, loss=0.5817 acc=0.8267
epoch 29, train 81/100, loss=0.9913 acc=0.6933
epoch 29, train 82/100, loss=0.6602 acc=0.7733
epoch 29, train 83/100, loss=0.4405 acc=0.8800
epoch 29, train 84/100, loss=1.1062 acc=0.5867
epoch 29, train 85/100, loss=0.1164 acc=0.9733
epoch 29, train 86/100, loss=0.1315 acc=0.9733
epoch 29, train 87/100, loss=0.1600 acc=0.9067
epoch 29, train 88/100, loss=1.1153 acc=0.6267
epoch 29, train 89/100, loss=0.3972 acc=0.8800
epoch 29, train 90/100, loss=0.5209 acc=0.8400
epoch 29, train 91/100, loss=0.4910 acc=0.8267
epoch 29, train 92/100, loss=1.0037 acc=0.6400
epoch 29, train 93/100, loss=0.2756 acc=0.9067
epoch 29, train 94/100, loss=0.4384 acc=0.8400
epoch 29, train 95/100, loss=0.6097 acc=0.6933
epoch 29, train 96/100, loss=0.8857 acc=0.7467
epoch 29, train 97/100, loss=1.5087 acc=0.6133
epoch 29, train 98/100, loss=0.3024 acc=0.8933
epoch 29, train 99/100, loss=0.0992 acc=0.9733
epoch 29, train 100/100, loss=0.2822 acc=0.8800
best epoch 26, best val acc=0.7125
epoch 29, val, loss=0.9781 acc=0.6956
ETA:1.9h/6.4h
epoch 30, train 1/100, loss=0.7818 acc=0.6933
epoch 30, train 2/100, loss=1.5047 acc=0.5467
epoch 30, train 3/100, loss=0.8694 acc=0.7200
epoch 30, train 4/100, loss=0.1912 acc=0.9467
epoch 30, train 5/100, loss=0.7319 acc=0.7067
epoch 30, train 6/100, loss=0.1783 acc=0.9067
epoch 30, train 7/100, loss=0.7331 acc=0.7467
epoch 30, train 8/100, loss=0.7340 acc=0.6667
epoch 30, train 9/100, loss=0.6575 acc=0.8267
epoch 30, train 10/100, loss=0.2041 acc=0.9467
epoch 30, train 11/100, loss=0.4843 acc=0.8667
epoch 30, train 12/100, loss=0.1548 acc=0.9467
epoch 30, train 13/100, loss=0.7662 acc=0.8267
epoch 30, train 14/100, loss=0.4892 acc=0.8400
epoch 30, train 15/100, loss=0.3171 acc=0.8800
epoch 30, train 16/100, loss=0.5128 acc=0.8800
epoch 30, train 17/100, loss=0.3821 acc=0.8667
epoch 30, train 18/100, loss=0.2261 acc=0.9200
epoch 30, train 19/100, loss=0.3108 acc=0.8800
epoch 30, train 20/100, loss=0.8932 acc=0.7067
epoch 30, train 21/100, loss=0.3914 acc=0.8533
epoch 30, train 22/100, loss=0.5132 acc=0.8667
epoch 30, train 23/100, loss=0.3330 acc=0.8933
epoch 30, train 24/100, loss=0.3977 acc=0.8800
epoch 30, train 25/100, loss=0.3109 acc=0.9067
epoch 30, train 26/100, loss=0.4440 acc=0.8933
epoch 30, train 27/100, loss=0.4548 acc=0.8533
epoch 30, train 28/100, loss=0.5476 acc=0.8267
epoch 30, train 29/100, loss=0.2657 acc=0.8933
epoch 30, train 30/100, loss=0.2322 acc=0.9200
epoch 30, train 31/100, loss=0.2243 acc=0.9067
epoch 30, train 32/100, loss=0.3041 acc=0.8800
epoch 30, train 33/100, loss=0.6269 acc=0.8533
epoch 30, train 34/100, loss=0.8730 acc=0.6800
epoch 30, train 35/100, loss=0.2730 acc=0.9067
epoch 30, train 36/100, loss=0.4808 acc=0.8267
epoch 30, train 37/100, loss=0.2311 acc=0.9200
epoch 30, train 38/100, loss=0.2344 acc=0.8933
epoch 30, train 39/100, loss=0.2430 acc=0.9600
epoch 30, train 40/100, loss=0.7387 acc=0.8000
epoch 30, train 41/100, loss=0.5557 acc=0.8133
epoch 30, train 42/100, loss=0.5324 acc=0.7067
epoch 30, train 43/100, loss=0.2764 acc=0.9467
epoch 30, train 44/100, loss=0.4501 acc=0.8400
epoch 30, train 45/100, loss=0.2671 acc=0.9067
epoch 30, train 46/100, loss=0.3582 acc=0.8400
epoch 30, train 47/100, loss=0.2667 acc=0.8800
epoch 30, train 48/100, loss=0.7441 acc=0.7333
epoch 30, train 49/100, loss=0.6001 acc=0.8400
epoch 30, train 50/100, loss=0.4139 acc=0.8267
epoch 30, train 51/100, loss=0.2230 acc=0.9467
epoch 30, train 52/100, loss=0.2420 acc=0.9333
epoch 30, train 53/100, loss=1.4768 acc=0.5600
epoch 30, train 54/100, loss=0.4218 acc=0.8533
epoch 30, train 55/100, loss=0.0871 acc=0.9733
epoch 30, train 56/100, loss=0.2278 acc=0.9067
epoch 30, train 57/100, loss=0.5869 acc=0.8267
epoch 30, train 58/100, loss=0.6037 acc=0.7333
epoch 30, train 59/100, loss=0.2604 acc=0.8800
epoch 30, train 60/100, loss=0.9753 acc=0.7067
epoch 30, train 61/100, loss=0.1183 acc=0.9733
epoch 30, train 62/100, loss=0.9406 acc=0.6533
epoch 30, train 63/100, loss=1.5283 acc=0.5467
epoch 30, train 64/100, loss=0.2564 acc=0.9067
epoch 30, train 65/100, loss=0.0422 acc=0.9867
epoch 30, train 66/100, loss=0.1892 acc=0.9333
epoch 30, train 67/100, loss=0.4103 acc=0.8267
epoch 30, train 68/100, loss=0.4468 acc=0.8667
epoch 30, train 69/100, loss=0.6350 acc=0.7467
epoch 30, train 70/100, loss=0.5588 acc=0.8133
epoch 30, train 71/100, loss=0.3570 acc=0.8800
epoch 30, train 72/100, loss=0.1359 acc=0.9333
epoch 30, train 73/100, loss=0.6879 acc=0.8000
epoch 30, train 74/100, loss=0.4299 acc=0.8800
epoch 30, train 75/100, loss=0.3989 acc=0.8400
epoch 30, train 76/100, loss=1.0369 acc=0.6800
epoch 30, train 77/100, loss=0.6057 acc=0.8000
epoch 30, train 78/100, loss=0.2283 acc=0.9467
epoch 30, train 79/100, loss=0.4395 acc=0.8133
epoch 30, train 80/100, loss=1.1175 acc=0.7067
epoch 30, train 81/100, loss=0.4731 acc=0.7467
epoch 30, train 82/100, loss=0.2031 acc=0.9067
epoch 30, train 83/100, loss=0.3782 acc=0.8533
epoch 30, train 84/100, loss=0.2671 acc=0.8933
epoch 30, train 85/100, loss=0.8835 acc=0.7733
epoch 30, train 86/100, loss=0.3662 acc=0.8800
epoch 30, train 87/100, loss=0.8329 acc=0.7467
epoch 30, train 88/100, loss=0.4863 acc=0.8400
epoch 30, train 89/100, loss=0.6614 acc=0.8133
epoch 30, train 90/100, loss=0.7129 acc=0.7733
epoch 30, train 91/100, loss=0.4948 acc=0.8800
epoch 30, train 92/100, loss=0.6693 acc=0.7733
epoch 30, train 93/100, loss=0.7408 acc=0.7733
epoch 30, train 94/100, loss=0.0216 acc=1.0000
epoch 30, train 95/100, loss=0.5323 acc=0.8533
epoch 30, train 96/100, loss=0.7559 acc=0.7733
epoch 30, train 97/100, loss=0.2514 acc=0.8933
epoch 30, train 98/100, loss=0.5757 acc=0.8267
epoch 30, train 99/100, loss=0.6435 acc=0.8000
epoch 30, train 100/100, loss=0.2238 acc=0.9600
best epoch 26, best val acc=0.7125
epoch 30, val, loss=0.9682 acc=0.7092
ETA:1.9h/6.4h
epoch 31, train 1/100, loss=0.7253 acc=0.6933
epoch 31, train 2/100, loss=0.1152 acc=0.9600
epoch 31, train 3/100, loss=1.7728 acc=0.6133
epoch 31, train 4/100, loss=0.8324 acc=0.7200
epoch 31, train 5/100, loss=0.4208 acc=0.8800
epoch 31, train 6/100, loss=1.9672 acc=0.4267
epoch 31, train 7/100, loss=0.9588 acc=0.7467
epoch 31, train 8/100, loss=0.4139 acc=0.8667
epoch 31, train 9/100, loss=0.9394 acc=0.7333
epoch 31, train 10/100, loss=0.8184 acc=0.7467
epoch 31, train 11/100, loss=0.5354 acc=0.7867
epoch 31, train 12/100, loss=0.4440 acc=0.8133
epoch 31, train 13/100, loss=0.2647 acc=0.9067
epoch 31, train 14/100, loss=0.3375 acc=0.8800
epoch 31, train 15/100, loss=1.4404 acc=0.5733
epoch 31, train 16/100, loss=0.6015 acc=0.7333
epoch 31, train 17/100, loss=0.4676 acc=0.8533
epoch 31, train 18/100, loss=0.0566 acc=1.0000
epoch 31, train 19/100, loss=0.4300 acc=0.8400
epoch 31, train 20/100, loss=0.1622 acc=0.9600
epoch 31, train 21/100, loss=0.3584 acc=0.9067
epoch 31, train 22/100, loss=0.5976 acc=0.7867
epoch 31, train 23/100, loss=0.6059 acc=0.7867
epoch 31, train 24/100, loss=0.8806 acc=0.6933
epoch 31, train 25/100, loss=0.3420 acc=0.9200
epoch 31, train 26/100, loss=0.3170 acc=0.9467
epoch 31, train 27/100, loss=1.4382 acc=0.6000
epoch 31, train 28/100, loss=0.2071 acc=0.9333
epoch 31, train 29/100, loss=0.5263 acc=0.8133
epoch 31, train 30/100, loss=0.6279 acc=0.7867
epoch 31, train 31/100, loss=0.5302 acc=0.8400
epoch 31, train 32/100, loss=0.1569 acc=0.9600
epoch 31, train 33/100, loss=0.7505 acc=0.7200
epoch 31, train 34/100, loss=1.1794 acc=0.7200
epoch 31, train 35/100, loss=0.3830 acc=0.8667
epoch 31, train 36/100, loss=0.5676 acc=0.8000
epoch 31, train 37/100, loss=1.1174 acc=0.6533
epoch 31, train 38/100, loss=0.5534 acc=0.7733
epoch 31, train 39/100, loss=0.5217 acc=0.8000
epoch 31, train 40/100, loss=0.7318 acc=0.7600
epoch 31, train 41/100, loss=1.2122 acc=0.6000
epoch 31, train 42/100, loss=0.6411 acc=0.7600
epoch 31, train 43/100, loss=0.9474 acc=0.6800
epoch 31, train 44/100, loss=0.7278 acc=0.7867
epoch 31, train 45/100, loss=0.3329 acc=0.8400
epoch 31, train 46/100, loss=0.4445 acc=0.8267
epoch 31, train 47/100, loss=0.1182 acc=0.9333
epoch 31, train 48/100, loss=0.1880 acc=0.9600
epoch 31, train 49/100, loss=0.3124 acc=0.8800
epoch 31, train 50/100, loss=0.3642 acc=0.8533
epoch 31, train 51/100, loss=0.9282 acc=0.7067
epoch 31, train 52/100, loss=0.6014 acc=0.8133
epoch 31, train 53/100, loss=1.0271 acc=0.7067
epoch 31, train 54/100, loss=0.3438 acc=0.9067
epoch 31, train 55/100, loss=0.1572 acc=0.9733
epoch 31, train 56/100, loss=0.3855 acc=0.8800
epoch 31, train 57/100, loss=0.2273 acc=0.8933
epoch 31, train 58/100, loss=0.3956 acc=0.8533
epoch 31, train 59/100, loss=0.4742 acc=0.8400
epoch 31, train 60/100, loss=0.1665 acc=0.9200
epoch 31, train 61/100, loss=0.3886 acc=0.8800
epoch 31, train 62/100, loss=0.4400 acc=0.8267
epoch 31, train 63/100, loss=0.5981 acc=0.8400
epoch 31, train 64/100, loss=0.8005 acc=0.7600
epoch 31, train 65/100, loss=0.9775 acc=0.6933
epoch 31, train 66/100, loss=0.6611 acc=0.7467
epoch 31, train 67/100, loss=0.6161 acc=0.8133
epoch 31, train 68/100, loss=0.5607 acc=0.7867
epoch 31, train 69/100, loss=0.9002 acc=0.6667
epoch 31, train 70/100, loss=0.8671 acc=0.7200
epoch 31, train 71/100, loss=0.2597 acc=0.9333
epoch 31, train 72/100, loss=1.8945 acc=0.4400
epoch 31, train 73/100, loss=0.2806 acc=0.9333
epoch 31, train 74/100, loss=0.4383 acc=0.8800
epoch 31, train 75/100, loss=0.1337 acc=0.9600
epoch 31, train 76/100, loss=0.4476 acc=0.8133
epoch 31, train 77/100, loss=0.5490 acc=0.8000
epoch 31, train 78/100, loss=0.3026 acc=0.8667
epoch 31, train 79/100, loss=0.7434 acc=0.6933
epoch 31, train 80/100, loss=0.4895 acc=0.8267
epoch 31, train 81/100, loss=0.9884 acc=0.6933
epoch 31, train 82/100, loss=0.9833 acc=0.6000
epoch 31, train 83/100, loss=0.4020 acc=0.8800
epoch 31, train 84/100, loss=0.2432 acc=0.8800
epoch 31, train 85/100, loss=0.3068 acc=0.9067
epoch 31, train 86/100, loss=0.6130 acc=0.7867
epoch 31, train 87/100, loss=0.5924 acc=0.7867
epoch 31, train 88/100, loss=0.3263 acc=0.9067
epoch 31, train 89/100, loss=0.6874 acc=0.8000
epoch 31, train 90/100, loss=0.7929 acc=0.7333
epoch 31, train 91/100, loss=0.6394 acc=0.8133
epoch 31, train 92/100, loss=0.3818 acc=0.8933
epoch 31, train 93/100, loss=0.3561 acc=0.8667
epoch 31, train 94/100, loss=0.5541 acc=0.8400
epoch 31, train 95/100, loss=0.1841 acc=0.9467
epoch 31, train 96/100, loss=0.4316 acc=0.8267
epoch 31, train 97/100, loss=0.8502 acc=0.7200
epoch 31, train 98/100, loss=0.5500 acc=0.8400
epoch 31, train 99/100, loss=1.1334 acc=0.6800
epoch 31, train 100/100, loss=0.3721 acc=0.8933
best epoch 26, best val acc=0.7125
epoch 31, val, loss=0.9934 acc=0.6991
ETA:2.0h/6.4h
epoch 32, train 1/100, loss=0.6907 acc=0.7467
epoch 32, train 2/100, loss=0.3980 acc=0.8533
epoch 32, train 3/100, loss=0.5407 acc=0.7733
epoch 32, train 4/100, loss=0.3779 acc=0.8800
epoch 32, train 5/100, loss=0.1580 acc=0.9467
epoch 32, train 6/100, loss=1.6542 acc=0.5867
epoch 32, train 7/100, loss=0.2289 acc=0.8800
epoch 32, train 8/100, loss=0.1166 acc=0.9467
epoch 32, train 9/100, loss=0.2112 acc=0.9067
epoch 32, train 10/100, loss=0.6846 acc=0.7867
epoch 32, train 11/100, loss=0.5457 acc=0.7733
epoch 32, train 12/100, loss=0.2802 acc=0.8933
epoch 32, train 13/100, loss=0.3907 acc=0.9067
epoch 32, train 14/100, loss=0.2198 acc=0.9067
epoch 32, train 15/100, loss=0.5029 acc=0.8400
epoch 32, train 16/100, loss=1.0870 acc=0.6933
epoch 32, train 17/100, loss=0.4095 acc=0.8800
epoch 32, train 18/100, loss=0.7804 acc=0.7600
epoch 32, train 19/100, loss=0.5179 acc=0.8133
epoch 32, train 20/100, loss=0.1198 acc=0.9467
epoch 32, train 21/100, loss=0.3467 acc=0.9067
epoch 32, train 22/100, loss=0.7152 acc=0.8000
epoch 32, train 23/100, loss=0.3572 acc=0.8533
epoch 32, train 24/100, loss=0.1615 acc=0.9333
epoch 32, train 25/100, loss=0.4032 acc=0.8400
epoch 32, train 26/100, loss=0.4685 acc=0.8800
epoch 32, train 27/100, loss=0.9658 acc=0.6667
epoch 32, train 28/100, loss=1.0761 acc=0.7333
epoch 32, train 29/100, loss=0.5187 acc=0.8267
epoch 32, train 30/100, loss=2.0074 acc=0.4133
epoch 32, train 31/100, loss=0.7835 acc=0.7333
epoch 32, train 32/100, loss=0.7918 acc=0.7733
epoch 32, train 33/100, loss=0.4694 acc=0.8933
epoch 32, train 34/100, loss=0.8969 acc=0.6933
epoch 32, train 35/100, loss=0.2022 acc=0.9067
epoch 32, train 36/100, loss=0.5079 acc=0.7733
epoch 32, train 37/100, loss=0.8680 acc=0.7067
epoch 32, train 38/100, loss=0.8263 acc=0.7333
epoch 32, train 39/100, loss=0.1841 acc=0.9333
epoch 32, train 40/100, loss=0.2878 acc=0.9067
epoch 32, train 41/100, loss=0.5532 acc=0.8133
epoch 32, train 42/100, loss=0.6626 acc=0.7867
epoch 32, train 43/100, loss=0.3684 acc=0.8933
epoch 32, train 44/100, loss=0.1774 acc=0.9067
epoch 32, train 45/100, loss=0.4055 acc=0.8533
epoch 32, train 46/100, loss=0.1478 acc=0.9200
epoch 32, train 47/100, loss=0.4716 acc=0.8000
epoch 32, train 48/100, loss=0.1780 acc=0.9467
epoch 32, train 49/100, loss=0.2809 acc=0.9200
epoch 32, train 50/100, loss=1.0382 acc=0.6133
epoch 32, train 51/100, loss=0.3314 acc=0.8667
epoch 32, train 52/100, loss=0.3461 acc=0.8667
epoch 32, train 53/100, loss=0.6397 acc=0.7733
epoch 32, train 54/100, loss=0.5310 acc=0.8267
epoch 32, train 55/100, loss=0.3407 acc=0.9200
epoch 32, train 56/100, loss=0.4187 acc=0.8000
epoch 32, train 57/100, loss=0.1937 acc=0.9467
epoch 32, train 58/100, loss=0.7861 acc=0.7467
epoch 32, train 59/100, loss=0.0862 acc=0.9600
epoch 32, train 60/100, loss=0.4743 acc=0.8933
epoch 32, train 61/100, loss=0.4111 acc=0.8667
epoch 32, train 62/100, loss=0.3110 acc=0.9333
epoch 32, train 63/100, loss=1.5281 acc=0.6000
epoch 32, train 64/100, loss=0.6805 acc=0.8533
epoch 32, train 65/100, loss=0.6403 acc=0.7333
epoch 32, train 66/100, loss=0.3569 acc=0.8533
epoch 32, train 67/100, loss=0.5110 acc=0.8400
epoch 32, train 68/100, loss=0.9669 acc=0.7467
epoch 32, train 69/100, loss=0.1864 acc=0.9733
epoch 32, train 70/100, loss=0.2065 acc=0.9067
epoch 32, train 71/100, loss=0.2596 acc=0.8933
epoch 32, train 72/100, loss=0.7445 acc=0.8133
epoch 32, train 73/100, loss=0.2042 acc=0.9200
epoch 32, train 74/100, loss=0.0519 acc=0.9867
epoch 32, train 75/100, loss=0.2189 acc=0.9067
epoch 32, train 76/100, loss=0.8947 acc=0.6533
epoch 32, train 77/100, loss=0.2407 acc=0.9067
epoch 32, train 78/100, loss=0.4785 acc=0.8800
epoch 32, train 79/100, loss=0.6973 acc=0.7467
epoch 32, train 80/100, loss=1.1180 acc=0.7467
epoch 32, train 81/100, loss=1.0010 acc=0.6667
epoch 32, train 82/100, loss=0.4082 acc=0.8533
epoch 32, train 83/100, loss=0.3866 acc=0.8533
epoch 32, train 84/100, loss=0.8499 acc=0.7333
epoch 32, train 85/100, loss=1.3882 acc=0.6133
epoch 32, train 86/100, loss=0.9307 acc=0.6000
epoch 32, train 87/100, loss=0.1163 acc=0.9600
epoch 32, train 88/100, loss=0.6589 acc=0.7333
epoch 32, train 89/100, loss=1.0158 acc=0.7200
epoch 32, train 90/100, loss=0.0144 acc=1.0000
epoch 32, train 91/100, loss=0.2291 acc=0.8933
epoch 32, train 92/100, loss=0.5999 acc=0.7733
epoch 32, train 93/100, loss=0.7332 acc=0.7067
epoch 32, train 94/100, loss=0.7972 acc=0.7333
epoch 32, train 95/100, loss=0.7744 acc=0.7200
epoch 32, train 96/100, loss=0.0957 acc=0.9867
epoch 32, train 97/100, loss=0.9093 acc=0.6667
epoch 32, train 98/100, loss=0.5626 acc=0.8533
epoch 32, train 99/100, loss=0.3852 acc=0.8667
epoch 32, train 100/100, loss=0.3214 acc=0.8667
best epoch 26, best val acc=0.7125
epoch 32, val, loss=0.9313 acc=0.7114
ETA:2.1h/6.4h
epoch 33, train 1/100, loss=0.6764 acc=0.7867
epoch 33, train 2/100, loss=0.2208 acc=0.9200
epoch 33, train 3/100, loss=0.3578 acc=0.8400
epoch 33, train 4/100, loss=0.5143 acc=0.8400
epoch 33, train 5/100, loss=0.8857 acc=0.7600
epoch 33, train 6/100, loss=0.3776 acc=0.8533
epoch 33, train 7/100, loss=0.9187 acc=0.7600
epoch 33, train 8/100, loss=0.3778 acc=0.8933
epoch 33, train 9/100, loss=0.7874 acc=0.7200
epoch 33, train 10/100, loss=0.5924 acc=0.8000
epoch 33, train 11/100, loss=0.3995 acc=0.8533
epoch 33, train 12/100, loss=0.6494 acc=0.7600
epoch 33, train 13/100, loss=0.2801 acc=0.9200
epoch 33, train 14/100, loss=0.2151 acc=0.9200
epoch 33, train 15/100, loss=0.2092 acc=0.9200
epoch 33, train 16/100, loss=0.5915 acc=0.8267
epoch 33, train 17/100, loss=0.4376 acc=0.8533
epoch 33, train 18/100, loss=0.4414 acc=0.8267
epoch 33, train 19/100, loss=0.8135 acc=0.7333
epoch 33, train 20/100, loss=0.5711 acc=0.8400
epoch 33, train 21/100, loss=0.2840 acc=0.9200
epoch 33, train 22/100, loss=0.3681 acc=0.8267
epoch 33, train 23/100, loss=1.0470 acc=0.6533
epoch 33, train 24/100, loss=0.7305 acc=0.8000
epoch 33, train 25/100, loss=0.2756 acc=0.9333
epoch 33, train 26/100, loss=0.5132 acc=0.8533
epoch 33, train 27/100, loss=0.8229 acc=0.7333
epoch 33, train 28/100, loss=0.3160 acc=0.8533
epoch 33, train 29/100, loss=0.4515 acc=0.8400
epoch 33, train 30/100, loss=0.6761 acc=0.7600
epoch 33, train 31/100, loss=0.2827 acc=0.9067
epoch 33, train 32/100, loss=0.8615 acc=0.6667
epoch 33, train 33/100, loss=0.2359 acc=0.9067
epoch 33, train 34/100, loss=0.1834 acc=0.9333
epoch 33, train 35/100, loss=0.6751 acc=0.7733
epoch 33, train 36/100, loss=0.5945 acc=0.7600
epoch 33, train 37/100, loss=0.6911 acc=0.8267
epoch 33, train 38/100, loss=0.3213 acc=0.8667
epoch 33, train 39/100, loss=0.1376 acc=0.9467
epoch 33, train 40/100, loss=0.3126 acc=0.9067
epoch 33, train 41/100, loss=0.7255 acc=0.7733
epoch 33, train 42/100, loss=0.3410 acc=0.9067
epoch 33, train 43/100, loss=0.3518 acc=0.8800
epoch 33, train 44/100, loss=0.2376 acc=0.9333
epoch 33, train 45/100, loss=1.0487 acc=0.6267
epoch 33, train 46/100, loss=0.3037 acc=0.9067
epoch 33, train 47/100, loss=0.5670 acc=0.8267
epoch 33, train 48/100, loss=0.3916 acc=0.8800
epoch 33, train 49/100, loss=0.5570 acc=0.8133
epoch 33, train 50/100, loss=0.9119 acc=0.6800
epoch 33, train 51/100, loss=0.7582 acc=0.7600
epoch 33, train 52/100, loss=0.4477 acc=0.8267
epoch 33, train 53/100, loss=0.5178 acc=0.8267
epoch 33, train 54/100, loss=0.7904 acc=0.6933
epoch 33, train 55/100, loss=0.9954 acc=0.7200
epoch 33, train 56/100, loss=1.3321 acc=0.5867
epoch 33, train 57/100, loss=0.5470 acc=0.8133
epoch 33, train 58/100, loss=0.4925 acc=0.8133
epoch 33, train 59/100, loss=0.5987 acc=0.8000
epoch 33, train 60/100, loss=0.5287 acc=0.8400
epoch 33, train 61/100, loss=0.3102 acc=0.8933
epoch 33, train 62/100, loss=1.0127 acc=0.6400
epoch 33, train 63/100, loss=0.4432 acc=0.8400
epoch 33, train 64/100, loss=0.4600 acc=0.8133
epoch 33, train 65/100, loss=0.3462 acc=0.8667
epoch 33, train 66/100, loss=0.5786 acc=0.8400
epoch 33, train 67/100, loss=0.1493 acc=0.9200
epoch 33, train 68/100, loss=0.2091 acc=0.9333
epoch 33, train 69/100, loss=1.1283 acc=0.7333
epoch 33, train 70/100, loss=0.7383 acc=0.7467
epoch 33, train 71/100, loss=0.5208 acc=0.8800
epoch 33, train 72/100, loss=0.2876 acc=0.8667
epoch 33, train 73/100, loss=0.5606 acc=0.7867
epoch 33, train 74/100, loss=1.3145 acc=0.5467
epoch 33, train 75/100, loss=0.5724 acc=0.8267
epoch 33, train 76/100, loss=0.3476 acc=0.8667
epoch 33, train 77/100, loss=0.2253 acc=0.9200
epoch 33, train 78/100, loss=0.2925 acc=0.9200
epoch 33, train 79/100, loss=0.7426 acc=0.7867
epoch 33, train 80/100, loss=0.5467 acc=0.8133
epoch 33, train 81/100, loss=0.2354 acc=0.9333
epoch 33, train 82/100, loss=0.2732 acc=0.9333
epoch 33, train 83/100, loss=0.2718 acc=0.8933
epoch 33, train 84/100, loss=0.6045 acc=0.7867
epoch 33, train 85/100, loss=0.4253 acc=0.8533
epoch 33, train 86/100, loss=0.6012 acc=0.7733
epoch 33, train 87/100, loss=0.5212 acc=0.8000
epoch 33, train 88/100, loss=0.4367 acc=0.8800
epoch 33, train 89/100, loss=0.1175 acc=0.9733
epoch 33, train 90/100, loss=0.3002 acc=0.8800
epoch 33, train 91/100, loss=0.7244 acc=0.7467
epoch 33, train 92/100, loss=1.5020 acc=0.5600
epoch 33, train 93/100, loss=0.6288 acc=0.8400
epoch 33, train 94/100, loss=0.2658 acc=0.9200
epoch 33, train 95/100, loss=0.2014 acc=0.9067
epoch 33, train 96/100, loss=0.5551 acc=0.8800
epoch 33, train 97/100, loss=0.6641 acc=0.7733
epoch 33, train 98/100, loss=0.3132 acc=0.9200
epoch 33, train 99/100, loss=0.6678 acc=0.7733
epoch 33, train 100/100, loss=1.1057 acc=0.7067
best epoch 26, best val acc=0.7125
epoch 33, val, loss=0.9664 acc=0.7069
ETA:2.1h/6.4h
epoch 34, train 1/100, loss=0.8200 acc=0.7467
epoch 34, train 2/100, loss=0.5593 acc=0.7867
epoch 34, train 3/100, loss=0.5442 acc=0.8667
epoch 34, train 4/100, loss=0.4172 acc=0.8933
epoch 34, train 5/100, loss=1.0449 acc=0.7467
epoch 34, train 6/100, loss=0.2440 acc=0.9200
epoch 34, train 7/100, loss=0.8638 acc=0.7333
epoch 34, train 8/100, loss=0.4624 acc=0.8667
epoch 34, train 9/100, loss=1.1086 acc=0.6667
epoch 34, train 10/100, loss=1.6263 acc=0.5200
epoch 34, train 11/100, loss=1.0386 acc=0.6667
epoch 34, train 12/100, loss=0.5917 acc=0.7867
epoch 34, train 13/100, loss=0.3779 acc=0.8267
epoch 34, train 14/100, loss=0.5827 acc=0.8400
epoch 34, train 15/100, loss=1.4836 acc=0.6533
epoch 34, train 16/100, loss=1.2597 acc=0.6267
epoch 34, train 17/100, loss=0.2264 acc=0.9200
epoch 34, train 18/100, loss=0.8601 acc=0.6667
epoch 34, train 19/100, loss=0.7411 acc=0.7200
epoch 34, train 20/100, loss=0.2638 acc=0.9200
epoch 34, train 21/100, loss=0.6498 acc=0.8000
epoch 34, train 22/100, loss=0.0336 acc=0.9867
epoch 34, train 23/100, loss=0.6593 acc=0.7067
epoch 34, train 24/100, loss=1.0384 acc=0.7333
epoch 34, train 25/100, loss=0.2545 acc=0.9200
epoch 34, train 26/100, loss=0.0110 acc=1.0000
epoch 34, train 27/100, loss=0.1633 acc=0.9333
epoch 34, train 28/100, loss=0.5691 acc=0.8133
epoch 34, train 29/100, loss=0.3608 acc=0.8933
epoch 34, train 30/100, loss=0.8236 acc=0.7467
epoch 34, train 31/100, loss=0.8486 acc=0.7333
epoch 34, train 32/100, loss=0.4017 acc=0.8533
epoch 34, train 33/100, loss=1.0818 acc=0.7867
epoch 34, train 34/100, loss=0.8307 acc=0.6667
epoch 34, train 35/100, loss=0.3506 acc=0.9067
epoch 34, train 36/100, loss=0.5550 acc=0.8267
epoch 34, train 37/100, loss=0.1684 acc=0.9467
epoch 34, train 38/100, loss=0.6973 acc=0.7867
epoch 34, train 39/100, loss=0.9920 acc=0.7067
epoch 34, train 40/100, loss=0.8888 acc=0.7467
epoch 34, train 41/100, loss=0.4419 acc=0.8533
epoch 34, train 42/100, loss=0.6419 acc=0.8000
epoch 34, train 43/100, loss=0.9754 acc=0.6933
epoch 34, train 44/100, loss=0.3627 acc=0.8667
epoch 34, train 45/100, loss=0.5497 acc=0.8000
epoch 34, train 46/100, loss=1.0386 acc=0.7200
epoch 34, train 47/100, loss=0.3954 acc=0.8267
epoch 34, train 48/100, loss=0.2549 acc=0.9067
epoch 34, train 49/100, loss=0.6272 acc=0.7200
epoch 34, train 50/100, loss=0.4232 acc=0.8133
epoch 34, train 51/100, loss=0.4097 acc=0.9067
epoch 34, train 52/100, loss=0.4512 acc=0.8267
epoch 34, train 53/100, loss=0.7371 acc=0.7200
epoch 34, train 54/100, loss=0.4856 acc=0.7867
epoch 34, train 55/100, loss=0.5282 acc=0.7867
epoch 34, train 56/100, loss=0.5269 acc=0.8000
epoch 34, train 57/100, loss=0.8091 acc=0.7067
epoch 34, train 58/100, loss=0.3510 acc=0.8933
epoch 34, train 59/100, loss=0.7534 acc=0.7600
epoch 34, train 60/100, loss=0.8301 acc=0.7333
epoch 34, train 61/100, loss=0.1031 acc=0.9467
epoch 34, train 62/100, loss=0.4156 acc=0.8667
epoch 34, train 63/100, loss=0.7682 acc=0.7733
epoch 34, train 64/100, loss=0.6245 acc=0.8267
epoch 34, train 65/100, loss=0.5234 acc=0.8400
epoch 34, train 66/100, loss=0.7054 acc=0.7467
epoch 34, train 67/100, loss=0.2224 acc=0.9333
epoch 34, train 68/100, loss=0.3474 acc=0.9067
epoch 34, train 69/100, loss=0.1881 acc=0.9067
epoch 34, train 70/100, loss=0.6303 acc=0.8400
epoch 34, train 71/100, loss=0.1805 acc=0.9467
epoch 34, train 72/100, loss=0.5396 acc=0.8667
epoch 34, train 73/100, loss=0.2722 acc=0.9200
epoch 34, train 74/100, loss=0.4545 acc=0.8933
epoch 34, train 75/100, loss=0.5833 acc=0.8400
epoch 34, train 76/100, loss=0.7632 acc=0.7467
epoch 34, train 77/100, loss=1.5105 acc=0.5200
epoch 34, train 78/100, loss=0.8892 acc=0.7333
epoch 34, train 79/100, loss=0.8348 acc=0.6800
epoch 34, train 80/100, loss=0.1444 acc=0.9600
epoch 34, train 81/100, loss=0.5088 acc=0.8667
epoch 34, train 82/100, loss=0.2229 acc=0.8800
epoch 34, train 83/100, loss=0.5069 acc=0.8000
epoch 34, train 84/100, loss=1.0249 acc=0.6533
epoch 34, train 85/100, loss=0.4646 acc=0.8800
epoch 34, train 86/100, loss=0.4603 acc=0.8400
epoch 34, train 87/100, loss=1.2342 acc=0.6267
epoch 34, train 88/100, loss=0.0554 acc=0.9733
epoch 34, train 89/100, loss=0.8004 acc=0.7600
epoch 34, train 90/100, loss=0.9285 acc=0.7867
epoch 34, train 91/100, loss=1.2808 acc=0.6400
epoch 34, train 92/100, loss=0.8015 acc=0.7200
epoch 34, train 93/100, loss=0.8482 acc=0.7067
epoch 34, train 94/100, loss=1.7165 acc=0.5733
epoch 34, train 95/100, loss=0.6592 acc=0.7600
epoch 34, train 96/100, loss=0.9465 acc=0.5867
epoch 34, train 97/100, loss=0.3315 acc=0.8667
epoch 34, train 98/100, loss=0.5092 acc=0.8267
epoch 34, train 99/100, loss=0.8076 acc=0.6533
epoch 34, train 100/100, loss=0.6864 acc=0.8000
best epoch 26, best val acc=0.7125
epoch 34, val, loss=0.9710 acc=0.7049
ETA:2.2h/6.4h
epoch 35, train 1/100, loss=0.5684 acc=0.8000
epoch 35, train 2/100, loss=0.4436 acc=0.7733
epoch 35, train 3/100, loss=0.6904 acc=0.7733
epoch 35, train 4/100, loss=1.0091 acc=0.6267
epoch 35, train 5/100, loss=0.8937 acc=0.7067
epoch 35, train 6/100, loss=0.3455 acc=0.8533
epoch 35, train 7/100, loss=0.4192 acc=0.8800
epoch 35, train 8/100, loss=0.4343 acc=0.8000
epoch 35, train 9/100, loss=0.5735 acc=0.7733
epoch 35, train 10/100, loss=0.2630 acc=0.9200
epoch 35, train 11/100, loss=1.0746 acc=0.6000
epoch 35, train 12/100, loss=0.8079 acc=0.7733
epoch 35, train 13/100, loss=1.2857 acc=0.6267
epoch 35, train 14/100, loss=0.2535 acc=0.9200
epoch 35, train 15/100, loss=0.3715 acc=0.8800
epoch 35, train 16/100, loss=0.7800 acc=0.7467
epoch 35, train 17/100, loss=0.5773 acc=0.7867
epoch 35, train 18/100, loss=0.4290 acc=0.8400
epoch 35, train 19/100, loss=0.1908 acc=0.9333
epoch 35, train 20/100, loss=0.4642 acc=0.8533
epoch 35, train 21/100, loss=0.8737 acc=0.7467
epoch 35, train 22/100, loss=0.7882 acc=0.7333
epoch 35, train 23/100, loss=1.2232 acc=0.5600
epoch 35, train 24/100, loss=0.5580 acc=0.7867
epoch 35, train 25/100, loss=0.5881 acc=0.7867
epoch 35, train 26/100, loss=0.3511 acc=0.8800
epoch 35, train 27/100, loss=0.5292 acc=0.8267
epoch 35, train 28/100, loss=1.0575 acc=0.6800
epoch 35, train 29/100, loss=0.2816 acc=0.8800
epoch 35, train 30/100, loss=0.9854 acc=0.6533
epoch 35, train 31/100, loss=0.5612 acc=0.7600
epoch 35, train 32/100, loss=0.8027 acc=0.7067
epoch 35, train 33/100, loss=0.4010 acc=0.8267
epoch 35, train 34/100, loss=0.6455 acc=0.7733
epoch 35, train 35/100, loss=0.2095 acc=0.9067
epoch 35, train 36/100, loss=0.2006 acc=0.9333
epoch 35, train 37/100, loss=1.0754 acc=0.6667
epoch 35, train 38/100, loss=0.2996 acc=0.8933
epoch 35, train 39/100, loss=0.8738 acc=0.7067
epoch 35, train 40/100, loss=0.4588 acc=0.8667
epoch 35, train 41/100, loss=0.6158 acc=0.7867
epoch 35, train 42/100, loss=0.7598 acc=0.7467
epoch 35, train 43/100, loss=0.0743 acc=0.9733
epoch 35, train 44/100, loss=0.5932 acc=0.8133
epoch 35, train 45/100, loss=0.2168 acc=0.9333
epoch 35, train 46/100, loss=0.6407 acc=0.7467
epoch 35, train 47/100, loss=0.5046 acc=0.8800
epoch 35, train 48/100, loss=0.2844 acc=0.9200
epoch 35, train 49/100, loss=0.6882 acc=0.7067
epoch 35, train 50/100, loss=0.1663 acc=0.9200
epoch 35, train 51/100, loss=0.3992 acc=0.9333
epoch 35, train 52/100, loss=0.3014 acc=0.9200
epoch 35, train 53/100, loss=0.9118 acc=0.6533
epoch 35, train 54/100, loss=0.6599 acc=0.8000
epoch 35, train 55/100, loss=0.5578 acc=0.8267
epoch 35, train 56/100, loss=0.3190 acc=0.8933
epoch 35, train 57/100, loss=0.3573 acc=0.8533
epoch 35, train 58/100, loss=0.4985 acc=0.8267
epoch 35, train 59/100, loss=0.1081 acc=0.9733
epoch 35, train 60/100, loss=0.3311 acc=0.8933
epoch 35, train 61/100, loss=0.2912 acc=0.8667
epoch 35, train 62/100, loss=0.3656 acc=0.8400
epoch 35, train 63/100, loss=1.0533 acc=0.6800
epoch 35, train 64/100, loss=0.4539 acc=0.8267
epoch 35, train 65/100, loss=0.5658 acc=0.8133
epoch 35, train 66/100, loss=1.0962 acc=0.6267
epoch 35, train 67/100, loss=0.5788 acc=0.7600
epoch 35, train 68/100, loss=0.4577 acc=0.8267
epoch 35, train 69/100, loss=0.3135 acc=0.8800
epoch 35, train 70/100, loss=0.8932 acc=0.7467
epoch 35, train 71/100, loss=0.5330 acc=0.7600
epoch 35, train 72/100, loss=0.2738 acc=0.8933
epoch 35, train 73/100, loss=0.9491 acc=0.6667
epoch 35, train 74/100, loss=0.9815 acc=0.6933
epoch 35, train 75/100, loss=0.1661 acc=0.9467
epoch 35, train 76/100, loss=1.3413 acc=0.7067
epoch 35, train 77/100, loss=1.0001 acc=0.7333
epoch 35, train 78/100, loss=0.5528 acc=0.7867
epoch 35, train 79/100, loss=0.3104 acc=0.8933
epoch 35, train 80/100, loss=0.2767 acc=0.8800
epoch 35, train 81/100, loss=0.6894 acc=0.7600
epoch 35, train 82/100, loss=0.6959 acc=0.7067
epoch 35, train 83/100, loss=0.3408 acc=0.8933
epoch 35, train 84/100, loss=0.6375 acc=0.7600
epoch 35, train 85/100, loss=0.7802 acc=0.7600
epoch 35, train 86/100, loss=0.6468 acc=0.7733
epoch 35, train 87/100, loss=0.2461 acc=0.9467
epoch 35, train 88/100, loss=0.5034 acc=0.8800
epoch 35, train 89/100, loss=0.2788 acc=0.8800
epoch 35, train 90/100, loss=0.7202 acc=0.7867
epoch 35, train 91/100, loss=0.3660 acc=0.8800
epoch 35, train 92/100, loss=0.4280 acc=0.8400
epoch 35, train 93/100, loss=0.6273 acc=0.8667
epoch 35, train 94/100, loss=0.2870 acc=0.8933
epoch 35, train 95/100, loss=0.8949 acc=0.6533
epoch 35, train 96/100, loss=0.4090 acc=0.8533
epoch 35, train 97/100, loss=0.8691 acc=0.6933
epoch 35, train 98/100, loss=0.6222 acc=0.8267
epoch 35, train 99/100, loss=0.4129 acc=0.8133
epoch 35, train 100/100, loss=0.4762 acc=0.7867
best epoch 26, best val acc=0.7125
epoch 35, val, loss=0.9548 acc=0.7087
ETA:2.2h/6.4h
epoch 36, train 1/100, loss=0.2573 acc=0.8933
epoch 36, train 2/100, loss=0.5567 acc=0.8533
epoch 36, train 3/100, loss=0.6606 acc=0.8133
epoch 36, train 4/100, loss=0.3227 acc=0.8800
epoch 36, train 5/100, loss=0.4425 acc=0.8533
epoch 36, train 6/100, loss=0.5583 acc=0.8400
epoch 36, train 7/100, loss=0.7711 acc=0.7467
epoch 36, train 8/100, loss=0.3188 acc=0.8667
epoch 36, train 9/100, loss=0.2839 acc=0.8800
epoch 36, train 10/100, loss=0.5487 acc=0.8267
epoch 36, train 11/100, loss=0.0702 acc=0.9867
epoch 36, train 12/100, loss=1.0722 acc=0.6400
epoch 36, train 13/100, loss=0.2488 acc=0.8933
epoch 36, train 14/100, loss=0.3892 acc=0.8267
epoch 36, train 15/100, loss=1.0078 acc=0.7067
epoch 36, train 16/100, loss=0.5597 acc=0.8133
epoch 36, train 17/100, loss=1.1075 acc=0.6400
epoch 36, train 18/100, loss=0.6489 acc=0.7600
epoch 36, train 19/100, loss=0.6136 acc=0.8133
epoch 36, train 20/100, loss=1.1270 acc=0.6267
epoch 36, train 21/100, loss=0.2242 acc=0.9467
epoch 36, train 22/100, loss=0.5178 acc=0.7867
epoch 36, train 23/100, loss=0.2800 acc=0.9200
epoch 36, train 24/100, loss=0.5477 acc=0.7600
epoch 36, train 25/100, loss=0.7026 acc=0.7867
epoch 36, train 26/100, loss=0.4781 acc=0.8267
epoch 36, train 27/100, loss=1.0588 acc=0.6667
epoch 36, train 28/100, loss=0.3846 acc=0.8933
epoch 36, train 29/100, loss=0.4108 acc=0.8800
epoch 36, train 30/100, loss=1.3883 acc=0.5867
epoch 36, train 31/100, loss=0.4258 acc=0.8933
epoch 36, train 32/100, loss=0.4688 acc=0.8533
epoch 36, train 33/100, loss=0.1258 acc=0.9600
epoch 36, train 34/100, loss=0.4622 acc=0.8800
epoch 36, train 35/100, loss=0.2965 acc=0.9067
epoch 36, train 36/100, loss=0.3503 acc=0.9067
epoch 36, train 37/100, loss=0.0734 acc=0.9600
epoch 36, train 38/100, loss=0.6613 acc=0.8133
epoch 36, train 39/100, loss=0.4236 acc=0.8400
epoch 36, train 40/100, loss=0.3007 acc=0.9067
epoch 36, train 41/100, loss=0.4610 acc=0.8267
epoch 36, train 42/100, loss=0.3126 acc=0.9067
epoch 36, train 43/100, loss=0.3829 acc=0.8933
epoch 36, train 44/100, loss=0.8209 acc=0.7200
epoch 36, train 45/100, loss=0.4562 acc=0.8400
epoch 36, train 46/100, loss=0.4411 acc=0.8667
epoch 36, train 47/100, loss=0.4062 acc=0.8533
epoch 36, train 48/100, loss=0.2110 acc=0.9200
epoch 36, train 49/100, loss=0.5579 acc=0.7600
epoch 36, train 50/100, loss=0.3560 acc=0.8533
epoch 36, train 51/100, loss=0.3814 acc=0.8533
epoch 36, train 52/100, loss=0.3126 acc=0.9067
epoch 36, train 53/100, loss=0.9068 acc=0.7600
epoch 36, train 54/100, loss=0.5582 acc=0.8133
epoch 36, train 55/100, loss=0.7393 acc=0.7600
epoch 36, train 56/100, loss=0.4243 acc=0.8267
epoch 36, train 57/100, loss=1.0185 acc=0.7200
epoch 36, train 58/100, loss=0.8839 acc=0.7467
epoch 36, train 59/100, loss=0.8155 acc=0.7467
epoch 36, train 60/100, loss=0.7507 acc=0.8000
epoch 36, train 61/100, loss=0.1697 acc=0.9467
epoch 36, train 62/100, loss=0.6331 acc=0.7600
epoch 36, train 63/100, loss=0.3931 acc=0.8800
epoch 36, train 64/100, loss=0.3614 acc=0.8800
epoch 36, train 65/100, loss=0.2582 acc=0.9200
epoch 36, train 66/100, loss=0.2947 acc=0.8933
epoch 36, train 67/100, loss=1.2075 acc=0.6933
epoch 36, train 68/100, loss=1.1746 acc=0.6933
epoch 36, train 69/100, loss=0.4155 acc=0.8800
epoch 36, train 70/100, loss=0.6120 acc=0.7600
epoch 36, train 71/100, loss=0.8617 acc=0.7867
epoch 36, train 72/100, loss=0.6507 acc=0.7600
epoch 36, train 73/100, loss=0.4093 acc=0.8667
epoch 36, train 74/100, loss=0.6838 acc=0.7200
epoch 36, train 75/100, loss=0.1678 acc=0.9333
epoch 36, train 76/100, loss=0.5252 acc=0.8000
epoch 36, train 77/100, loss=0.4688 acc=0.8267
epoch 36, train 78/100, loss=0.5625 acc=0.8400
epoch 36, train 79/100, loss=0.5357 acc=0.8133
epoch 36, train 80/100, loss=0.3871 acc=0.9067
epoch 36, train 81/100, loss=0.2560 acc=0.9067
epoch 36, train 82/100, loss=0.3483 acc=0.8933
epoch 36, train 83/100, loss=0.5619 acc=0.8000
epoch 36, train 84/100, loss=0.4734 acc=0.8533
epoch 36, train 85/100, loss=0.5228 acc=0.8000
epoch 36, train 86/100, loss=0.6999 acc=0.7200
epoch 36, train 87/100, loss=0.7995 acc=0.7733
epoch 36, train 88/100, loss=1.1382 acc=0.6667
epoch 36, train 89/100, loss=0.5966 acc=0.8267
epoch 36, train 90/100, loss=0.3234 acc=0.8533
epoch 36, train 91/100, loss=0.2709 acc=0.9200
epoch 36, train 92/100, loss=0.3457 acc=0.8533
epoch 36, train 93/100, loss=0.7277 acc=0.7467
epoch 36, train 94/100, loss=0.4167 acc=0.8533
epoch 36, train 95/100, loss=0.3510 acc=0.8933
epoch 36, train 96/100, loss=0.5082 acc=0.8000
epoch 36, train 97/100, loss=0.8729 acc=0.7333
epoch 36, train 98/100, loss=0.5189 acc=0.8400
epoch 36, train 99/100, loss=0.3720 acc=0.8800
epoch 36, train 100/100, loss=0.6656 acc=0.7733
best epoch 26, best val acc=0.7125
epoch 36, val, loss=0.9599 acc=0.7108
ETA:2.3h/6.4h
epoch 37, train 1/100, loss=1.0615 acc=0.6533
epoch 37, train 2/100, loss=0.7066 acc=0.7733
epoch 37, train 3/100, loss=1.0584 acc=0.6133
epoch 37, train 4/100, loss=0.3872 acc=0.8533
epoch 37, train 5/100, loss=0.2561 acc=0.8800
epoch 37, train 6/100, loss=0.1842 acc=0.9467
epoch 37, train 7/100, loss=0.8398 acc=0.7200
epoch 37, train 8/100, loss=0.7851 acc=0.7333
epoch 37, train 9/100, loss=0.5372 acc=0.8000
epoch 37, train 10/100, loss=0.6540 acc=0.7333
epoch 37, train 11/100, loss=0.3386 acc=0.9067
epoch 37, train 12/100, loss=0.3191 acc=0.9067
epoch 37, train 13/100, loss=1.1951 acc=0.6267
epoch 37, train 14/100, loss=0.3161 acc=0.8933
epoch 37, train 15/100, loss=0.4324 acc=0.8267
epoch 37, train 16/100, loss=0.3491 acc=0.9067
epoch 37, train 17/100, loss=0.0963 acc=0.9733
epoch 37, train 18/100, loss=2.1617 acc=0.4800
epoch 37, train 19/100, loss=0.5214 acc=0.8267
epoch 37, train 20/100, loss=0.9394 acc=0.6800
epoch 37, train 21/100, loss=0.7844 acc=0.7467
epoch 37, train 22/100, loss=0.6757 acc=0.8267
epoch 37, train 23/100, loss=0.2000 acc=0.9467
epoch 37, train 24/100, loss=0.6267 acc=0.7467
epoch 37, train 25/100, loss=0.2493 acc=0.8933
epoch 37, train 26/100, loss=1.1338 acc=0.6533
epoch 37, train 27/100, loss=0.2589 acc=0.9200
epoch 37, train 28/100, loss=0.3741 acc=0.8533
epoch 37, train 29/100, loss=1.1626 acc=0.6000
epoch 37, train 30/100, loss=0.3108 acc=0.8800
epoch 37, train 31/100, loss=0.2014 acc=0.9200
epoch 37, train 32/100, loss=1.0927 acc=0.6800
epoch 37, train 33/100, loss=0.4243 acc=0.8533
epoch 37, train 34/100, loss=0.8668 acc=0.7600
epoch 37, train 35/100, loss=1.3090 acc=0.5333
epoch 37, train 36/100, loss=0.3854 acc=0.8933
epoch 37, train 37/100, loss=0.3809 acc=0.8133
epoch 37, train 38/100, loss=0.4449 acc=0.8267
epoch 37, train 39/100, loss=0.2504 acc=0.9067
epoch 37, train 40/100, loss=0.7468 acc=0.7067
epoch 37, train 41/100, loss=0.3271 acc=0.8933
epoch 37, train 42/100, loss=0.4481 acc=0.8800
epoch 37, train 43/100, loss=0.6131 acc=0.8000
epoch 37, train 44/100, loss=0.2880 acc=0.8800
epoch 37, train 45/100, loss=0.6638 acc=0.8000
epoch 37, train 46/100, loss=0.6593 acc=0.8000
epoch 37, train 47/100, loss=0.3499 acc=0.8933
epoch 37, train 48/100, loss=0.6987 acc=0.8000
epoch 37, train 49/100, loss=0.2533 acc=0.9333
epoch 37, train 50/100, loss=0.9501 acc=0.6933
epoch 37, train 51/100, loss=0.1998 acc=0.9067
epoch 37, train 52/100, loss=0.4805 acc=0.8133
epoch 37, train 53/100, loss=0.2766 acc=0.8400
epoch 37, train 54/100, loss=0.3131 acc=0.9067
epoch 37, train 55/100, loss=0.3877 acc=0.8400
epoch 37, train 56/100, loss=0.1911 acc=0.9200
epoch 37, train 57/100, loss=0.4113 acc=0.8400
epoch 37, train 58/100, loss=0.7667 acc=0.7467
epoch 37, train 59/100, loss=0.8354 acc=0.6933
epoch 37, train 60/100, loss=0.8135 acc=0.7733
epoch 37, train 61/100, loss=0.1294 acc=0.9600
epoch 37, train 62/100, loss=0.0977 acc=0.9600
epoch 37, train 63/100, loss=0.2967 acc=0.9067
epoch 37, train 64/100, loss=0.3924 acc=0.8800
epoch 37, train 65/100, loss=0.1736 acc=0.9333
epoch 37, train 66/100, loss=0.1596 acc=0.9333
epoch 37, train 67/100, loss=0.7777 acc=0.8133
epoch 37, train 68/100, loss=0.1087 acc=0.9600
epoch 37, train 69/100, loss=0.7888 acc=0.8000
epoch 37, train 70/100, loss=0.4282 acc=0.8267
epoch 37, train 71/100, loss=0.3488 acc=0.8533
epoch 37, train 72/100, loss=0.9441 acc=0.6400
epoch 37, train 73/100, loss=0.9700 acc=0.6667
epoch 37, train 74/100, loss=0.7481 acc=0.7867
epoch 37, train 75/100, loss=1.0156 acc=0.6533
epoch 37, train 76/100, loss=1.0687 acc=0.6133
epoch 37, train 77/100, loss=0.8467 acc=0.7867
epoch 37, train 78/100, loss=0.4204 acc=0.8267
epoch 37, train 79/100, loss=0.1212 acc=0.9600
epoch 37, train 80/100, loss=0.1521 acc=0.9600
epoch 37, train 81/100, loss=0.2719 acc=0.9200
epoch 37, train 82/100, loss=1.6771 acc=0.4800
epoch 37, train 83/100, loss=0.2598 acc=0.9200
epoch 37, train 84/100, loss=0.3364 acc=0.8933
epoch 37, train 85/100, loss=0.4225 acc=0.8267
epoch 37, train 86/100, loss=0.1819 acc=0.9333
epoch 37, train 87/100, loss=0.3793 acc=0.8933
epoch 37, train 88/100, loss=0.6220 acc=0.7867
epoch 37, train 89/100, loss=0.1526 acc=0.9600
epoch 37, train 90/100, loss=0.2445 acc=0.9600
epoch 37, train 91/100, loss=0.2468 acc=0.9067
epoch 37, train 92/100, loss=0.2427 acc=0.9467
epoch 37, train 93/100, loss=0.4164 acc=0.8533
epoch 37, train 94/100, loss=0.3473 acc=0.9067
epoch 37, train 95/100, loss=0.6655 acc=0.8000
epoch 37, train 96/100, loss=0.4719 acc=0.8667
epoch 37, train 97/100, loss=0.1350 acc=0.9600
epoch 37, train 98/100, loss=0.9579 acc=0.7200
epoch 37, train 99/100, loss=0.9881 acc=0.6533
epoch 37, train 100/100, loss=0.4047 acc=0.8533
best epoch 26, best val acc=0.7125
epoch 37, val, loss=0.9664 acc=0.7059
ETA:2.4h/6.4h
epoch 38, train 1/100, loss=0.4419 acc=0.8667
epoch 38, train 2/100, loss=0.7676 acc=0.7200
epoch 38, train 3/100, loss=0.5494 acc=0.8533
epoch 38, train 4/100, loss=0.2175 acc=0.9333
epoch 38, train 5/100, loss=0.7199 acc=0.7333
epoch 38, train 6/100, loss=1.0583 acc=0.7200
epoch 38, train 7/100, loss=0.7852 acc=0.7733
epoch 38, train 8/100, loss=0.6624 acc=0.7867
epoch 38, train 9/100, loss=1.3988 acc=0.5600
epoch 38, train 10/100, loss=0.6809 acc=0.7467
epoch 38, train 11/100, loss=0.3895 acc=0.8933
epoch 38, train 12/100, loss=2.0533 acc=0.4667
epoch 38, train 13/100, loss=0.8384 acc=0.7200
epoch 38, train 14/100, loss=0.7813 acc=0.6933
epoch 38, train 15/100, loss=0.7507 acc=0.7867
epoch 38, train 16/100, loss=0.8938 acc=0.6533
epoch 38, train 17/100, loss=1.1715 acc=0.6800
epoch 38, train 18/100, loss=0.9677 acc=0.7600
epoch 38, train 19/100, loss=0.3286 acc=0.8667
epoch 38, train 20/100, loss=1.5223 acc=0.5333
epoch 38, train 21/100, loss=0.2809 acc=0.9067
epoch 38, train 22/100, loss=0.1572 acc=0.9333
epoch 38, train 23/100, loss=0.4787 acc=0.8667
epoch 38, train 24/100, loss=0.1431 acc=0.9600
epoch 38, train 25/100, loss=0.6562 acc=0.7733
epoch 38, train 26/100, loss=0.4202 acc=0.8800
epoch 38, train 27/100, loss=0.5616 acc=0.8000
epoch 38, train 28/100, loss=0.6819 acc=0.7200
epoch 38, train 29/100, loss=0.7136 acc=0.6933
epoch 38, train 30/100, loss=0.2124 acc=0.9200
epoch 38, train 31/100, loss=0.6262 acc=0.7333
epoch 38, train 32/100, loss=0.4916 acc=0.7733
epoch 38, train 33/100, loss=0.5399 acc=0.8667
epoch 38, train 34/100, loss=0.2725 acc=0.9467
epoch 38, train 35/100, loss=0.1208 acc=0.9333
epoch 38, train 36/100, loss=0.5990 acc=0.7867
epoch 38, train 37/100, loss=0.4224 acc=0.8933
epoch 38, train 38/100, loss=0.7120 acc=0.7600
epoch 38, train 39/100, loss=0.5773 acc=0.8000
epoch 38, train 40/100, loss=0.3433 acc=0.8800
epoch 38, train 41/100, loss=0.3698 acc=0.8267
epoch 38, train 42/100, loss=0.8862 acc=0.7200
epoch 38, train 43/100, loss=0.4211 acc=0.8667
epoch 38, train 44/100, loss=0.3142 acc=0.9200
epoch 38, train 45/100, loss=0.2245 acc=0.9200
epoch 38, train 46/100, loss=1.1649 acc=0.6400
epoch 38, train 47/100, loss=0.6599 acc=0.7467
epoch 38, train 48/100, loss=0.4112 acc=0.8667
epoch 38, train 49/100, loss=0.8501 acc=0.7733
epoch 38, train 50/100, loss=0.8156 acc=0.7333
epoch 38, train 51/100, loss=0.7181 acc=0.7200
epoch 38, train 52/100, loss=0.5504 acc=0.8533
epoch 38, train 53/100, loss=1.0089 acc=0.7600
epoch 38, train 54/100, loss=0.2748 acc=0.8933
epoch 38, train 55/100, loss=0.2789 acc=0.9200
epoch 38, train 56/100, loss=0.4494 acc=0.8133
epoch 38, train 57/100, loss=1.6265 acc=0.5600
epoch 38, train 58/100, loss=0.6191 acc=0.7733
epoch 38, train 59/100, loss=0.4659 acc=0.8400
epoch 38, train 60/100, loss=0.5302 acc=0.8000
epoch 38, train 61/100, loss=0.4451 acc=0.8400
epoch 38, train 62/100, loss=0.3446 acc=0.9200
epoch 38, train 63/100, loss=0.3284 acc=0.8267
epoch 38, train 64/100, loss=1.2693 acc=0.6000
epoch 38, train 65/100, loss=0.4975 acc=0.8533
epoch 38, train 66/100, loss=0.6528 acc=0.8133
epoch 38, train 67/100, loss=0.4230 acc=0.8800
epoch 38, train 68/100, loss=0.9211 acc=0.7467
epoch 38, train 69/100, loss=0.9521 acc=0.7200
epoch 38, train 70/100, loss=0.6833 acc=0.7600
epoch 38, train 71/100, loss=0.5468 acc=0.7867
epoch 38, train 72/100, loss=0.7143 acc=0.7733
epoch 38, train 73/100, loss=0.9987 acc=0.6800
epoch 38, train 74/100, loss=0.6247 acc=0.7867
epoch 38, train 75/100, loss=0.6471 acc=0.7333
epoch 38, train 76/100, loss=1.0290 acc=0.7067
epoch 38, train 77/100, loss=0.7666 acc=0.7733
epoch 38, train 78/100, loss=0.8701 acc=0.6933
epoch 38, train 79/100, loss=1.1790 acc=0.6267
epoch 38, train 80/100, loss=0.4913 acc=0.8400
epoch 38, train 81/100, loss=0.5075 acc=0.8133
epoch 38, train 82/100, loss=0.1228 acc=0.9733
epoch 38, train 83/100, loss=1.0738 acc=0.6800
epoch 38, train 84/100, loss=0.0758 acc=0.9733
epoch 38, train 85/100, loss=1.2035 acc=0.6533
epoch 38, train 86/100, loss=0.3310 acc=0.8667
epoch 38, train 87/100, loss=2.1696 acc=0.4000
epoch 38, train 88/100, loss=0.3942 acc=0.8400
epoch 38, train 89/100, loss=0.1355 acc=0.9600
epoch 38, train 90/100, loss=0.4825 acc=0.8133
epoch 38, train 91/100, loss=0.9698 acc=0.6933
epoch 38, train 92/100, loss=0.3658 acc=0.8400
epoch 38, train 93/100, loss=0.6495 acc=0.8000
epoch 38, train 94/100, loss=0.8691 acc=0.7067
epoch 38, train 95/100, loss=0.6395 acc=0.7733
epoch 38, train 96/100, loss=0.8182 acc=0.8267
epoch 38, train 97/100, loss=0.3714 acc=0.8533
epoch 38, train 98/100, loss=0.8140 acc=0.6800
epoch 38, train 99/100, loss=0.5042 acc=0.8000
epoch 38, train 100/100, loss=0.1637 acc=0.9467
best epoch 26, best val acc=0.7125
epoch 38, val, loss=0.9853 acc=0.7034
ETA:2.4h/6.4h
epoch 39, train 1/100, loss=1.0612 acc=0.7067
epoch 39, train 2/100, loss=0.6671 acc=0.7467
epoch 39, train 3/100, loss=0.6112 acc=0.8667
epoch 39, train 4/100, loss=0.7185 acc=0.8000
epoch 39, train 5/100, loss=2.4868 acc=0.4133
epoch 39, train 6/100, loss=0.3154 acc=0.9200
epoch 39, train 7/100, loss=0.2165 acc=0.9200
epoch 39, train 8/100, loss=0.7123 acc=0.7733
epoch 39, train 9/100, loss=0.6241 acc=0.7867
epoch 39, train 10/100, loss=0.3578 acc=0.8800
epoch 39, train 11/100, loss=1.5009 acc=0.6400
epoch 39, train 12/100, loss=0.4088 acc=0.8933
epoch 39, train 13/100, loss=0.1189 acc=0.9600
epoch 39, train 14/100, loss=0.3456 acc=0.8533
epoch 39, train 15/100, loss=1.0595 acc=0.6400
epoch 39, train 16/100, loss=1.4589 acc=0.4933
epoch 39, train 17/100, loss=0.1451 acc=0.9867
epoch 39, train 18/100, loss=0.6057 acc=0.7333
epoch 39, train 19/100, loss=0.3656 acc=0.8667
epoch 39, train 20/100, loss=0.3319 acc=0.9200
epoch 39, train 21/100, loss=0.0552 acc=1.0000
epoch 39, train 22/100, loss=0.3706 acc=0.8533
epoch 39, train 23/100, loss=0.6833 acc=0.7867
epoch 39, train 24/100, loss=0.1905 acc=0.9333
epoch 39, train 25/100, loss=1.0245 acc=0.6400
epoch 39, train 26/100, loss=0.5957 acc=0.7733
epoch 39, train 27/100, loss=0.5810 acc=0.8400
epoch 39, train 28/100, loss=0.2999 acc=0.9067
epoch 39, train 29/100, loss=0.3885 acc=0.8533
epoch 39, train 30/100, loss=0.8175 acc=0.7067
epoch 39, train 31/100, loss=1.6574 acc=0.5600
epoch 39, train 32/100, loss=0.0591 acc=0.9867
epoch 39, train 33/100, loss=0.4421 acc=0.8533
epoch 39, train 34/100, loss=1.2250 acc=0.5733
epoch 39, train 35/100, loss=0.5263 acc=0.8667
epoch 39, train 36/100, loss=0.2920 acc=0.9067
epoch 39, train 37/100, loss=0.4124 acc=0.8800
epoch 39, train 38/100, loss=0.2624 acc=0.9067
epoch 39, train 39/100, loss=0.2103 acc=0.9333
epoch 39, train 40/100, loss=0.1204 acc=0.9467
epoch 39, train 41/100, loss=0.3405 acc=0.8800
epoch 39, train 42/100, loss=0.3017 acc=0.9333
epoch 39, train 43/100, loss=0.5453 acc=0.8133
epoch 39, train 44/100, loss=0.9388 acc=0.6267
epoch 39, train 45/100, loss=0.5851 acc=0.8533
epoch 39, train 46/100, loss=0.8066 acc=0.7600
epoch 39, train 47/100, loss=0.3170 acc=0.8800
epoch 39, train 48/100, loss=0.2432 acc=0.8933
epoch 39, train 49/100, loss=0.6578 acc=0.7467
epoch 39, train 50/100, loss=0.4666 acc=0.8533
epoch 39, train 51/100, loss=0.0398 acc=0.9867
epoch 39, train 52/100, loss=0.7421 acc=0.8000
epoch 39, train 53/100, loss=0.8691 acc=0.6933
epoch 39, train 54/100, loss=0.3309 acc=0.9067
epoch 39, train 55/100, loss=0.5106 acc=0.8400
epoch 39, train 56/100, loss=0.5295 acc=0.8400
epoch 39, train 57/100, loss=0.2329 acc=0.9200
epoch 39, train 58/100, loss=0.3339 acc=0.8800
epoch 39, train 59/100, loss=1.3942 acc=0.5333
epoch 39, train 60/100, loss=0.7462 acc=0.7200
epoch 39, train 61/100, loss=0.3986 acc=0.8667
epoch 39, train 62/100, loss=0.2739 acc=0.9200
epoch 39, train 63/100, loss=0.4316 acc=0.8400
epoch 39, train 64/100, loss=0.6473 acc=0.8000
epoch 39, train 65/100, loss=0.6047 acc=0.7600
epoch 39, train 66/100, loss=0.6805 acc=0.7733
epoch 39, train 67/100, loss=0.4451 acc=0.8400
epoch 39, train 68/100, loss=0.2602 acc=0.9067
epoch 39, train 69/100, loss=0.3928 acc=0.8667
epoch 39, train 70/100, loss=0.3221 acc=0.8667
epoch 39, train 71/100, loss=0.3774 acc=0.8400
epoch 39, train 72/100, loss=0.6698 acc=0.8533
epoch 39, train 73/100, loss=0.1373 acc=0.9733
epoch 39, train 74/100, loss=1.2893 acc=0.6533
epoch 39, train 75/100, loss=0.2598 acc=0.8667
epoch 39, train 76/100, loss=0.7448 acc=0.7467
epoch 39, train 77/100, loss=0.3391 acc=0.9067
epoch 39, train 78/100, loss=0.6889 acc=0.8133
epoch 39, train 79/100, loss=0.4254 acc=0.8667
epoch 39, train 80/100, loss=0.1002 acc=0.9733
epoch 39, train 81/100, loss=0.4361 acc=0.8267
epoch 39, train 82/100, loss=0.8532 acc=0.6933
epoch 39, train 83/100, loss=0.5232 acc=0.8267
epoch 39, train 84/100, loss=1.0324 acc=0.7467
epoch 39, train 85/100, loss=1.3013 acc=0.5200
epoch 39, train 86/100, loss=0.7797 acc=0.7600
epoch 39, train 87/100, loss=1.4849 acc=0.5333
epoch 39, train 88/100, loss=0.5229 acc=0.8800
epoch 39, train 89/100, loss=0.2081 acc=0.8933
epoch 39, train 90/100, loss=0.2288 acc=0.8933
epoch 39, train 91/100, loss=2.4355 acc=0.3333
epoch 39, train 92/100, loss=0.4145 acc=0.8000
epoch 39, train 93/100, loss=0.1584 acc=0.9467
epoch 39, train 94/100, loss=0.5419 acc=0.8400
epoch 39, train 95/100, loss=0.8018 acc=0.7600
epoch 39, train 96/100, loss=0.2631 acc=0.9333
epoch 39, train 97/100, loss=0.5436 acc=0.8267
epoch 39, train 98/100, loss=0.4651 acc=0.8400
epoch 39, train 99/100, loss=0.5645 acc=0.8133
epoch 39, train 100/100, loss=0.6602 acc=0.8133
best epoch 26, best val acc=0.7125
epoch 39, val, loss=0.9645 acc=0.6991
ETA:2.5h/6.4h
epoch 40, train 1/100, loss=0.4085 acc=0.8400
epoch 40, train 2/100, loss=0.6689 acc=0.7600
epoch 40, train 3/100, loss=0.5007 acc=0.8000
epoch 40, train 4/100, loss=0.4319 acc=0.8667
epoch 40, train 5/100, loss=0.4154 acc=0.9067
epoch 40, train 6/100, loss=0.2339 acc=0.9333
epoch 40, train 7/100, loss=0.8427 acc=0.6933
epoch 40, train 8/100, loss=0.0619 acc=0.9867
epoch 40, train 9/100, loss=0.6778 acc=0.8133
epoch 40, train 10/100, loss=0.1749 acc=0.9333
epoch 40, train 11/100, loss=1.0720 acc=0.6533
epoch 40, train 12/100, loss=0.8564 acc=0.7200
epoch 40, train 13/100, loss=0.6135 acc=0.7467
epoch 40, train 14/100, loss=1.2262 acc=0.6667
epoch 40, train 15/100, loss=0.7639 acc=0.7200
epoch 40, train 16/100, loss=0.0948 acc=0.9467
epoch 40, train 17/100, loss=1.0647 acc=0.7200
epoch 40, train 18/100, loss=1.5599 acc=0.5200
epoch 40, train 19/100, loss=0.4774 acc=0.8267
epoch 40, train 20/100, loss=1.0515 acc=0.5867
epoch 40, train 21/100, loss=0.8274 acc=0.7467
epoch 40, train 22/100, loss=0.4031 acc=0.9067
epoch 40, train 23/100, loss=0.3014 acc=0.8933
epoch 40, train 24/100, loss=0.2380 acc=0.8667
epoch 40, train 25/100, loss=0.3033 acc=0.9200
epoch 40, train 26/100, loss=1.2618 acc=0.5600
epoch 40, train 27/100, loss=0.1343 acc=0.9600
epoch 40, train 28/100, loss=0.1592 acc=0.9600
epoch 40, train 29/100, loss=0.0997 acc=0.9600
epoch 40, train 30/100, loss=0.1503 acc=0.9467
epoch 40, train 31/100, loss=0.3711 acc=0.8533
epoch 40, train 32/100, loss=0.3149 acc=0.9067
epoch 40, train 33/100, loss=0.4656 acc=0.8400
epoch 40, train 34/100, loss=1.2576 acc=0.5733
epoch 40, train 35/100, loss=0.2239 acc=0.9200
epoch 40, train 36/100, loss=0.3985 acc=0.8400
epoch 40, train 37/100, loss=0.3658 acc=0.8667
epoch 40, train 38/100, loss=0.3129 acc=0.8667
epoch 40, train 39/100, loss=1.4228 acc=0.5733
epoch 40, train 40/100, loss=0.7269 acc=0.7467
epoch 40, train 41/100, loss=0.2074 acc=0.9467
epoch 40, train 42/100, loss=0.2458 acc=0.8933
epoch 40, train 43/100, loss=0.3660 acc=0.8667
epoch 40, train 44/100, loss=0.6800 acc=0.7333
epoch 40, train 45/100, loss=0.1807 acc=0.9467
epoch 40, train 46/100, loss=0.1552 acc=0.9600
epoch 40, train 47/100, loss=0.3173 acc=0.8667
epoch 40, train 48/100, loss=0.1957 acc=0.9067
epoch 40, train 49/100, loss=0.6893 acc=0.7600
epoch 40, train 50/100, loss=0.5603 acc=0.8133
epoch 40, train 51/100, loss=2.7723 acc=0.4667
epoch 40, train 52/100, loss=0.5723 acc=0.8533
epoch 40, train 53/100, loss=0.9015 acc=0.6933
epoch 40, train 54/100, loss=0.7343 acc=0.7600
epoch 40, train 55/100, loss=1.2761 acc=0.6133
epoch 40, train 56/100, loss=0.8197 acc=0.7333
epoch 40, train 57/100, loss=0.2033 acc=0.9200
epoch 40, train 58/100, loss=0.4643 acc=0.8267
epoch 40, train 59/100, loss=0.2323 acc=0.9333
epoch 40, train 60/100, loss=0.1963 acc=0.9333
epoch 40, train 61/100, loss=1.1770 acc=0.6267
epoch 40, train 62/100, loss=0.6357 acc=0.8400
epoch 40, train 63/100, loss=0.7185 acc=0.7200
epoch 40, train 64/100, loss=0.2432 acc=0.9067
epoch 40, train 65/100, loss=0.2823 acc=0.9067
epoch 40, train 66/100, loss=0.5595 acc=0.8400
epoch 40, train 67/100, loss=0.1191 acc=0.9467
epoch 40, train 68/100, loss=0.4998 acc=0.8400
epoch 40, train 69/100, loss=0.2385 acc=0.9333
epoch 40, train 70/100, loss=0.3100 acc=0.8667
epoch 40, train 71/100, loss=0.2757 acc=0.9200
epoch 40, train 72/100, loss=0.3496 acc=0.8667
epoch 40, train 73/100, loss=0.3529 acc=0.8133
epoch 40, train 74/100, loss=0.6825 acc=0.7867
epoch 40, train 75/100, loss=0.9595 acc=0.7200
epoch 40, train 76/100, loss=0.7034 acc=0.7867
epoch 40, train 77/100, loss=0.4320 acc=0.8000
epoch 40, train 78/100, loss=0.0904 acc=0.9733
epoch 40, train 79/100, loss=0.5129 acc=0.8267
epoch 40, train 80/100, loss=0.3033 acc=0.8667
epoch 40, train 81/100, loss=1.2277 acc=0.6000
epoch 40, train 82/100, loss=0.3880 acc=0.8533
epoch 40, train 83/100, loss=0.3799 acc=0.8667
epoch 40, train 84/100, loss=0.7035 acc=0.7733
epoch 40, train 85/100, loss=0.5062 acc=0.8000
epoch 40, train 86/100, loss=0.6374 acc=0.8267
epoch 40, train 87/100, loss=0.3449 acc=0.8667
epoch 40, train 88/100, loss=0.5748 acc=0.8400
epoch 40, train 89/100, loss=0.7542 acc=0.7600
epoch 40, train 90/100, loss=0.5778 acc=0.8533
epoch 40, train 91/100, loss=0.2273 acc=0.9333
epoch 40, train 92/100, loss=0.4613 acc=0.8000
epoch 40, train 93/100, loss=0.2919 acc=0.8667
epoch 40, train 94/100, loss=1.1041 acc=0.6400
epoch 40, train 95/100, loss=1.1397 acc=0.6400
epoch 40, train 96/100, loss=1.0355 acc=0.6667
epoch 40, train 97/100, loss=0.3543 acc=0.8667
epoch 40, train 98/100, loss=0.4078 acc=0.8533
epoch 40, train 99/100, loss=1.5370 acc=0.5600
epoch 40, train 100/100, loss=0.3311 acc=0.8800
best epoch 26, best val acc=0.7125
epoch 40, val, loss=0.9169 acc=0.7157
ETA:2.6h/6.4h
epoch 41, train 1/100, loss=1.2764 acc=0.5867
epoch 41, train 2/100, loss=0.6707 acc=0.7333
epoch 41, train 3/100, loss=0.5023 acc=0.8267
epoch 41, train 4/100, loss=0.4037 acc=0.8400
epoch 41, train 5/100, loss=1.1463 acc=0.7200
epoch 41, train 6/100, loss=1.5796 acc=0.5733
epoch 41, train 7/100, loss=0.6252 acc=0.7733
epoch 41, train 8/100, loss=0.3063 acc=0.9067
epoch 41, train 9/100, loss=0.1893 acc=0.9200
epoch 41, train 10/100, loss=0.4465 acc=0.8667
epoch 41, train 11/100, loss=0.3785 acc=0.8800
epoch 41, train 12/100, loss=0.0831 acc=0.9600
epoch 41, train 13/100, loss=0.2427 acc=0.9467
epoch 41, train 14/100, loss=0.7068 acc=0.8133
epoch 41, train 15/100, loss=0.6178 acc=0.8000
epoch 41, train 16/100, loss=1.0943 acc=0.7200
epoch 41, train 17/100, loss=0.4345 acc=0.8400
epoch 41, train 18/100, loss=0.3052 acc=0.9200
epoch 41, train 19/100, loss=0.4072 acc=0.8533
epoch 41, train 20/100, loss=0.3160 acc=0.9200
epoch 41, train 21/100, loss=1.8134 acc=0.5467
epoch 41, train 22/100, loss=0.7500 acc=0.7467
epoch 41, train 23/100, loss=1.7233 acc=0.6267
epoch 41, train 24/100, loss=0.2432 acc=0.9333
epoch 41, train 25/100, loss=0.0875 acc=0.9733
epoch 41, train 26/100, loss=0.3531 acc=0.8533
epoch 41, train 27/100, loss=0.4016 acc=0.8800
epoch 41, train 28/100, loss=0.4244 acc=0.8800
epoch 41, train 29/100, loss=0.2387 acc=0.9067
epoch 41, train 30/100, loss=0.5273 acc=0.8000
epoch 41, train 31/100, loss=1.5282 acc=0.5200
epoch 41, train 32/100, loss=0.4719 acc=0.8533
epoch 41, train 33/100, loss=0.4795 acc=0.8533
epoch 41, train 34/100, loss=0.3561 acc=0.8800
epoch 41, train 35/100, loss=0.5093 acc=0.7867
epoch 41, train 36/100, loss=0.3776 acc=0.8667
epoch 41, train 37/100, loss=0.9825 acc=0.6667
epoch 41, train 38/100, loss=0.6663 acc=0.8000
epoch 41, train 39/100, loss=0.2195 acc=0.9333
epoch 41, train 40/100, loss=0.2387 acc=0.9200
epoch 41, train 41/100, loss=0.7641 acc=0.7467
epoch 41, train 42/100, loss=0.3008 acc=0.8800
epoch 41, train 43/100, loss=0.1735 acc=0.9333
epoch 41, train 44/100, loss=0.6644 acc=0.7733
epoch 41, train 45/100, loss=0.3875 acc=0.9067
epoch 41, train 46/100, loss=0.4994 acc=0.8267
epoch 41, train 47/100, loss=0.5683 acc=0.7600
epoch 41, train 48/100, loss=0.1331 acc=0.9067
epoch 41, train 49/100, loss=0.3206 acc=0.8800
epoch 41, train 50/100, loss=0.6560 acc=0.7867
epoch 41, train 51/100, loss=0.6267 acc=0.7733
epoch 41, train 52/100, loss=0.2619 acc=0.9067
epoch 41, train 53/100, loss=0.8127 acc=0.7200
epoch 41, train 54/100, loss=0.2462 acc=0.9200
epoch 41, train 55/100, loss=0.8365 acc=0.7600
epoch 41, train 56/100, loss=0.2731 acc=0.8667
epoch 41, train 57/100, loss=0.8225 acc=0.7733
epoch 41, train 58/100, loss=0.4963 acc=0.8533
epoch 41, train 59/100, loss=0.7219 acc=0.8267
epoch 41, train 60/100, loss=0.6366 acc=0.8267
epoch 41, train 61/100, loss=0.6933 acc=0.7600
epoch 41, train 62/100, loss=0.3074 acc=0.9067
epoch 41, train 63/100, loss=0.2379 acc=0.9333
epoch 41, train 64/100, loss=0.2935 acc=0.8533
epoch 41, train 65/100, loss=0.4338 acc=0.8133
epoch 41, train 66/100, loss=0.3206 acc=0.9067
epoch 41, train 67/100, loss=0.4912 acc=0.7867
epoch 41, train 68/100, loss=0.4012 acc=0.8533
epoch 41, train 69/100, loss=0.4180 acc=0.8533
epoch 41, train 70/100, loss=0.6305 acc=0.7733
epoch 41, train 71/100, loss=0.1664 acc=0.9600
epoch 41, train 72/100, loss=0.2923 acc=0.9200
epoch 41, train 73/100, loss=0.4522 acc=0.8000
epoch 41, train 74/100, loss=0.4338 acc=0.8267
epoch 41, train 75/100, loss=0.1708 acc=0.9067
epoch 41, train 76/100, loss=0.3290 acc=0.8800
epoch 41, train 77/100, loss=0.4987 acc=0.8000
epoch 41, train 78/100, loss=0.9328 acc=0.6800
epoch 41, train 79/100, loss=0.1718 acc=0.9200
epoch 41, train 80/100, loss=1.2213 acc=0.5733
epoch 41, train 81/100, loss=0.7728 acc=0.7600
epoch 41, train 82/100, loss=0.5713 acc=0.7867
epoch 41, train 83/100, loss=0.2448 acc=0.8933
epoch 41, train 84/100, loss=0.4639 acc=0.8667
epoch 41, train 85/100, loss=0.2224 acc=0.9067
epoch 41, train 86/100, loss=0.6786 acc=0.7733
epoch 41, train 87/100, loss=0.1620 acc=0.9333
epoch 41, train 88/100, loss=0.8493 acc=0.6533
epoch 41, train 89/100, loss=0.1685 acc=0.9333
epoch 41, train 90/100, loss=0.1307 acc=0.9333
epoch 41, train 91/100, loss=0.1672 acc=0.9200
epoch 41, train 92/100, loss=0.2194 acc=0.9200
epoch 41, train 93/100, loss=0.3713 acc=0.8933
epoch 41, train 94/100, loss=0.8860 acc=0.6800
epoch 41, train 95/100, loss=0.5719 acc=0.8400
epoch 41, train 96/100, loss=0.9664 acc=0.7333
epoch 41, train 97/100, loss=0.6370 acc=0.7600
epoch 41, train 98/100, loss=0.2283 acc=0.8933
epoch 41, train 99/100, loss=1.0139 acc=0.6933
epoch 41, train 100/100, loss=1.3727 acc=0.5200
best epoch 40, best val acc=0.7157
epoch 41, val, loss=0.9434 acc=0.7162
ETA:2.6h/6.4h
epoch 42, train 1/100, loss=0.8638 acc=0.6533
epoch 42, train 2/100, loss=0.3444 acc=0.8800
epoch 42, train 3/100, loss=0.0959 acc=0.9467
epoch 42, train 4/100, loss=0.2996 acc=0.9200
epoch 42, train 5/100, loss=0.6068 acc=0.8000
epoch 42, train 6/100, loss=0.7406 acc=0.7200
epoch 42, train 7/100, loss=0.4182 acc=0.8800
epoch 42, train 8/100, loss=0.7332 acc=0.7600
epoch 42, train 9/100, loss=0.1916 acc=0.9467
epoch 42, train 10/100, loss=0.2976 acc=0.9067
epoch 42, train 11/100, loss=0.2924 acc=0.8667
epoch 42, train 12/100, loss=0.6494 acc=0.7067
epoch 42, train 13/100, loss=0.1825 acc=0.9200
epoch 42, train 14/100, loss=0.6543 acc=0.7200
epoch 42, train 15/100, loss=1.3633 acc=0.6000
epoch 42, train 16/100, loss=0.2137 acc=0.9333
epoch 42, train 17/100, loss=0.1061 acc=0.9733
epoch 42, train 18/100, loss=0.6287 acc=0.7333
epoch 42, train 19/100, loss=0.8734 acc=0.6533
epoch 42, train 20/100, loss=0.6983 acc=0.7600
epoch 42, train 21/100, loss=1.0742 acc=0.6533
epoch 42, train 22/100, loss=0.4702 acc=0.8267
epoch 42, train 23/100, loss=0.8020 acc=0.7600
epoch 42, train 24/100, loss=0.9153 acc=0.7200
epoch 42, train 25/100, loss=2.3107 acc=0.2000
epoch 42, train 26/100, loss=0.7533 acc=0.7200
epoch 42, train 27/100, loss=0.5463 acc=0.7733
epoch 42, train 28/100, loss=0.2404 acc=0.9200
epoch 42, train 29/100, loss=0.2372 acc=0.9067
epoch 42, train 30/100, loss=0.7991 acc=0.6800
epoch 42, train 31/100, loss=0.2584 acc=0.9467
epoch 42, train 32/100, loss=0.7691 acc=0.7733
epoch 42, train 33/100, loss=1.5343 acc=0.6133
epoch 42, train 34/100, loss=0.8943 acc=0.6667
epoch 42, train 35/100, loss=1.3895 acc=0.6133
epoch 42, train 36/100, loss=0.6690 acc=0.8133
epoch 42, train 37/100, loss=0.5016 acc=0.7733
epoch 42, train 38/100, loss=0.5106 acc=0.8000
epoch 42, train 39/100, loss=1.3738 acc=0.5600
epoch 42, train 40/100, loss=0.3956 acc=0.8667
epoch 42, train 41/100, loss=0.2327 acc=0.9200
epoch 42, train 42/100, loss=0.7776 acc=0.8000
epoch 42, train 43/100, loss=0.1546 acc=0.9200
epoch 42, train 44/100, loss=0.2584 acc=0.9067
epoch 42, train 45/100, loss=0.3615 acc=0.8533
epoch 42, train 46/100, loss=1.7389 acc=0.5467
epoch 42, train 47/100, loss=0.2480 acc=0.9333
epoch 42, train 48/100, loss=0.5187 acc=0.8133
epoch 42, train 49/100, loss=0.4103 acc=0.8533
epoch 42, train 50/100, loss=0.4362 acc=0.8933
epoch 42, train 51/100, loss=0.8029 acc=0.7333
epoch 42, train 52/100, loss=0.2450 acc=0.9333
epoch 42, train 53/100, loss=0.3008 acc=0.8933
epoch 42, train 54/100, loss=0.3615 acc=0.8533
epoch 42, train 55/100, loss=0.6230 acc=0.8000
epoch 42, train 56/100, loss=0.3310 acc=0.9067
epoch 42, train 57/100, loss=0.3937 acc=0.8400
epoch 42, train 58/100, loss=0.4419 acc=0.9067
epoch 42, train 59/100, loss=0.5226 acc=0.8533
epoch 42, train 60/100, loss=0.3087 acc=0.9200
epoch 42, train 61/100, loss=0.9552 acc=0.6933
epoch 42, train 62/100, loss=0.5981 acc=0.7733
epoch 42, train 63/100, loss=0.1935 acc=0.9467
epoch 42, train 64/100, loss=0.5736 acc=0.8533
epoch 42, train 65/100, loss=0.1901 acc=0.9333
epoch 42, train 66/100, loss=0.1219 acc=0.9200
epoch 42, train 67/100, loss=0.9173 acc=0.7200
epoch 42, train 68/100, loss=0.5201 acc=0.8400
epoch 42, train 69/100, loss=0.6309 acc=0.7733
epoch 42, train 70/100, loss=0.2258 acc=0.9200
epoch 42, train 71/100, loss=0.3899 acc=0.8533
epoch 42, train 72/100, loss=0.2909 acc=0.8400
epoch 42, train 73/100, loss=0.5546 acc=0.8400
epoch 42, train 74/100, loss=0.8659 acc=0.6933
epoch 42, train 75/100, loss=0.0200 acc=1.0000
epoch 42, train 76/100, loss=0.3338 acc=0.8800
epoch 42, train 77/100, loss=0.4019 acc=0.8267
epoch 42, train 78/100, loss=0.1765 acc=0.9333
epoch 42, train 79/100, loss=0.4385 acc=0.8800
epoch 42, train 80/100, loss=0.2447 acc=0.9200
epoch 42, train 81/100, loss=0.4596 acc=0.8533
epoch 42, train 82/100, loss=0.4208 acc=0.9067
epoch 42, train 83/100, loss=0.6400 acc=0.8000
epoch 42, train 84/100, loss=0.4934 acc=0.8133
epoch 42, train 85/100, loss=0.3258 acc=0.8800
epoch 42, train 86/100, loss=1.4432 acc=0.4533
epoch 42, train 87/100, loss=1.0043 acc=0.7200
epoch 42, train 88/100, loss=0.7741 acc=0.7200
epoch 42, train 89/100, loss=1.1007 acc=0.6400
epoch 42, train 90/100, loss=0.2354 acc=0.9467
epoch 42, train 91/100, loss=0.2300 acc=0.9333
epoch 42, train 92/100, loss=0.2736 acc=0.9467
epoch 42, train 93/100, loss=0.2231 acc=0.9600
epoch 42, train 94/100, loss=0.2961 acc=0.9067
epoch 42, train 95/100, loss=0.1733 acc=0.9600
epoch 42, train 96/100, loss=0.7083 acc=0.7733
epoch 42, train 97/100, loss=0.4906 acc=0.8267
epoch 42, train 98/100, loss=0.5735 acc=0.7867
epoch 42, train 99/100, loss=0.2501 acc=0.9333
epoch 42, train 100/100, loss=0.8952 acc=0.7467
best epoch 41, best val acc=0.7162
epoch 42, val, loss=0.9315 acc=0.7113
ETA:2.7h/6.4h
epoch 43, train 1/100, loss=0.2744 acc=0.8800
epoch 43, train 2/100, loss=0.3871 acc=0.8800
epoch 43, train 3/100, loss=0.8360 acc=0.7467
epoch 43, train 4/100, loss=0.2838 acc=0.9067
epoch 43, train 5/100, loss=0.5802 acc=0.8800
epoch 43, train 6/100, loss=0.1110 acc=0.9467
epoch 43, train 7/100, loss=0.7469 acc=0.7867
epoch 43, train 8/100, loss=0.1210 acc=0.9467
epoch 43, train 9/100, loss=0.5403 acc=0.7733
epoch 43, train 10/100, loss=0.9614 acc=0.6667
epoch 43, train 11/100, loss=0.7931 acc=0.7600
epoch 43, train 12/100, loss=0.3987 acc=0.8933
epoch 43, train 13/100, loss=0.4817 acc=0.8533
epoch 43, train 14/100, loss=0.2722 acc=0.8933
epoch 43, train 15/100, loss=1.0666 acc=0.7067
epoch 43, train 16/100, loss=0.4073 acc=0.8933
epoch 43, train 17/100, loss=0.3026 acc=0.8667
epoch 43, train 18/100, loss=1.6308 acc=0.5200
epoch 43, train 19/100, loss=1.3015 acc=0.5733
epoch 43, train 20/100, loss=0.7070 acc=0.7867
epoch 43, train 21/100, loss=0.4622 acc=0.8267
epoch 43, train 22/100, loss=0.1351 acc=0.9333
epoch 43, train 23/100, loss=0.2422 acc=0.8933
epoch 43, train 24/100, loss=0.5314 acc=0.8133
epoch 43, train 25/100, loss=0.4964 acc=0.8133
epoch 43, train 26/100, loss=0.8009 acc=0.7200
epoch 43, train 27/100, loss=0.6791 acc=0.7600
epoch 43, train 28/100, loss=0.4058 acc=0.8000
epoch 43, train 29/100, loss=0.6993 acc=0.8533
epoch 43, train 30/100, loss=0.9983 acc=0.6267
epoch 43, train 31/100, loss=0.7673 acc=0.7733
epoch 43, train 32/100, loss=0.1805 acc=0.9600
epoch 43, train 33/100, loss=0.5443 acc=0.8133
epoch 43, train 34/100, loss=0.4277 acc=0.8400
epoch 43, train 35/100, loss=0.5673 acc=0.8133
epoch 43, train 36/100, loss=0.4453 acc=0.8400
epoch 43, train 37/100, loss=0.6789 acc=0.8133
epoch 43, train 38/100, loss=0.2548 acc=0.8933
epoch 43, train 39/100, loss=0.3011 acc=0.9200
epoch 43, train 40/100, loss=0.6525 acc=0.6933
epoch 43, train 41/100, loss=0.3595 acc=0.8800
epoch 43, train 42/100, loss=0.4978 acc=0.8667
epoch 43, train 43/100, loss=0.5246 acc=0.7867
epoch 43, train 44/100, loss=0.7482 acc=0.8000
epoch 43, train 45/100, loss=0.6456 acc=0.8000
epoch 43, train 46/100, loss=0.4835 acc=0.8933
epoch 43, train 47/100, loss=0.4919 acc=0.8533
epoch 43, train 48/100, loss=0.1048 acc=0.9467
epoch 43, train 49/100, loss=0.6094 acc=0.7467
epoch 43, train 50/100, loss=0.6826 acc=0.7867
epoch 43, train 51/100, loss=0.7665 acc=0.7200
epoch 43, train 52/100, loss=0.3993 acc=0.8667
epoch 43, train 53/100, loss=0.2611 acc=0.9067
epoch 43, train 54/100, loss=0.3538 acc=0.9200
epoch 43, train 55/100, loss=0.2098 acc=0.9600
epoch 43, train 56/100, loss=0.2651 acc=0.9200
epoch 43, train 57/100, loss=0.4333 acc=0.8800
epoch 43, train 58/100, loss=0.1687 acc=0.9600
epoch 43, train 59/100, loss=1.3704 acc=0.5733
epoch 43, train 60/100, loss=0.6321 acc=0.8000
epoch 43, train 61/100, loss=0.7346 acc=0.7333
epoch 43, train 62/100, loss=1.0803 acc=0.6667
epoch 43, train 63/100, loss=0.2714 acc=0.8533
epoch 43, train 64/100, loss=0.3958 acc=0.8400
epoch 43, train 65/100, loss=0.6893 acc=0.7733
epoch 43, train 66/100, loss=0.2380 acc=0.9467
epoch 43, train 67/100, loss=0.5388 acc=0.8400
epoch 43, train 68/100, loss=0.8209 acc=0.8133
epoch 43, train 69/100, loss=0.3482 acc=0.8800
epoch 43, train 70/100, loss=0.7160 acc=0.7467
epoch 43, train 71/100, loss=1.5484 acc=0.5733
epoch 43, train 72/100, loss=0.0468 acc=0.9867
epoch 43, train 73/100, loss=0.3938 acc=0.8800
epoch 43, train 74/100, loss=0.4326 acc=0.8400
epoch 43, train 75/100, loss=0.3746 acc=0.8667
epoch 43, train 76/100, loss=0.5399 acc=0.8533
epoch 43, train 77/100, loss=0.4785 acc=0.7733
epoch 43, train 78/100, loss=1.1074 acc=0.6800
epoch 43, train 79/100, loss=0.3335 acc=0.8400
epoch 43, train 80/100, loss=0.8187 acc=0.7733
epoch 43, train 81/100, loss=0.2426 acc=0.9467
epoch 43, train 82/100, loss=0.7380 acc=0.7733
epoch 43, train 83/100, loss=0.7051 acc=0.8000
epoch 43, train 84/100, loss=0.1720 acc=0.9600
epoch 43, train 85/100, loss=0.4770 acc=0.8000
epoch 43, train 86/100, loss=0.3071 acc=0.9067
epoch 43, train 87/100, loss=0.3755 acc=0.8800
epoch 43, train 88/100, loss=0.6833 acc=0.7467
epoch 43, train 89/100, loss=0.1092 acc=0.9733
epoch 43, train 90/100, loss=0.7864 acc=0.6800
epoch 43, train 91/100, loss=0.7660 acc=0.7333
epoch 43, train 92/100, loss=0.6879 acc=0.7867
epoch 43, train 93/100, loss=0.6369 acc=0.8133
epoch 43, train 94/100, loss=1.4940 acc=0.5333
epoch 43, train 95/100, loss=0.6341 acc=0.8133
epoch 43, train 96/100, loss=0.5671 acc=0.7733
epoch 43, train 97/100, loss=0.3617 acc=0.9067
epoch 43, train 98/100, loss=1.0875 acc=0.5867
epoch 43, train 99/100, loss=0.1955 acc=0.9200
epoch 43, train 100/100, loss=0.9251 acc=0.7333
best epoch 41, best val acc=0.7162
epoch 43, val, loss=0.9379 acc=0.7130
ETA:2.8h/6.4h
epoch 44, train 1/100, loss=0.4269 acc=0.8400
epoch 44, train 2/100, loss=0.9588 acc=0.7067
epoch 44, train 3/100, loss=0.5335 acc=0.8133
epoch 44, train 4/100, loss=0.1534 acc=0.9733
epoch 44, train 5/100, loss=0.4602 acc=0.8267
epoch 44, train 6/100, loss=0.5529 acc=0.8000
epoch 44, train 7/100, loss=0.4332 acc=0.8800
epoch 44, train 8/100, loss=1.2500 acc=0.6000
epoch 44, train 9/100, loss=1.1075 acc=0.6800
epoch 44, train 10/100, loss=1.4461 acc=0.5600
epoch 44, train 11/100, loss=0.7777 acc=0.7467
epoch 44, train 12/100, loss=0.9330 acc=0.6800
epoch 44, train 13/100, loss=0.2754 acc=0.8933
epoch 44, train 14/100, loss=0.1942 acc=0.9333
epoch 44, train 15/100, loss=0.5010 acc=0.8533
epoch 44, train 16/100, loss=0.3661 acc=0.8800
epoch 44, train 17/100, loss=1.0883 acc=0.7200
epoch 44, train 18/100, loss=0.2545 acc=0.9067
epoch 44, train 19/100, loss=0.5542 acc=0.7333
epoch 44, train 20/100, loss=1.1105 acc=0.6267
epoch 44, train 21/100, loss=0.4970 acc=0.8133
epoch 44, train 22/100, loss=0.1959 acc=0.9067
epoch 44, train 23/100, loss=1.2076 acc=0.6267
epoch 44, train 24/100, loss=0.7792 acc=0.7867
epoch 44, train 25/100, loss=0.3741 acc=0.8533
epoch 44, train 26/100, loss=1.4162 acc=0.6000
epoch 44, train 27/100, loss=1.4504 acc=0.5733
epoch 44, train 28/100, loss=0.5448 acc=0.8133
epoch 44, train 29/100, loss=0.7931 acc=0.7200
epoch 44, train 30/100, loss=0.9611 acc=0.7200
epoch 44, train 31/100, loss=0.9551 acc=0.7067
epoch 44, train 32/100, loss=0.5578 acc=0.8400
epoch 44, train 33/100, loss=0.3208 acc=0.9067
epoch 44, train 34/100, loss=1.6389 acc=0.5333
epoch 44, train 35/100, loss=0.1377 acc=0.9600
epoch 44, train 36/100, loss=0.9542 acc=0.7600
epoch 44, train 37/100, loss=0.5717 acc=0.7200
epoch 44, train 38/100, loss=0.4341 acc=0.8800
epoch 44, train 39/100, loss=0.1127 acc=0.9733
epoch 44, train 40/100, loss=1.0862 acc=0.7067
epoch 44, train 41/100, loss=1.0820 acc=0.6133
epoch 44, train 42/100, loss=0.3464 acc=0.9067
epoch 44, train 43/100, loss=0.8607 acc=0.7467
epoch 44, train 44/100, loss=0.4674 acc=0.8667
epoch 44, train 45/100, loss=1.0204 acc=0.7733
epoch 44, train 46/100, loss=0.8890 acc=0.6933
epoch 44, train 47/100, loss=0.4246 acc=0.8400
epoch 44, train 48/100, loss=0.2352 acc=0.9467
epoch 44, train 49/100, loss=1.4304 acc=0.6667
epoch 44, train 50/100, loss=0.3889 acc=0.8000
epoch 44, train 51/100, loss=0.3503 acc=0.8667
epoch 44, train 52/100, loss=1.0486 acc=0.6800
epoch 44, train 53/100, loss=0.3414 acc=0.8800
epoch 44, train 54/100, loss=0.2252 acc=0.8933
epoch 44, train 55/100, loss=0.1056 acc=0.9600
epoch 44, train 56/100, loss=0.6099 acc=0.7600
epoch 44, train 57/100, loss=0.9479 acc=0.7200
epoch 44, train 58/100, loss=0.4724 acc=0.8400
epoch 44, train 59/100, loss=0.1920 acc=0.9333
epoch 44, train 60/100, loss=0.3898 acc=0.8667
epoch 44, train 61/100, loss=1.2246 acc=0.7200
epoch 44, train 62/100, loss=0.8610 acc=0.6667
epoch 44, train 63/100, loss=1.2611 acc=0.5467
epoch 44, train 64/100, loss=0.4861 acc=0.8533
epoch 44, train 65/100, loss=0.7725 acc=0.7333
epoch 44, train 66/100, loss=0.2555 acc=0.9200
epoch 44, train 67/100, loss=0.1143 acc=0.9600
epoch 44, train 68/100, loss=0.1757 acc=0.9733
epoch 44, train 69/100, loss=0.1575 acc=0.9333
epoch 44, train 70/100, loss=1.0926 acc=0.6400
epoch 44, train 71/100, loss=1.1300 acc=0.6400
epoch 44, train 72/100, loss=1.6094 acc=0.5733
epoch 44, train 73/100, loss=0.2272 acc=0.9333
epoch 44, train 74/100, loss=0.8149 acc=0.7333
epoch 44, train 75/100, loss=0.3682 acc=0.8533
epoch 44, train 76/100, loss=0.6070 acc=0.7600
epoch 44, train 77/100, loss=0.1934 acc=0.9333
epoch 44, train 78/100, loss=1.1485 acc=0.5467
epoch 44, train 79/100, loss=0.7926 acc=0.7600
epoch 44, train 80/100, loss=0.4291 acc=0.8533
epoch 44, train 81/100, loss=0.5561 acc=0.8400
epoch 44, train 82/100, loss=1.4277 acc=0.5600
epoch 44, train 83/100, loss=1.1092 acc=0.6533
epoch 44, train 84/100, loss=0.3113 acc=0.9067
epoch 44, train 85/100, loss=0.7904 acc=0.7067
epoch 44, train 86/100, loss=1.3102 acc=0.6533
epoch 44, train 87/100, loss=0.0371 acc=1.0000
epoch 44, train 88/100, loss=0.3849 acc=0.8667
epoch 44, train 89/100, loss=0.7903 acc=0.7733
epoch 44, train 90/100, loss=0.3494 acc=0.8400
epoch 44, train 91/100, loss=0.4152 acc=0.8000
epoch 44, train 92/100, loss=0.1828 acc=0.9600
epoch 44, train 93/100, loss=0.7180 acc=0.6800
epoch 44, train 94/100, loss=0.4958 acc=0.8800
epoch 44, train 95/100, loss=0.7546 acc=0.7333
epoch 44, train 96/100, loss=0.5246 acc=0.8400
epoch 44, train 97/100, loss=0.4656 acc=0.8667
epoch 44, train 98/100, loss=0.4579 acc=0.8400
epoch 44, train 99/100, loss=0.4453 acc=0.8267
epoch 44, train 100/100, loss=0.2942 acc=0.9067
best epoch 41, best val acc=0.7162
epoch 44, val, loss=0.9448 acc=0.7085
ETA:2.8h/6.4h
epoch 45, train 1/100, loss=0.0945 acc=0.9600
epoch 45, train 2/100, loss=0.4008 acc=0.8667
epoch 45, train 3/100, loss=1.3949 acc=0.5467
epoch 45, train 4/100, loss=2.2858 acc=0.4000
epoch 45, train 5/100, loss=0.1343 acc=0.9467
epoch 45, train 6/100, loss=3.5205 acc=0.2000
epoch 45, train 7/100, loss=0.6166 acc=0.8000
epoch 45, train 8/100, loss=1.0673 acc=0.6533
epoch 45, train 9/100, loss=0.5194 acc=0.8133
epoch 45, train 10/100, loss=1.1962 acc=0.6533
epoch 45, train 11/100, loss=0.4492 acc=0.8933
epoch 45, train 12/100, loss=0.6012 acc=0.8133
epoch 45, train 13/100, loss=0.6647 acc=0.8000
epoch 45, train 14/100, loss=0.1001 acc=0.9467
epoch 45, train 15/100, loss=0.6364 acc=0.7867
epoch 45, train 16/100, loss=0.0984 acc=0.9867
epoch 45, train 17/100, loss=0.3721 acc=0.8533
epoch 45, train 18/100, loss=0.5349 acc=0.7867
epoch 45, train 19/100, loss=0.1699 acc=0.9067
epoch 45, train 20/100, loss=0.6268 acc=0.7467
epoch 45, train 21/100, loss=0.4254 acc=0.8533
epoch 45, train 22/100, loss=0.4968 acc=0.8267
epoch 45, train 23/100, loss=1.9826 acc=0.3867
epoch 45, train 24/100, loss=0.7060 acc=0.8133
epoch 45, train 25/100, loss=0.9771 acc=0.6933
epoch 45, train 26/100, loss=0.0754 acc=0.9600
epoch 45, train 27/100, loss=0.1049 acc=0.9733
epoch 45, train 28/100, loss=0.5446 acc=0.8400
epoch 45, train 29/100, loss=0.8301 acc=0.7600
epoch 45, train 30/100, loss=0.3201 acc=0.9067
epoch 45, train 31/100, loss=0.2867 acc=0.9067
epoch 45, train 32/100, loss=0.4442 acc=0.8400
epoch 45, train 33/100, loss=1.2380 acc=0.5467
epoch 45, train 34/100, loss=1.0204 acc=0.7333
epoch 45, train 35/100, loss=1.4318 acc=0.6400
epoch 45, train 36/100, loss=0.7982 acc=0.7600
epoch 45, train 37/100, loss=0.3486 acc=0.8667
epoch 45, train 38/100, loss=0.4083 acc=0.9200
epoch 45, train 39/100, loss=0.7337 acc=0.8000
epoch 45, train 40/100, loss=0.5933 acc=0.8267
epoch 45, train 41/100, loss=1.6911 acc=0.5333
epoch 45, train 42/100, loss=1.0108 acc=0.6400
epoch 45, train 43/100, loss=0.5414 acc=0.7733
epoch 45, train 44/100, loss=0.8093 acc=0.7200
epoch 45, train 45/100, loss=0.3036 acc=0.8800
epoch 45, train 46/100, loss=0.6616 acc=0.7467
epoch 45, train 47/100, loss=1.0771 acc=0.6533
epoch 45, train 48/100, loss=1.1586 acc=0.6933
epoch 45, train 49/100, loss=0.2172 acc=0.9200
epoch 45, train 50/100, loss=0.3972 acc=0.8400
epoch 45, train 51/100, loss=0.2692 acc=0.8933
epoch 45, train 52/100, loss=0.6894 acc=0.7733
epoch 45, train 53/100, loss=0.2149 acc=0.9200
epoch 45, train 54/100, loss=0.1813 acc=0.9067
epoch 45, train 55/100, loss=0.6985 acc=0.7867
epoch 45, train 56/100, loss=0.9646 acc=0.6267
epoch 45, train 57/100, loss=0.5256 acc=0.7867
epoch 45, train 58/100, loss=0.3989 acc=0.8400
epoch 45, train 59/100, loss=0.4262 acc=0.8667
epoch 45, train 60/100, loss=0.6598 acc=0.8000
epoch 45, train 61/100, loss=1.2308 acc=0.6133
epoch 45, train 62/100, loss=0.7229 acc=0.7067
epoch 45, train 63/100, loss=0.1769 acc=0.9200
epoch 45, train 64/100, loss=0.4091 acc=0.8800
epoch 45, train 65/100, loss=0.2540 acc=0.9200
epoch 45, train 66/100, loss=1.1075 acc=0.6533
epoch 45, train 67/100, loss=0.3101 acc=0.9200
epoch 45, train 68/100, loss=0.2024 acc=0.9067
epoch 45, train 69/100, loss=1.3266 acc=0.5333
epoch 45, train 70/100, loss=0.3964 acc=0.8667
epoch 45, train 71/100, loss=0.8588 acc=0.7733
epoch 45, train 72/100, loss=1.0981 acc=0.6933
epoch 45, train 73/100, loss=0.3629 acc=0.8400
epoch 45, train 74/100, loss=0.2748 acc=0.8800
epoch 45, train 75/100, loss=0.3682 acc=0.8800
epoch 45, train 76/100, loss=0.4777 acc=0.9067
epoch 45, train 77/100, loss=0.4926 acc=0.8133
epoch 45, train 78/100, loss=0.4604 acc=0.8667
epoch 45, train 79/100, loss=0.2087 acc=0.8933
epoch 45, train 80/100, loss=0.2958 acc=0.8800
epoch 45, train 81/100, loss=0.0419 acc=0.9867
epoch 45, train 82/100, loss=0.0857 acc=0.9733
epoch 45, train 83/100, loss=0.2582 acc=0.9333
epoch 45, train 84/100, loss=0.9228 acc=0.7333
epoch 45, train 85/100, loss=0.8170 acc=0.7200
epoch 45, train 86/100, loss=0.4561 acc=0.8667
epoch 45, train 87/100, loss=0.2711 acc=0.8933
epoch 45, train 88/100, loss=0.4815 acc=0.7867
epoch 45, train 89/100, loss=0.6424 acc=0.7733
epoch 45, train 90/100, loss=1.2807 acc=0.7333
epoch 45, train 91/100, loss=0.8260 acc=0.7067
epoch 45, train 92/100, loss=1.0942 acc=0.6800
epoch 45, train 93/100, loss=1.0718 acc=0.6400
epoch 45, train 94/100, loss=0.3611 acc=0.8533
epoch 45, train 95/100, loss=0.6446 acc=0.8267
epoch 45, train 96/100, loss=0.2552 acc=0.9067
epoch 45, train 97/100, loss=0.1118 acc=0.9467
epoch 45, train 98/100, loss=0.3109 acc=0.9200
epoch 45, train 99/100, loss=0.3270 acc=0.8533
epoch 45, train 100/100, loss=0.6497 acc=0.8133
best epoch 41, best val acc=0.7162
epoch 45, val, loss=0.9270 acc=0.7083
ETA:2.9h/6.4h
epoch 46, train 1/100, loss=1.2766 acc=0.6533
epoch 46, train 2/100, loss=0.4271 acc=0.8267
epoch 46, train 3/100, loss=0.2751 acc=0.9067
epoch 46, train 4/100, loss=0.6497 acc=0.7600
epoch 46, train 5/100, loss=0.8457 acc=0.7600
epoch 46, train 6/100, loss=0.4495 acc=0.8533
epoch 46, train 7/100, loss=0.3427 acc=0.8933
epoch 46, train 8/100, loss=0.6248 acc=0.7600
epoch 46, train 9/100, loss=0.6280 acc=0.7867
epoch 46, train 10/100, loss=0.3037 acc=0.8800
epoch 46, train 11/100, loss=0.3841 acc=0.8400
epoch 46, train 12/100, loss=0.8477 acc=0.7200
epoch 46, train 13/100, loss=1.0820 acc=0.6800
epoch 46, train 14/100, loss=0.6293 acc=0.7200
epoch 46, train 15/100, loss=0.2923 acc=0.8933
epoch 46, train 16/100, loss=0.5057 acc=0.8400
epoch 46, train 17/100, loss=1.0842 acc=0.6800
epoch 46, train 18/100, loss=0.7937 acc=0.7200
epoch 46, train 19/100, loss=0.5238 acc=0.8800
epoch 46, train 20/100, loss=0.2751 acc=0.9333
epoch 46, train 21/100, loss=0.2297 acc=0.9333
epoch 46, train 22/100, loss=0.4695 acc=0.8400
epoch 46, train 23/100, loss=0.3418 acc=0.9067
epoch 46, train 24/100, loss=0.2440 acc=0.8933
epoch 46, train 25/100, loss=0.8586 acc=0.7467
epoch 46, train 26/100, loss=0.1914 acc=0.9333
epoch 46, train 27/100, loss=0.7646 acc=0.7467
epoch 46, train 28/100, loss=0.2024 acc=0.8933
epoch 46, train 29/100, loss=1.1683 acc=0.6267
epoch 46, train 30/100, loss=0.6551 acc=0.8000
epoch 46, train 31/100, loss=0.3317 acc=0.8933
epoch 46, train 32/100, loss=0.7504 acc=0.7067
epoch 46, train 33/100, loss=0.5063 acc=0.8000
epoch 46, train 34/100, loss=0.7287 acc=0.7333
epoch 46, train 35/100, loss=0.2654 acc=0.9067
epoch 46, train 36/100, loss=0.4586 acc=0.8267
epoch 46, train 37/100, loss=0.6889 acc=0.7200
epoch 46, train 38/100, loss=0.4961 acc=0.8000
epoch 46, train 39/100, loss=0.8776 acc=0.8000
epoch 46, train 40/100, loss=0.2415 acc=0.9333
epoch 46, train 41/100, loss=0.1820 acc=0.9467
epoch 46, train 42/100, loss=0.3260 acc=0.8800
epoch 46, train 43/100, loss=0.2730 acc=0.8800
epoch 46, train 44/100, loss=0.5356 acc=0.8133
epoch 46, train 45/100, loss=0.4896 acc=0.8667
epoch 46, train 46/100, loss=0.6762 acc=0.8267
epoch 46, train 47/100, loss=0.9529 acc=0.7200
epoch 46, train 48/100, loss=0.5549 acc=0.8533
epoch 46, train 49/100, loss=0.3773 acc=0.8800
epoch 46, train 50/100, loss=0.5557 acc=0.8000
epoch 46, train 51/100, loss=0.4008 acc=0.8800
epoch 46, train 52/100, loss=0.6955 acc=0.8000
epoch 46, train 53/100, loss=0.6238 acc=0.8267
epoch 46, train 54/100, loss=0.6062 acc=0.7733
epoch 46, train 55/100, loss=0.5355 acc=0.8533
epoch 46, train 56/100, loss=0.4582 acc=0.8533
epoch 46, train 57/100, loss=0.5754 acc=0.7733
epoch 46, train 58/100, loss=0.3595 acc=0.8800
epoch 46, train 59/100, loss=0.3962 acc=0.8533
epoch 46, train 60/100, loss=0.3366 acc=0.9067
epoch 46, train 61/100, loss=0.3925 acc=0.8133
epoch 46, train 62/100, loss=0.3895 acc=0.8933
epoch 46, train 63/100, loss=0.5219 acc=0.8267
epoch 46, train 64/100, loss=0.7725 acc=0.7867
epoch 46, train 65/100, loss=0.6474 acc=0.8133
epoch 46, train 66/100, loss=0.3213 acc=0.8400
epoch 46, train 67/100, loss=0.2465 acc=0.9467
epoch 46, train 68/100, loss=0.1745 acc=0.9600
epoch 46, train 69/100, loss=1.0821 acc=0.5733
epoch 46, train 70/100, loss=0.7805 acc=0.7467
epoch 46, train 71/100, loss=1.3292 acc=0.6133
epoch 46, train 72/100, loss=0.6564 acc=0.7600
epoch 46, train 73/100, loss=0.3926 acc=0.8800
epoch 46, train 74/100, loss=0.6150 acc=0.7600
epoch 46, train 75/100, loss=0.5159 acc=0.8400
epoch 46, train 76/100, loss=0.1438 acc=0.9467
epoch 46, train 77/100, loss=0.6232 acc=0.7867
epoch 46, train 78/100, loss=0.9399 acc=0.6533
epoch 46, train 79/100, loss=0.6338 acc=0.8133
epoch 46, train 80/100, loss=0.6090 acc=0.7867
epoch 46, train 81/100, loss=0.8866 acc=0.6133
epoch 46, train 82/100, loss=0.7929 acc=0.7733
epoch 46, train 83/100, loss=1.1588 acc=0.6533
epoch 46, train 84/100, loss=0.4114 acc=0.8133
epoch 46, train 85/100, loss=0.4524 acc=0.8533
epoch 46, train 86/100, loss=0.2732 acc=0.9333
epoch 46, train 87/100, loss=0.4345 acc=0.8267
epoch 46, train 88/100, loss=0.6912 acc=0.7467
epoch 46, train 89/100, loss=0.4848 acc=0.9067
epoch 46, train 90/100, loss=0.0974 acc=0.9733
epoch 46, train 91/100, loss=0.3959 acc=0.8667
epoch 46, train 92/100, loss=0.3167 acc=0.8800
epoch 46, train 93/100, loss=0.2285 acc=0.9333
epoch 46, train 94/100, loss=1.1125 acc=0.6533
epoch 46, train 95/100, loss=0.3966 acc=0.8267
epoch 46, train 96/100, loss=0.6098 acc=0.7867
epoch 46, train 97/100, loss=0.2799 acc=0.9067
epoch 46, train 98/100, loss=0.2567 acc=0.9067
epoch 46, train 99/100, loss=0.7789 acc=0.7733
epoch 46, train 100/100, loss=0.7136 acc=0.8400
best epoch 41, best val acc=0.7162
epoch 46, val, loss=0.9461 acc=0.7091
ETA:3.0h/6.4h
epoch 47, train 1/100, loss=0.1452 acc=0.9733
epoch 47, train 2/100, loss=0.2842 acc=0.9333
epoch 47, train 3/100, loss=0.2161 acc=0.9067
epoch 47, train 4/100, loss=0.7649 acc=0.7067
epoch 47, train 5/100, loss=0.3145 acc=0.8800
epoch 47, train 6/100, loss=0.3059 acc=0.8933
epoch 47, train 7/100, loss=1.2427 acc=0.6400
epoch 47, train 8/100, loss=0.4878 acc=0.8133
epoch 47, train 9/100, loss=1.3808 acc=0.5867
epoch 47, train 10/100, loss=0.2800 acc=0.9200
epoch 47, train 11/100, loss=0.3134 acc=0.9333
epoch 47, train 12/100, loss=0.3578 acc=0.8533
epoch 47, train 13/100, loss=0.2447 acc=0.9467
epoch 47, train 14/100, loss=1.0404 acc=0.7200
epoch 47, train 15/100, loss=0.3651 acc=0.8800
epoch 47, train 16/100, loss=0.4236 acc=0.8800
epoch 47, train 17/100, loss=0.2085 acc=0.9333
epoch 47, train 18/100, loss=0.8536 acc=0.7200
epoch 47, train 19/100, loss=0.9157 acc=0.6000
epoch 47, train 20/100, loss=0.2566 acc=0.9333
epoch 47, train 21/100, loss=0.2038 acc=0.9600
epoch 47, train 22/100, loss=0.0625 acc=0.9733
epoch 47, train 23/100, loss=0.4445 acc=0.7867
epoch 47, train 24/100, loss=0.3957 acc=0.8533
epoch 47, train 25/100, loss=0.6984 acc=0.7200
epoch 47, train 26/100, loss=0.6805 acc=0.8267
epoch 47, train 27/100, loss=0.4510 acc=0.8800
epoch 47, train 28/100, loss=0.1680 acc=0.9600
epoch 47, train 29/100, loss=0.4541 acc=0.8400
epoch 47, train 30/100, loss=0.6075 acc=0.8533
epoch 47, train 31/100, loss=0.2747 acc=0.8800
epoch 47, train 32/100, loss=0.4093 acc=0.8933
epoch 47, train 33/100, loss=1.1349 acc=0.6667
epoch 47, train 34/100, loss=0.1242 acc=0.9600
epoch 47, train 35/100, loss=0.3773 acc=0.8267
epoch 47, train 36/100, loss=0.4022 acc=0.7600
epoch 47, train 37/100, loss=0.2418 acc=0.9200
epoch 47, train 38/100, loss=0.2382 acc=0.9333
epoch 47, train 39/100, loss=0.2175 acc=0.9067
epoch 47, train 40/100, loss=0.5903 acc=0.7867
epoch 47, train 41/100, loss=0.7568 acc=0.8000
epoch 47, train 42/100, loss=0.2538 acc=0.8533
epoch 47, train 43/100, loss=0.4108 acc=0.8267
epoch 47, train 44/100, loss=0.1293 acc=0.9467
epoch 47, train 45/100, loss=0.0832 acc=0.9867
epoch 47, train 46/100, loss=0.5551 acc=0.8267
epoch 47, train 47/100, loss=0.7215 acc=0.7333
epoch 47, train 48/100, loss=0.7814 acc=0.7200
epoch 47, train 49/100, loss=0.9068 acc=0.6933
epoch 47, train 50/100, loss=1.1583 acc=0.6000
epoch 47, train 51/100, loss=0.0860 acc=0.9733
epoch 47, train 52/100, loss=0.4671 acc=0.8000
epoch 47, train 53/100, loss=0.7828 acc=0.8000
epoch 47, train 54/100, loss=0.6153 acc=0.8267
epoch 47, train 55/100, loss=0.2109 acc=0.9200
epoch 47, train 56/100, loss=0.4643 acc=0.8400
epoch 47, train 57/100, loss=1.1684 acc=0.5733
epoch 47, train 58/100, loss=0.0650 acc=0.9600
epoch 47, train 59/100, loss=0.3021 acc=0.8267
epoch 47, train 60/100, loss=0.6315 acc=0.7600
epoch 47, train 61/100, loss=1.0234 acc=0.7333
epoch 47, train 62/100, loss=0.4901 acc=0.8133
epoch 47, train 63/100, loss=0.2652 acc=0.9067
epoch 47, train 64/100, loss=0.4140 acc=0.8667
epoch 47, train 65/100, loss=0.3127 acc=0.8533
epoch 47, train 66/100, loss=0.2007 acc=0.9333
epoch 47, train 67/100, loss=0.2288 acc=0.9200
epoch 47, train 68/100, loss=1.0155 acc=0.6400
epoch 47, train 69/100, loss=0.7556 acc=0.7333
epoch 47, train 70/100, loss=0.7679 acc=0.7867
epoch 47, train 71/100, loss=0.5050 acc=0.8533
epoch 47, train 72/100, loss=0.6071 acc=0.7600
epoch 47, train 73/100, loss=0.7571 acc=0.8000
epoch 47, train 74/100, loss=0.2836 acc=0.8800
epoch 47, train 75/100, loss=0.5664 acc=0.7867
epoch 47, train 76/100, loss=0.5969 acc=0.7733
epoch 47, train 77/100, loss=0.1763 acc=0.9733
epoch 47, train 78/100, loss=0.3184 acc=0.8533
epoch 47, train 79/100, loss=0.4714 acc=0.8533
epoch 47, train 80/100, loss=0.3368 acc=0.9200
epoch 47, train 81/100, loss=0.5255 acc=0.8533
epoch 47, train 82/100, loss=0.4522 acc=0.8533
epoch 47, train 83/100, loss=0.4864 acc=0.8533
epoch 47, train 84/100, loss=0.5493 acc=0.8000
epoch 47, train 85/100, loss=0.2020 acc=0.9200
epoch 47, train 86/100, loss=1.3429 acc=0.5600
epoch 47, train 87/100, loss=0.8054 acc=0.7067
epoch 47, train 88/100, loss=0.1182 acc=0.9600
epoch 47, train 89/100, loss=0.4475 acc=0.8933
epoch 47, train 90/100, loss=0.8432 acc=0.7200
epoch 47, train 91/100, loss=0.5534 acc=0.7600
epoch 47, train 92/100, loss=0.4741 acc=0.8400
epoch 47, train 93/100, loss=0.3717 acc=0.8267
epoch 47, train 94/100, loss=0.1756 acc=0.9467
epoch 47, train 95/100, loss=0.6065 acc=0.7600
epoch 47, train 96/100, loss=0.6236 acc=0.8267
epoch 47, train 97/100, loss=0.8212 acc=0.7467
epoch 47, train 98/100, loss=0.7652 acc=0.7333
epoch 47, train 99/100, loss=0.5478 acc=0.7867
epoch 47, train 100/100, loss=0.2093 acc=0.9333
best epoch 41, best val acc=0.7162
epoch 47, val, loss=0.9484 acc=0.7106
ETA:3.0h/6.4h
epoch 48, train 1/100, loss=0.3369 acc=0.8800
epoch 48, train 2/100, loss=0.6439 acc=0.8133
epoch 48, train 3/100, loss=0.1823 acc=0.8933
epoch 48, train 4/100, loss=0.1938 acc=0.9333
epoch 48, train 5/100, loss=0.5197 acc=0.8267
epoch 48, train 6/100, loss=0.2015 acc=0.9200
epoch 48, train 7/100, loss=0.3886 acc=0.8800
epoch 48, train 8/100, loss=0.6076 acc=0.8267
epoch 48, train 9/100, loss=0.2381 acc=0.9333
epoch 48, train 10/100, loss=0.3478 acc=0.8800
epoch 48, train 11/100, loss=0.3054 acc=0.9067
epoch 48, train 12/100, loss=0.9310 acc=0.7333
epoch 48, train 13/100, loss=0.4131 acc=0.9067
epoch 48, train 14/100, loss=0.6072 acc=0.7600
epoch 48, train 15/100, loss=0.3786 acc=0.8667
epoch 48, train 16/100, loss=0.9334 acc=0.7733
epoch 48, train 17/100, loss=0.4644 acc=0.8933
epoch 48, train 18/100, loss=0.2293 acc=0.9600
epoch 48, train 19/100, loss=0.2724 acc=0.9200
epoch 48, train 20/100, loss=0.4741 acc=0.8533
epoch 48, train 21/100, loss=0.8185 acc=0.7467
epoch 48, train 22/100, loss=0.3045 acc=0.8800
epoch 48, train 23/100, loss=0.4493 acc=0.8800
epoch 48, train 24/100, loss=0.6317 acc=0.7733
epoch 48, train 25/100, loss=0.4600 acc=0.8667
epoch 48, train 26/100, loss=0.3441 acc=0.8400
epoch 48, train 27/100, loss=0.6040 acc=0.7600
epoch 48, train 28/100, loss=0.3863 acc=0.8800
epoch 48, train 29/100, loss=0.7932 acc=0.6800
epoch 48, train 30/100, loss=0.7847 acc=0.7467
epoch 48, train 31/100, loss=0.4060 acc=0.8533
epoch 48, train 32/100, loss=0.2751 acc=0.8933
epoch 48, train 33/100, loss=0.1407 acc=0.9600
epoch 48, train 34/100, loss=0.3656 acc=0.8667
epoch 48, train 35/100, loss=1.2967 acc=0.5867
epoch 48, train 36/100, loss=0.4950 acc=0.8533
epoch 48, train 37/100, loss=0.3059 acc=0.9200
epoch 48, train 38/100, loss=1.3160 acc=0.6667
epoch 48, train 39/100, loss=0.4514 acc=0.8000
epoch 48, train 40/100, loss=0.0792 acc=0.9600
epoch 48, train 41/100, loss=0.2748 acc=0.9467
epoch 48, train 42/100, loss=0.9184 acc=0.7333
epoch 48, train 43/100, loss=1.1550 acc=0.6800
epoch 48, train 44/100, loss=1.2810 acc=0.5733
epoch 48, train 45/100, loss=0.2159 acc=0.9200
epoch 48, train 46/100, loss=0.8585 acc=0.7200
epoch 48, train 47/100, loss=0.2548 acc=0.8933
epoch 48, train 48/100, loss=0.4061 acc=0.8400
epoch 48, train 49/100, loss=0.4518 acc=0.8000
epoch 48, train 50/100, loss=1.0940 acc=0.6267
epoch 48, train 51/100, loss=0.4549 acc=0.8933
epoch 48, train 52/100, loss=0.7850 acc=0.7600
epoch 48, train 53/100, loss=0.8931 acc=0.7200
epoch 48, train 54/100, loss=0.3171 acc=0.8933
epoch 48, train 55/100, loss=0.3567 acc=0.8933
epoch 48, train 56/100, loss=0.1166 acc=0.9467
epoch 48, train 57/100, loss=1.0638 acc=0.6400
epoch 48, train 58/100, loss=0.2632 acc=0.9067
epoch 48, train 59/100, loss=0.1067 acc=0.9600
epoch 48, train 60/100, loss=1.0962 acc=0.6133
epoch 48, train 61/100, loss=0.5645 acc=0.8133
epoch 48, train 62/100, loss=0.4980 acc=0.8133
epoch 48, train 63/100, loss=0.1352 acc=0.9867
epoch 48, train 64/100, loss=0.5777 acc=0.7733
epoch 48, train 65/100, loss=0.3091 acc=0.9067
epoch 48, train 66/100, loss=0.5379 acc=0.8267
epoch 48, train 67/100, loss=0.0887 acc=0.9600
epoch 48, train 68/100, loss=0.7234 acc=0.7867
epoch 48, train 69/100, loss=0.2974 acc=0.8667
epoch 48, train 70/100, loss=0.8116 acc=0.7200
epoch 48, train 71/100, loss=0.2616 acc=0.9200
epoch 48, train 72/100, loss=0.6198 acc=0.7600
epoch 48, train 73/100, loss=1.5722 acc=0.6000
epoch 48, train 74/100, loss=0.8217 acc=0.6533
epoch 48, train 75/100, loss=0.4687 acc=0.8533
epoch 48, train 76/100, loss=0.8429 acc=0.6400
epoch 48, train 77/100, loss=0.3411 acc=0.8800
epoch 48, train 78/100, loss=0.1539 acc=0.9600
epoch 48, train 79/100, loss=0.3351 acc=0.8800
epoch 48, train 80/100, loss=0.9039 acc=0.6400
epoch 48, train 81/100, loss=1.5339 acc=0.6267
epoch 48, train 82/100, loss=0.0728 acc=0.9867
epoch 48, train 83/100, loss=0.1594 acc=0.9067
epoch 48, train 84/100, loss=0.5239 acc=0.8267
epoch 48, train 85/100, loss=0.3544 acc=0.8800
epoch 48, train 86/100, loss=0.4792 acc=0.8800
epoch 48, train 87/100, loss=1.1269 acc=0.6533
epoch 48, train 88/100, loss=0.6506 acc=0.7467
epoch 48, train 89/100, loss=0.3362 acc=0.9067
epoch 48, train 90/100, loss=1.2620 acc=0.5600
epoch 48, train 91/100, loss=0.5349 acc=0.7467
epoch 48, train 92/100, loss=0.6179 acc=0.7733
epoch 48, train 93/100, loss=0.4283 acc=0.8667
epoch 48, train 94/100, loss=0.4185 acc=0.8533
epoch 48, train 95/100, loss=0.6763 acc=0.8000
epoch 48, train 96/100, loss=0.1740 acc=0.9200
epoch 48, train 97/100, loss=0.4721 acc=0.8000
epoch 48, train 98/100, loss=0.4777 acc=0.8133
epoch 48, train 99/100, loss=0.2113 acc=0.9200
epoch 48, train 100/100, loss=0.6858 acc=0.7600
best epoch 41, best val acc=0.7162
epoch 48, val, loss=0.9611 acc=0.7058
ETA:3.1h/6.4h
epoch 49, train 1/100, loss=0.5488 acc=0.8133
epoch 49, train 2/100, loss=0.3209 acc=0.8933
epoch 49, train 3/100, loss=0.0671 acc=0.9733
epoch 49, train 4/100, loss=0.0778 acc=0.9733
epoch 49, train 5/100, loss=1.0058 acc=0.6933
epoch 49, train 6/100, loss=0.3671 acc=0.9200
epoch 49, train 7/100, loss=0.9675 acc=0.7200
epoch 49, train 8/100, loss=0.6879 acc=0.7333
epoch 49, train 9/100, loss=0.9185 acc=0.7333
epoch 49, train 10/100, loss=0.5825 acc=0.8267
epoch 49, train 11/100, loss=0.3504 acc=0.8667
epoch 49, train 12/100, loss=0.5338 acc=0.8400
epoch 49, train 13/100, loss=0.7135 acc=0.7467
epoch 49, train 14/100, loss=0.6496 acc=0.8667
epoch 49, train 15/100, loss=0.9048 acc=0.6933
epoch 49, train 16/100, loss=0.2834 acc=0.9200
epoch 49, train 17/100, loss=1.2681 acc=0.6000
epoch 49, train 18/100, loss=0.2311 acc=0.9333
epoch 49, train 19/100, loss=0.5150 acc=0.8133
epoch 49, train 20/100, loss=0.9583 acc=0.6000
epoch 49, train 21/100, loss=0.3121 acc=0.9200
epoch 49, train 22/100, loss=0.2615 acc=0.9067
epoch 49, train 23/100, loss=0.3649 acc=0.8933
epoch 49, train 24/100, loss=0.7938 acc=0.7200
epoch 49, train 25/100, loss=0.5820 acc=0.7867
epoch 49, train 26/100, loss=0.5239 acc=0.8000
epoch 49, train 27/100, loss=0.2973 acc=0.8533
epoch 49, train 28/100, loss=0.7924 acc=0.7467
epoch 49, train 29/100, loss=0.5700 acc=0.8000
epoch 49, train 30/100, loss=0.7044 acc=0.8000
epoch 49, train 31/100, loss=0.7073 acc=0.7733
epoch 49, train 32/100, loss=0.4844 acc=0.8667
epoch 49, train 33/100, loss=0.2098 acc=0.8933
epoch 49, train 34/100, loss=0.3979 acc=0.8933
epoch 49, train 35/100, loss=0.4050 acc=0.8400
epoch 49, train 36/100, loss=1.0941 acc=0.7067
epoch 49, train 37/100, loss=0.7829 acc=0.7600
epoch 49, train 38/100, loss=0.4604 acc=0.8667
epoch 49, train 39/100, loss=0.3918 acc=0.8533
epoch 49, train 40/100, loss=0.3650 acc=0.8667
epoch 49, train 41/100, loss=0.2996 acc=0.8933
epoch 49, train 42/100, loss=0.5402 acc=0.8267
epoch 49, train 43/100, loss=0.9068 acc=0.7333
epoch 49, train 44/100, loss=0.5024 acc=0.8267
epoch 49, train 45/100, loss=0.2528 acc=0.8933
epoch 49, train 46/100, loss=0.5650 acc=0.7867
epoch 49, train 47/100, loss=1.8576 acc=0.4933
epoch 49, train 48/100, loss=1.1272 acc=0.5867
epoch 49, train 49/100, loss=1.1172 acc=0.7067
epoch 49, train 50/100, loss=0.3002 acc=0.8933
epoch 49, train 51/100, loss=0.8834 acc=0.7867
epoch 49, train 52/100, loss=0.5422 acc=0.8533
epoch 49, train 53/100, loss=0.6033 acc=0.8267
epoch 49, train 54/100, loss=0.9941 acc=0.6133
epoch 49, train 55/100, loss=0.1624 acc=0.9467
epoch 49, train 56/100, loss=0.4727 acc=0.8800
epoch 49, train 57/100, loss=0.8603 acc=0.6667
epoch 49, train 58/100, loss=0.4369 acc=0.8267
epoch 49, train 59/100, loss=0.6322 acc=0.7600
epoch 49, train 60/100, loss=0.0805 acc=0.9600
epoch 49, train 61/100, loss=0.3212 acc=0.9067
epoch 49, train 62/100, loss=0.7190 acc=0.7867
epoch 49, train 63/100, loss=0.5565 acc=0.8133
epoch 49, train 64/100, loss=1.1256 acc=0.6800
epoch 49, train 65/100, loss=1.2279 acc=0.6133
epoch 49, train 66/100, loss=0.5819 acc=0.8000
epoch 49, train 67/100, loss=0.4744 acc=0.8533
epoch 49, train 68/100, loss=0.3240 acc=0.8933
epoch 49, train 69/100, loss=1.5561 acc=0.5333
epoch 49, train 70/100, loss=0.1515 acc=0.9600
epoch 49, train 71/100, loss=0.3151 acc=0.9333
epoch 49, train 72/100, loss=0.6309 acc=0.8000
epoch 49, train 73/100, loss=0.9279 acc=0.6933
epoch 49, train 74/100, loss=1.1574 acc=0.6133
epoch 49, train 75/100, loss=0.6743 acc=0.7467
epoch 49, train 76/100, loss=0.5639 acc=0.7733
epoch 49, train 77/100, loss=1.4202 acc=0.6000
epoch 49, train 78/100, loss=0.4940 acc=0.8533
epoch 49, train 79/100, loss=0.2363 acc=0.9067
epoch 49, train 80/100, loss=0.1595 acc=0.9333
epoch 49, train 81/100, loss=0.2832 acc=0.8800
epoch 49, train 82/100, loss=1.5769 acc=0.5467
epoch 49, train 83/100, loss=1.7942 acc=0.4667
epoch 49, train 84/100, loss=0.4873 acc=0.8533
epoch 49, train 85/100, loss=1.2701 acc=0.6400
epoch 49, train 86/100, loss=0.5019 acc=0.8400
epoch 49, train 87/100, loss=0.8524 acc=0.7333
epoch 49, train 88/100, loss=0.4068 acc=0.8267
epoch 49, train 89/100, loss=0.3723 acc=0.8533
epoch 49, train 90/100, loss=0.1708 acc=0.9467
epoch 49, train 91/100, loss=0.1462 acc=0.9600
epoch 49, train 92/100, loss=0.6226 acc=0.8533
epoch 49, train 93/100, loss=0.4477 acc=0.8667
epoch 49, train 94/100, loss=0.9525 acc=0.8133
epoch 49, train 95/100, loss=0.5381 acc=0.8400
epoch 49, train 96/100, loss=0.1183 acc=0.9600
epoch 49, train 97/100, loss=0.3504 acc=0.8800
epoch 49, train 98/100, loss=0.2727 acc=0.8933
epoch 49, train 99/100, loss=0.6954 acc=0.6667
epoch 49, train 100/100, loss=0.8995 acc=0.7600
best epoch 41, best val acc=0.7162
epoch 49, val, loss=0.8984 acc=0.7193
ETA:3.1h/6.4h
epoch 50, train 1/100, loss=0.7733 acc=0.8000
epoch 50, train 2/100, loss=0.2112 acc=0.9333
epoch 50, train 3/100, loss=0.3332 acc=0.9333
epoch 50, train 4/100, loss=0.2237 acc=0.8933
epoch 50, train 5/100, loss=0.1643 acc=0.9467
epoch 50, train 6/100, loss=0.6519 acc=0.8133
epoch 50, train 7/100, loss=0.2314 acc=0.9067
epoch 50, train 8/100, loss=1.3970 acc=0.6000
epoch 50, train 9/100, loss=0.3329 acc=0.8933
epoch 50, train 10/100, loss=1.2975 acc=0.5600
epoch 50, train 11/100, loss=0.7413 acc=0.7867
epoch 50, train 12/100, loss=0.5004 acc=0.8133
epoch 50, train 13/100, loss=0.5998 acc=0.8400
epoch 50, train 14/100, loss=0.2034 acc=0.9200
epoch 50, train 15/100, loss=0.2457 acc=0.9467
epoch 50, train 16/100, loss=0.4540 acc=0.8400
epoch 50, train 17/100, loss=0.8506 acc=0.7600
epoch 50, train 18/100, loss=0.5801 acc=0.7867
epoch 50, train 19/100, loss=0.4224 acc=0.8800
epoch 50, train 20/100, loss=0.2906 acc=0.9067
epoch 50, train 21/100, loss=0.2806 acc=0.8933
epoch 50, train 22/100, loss=0.9306 acc=0.7467
epoch 50, train 23/100, loss=0.1397 acc=0.9600
epoch 50, train 24/100, loss=0.7954 acc=0.7600
epoch 50, train 25/100, loss=0.1907 acc=0.9200
epoch 50, train 26/100, loss=0.2740 acc=0.8933
epoch 50, train 27/100, loss=0.1894 acc=0.9333
epoch 50, train 28/100, loss=1.0709 acc=0.6667
epoch 50, train 29/100, loss=0.2005 acc=0.9067
epoch 50, train 30/100, loss=0.7633 acc=0.7333
epoch 50, train 31/100, loss=0.1281 acc=0.9600
epoch 50, train 32/100, loss=0.5352 acc=0.8533
epoch 50, train 33/100, loss=0.0126 acc=1.0000
epoch 50, train 34/100, loss=0.4914 acc=0.8267
epoch 50, train 35/100, loss=1.6368 acc=0.6133
epoch 50, train 36/100, loss=0.5214 acc=0.8000
epoch 50, train 37/100, loss=0.4428 acc=0.8533
epoch 50, train 38/100, loss=1.5847 acc=0.5467
epoch 50, train 39/100, loss=0.2631 acc=0.9067
epoch 50, train 40/100, loss=1.8493 acc=0.4400
epoch 50, train 41/100, loss=1.0976 acc=0.6667
epoch 50, train 42/100, loss=0.2674 acc=0.8933
epoch 50, train 43/100, loss=1.0938 acc=0.6000
epoch 50, train 44/100, loss=0.7259 acc=0.7733
epoch 50, train 45/100, loss=0.4058 acc=0.8800
epoch 50, train 46/100, loss=0.3690 acc=0.8800
epoch 50, train 47/100, loss=0.6934 acc=0.7333
epoch 50, train 48/100, loss=0.6559 acc=0.7867
epoch 50, train 49/100, loss=0.0305 acc=1.0000
epoch 50, train 50/100, loss=0.4448 acc=0.8533
epoch 50, train 51/100, loss=0.4525 acc=0.8400
epoch 50, train 52/100, loss=0.7452 acc=0.7600
epoch 50, train 53/100, loss=1.1239 acc=0.6133
epoch 50, train 54/100, loss=0.3020 acc=0.9467
epoch 50, train 55/100, loss=0.5537 acc=0.8133
epoch 50, train 56/100, loss=0.5681 acc=0.7600
epoch 50, train 57/100, loss=0.2868 acc=0.8933
epoch 50, train 58/100, loss=0.5961 acc=0.8667
epoch 50, train 59/100, loss=0.6919 acc=0.7867
epoch 50, train 60/100, loss=0.4109 acc=0.8533
epoch 50, train 61/100, loss=0.9053 acc=0.6400
epoch 50, train 62/100, loss=1.3853 acc=0.4667
epoch 50, train 63/100, loss=0.0830 acc=0.9600
epoch 50, train 64/100, loss=0.5843 acc=0.7333
epoch 50, train 65/100, loss=0.3770 acc=0.8533
epoch 50, train 66/100, loss=0.2438 acc=0.9200
epoch 50, train 67/100, loss=0.3305 acc=0.8267
epoch 50, train 68/100, loss=0.2690 acc=0.9200
epoch 50, train 69/100, loss=0.5692 acc=0.8267
epoch 50, train 70/100, loss=0.1148 acc=0.9733
epoch 50, train 71/100, loss=0.4485 acc=0.8533
epoch 50, train 72/100, loss=0.7397 acc=0.7200
epoch 50, train 73/100, loss=0.6461 acc=0.8000
epoch 50, train 74/100, loss=0.0716 acc=0.9733
epoch 50, train 75/100, loss=0.2543 acc=0.8800
epoch 50, train 76/100, loss=0.6268 acc=0.7200
epoch 50, train 77/100, loss=0.7484 acc=0.7600
epoch 50, train 78/100, loss=1.3483 acc=0.5867
epoch 50, train 79/100, loss=0.3857 acc=0.8533
epoch 50, train 80/100, loss=0.4069 acc=0.8667
epoch 50, train 81/100, loss=0.5504 acc=0.8267
epoch 50, train 82/100, loss=0.8422 acc=0.7333
epoch 50, train 83/100, loss=0.4182 acc=0.8800
epoch 50, train 84/100, loss=0.2460 acc=0.9467
epoch 50, train 85/100, loss=0.7446 acc=0.7733
epoch 50, train 86/100, loss=0.3279 acc=0.9467
epoch 50, train 87/100, loss=0.4238 acc=0.8533
epoch 50, train 88/100, loss=0.0799 acc=0.9867
epoch 50, train 89/100, loss=0.2916 acc=0.8800
epoch 50, train 90/100, loss=0.2711 acc=0.9200
epoch 50, train 91/100, loss=0.7732 acc=0.7733
epoch 50, train 92/100, loss=0.1398 acc=0.9333
epoch 50, train 93/100, loss=1.0322 acc=0.6933
epoch 50, train 94/100, loss=0.5264 acc=0.8533
epoch 50, train 95/100, loss=0.4341 acc=0.8800
epoch 50, train 96/100, loss=0.3093 acc=0.8933
epoch 50, train 97/100, loss=0.1553 acc=0.9067
epoch 50, train 98/100, loss=0.1777 acc=0.9467
epoch 50, train 99/100, loss=1.1270 acc=0.6000
epoch 50, train 100/100, loss=0.1058 acc=0.9467
best epoch 49, best val acc=0.7193
epoch 50, val, loss=0.8983 acc=0.7228
ETA:3.2h/6.4h
epoch 51, train 1/100, loss=0.4123 acc=0.8267
epoch 51, train 2/100, loss=0.2354 acc=0.9467
epoch 51, train 3/100, loss=1.0002 acc=0.6800
epoch 51, train 4/100, loss=0.3553 acc=0.8933
epoch 51, train 5/100, loss=0.5846 acc=0.7600
epoch 51, train 6/100, loss=0.7322 acc=0.8000
epoch 51, train 7/100, loss=0.1541 acc=0.9333
epoch 51, train 8/100, loss=0.1662 acc=0.9333
epoch 51, train 9/100, loss=0.3914 acc=0.8267
epoch 51, train 10/100, loss=0.3011 acc=0.9200
epoch 51, train 11/100, loss=0.4964 acc=0.8133
epoch 51, train 12/100, loss=0.4272 acc=0.8533
epoch 51, train 13/100, loss=0.2886 acc=0.9067
epoch 51, train 14/100, loss=0.4249 acc=0.8533
epoch 51, train 15/100, loss=0.8124 acc=0.7333
epoch 51, train 16/100, loss=0.6367 acc=0.8667
epoch 51, train 17/100, loss=0.4620 acc=0.8133
epoch 51, train 18/100, loss=0.2948 acc=0.8667
epoch 51, train 19/100, loss=1.4133 acc=0.4933
epoch 51, train 20/100, loss=0.5561 acc=0.8000
epoch 51, train 21/100, loss=0.4994 acc=0.8133
epoch 51, train 22/100, loss=0.3079 acc=0.9067
epoch 51, train 23/100, loss=1.0488 acc=0.5867
epoch 51, train 24/100, loss=0.7301 acc=0.7200
epoch 51, train 25/100, loss=0.6311 acc=0.8133
epoch 51, train 26/100, loss=0.2410 acc=0.9200
epoch 51, train 27/100, loss=0.6146 acc=0.8133
epoch 51, train 28/100, loss=0.4819 acc=0.8000
epoch 51, train 29/100, loss=0.4030 acc=0.8267
epoch 51, train 30/100, loss=0.3601 acc=0.8800
epoch 51, train 31/100, loss=0.1267 acc=0.9333
epoch 51, train 32/100, loss=0.7849 acc=0.7733
epoch 51, train 33/100, loss=1.2971 acc=0.6267
epoch 51, train 34/100, loss=0.4489 acc=0.8000
epoch 51, train 35/100, loss=1.0446 acc=0.6800
epoch 51, train 36/100, loss=0.9996 acc=0.7600
epoch 51, train 37/100, loss=0.4987 acc=0.8000
epoch 51, train 38/100, loss=0.5770 acc=0.7867
epoch 51, train 39/100, loss=0.1825 acc=0.9600
epoch 51, train 40/100, loss=0.3769 acc=0.8533
epoch 51, train 41/100, loss=0.7014 acc=0.8000
epoch 51, train 42/100, loss=1.1633 acc=0.5333
epoch 51, train 43/100, loss=0.6159 acc=0.7200
epoch 51, train 44/100, loss=0.8701 acc=0.7200
epoch 51, train 45/100, loss=0.6911 acc=0.7200
epoch 51, train 46/100, loss=1.0187 acc=0.6933
epoch 51, train 47/100, loss=0.4006 acc=0.8800
epoch 51, train 48/100, loss=1.0635 acc=0.6533
epoch 51, train 49/100, loss=0.3308 acc=0.8133
epoch 51, train 50/100, loss=0.9994 acc=0.6400
epoch 51, train 51/100, loss=0.7958 acc=0.7733
epoch 51, train 52/100, loss=0.4499 acc=0.8533
epoch 51, train 53/100, loss=0.3066 acc=0.8933
epoch 51, train 54/100, loss=1.0783 acc=0.6133
epoch 51, train 55/100, loss=0.2576 acc=0.9067
epoch 51, train 56/100, loss=0.6687 acc=0.8000
epoch 51, train 57/100, loss=0.3232 acc=0.8800
epoch 51, train 58/100, loss=0.2777 acc=0.8667
epoch 51, train 59/100, loss=1.1993 acc=0.6133
epoch 51, train 60/100, loss=0.3159 acc=0.8800
epoch 51, train 61/100, loss=0.4592 acc=0.8400
epoch 51, train 62/100, loss=1.0877 acc=0.6667
epoch 51, train 63/100, loss=0.5141 acc=0.8667
epoch 51, train 64/100, loss=0.9480 acc=0.7067
epoch 51, train 65/100, loss=0.8845 acc=0.6800
epoch 51, train 66/100, loss=0.3081 acc=0.8800
epoch 51, train 67/100, loss=0.2434 acc=0.9200
epoch 51, train 68/100, loss=0.2332 acc=0.9333
epoch 51, train 69/100, loss=0.6416 acc=0.7733
epoch 51, train 70/100, loss=0.0800 acc=0.9600
epoch 51, train 71/100, loss=0.2951 acc=0.9200
epoch 51, train 72/100, loss=0.2088 acc=0.9333
epoch 51, train 73/100, loss=0.9407 acc=0.7333
epoch 51, train 74/100, loss=0.3081 acc=0.9067
epoch 51, train 75/100, loss=0.6817 acc=0.7600
epoch 51, train 76/100, loss=0.5412 acc=0.8533
epoch 51, train 77/100, loss=0.3712 acc=0.8933
epoch 51, train 78/100, loss=0.5923 acc=0.7600
epoch 51, train 79/100, loss=0.3717 acc=0.8800
epoch 51, train 80/100, loss=0.7782 acc=0.7067
epoch 51, train 81/100, loss=0.8159 acc=0.7067
epoch 51, train 82/100, loss=0.0302 acc=1.0000
epoch 51, train 83/100, loss=1.1094 acc=0.6400
epoch 51, train 84/100, loss=0.3692 acc=0.8667
epoch 51, train 85/100, loss=0.2211 acc=0.9067
epoch 51, train 86/100, loss=0.4099 acc=0.8400
epoch 51, train 87/100, loss=0.4758 acc=0.8000
epoch 51, train 88/100, loss=0.6617 acc=0.8800
epoch 51, train 89/100, loss=0.2818 acc=0.8933
epoch 51, train 90/100, loss=0.6039 acc=0.7600
epoch 51, train 91/100, loss=0.1580 acc=0.9600
epoch 51, train 92/100, loss=0.4762 acc=0.8133
epoch 51, train 93/100, loss=0.5013 acc=0.8800
epoch 51, train 94/100, loss=0.2141 acc=0.9200
epoch 51, train 95/100, loss=0.3865 acc=0.8800
epoch 51, train 96/100, loss=1.2543 acc=0.6800
epoch 51, train 97/100, loss=0.6610 acc=0.7467
epoch 51, train 98/100, loss=0.4161 acc=0.9200
epoch 51, train 99/100, loss=0.5576 acc=0.8000
epoch 51, train 100/100, loss=0.5484 acc=0.8133
best epoch 50, best val acc=0.7228
epoch 51, val, loss=0.9192 acc=0.7146
ETA:3.3h/6.4h
epoch 52, train 1/100, loss=0.3448 acc=0.8267
epoch 52, train 2/100, loss=0.8198 acc=0.7333
epoch 52, train 3/100, loss=0.4210 acc=0.8667
epoch 52, train 4/100, loss=0.2275 acc=0.9200
epoch 52, train 5/100, loss=0.3582 acc=0.8400
epoch 52, train 6/100, loss=0.9955 acc=0.7067
epoch 52, train 7/100, loss=0.2176 acc=0.9067
epoch 52, train 8/100, loss=0.2335 acc=0.9333
epoch 52, train 9/100, loss=0.5995 acc=0.8133
epoch 52, train 10/100, loss=0.5549 acc=0.8267
epoch 52, train 11/100, loss=0.4392 acc=0.8933
epoch 52, train 12/100, loss=0.8983 acc=0.7200
epoch 52, train 13/100, loss=0.2396 acc=0.8400
epoch 52, train 14/100, loss=0.0594 acc=0.9867
epoch 52, train 15/100, loss=0.1082 acc=0.9600
epoch 52, train 16/100, loss=0.4486 acc=0.8400
epoch 52, train 17/100, loss=0.4570 acc=0.8400
epoch 52, train 18/100, loss=0.1567 acc=0.9467
epoch 52, train 19/100, loss=0.3012 acc=0.8933
epoch 52, train 20/100, loss=0.7325 acc=0.6800
epoch 52, train 21/100, loss=0.6750 acc=0.7200
epoch 52, train 22/100, loss=0.3284 acc=0.8933
epoch 52, train 23/100, loss=1.4875 acc=0.5333
epoch 52, train 24/100, loss=0.0824 acc=0.9600
epoch 52, train 25/100, loss=0.9308 acc=0.7200
epoch 52, train 26/100, loss=0.2335 acc=0.9067
epoch 52, train 27/100, loss=0.2366 acc=0.9467
epoch 52, train 28/100, loss=0.5029 acc=0.8267
epoch 52, train 29/100, loss=0.5028 acc=0.8000
epoch 52, train 30/100, loss=0.2117 acc=0.9067
epoch 52, train 31/100, loss=0.7726 acc=0.7333
epoch 52, train 32/100, loss=0.6383 acc=0.8267
epoch 52, train 33/100, loss=0.2014 acc=0.9200
epoch 52, train 34/100, loss=0.2056 acc=0.9200
epoch 52, train 35/100, loss=0.2540 acc=0.8933
epoch 52, train 36/100, loss=0.1560 acc=0.9867
epoch 52, train 37/100, loss=0.3223 acc=0.9333
epoch 52, train 38/100, loss=0.7319 acc=0.7467
epoch 52, train 39/100, loss=0.7723 acc=0.6933
epoch 52, train 40/100, loss=0.1733 acc=0.9200
epoch 52, train 41/100, loss=0.1570 acc=0.9467
epoch 52, train 42/100, loss=0.3285 acc=0.8933
epoch 52, train 43/100, loss=0.7061 acc=0.7600
epoch 52, train 44/100, loss=0.1399 acc=0.9600
epoch 52, train 45/100, loss=0.3961 acc=0.8400
epoch 52, train 46/100, loss=0.3438 acc=0.8800
epoch 52, train 47/100, loss=0.6229 acc=0.8267
epoch 52, train 48/100, loss=0.5978 acc=0.7333
epoch 52, train 49/100, loss=1.3133 acc=0.6000
epoch 52, train 50/100, loss=0.1524 acc=0.9467
epoch 52, train 51/100, loss=0.3763 acc=0.8667
epoch 52, train 52/100, loss=0.6082 acc=0.8133
epoch 52, train 53/100, loss=0.1895 acc=0.9333
epoch 52, train 54/100, loss=0.3112 acc=0.8933
epoch 52, train 55/100, loss=0.7344 acc=0.6933
epoch 52, train 56/100, loss=0.7762 acc=0.6667
epoch 52, train 57/100, loss=0.1981 acc=0.9333
epoch 52, train 58/100, loss=0.6380 acc=0.8133
epoch 52, train 59/100, loss=0.4412 acc=0.8267
epoch 52, train 60/100, loss=0.6656 acc=0.7200
epoch 52, train 61/100, loss=0.8890 acc=0.7067
epoch 52, train 62/100, loss=1.1744 acc=0.6533
epoch 52, train 63/100, loss=0.6681 acc=0.7333
epoch 52, train 64/100, loss=0.1823 acc=0.9333
epoch 52, train 65/100, loss=0.5869 acc=0.7733
epoch 52, train 66/100, loss=0.3059 acc=0.9200
epoch 52, train 67/100, loss=0.3606 acc=0.8933
epoch 52, train 68/100, loss=0.2199 acc=0.9200
epoch 52, train 69/100, loss=0.4699 acc=0.8533
epoch 52, train 70/100, loss=0.1576 acc=0.9333
epoch 52, train 71/100, loss=0.4784 acc=0.8667
epoch 52, train 72/100, loss=0.3074 acc=0.9200
epoch 52, train 73/100, loss=0.1993 acc=0.9067
epoch 52, train 74/100, loss=0.2912 acc=0.8933
epoch 52, train 75/100, loss=0.5097 acc=0.8400
epoch 52, train 76/100, loss=0.2098 acc=0.9333
epoch 52, train 77/100, loss=0.3511 acc=0.8933
epoch 52, train 78/100, loss=0.6417 acc=0.7467
epoch 52, train 79/100, loss=0.6272 acc=0.8000
epoch 52, train 80/100, loss=0.1954 acc=0.9200
epoch 52, train 81/100, loss=0.5562 acc=0.8133
epoch 52, train 82/100, loss=0.1762 acc=0.9467
epoch 52, train 83/100, loss=0.2072 acc=0.9333
epoch 52, train 84/100, loss=0.8917 acc=0.7333
epoch 52, train 85/100, loss=0.8676 acc=0.6800
epoch 52, train 86/100, loss=0.1595 acc=0.9467
epoch 52, train 87/100, loss=0.6642 acc=0.8267
epoch 52, train 88/100, loss=0.2143 acc=0.9067
epoch 52, train 89/100, loss=0.3976 acc=0.8933
epoch 52, train 90/100, loss=0.7670 acc=0.7733
epoch 52, train 91/100, loss=0.1754 acc=0.9200
epoch 52, train 92/100, loss=0.9746 acc=0.6533
epoch 52, train 93/100, loss=0.3517 acc=0.8800
epoch 52, train 94/100, loss=0.6010 acc=0.7733
epoch 52, train 95/100, loss=0.8068 acc=0.7200
epoch 52, train 96/100, loss=0.7749 acc=0.7200
epoch 52, train 97/100, loss=0.8368 acc=0.7067
epoch 52, train 98/100, loss=0.6652 acc=0.8133
epoch 52, train 99/100, loss=0.9325 acc=0.7600
epoch 52, train 100/100, loss=1.1216 acc=0.6267
best epoch 50, best val acc=0.7228
epoch 52, val, loss=0.9365 acc=0.7095
ETA:3.3h/6.4h
epoch 53, train 1/100, loss=0.6484 acc=0.7600
epoch 53, train 2/100, loss=0.1448 acc=0.9600
epoch 53, train 3/100, loss=1.2049 acc=0.6667
epoch 53, train 4/100, loss=0.4942 acc=0.8267
epoch 53, train 5/100, loss=0.3333 acc=0.8533
epoch 53, train 6/100, loss=0.3279 acc=0.8667
epoch 53, train 7/100, loss=0.2417 acc=0.9200
epoch 53, train 8/100, loss=0.3774 acc=0.9467
epoch 53, train 9/100, loss=0.2167 acc=0.9333
epoch 53, train 10/100, loss=0.2117 acc=0.9333
epoch 53, train 11/100, loss=0.1039 acc=0.9600
epoch 53, train 12/100, loss=0.5837 acc=0.8000
epoch 53, train 13/100, loss=0.2159 acc=0.9200
epoch 53, train 14/100, loss=0.6596 acc=0.7200
epoch 53, train 15/100, loss=0.5218 acc=0.8133
epoch 53, train 16/100, loss=0.4504 acc=0.8533
epoch 53, train 17/100, loss=0.5298 acc=0.8133
epoch 53, train 18/100, loss=0.1826 acc=0.9200
epoch 53, train 19/100, loss=0.3290 acc=0.8533
epoch 53, train 20/100, loss=1.3990 acc=0.5333
epoch 53, train 21/100, loss=0.4190 acc=0.8667
epoch 53, train 22/100, loss=0.7156 acc=0.7333
epoch 53, train 23/100, loss=0.4587 acc=0.8400
epoch 53, train 24/100, loss=0.3457 acc=0.8667
epoch 53, train 25/100, loss=0.4116 acc=0.8533
epoch 53, train 26/100, loss=0.9403 acc=0.6667
epoch 53, train 27/100, loss=1.0661 acc=0.6533
epoch 53, train 28/100, loss=0.9232 acc=0.7200
epoch 53, train 29/100, loss=0.1147 acc=0.9733
epoch 53, train 30/100, loss=0.4072 acc=0.8933
epoch 53, train 31/100, loss=0.4992 acc=0.8533
epoch 53, train 32/100, loss=0.5039 acc=0.8267
epoch 53, train 33/100, loss=0.6168 acc=0.7467
epoch 53, train 34/100, loss=0.6883 acc=0.8133
epoch 53, train 35/100, loss=0.2174 acc=0.9200
epoch 53, train 36/100, loss=0.6083 acc=0.8400
epoch 53, train 37/100, loss=0.1680 acc=0.9333
epoch 53, train 38/100, loss=0.5094 acc=0.8400
epoch 53, train 39/100, loss=0.2254 acc=0.9067
epoch 53, train 40/100, loss=0.5711 acc=0.7600
epoch 53, train 41/100, loss=0.3327 acc=0.8667
epoch 53, train 42/100, loss=0.6870 acc=0.7467
epoch 53, train 43/100, loss=0.4854 acc=0.8400
epoch 53, train 44/100, loss=0.7590 acc=0.7600
epoch 53, train 45/100, loss=0.4472 acc=0.8800
epoch 53, train 46/100, loss=0.4630 acc=0.8267
epoch 53, train 47/100, loss=0.3366 acc=0.9067
epoch 53, train 48/100, loss=0.2968 acc=0.9067
epoch 53, train 49/100, loss=0.1171 acc=0.9733
epoch 53, train 50/100, loss=0.3602 acc=0.9067
epoch 53, train 51/100, loss=0.5116 acc=0.8533
epoch 53, train 52/100, loss=0.5922 acc=0.7867
epoch 53, train 53/100, loss=0.6990 acc=0.7333
epoch 53, train 54/100, loss=0.1955 acc=0.9467
epoch 53, train 55/100, loss=0.7497 acc=0.8133
epoch 53, train 56/100, loss=0.3577 acc=0.8400
epoch 53, train 57/100, loss=0.5722 acc=0.8000
epoch 53, train 58/100, loss=0.3442 acc=0.8667
epoch 53, train 59/100, loss=0.8817 acc=0.7067
epoch 53, train 60/100, loss=0.6086 acc=0.8000
epoch 53, train 61/100, loss=0.2064 acc=0.9200
epoch 53, train 62/100, loss=0.1700 acc=0.9600
epoch 53, train 63/100, loss=0.6508 acc=0.7733
epoch 53, train 64/100, loss=0.2198 acc=0.8800
epoch 53, train 65/100, loss=0.3622 acc=0.8800
epoch 53, train 66/100, loss=0.7052 acc=0.8133
epoch 53, train 67/100, loss=0.7523 acc=0.7600
epoch 53, train 68/100, loss=1.0025 acc=0.7067
epoch 53, train 69/100, loss=0.9839 acc=0.6667
epoch 53, train 70/100, loss=2.0312 acc=0.3467
epoch 53, train 71/100, loss=0.3071 acc=0.8800
epoch 53, train 72/100, loss=0.4082 acc=0.8800
epoch 53, train 73/100, loss=0.1493 acc=0.9600
epoch 53, train 74/100, loss=0.1996 acc=0.9600
epoch 53, train 75/100, loss=0.0559 acc=0.9867
epoch 53, train 76/100, loss=0.2289 acc=0.9333
epoch 53, train 77/100, loss=0.2648 acc=0.8800
epoch 53, train 78/100, loss=0.4511 acc=0.8533
epoch 53, train 79/100, loss=1.9837 acc=0.4267
epoch 53, train 80/100, loss=0.7127 acc=0.7600
epoch 53, train 81/100, loss=0.1712 acc=0.9467
epoch 53, train 82/100, loss=0.3964 acc=0.8267
epoch 53, train 83/100, loss=0.4236 acc=0.8933
epoch 53, train 84/100, loss=0.2552 acc=0.8800
epoch 53, train 85/100, loss=0.3533 acc=0.8933
epoch 53, train 86/100, loss=0.5076 acc=0.8400
epoch 53, train 87/100, loss=0.1986 acc=0.9200
epoch 53, train 88/100, loss=0.8211 acc=0.7200
epoch 53, train 89/100, loss=0.1760 acc=0.9333
epoch 53, train 90/100, loss=0.7367 acc=0.8000
epoch 53, train 91/100, loss=0.1625 acc=0.9333
epoch 53, train 92/100, loss=0.2282 acc=0.9200
epoch 53, train 93/100, loss=0.3302 acc=0.8933
epoch 53, train 94/100, loss=0.3314 acc=0.8667
epoch 53, train 95/100, loss=0.6014 acc=0.8667
epoch 53, train 96/100, loss=0.1733 acc=0.9600
epoch 53, train 97/100, loss=0.3795 acc=0.9067
epoch 53, train 98/100, loss=1.0371 acc=0.6133
epoch 53, train 99/100, loss=0.2317 acc=0.9467
epoch 53, train 100/100, loss=0.4827 acc=0.8400
best epoch 50, best val acc=0.7228
epoch 53, val, loss=0.9399 acc=0.7064
ETA:3.4h/6.4h
epoch 54, train 1/100, loss=0.7274 acc=0.7867
epoch 54, train 2/100, loss=0.3245 acc=0.8667
epoch 54, train 3/100, loss=0.7628 acc=0.6933
epoch 54, train 4/100, loss=0.3556 acc=0.8667
epoch 54, train 5/100, loss=0.5140 acc=0.8133
epoch 54, train 6/100, loss=1.0025 acc=0.7333
epoch 54, train 7/100, loss=0.6616 acc=0.7867
epoch 54, train 8/100, loss=0.2655 acc=0.8933
epoch 54, train 9/100, loss=0.7801 acc=0.7600
epoch 54, train 10/100, loss=0.1727 acc=0.9467
epoch 54, train 11/100, loss=0.3225 acc=0.8667
epoch 54, train 12/100, loss=0.6751 acc=0.7733
epoch 54, train 13/100, loss=0.1614 acc=0.9200
epoch 54, train 14/100, loss=0.0918 acc=0.9467
epoch 54, train 15/100, loss=0.1731 acc=0.9333
epoch 54, train 16/100, loss=0.3814 acc=0.9333
epoch 54, train 17/100, loss=0.2023 acc=0.9333
epoch 54, train 18/100, loss=0.8126 acc=0.7067
epoch 54, train 19/100, loss=0.3284 acc=0.8933
epoch 54, train 20/100, loss=0.3425 acc=0.8667
epoch 54, train 21/100, loss=0.5139 acc=0.7867
epoch 54, train 22/100, loss=0.7849 acc=0.7467
epoch 54, train 23/100, loss=0.9253 acc=0.6400
epoch 54, train 24/100, loss=0.4260 acc=0.8400
epoch 54, train 25/100, loss=0.3470 acc=0.9067
epoch 54, train 26/100, loss=0.3623 acc=0.8800
epoch 54, train 27/100, loss=0.2183 acc=0.9333
epoch 54, train 28/100, loss=0.2279 acc=0.9200
epoch 54, train 29/100, loss=0.8095 acc=0.7067
epoch 54, train 30/100, loss=0.3131 acc=0.9067
epoch 54, train 31/100, loss=0.2933 acc=0.8933
epoch 54, train 32/100, loss=0.4330 acc=0.8133
epoch 54, train 33/100, loss=0.3463 acc=0.8533
epoch 54, train 34/100, loss=0.3846 acc=0.8667
epoch 54, train 35/100, loss=0.0441 acc=0.9867
epoch 54, train 36/100, loss=0.4773 acc=0.8400
epoch 54, train 37/100, loss=1.1504 acc=0.6800
epoch 54, train 38/100, loss=0.4545 acc=0.8667
epoch 54, train 39/100, loss=0.2504 acc=0.8800
epoch 54, train 40/100, loss=0.2483 acc=0.9067
epoch 54, train 41/100, loss=0.5984 acc=0.7867
epoch 54, train 42/100, loss=0.4535 acc=0.7867
epoch 54, train 43/100, loss=2.5158 acc=0.4133
epoch 54, train 44/100, loss=0.2018 acc=0.9467
epoch 54, train 45/100, loss=0.2104 acc=0.9467
epoch 54, train 46/100, loss=0.3898 acc=0.8533
epoch 54, train 47/100, loss=0.0686 acc=0.9733
epoch 54, train 48/100, loss=0.4632 acc=0.8133
epoch 54, train 49/100, loss=0.2578 acc=0.9333
epoch 54, train 50/100, loss=0.4341 acc=0.8533
epoch 54, train 51/100, loss=0.3063 acc=0.8800
epoch 54, train 52/100, loss=0.1590 acc=0.9600
epoch 54, train 53/100, loss=0.3018 acc=0.8800
epoch 54, train 54/100, loss=0.7411 acc=0.7733
epoch 54, train 55/100, loss=0.7972 acc=0.7067
epoch 54, train 56/100, loss=1.3040 acc=0.5600
epoch 54, train 57/100, loss=0.5311 acc=0.8000
epoch 54, train 58/100, loss=0.8893 acc=0.6400
epoch 54, train 59/100, loss=0.4455 acc=0.8133
epoch 54, train 60/100, loss=1.0713 acc=0.6667
epoch 54, train 61/100, loss=0.1491 acc=0.9600
epoch 54, train 62/100, loss=0.3173 acc=0.9067
epoch 54, train 63/100, loss=0.1703 acc=0.9333
epoch 54, train 64/100, loss=0.3042 acc=0.8933
epoch 54, train 65/100, loss=0.4272 acc=0.8667
epoch 54, train 66/100, loss=1.1260 acc=0.6267
epoch 54, train 67/100, loss=0.8088 acc=0.7067
epoch 54, train 68/100, loss=0.5914 acc=0.8400
epoch 54, train 69/100, loss=0.3601 acc=0.8933
epoch 54, train 70/100, loss=0.4267 acc=0.8133
epoch 54, train 71/100, loss=0.0849 acc=0.9733
epoch 54, train 72/100, loss=0.5878 acc=0.7467
epoch 54, train 73/100, loss=0.5139 acc=0.8133
epoch 54, train 74/100, loss=0.6531 acc=0.8267
epoch 54, train 75/100, loss=0.0381 acc=0.9867
epoch 54, train 76/100, loss=0.5746 acc=0.8133
epoch 54, train 77/100, loss=0.8407 acc=0.6800
epoch 54, train 78/100, loss=0.2113 acc=0.9200
epoch 54, train 79/100, loss=0.3360 acc=0.8267
epoch 54, train 80/100, loss=0.5721 acc=0.8267
epoch 54, train 81/100, loss=0.4009 acc=0.8533
epoch 54, train 82/100, loss=0.1073 acc=0.9467
epoch 54, train 83/100, loss=0.6410 acc=0.8400
epoch 54, train 84/100, loss=0.8726 acc=0.7067
epoch 54, train 85/100, loss=0.2713 acc=0.8800
epoch 54, train 86/100, loss=0.2169 acc=0.9333
epoch 54, train 87/100, loss=0.5673 acc=0.8000
epoch 54, train 88/100, loss=0.2022 acc=0.9200
epoch 54, train 89/100, loss=0.3190 acc=0.9200
epoch 54, train 90/100, loss=0.5699 acc=0.8133
epoch 54, train 91/100, loss=0.1495 acc=0.9467
epoch 54, train 92/100, loss=0.3194 acc=0.8800
epoch 54, train 93/100, loss=0.3141 acc=0.9067
epoch 54, train 94/100, loss=0.3766 acc=0.9067
epoch 54, train 95/100, loss=0.2549 acc=0.9067
epoch 54, train 96/100, loss=0.3818 acc=0.9200
epoch 54, train 97/100, loss=0.4578 acc=0.8533
epoch 54, train 98/100, loss=0.2147 acc=0.9067
epoch 54, train 99/100, loss=0.3918 acc=0.8800
epoch 54, train 100/100, loss=0.0834 acc=0.9600
best epoch 50, best val acc=0.7228
epoch 54, val, loss=0.9179 acc=0.7093
ETA:3.5h/6.4h
epoch 55, train 1/100, loss=0.4715 acc=0.8133
epoch 55, train 2/100, loss=0.3981 acc=0.8667
epoch 55, train 3/100, loss=0.9017 acc=0.7067
epoch 55, train 4/100, loss=0.9776 acc=0.6667
epoch 55, train 5/100, loss=0.5636 acc=0.7467
epoch 55, train 6/100, loss=0.6159 acc=0.8667
epoch 55, train 7/100, loss=0.6471 acc=0.8133
epoch 55, train 8/100, loss=0.4286 acc=0.8933
epoch 55, train 9/100, loss=1.5959 acc=0.5467
epoch 55, train 10/100, loss=0.3704 acc=0.8933
epoch 55, train 11/100, loss=0.2212 acc=0.9200
epoch 55, train 12/100, loss=0.1583 acc=0.9200
epoch 55, train 13/100, loss=0.4801 acc=0.8267
epoch 55, train 14/100, loss=0.1952 acc=0.9333
epoch 55, train 15/100, loss=0.9675 acc=0.6400
epoch 55, train 16/100, loss=0.5267 acc=0.8400
epoch 55, train 17/100, loss=0.5313 acc=0.8267
epoch 55, train 18/100, loss=0.6097 acc=0.8000
epoch 55, train 19/100, loss=0.3456 acc=0.9067
epoch 55, train 20/100, loss=0.4992 acc=0.8533
epoch 55, train 21/100, loss=0.5072 acc=0.8000
epoch 55, train 22/100, loss=1.3176 acc=0.6533
epoch 55, train 23/100, loss=0.8179 acc=0.7600
epoch 55, train 24/100, loss=0.5364 acc=0.8400
epoch 55, train 25/100, loss=0.3611 acc=0.8000
epoch 55, train 26/100, loss=0.2660 acc=0.8933
epoch 55, train 27/100, loss=0.8399 acc=0.7467
epoch 55, train 28/100, loss=0.3710 acc=0.9067
epoch 55, train 29/100, loss=0.2109 acc=0.9200
epoch 55, train 30/100, loss=0.9857 acc=0.8000
epoch 55, train 31/100, loss=0.1656 acc=0.9333
epoch 55, train 32/100, loss=0.5805 acc=0.8400
epoch 55, train 33/100, loss=1.6198 acc=0.5333
epoch 55, train 34/100, loss=0.3486 acc=0.8667
epoch 55, train 35/100, loss=0.1931 acc=0.9333
epoch 55, train 36/100, loss=0.9782 acc=0.6533
epoch 55, train 37/100, loss=0.2424 acc=0.9333
epoch 55, train 38/100, loss=0.3893 acc=0.8800
epoch 55, train 39/100, loss=0.9206 acc=0.7067
epoch 55, train 40/100, loss=0.8638 acc=0.7467
epoch 55, train 41/100, loss=0.6517 acc=0.7467
epoch 55, train 42/100, loss=0.1522 acc=0.9600
epoch 55, train 43/100, loss=0.5433 acc=0.8133
epoch 55, train 44/100, loss=0.5777 acc=0.8533
epoch 55, train 45/100, loss=0.4446 acc=0.8933
epoch 55, train 46/100, loss=0.3201 acc=0.8533
epoch 55, train 47/100, loss=0.5561 acc=0.7467
epoch 55, train 48/100, loss=1.3131 acc=0.6400
epoch 55, train 49/100, loss=0.8881 acc=0.6800
epoch 55, train 50/100, loss=0.4490 acc=0.8400
epoch 55, train 51/100, loss=0.9882 acc=0.6933
epoch 55, train 52/100, loss=0.1362 acc=0.9600
epoch 55, train 53/100, loss=0.4312 acc=0.8400
epoch 55, train 54/100, loss=0.7435 acc=0.7733
epoch 55, train 55/100, loss=0.5784 acc=0.7600
epoch 55, train 56/100, loss=0.3601 acc=0.8800
epoch 55, train 57/100, loss=0.8618 acc=0.7333
epoch 55, train 58/100, loss=0.3954 acc=0.8000
epoch 55, train 59/100, loss=0.2612 acc=0.8800
epoch 55, train 60/100, loss=0.1881 acc=0.9467
epoch 55, train 61/100, loss=0.1674 acc=0.9200
epoch 55, train 62/100, loss=0.4394 acc=0.7867
epoch 55, train 63/100, loss=0.4622 acc=0.8400
epoch 55, train 64/100, loss=1.0896 acc=0.6267
epoch 55, train 65/100, loss=0.5604 acc=0.8000
epoch 55, train 66/100, loss=0.2429 acc=0.9200
epoch 55, train 67/100, loss=0.1910 acc=0.9200
epoch 55, train 68/100, loss=0.8538 acc=0.7867
epoch 55, train 69/100, loss=0.2560 acc=0.9200
epoch 55, train 70/100, loss=0.2987 acc=0.8667
epoch 55, train 71/100, loss=0.6688 acc=0.8133
epoch 55, train 72/100, loss=0.4513 acc=0.8800
epoch 55, train 73/100, loss=0.3149 acc=0.8933
epoch 55, train 74/100, loss=0.2596 acc=0.9333
epoch 55, train 75/100, loss=0.9321 acc=0.7333
epoch 55, train 76/100, loss=0.0827 acc=0.9733
epoch 55, train 77/100, loss=0.1852 acc=0.9200
epoch 55, train 78/100, loss=0.5929 acc=0.8400
epoch 55, train 79/100, loss=0.7291 acc=0.7867
epoch 55, train 80/100, loss=0.5429 acc=0.8267
epoch 55, train 81/100, loss=0.4333 acc=0.9067
epoch 55, train 82/100, loss=0.6223 acc=0.7867
epoch 55, train 83/100, loss=0.7400 acc=0.7600
epoch 55, train 84/100, loss=0.5147 acc=0.8133
epoch 55, train 85/100, loss=0.2775 acc=0.9067
epoch 55, train 86/100, loss=0.0193 acc=1.0000
epoch 55, train 87/100, loss=0.7448 acc=0.7067
epoch 55, train 88/100, loss=0.4301 acc=0.8533
epoch 55, train 89/100, loss=0.3420 acc=0.8800
epoch 55, train 90/100, loss=0.7649 acc=0.7467
epoch 55, train 91/100, loss=0.3358 acc=0.9067
epoch 55, train 92/100, loss=1.1272 acc=0.6133
epoch 55, train 93/100, loss=0.5346 acc=0.7867
epoch 55, train 94/100, loss=0.4100 acc=0.8133
epoch 55, train 95/100, loss=0.8453 acc=0.7867
epoch 55, train 96/100, loss=0.1185 acc=0.9600
epoch 55, train 97/100, loss=0.2167 acc=0.9600
epoch 55, train 98/100, loss=0.0933 acc=0.9733
epoch 55, train 99/100, loss=0.2012 acc=0.9200
epoch 55, train 100/100, loss=0.6164 acc=0.7867
best epoch 50, best val acc=0.7228
epoch 55, val, loss=0.9196 acc=0.7158
ETA:3.5h/6.4h
epoch 56, train 1/100, loss=0.5528 acc=0.7733
epoch 56, train 2/100, loss=0.2280 acc=0.9333
epoch 56, train 3/100, loss=1.1726 acc=0.6000
epoch 56, train 4/100, loss=0.2202 acc=0.9067
epoch 56, train 5/100, loss=1.3890 acc=0.6133
epoch 56, train 6/100, loss=0.1308 acc=0.9467
epoch 56, train 7/100, loss=0.3207 acc=0.9200
epoch 56, train 8/100, loss=0.8042 acc=0.7333
epoch 56, train 9/100, loss=0.5024 acc=0.7733
epoch 56, train 10/100, loss=0.6264 acc=0.8000
epoch 56, train 11/100, loss=0.3972 acc=0.8267
epoch 56, train 12/100, loss=0.7407 acc=0.7333
epoch 56, train 13/100, loss=0.4426 acc=0.8267
epoch 56, train 14/100, loss=0.5047 acc=0.7600
epoch 56, train 15/100, loss=0.2646 acc=0.8800
epoch 56, train 16/100, loss=0.2577 acc=0.9333
epoch 56, train 17/100, loss=0.9358 acc=0.7067
epoch 56, train 18/100, loss=0.1804 acc=0.9333
epoch 56, train 19/100, loss=1.0329 acc=0.6800
epoch 56, train 20/100, loss=0.9556 acc=0.7200
epoch 56, train 21/100, loss=0.4992 acc=0.8400
epoch 56, train 22/100, loss=0.6036 acc=0.7733
epoch 56, train 23/100, loss=0.3652 acc=0.8533
epoch 56, train 24/100, loss=0.4136 acc=0.8533
epoch 56, train 25/100, loss=0.1352 acc=0.9600
epoch 56, train 26/100, loss=0.3668 acc=0.8267
epoch 56, train 27/100, loss=0.5302 acc=0.8267
epoch 56, train 28/100, loss=0.5780 acc=0.8933
epoch 56, train 29/100, loss=0.4572 acc=0.8133
epoch 56, train 30/100, loss=1.2389 acc=0.6533
epoch 56, train 31/100, loss=0.3347 acc=0.9200
epoch 56, train 32/100, loss=0.3287 acc=0.8800
epoch 56, train 33/100, loss=0.5324 acc=0.8400
epoch 56, train 34/100, loss=0.2468 acc=0.9333
epoch 56, train 35/100, loss=0.5266 acc=0.8400
epoch 56, train 36/100, loss=0.3317 acc=0.8800
epoch 56, train 37/100, loss=0.4319 acc=0.8667
epoch 56, train 38/100, loss=0.1378 acc=0.9600
epoch 56, train 39/100, loss=0.5687 acc=0.8133
epoch 56, train 40/100, loss=0.2024 acc=0.9467
epoch 56, train 41/100, loss=0.6534 acc=0.7733
epoch 56, train 42/100, loss=0.2353 acc=0.8800
epoch 56, train 43/100, loss=1.3843 acc=0.5867
epoch 56, train 44/100, loss=0.2824 acc=0.9067
epoch 56, train 45/100, loss=0.7693 acc=0.6800
epoch 56, train 46/100, loss=0.1934 acc=0.9067
epoch 56, train 47/100, loss=1.3520 acc=0.6133
epoch 56, train 48/100, loss=0.6416 acc=0.7467
epoch 56, train 49/100, loss=1.0112 acc=0.6933
epoch 56, train 50/100, loss=0.1825 acc=0.8933
epoch 56, train 51/100, loss=0.5856 acc=0.8133
epoch 56, train 52/100, loss=0.0603 acc=0.9600
epoch 56, train 53/100, loss=0.7258 acc=0.7867
epoch 56, train 54/100, loss=0.4465 acc=0.8267
epoch 56, train 55/100, loss=0.5043 acc=0.8267
epoch 56, train 56/100, loss=0.6113 acc=0.7867
epoch 56, train 57/100, loss=0.2530 acc=0.9200
epoch 56, train 58/100, loss=1.2572 acc=0.6667
epoch 56, train 59/100, loss=0.9032 acc=0.7733
epoch 56, train 60/100, loss=0.1827 acc=0.9200
epoch 56, train 61/100, loss=0.2363 acc=0.9333
epoch 56, train 62/100, loss=0.2394 acc=0.9333
epoch 56, train 63/100, loss=0.5708 acc=0.8133
epoch 56, train 64/100, loss=0.3678 acc=0.8533
epoch 56, train 65/100, loss=0.1957 acc=0.9467
epoch 56, train 66/100, loss=1.4785 acc=0.6400
epoch 56, train 67/100, loss=0.3249 acc=0.8933
epoch 56, train 68/100, loss=0.1686 acc=0.9600
epoch 56, train 69/100, loss=0.7564 acc=0.7467
epoch 56, train 70/100, loss=0.2365 acc=0.9200
epoch 56, train 71/100, loss=0.1992 acc=0.9333
epoch 56, train 72/100, loss=0.2481 acc=0.9067
epoch 56, train 73/100, loss=0.6603 acc=0.7867
epoch 56, train 74/100, loss=0.4781 acc=0.7733
epoch 56, train 75/100, loss=0.0989 acc=0.9733
epoch 56, train 76/100, loss=0.4759 acc=0.8400
epoch 56, train 77/100, loss=0.9767 acc=0.6400
epoch 56, train 78/100, loss=0.4118 acc=0.8933
epoch 56, train 79/100, loss=0.2951 acc=0.8933
epoch 56, train 80/100, loss=0.2796 acc=0.8800
epoch 56, train 81/100, loss=0.1910 acc=0.9200
epoch 56, train 82/100, loss=0.3517 acc=0.9067
epoch 56, train 83/100, loss=0.7685 acc=0.7733
epoch 56, train 84/100, loss=0.1631 acc=0.9467
epoch 56, train 85/100, loss=0.4910 acc=0.8533
epoch 56, train 86/100, loss=0.2267 acc=0.9467
epoch 56, train 87/100, loss=0.5788 acc=0.8000
epoch 56, train 88/100, loss=0.2910 acc=0.9067
epoch 56, train 89/100, loss=0.8748 acc=0.6800
epoch 56, train 90/100, loss=0.7731 acc=0.7333
epoch 56, train 91/100, loss=0.5694 acc=0.7733
epoch 56, train 92/100, loss=0.7136 acc=0.7600
epoch 56, train 93/100, loss=0.3845 acc=0.8533
epoch 56, train 94/100, loss=0.3610 acc=0.8667
epoch 56, train 95/100, loss=0.3047 acc=0.9067
epoch 56, train 96/100, loss=0.2018 acc=0.9200
epoch 56, train 97/100, loss=0.3113 acc=0.8800
epoch 56, train 98/100, loss=0.3841 acc=0.8933
epoch 56, train 99/100, loss=0.4596 acc=0.8000
epoch 56, train 100/100, loss=0.9314 acc=0.8133
best epoch 50, best val acc=0.7228
epoch 56, val, loss=0.9061 acc=0.7166
ETA:3.6h/6.4h
epoch 57, train 1/100, loss=0.3144 acc=0.8533
epoch 57, train 2/100, loss=1.4070 acc=0.6133
epoch 57, train 3/100, loss=0.3938 acc=0.8400
epoch 57, train 4/100, loss=0.4288 acc=0.8267
epoch 57, train 5/100, loss=0.8190 acc=0.7333
epoch 57, train 6/100, loss=0.8855 acc=0.7067
epoch 57, train 7/100, loss=0.5369 acc=0.8267
epoch 57, train 8/100, loss=0.5097 acc=0.8667
epoch 57, train 9/100, loss=0.5863 acc=0.7867
epoch 57, train 10/100, loss=0.6112 acc=0.8133
epoch 57, train 11/100, loss=0.6222 acc=0.7867
epoch 57, train 12/100, loss=0.4333 acc=0.8667
epoch 57, train 13/100, loss=0.9693 acc=0.6800
epoch 57, train 14/100, loss=0.4878 acc=0.8267
epoch 57, train 15/100, loss=0.5328 acc=0.8000
epoch 57, train 16/100, loss=0.7107 acc=0.7733
epoch 57, train 17/100, loss=0.8534 acc=0.6933
epoch 57, train 18/100, loss=0.3066 acc=0.8533
epoch 57, train 19/100, loss=0.2168 acc=0.9467
epoch 57, train 20/100, loss=0.7451 acc=0.7200
epoch 57, train 21/100, loss=0.6738 acc=0.7200
epoch 57, train 22/100, loss=0.4584 acc=0.8533
epoch 57, train 23/100, loss=1.2059 acc=0.6800
epoch 57, train 24/100, loss=0.7016 acc=0.8000
epoch 57, train 25/100, loss=0.4773 acc=0.8133
epoch 57, train 26/100, loss=0.5415 acc=0.8000
epoch 57, train 27/100, loss=0.4570 acc=0.8533
epoch 57, train 28/100, loss=0.2192 acc=0.9600
epoch 57, train 29/100, loss=0.4090 acc=0.9067
epoch 57, train 30/100, loss=0.5375 acc=0.8000
epoch 57, train 31/100, loss=0.4136 acc=0.8133
epoch 57, train 32/100, loss=0.3707 acc=0.8933
epoch 57, train 33/100, loss=0.4705 acc=0.8400
epoch 57, train 34/100, loss=0.4726 acc=0.8267
epoch 57, train 35/100, loss=0.5801 acc=0.7600
epoch 57, train 36/100, loss=0.1241 acc=0.9733
epoch 57, train 37/100, loss=0.4827 acc=0.8667
epoch 57, train 38/100, loss=0.8865 acc=0.7200
epoch 57, train 39/100, loss=0.1657 acc=0.9333
epoch 57, train 40/100, loss=0.1680 acc=0.9467
epoch 57, train 41/100, loss=0.6464 acc=0.7867
epoch 57, train 42/100, loss=0.5320 acc=0.8400
epoch 57, train 43/100, loss=0.6228 acc=0.7067
epoch 57, train 44/100, loss=0.1237 acc=0.9600
epoch 57, train 45/100, loss=1.4082 acc=0.5600
epoch 57, train 46/100, loss=0.7840 acc=0.6933
epoch 57, train 47/100, loss=0.6175 acc=0.7200
epoch 57, train 48/100, loss=0.5883 acc=0.6933
epoch 57, train 49/100, loss=0.3355 acc=0.8933
epoch 57, train 50/100, loss=0.9478 acc=0.6533
epoch 57, train 51/100, loss=0.7600 acc=0.6667
epoch 57, train 52/100, loss=0.6435 acc=0.8000
epoch 57, train 53/100, loss=0.1968 acc=0.9333
epoch 57, train 54/100, loss=0.1468 acc=0.9600
epoch 57, train 55/100, loss=1.2897 acc=0.5867
epoch 57, train 56/100, loss=1.3631 acc=0.5867
epoch 57, train 57/100, loss=0.7254 acc=0.7333
epoch 57, train 58/100, loss=1.2081 acc=0.6533
epoch 57, train 59/100, loss=0.4276 acc=0.8667
epoch 57, train 60/100, loss=0.9712 acc=0.6667
epoch 57, train 61/100, loss=0.5610 acc=0.8533
epoch 57, train 62/100, loss=0.2604 acc=0.8933
epoch 57, train 63/100, loss=0.5193 acc=0.8400
epoch 57, train 64/100, loss=0.1982 acc=0.9200
epoch 57, train 65/100, loss=0.5180 acc=0.7600
epoch 57, train 66/100, loss=0.7628 acc=0.7333
epoch 57, train 67/100, loss=0.5950 acc=0.8267
epoch 57, train 68/100, loss=0.3796 acc=0.8800
epoch 57, train 69/100, loss=0.6353 acc=0.7867
epoch 57, train 70/100, loss=1.4013 acc=0.5733
epoch 57, train 71/100, loss=0.1707 acc=0.9333
epoch 57, train 72/100, loss=0.9976 acc=0.6667
epoch 57, train 73/100, loss=0.1413 acc=0.9600
epoch 57, train 74/100, loss=0.4127 acc=0.8533
epoch 57, train 75/100, loss=0.5078 acc=0.8933
epoch 57, train 76/100, loss=0.3225 acc=0.8800
epoch 57, train 77/100, loss=0.5002 acc=0.7733
epoch 57, train 78/100, loss=0.0235 acc=1.0000
epoch 57, train 79/100, loss=0.8541 acc=0.6933
epoch 57, train 80/100, loss=0.5687 acc=0.8400
epoch 57, train 81/100, loss=1.2235 acc=0.5733
epoch 57, train 82/100, loss=0.4603 acc=0.8400
epoch 57, train 83/100, loss=0.7097 acc=0.8000
epoch 57, train 84/100, loss=0.0428 acc=1.0000
epoch 57, train 85/100, loss=0.7349 acc=0.7067
epoch 57, train 86/100, loss=0.4058 acc=0.8533
epoch 57, train 87/100, loss=1.3007 acc=0.6133
epoch 57, train 88/100, loss=0.5744 acc=0.8400
epoch 57, train 89/100, loss=0.1597 acc=0.9467
epoch 57, train 90/100, loss=0.7252 acc=0.7733
epoch 57, train 91/100, loss=0.2904 acc=0.9200
epoch 57, train 92/100, loss=0.3650 acc=0.9067
epoch 57, train 93/100, loss=0.7497 acc=0.7867
epoch 57, train 94/100, loss=0.6540 acc=0.8533
epoch 57, train 95/100, loss=0.4927 acc=0.8000
epoch 57, train 96/100, loss=0.5992 acc=0.7867
epoch 57, train 97/100, loss=1.0308 acc=0.6933
epoch 57, train 98/100, loss=0.6950 acc=0.7200
epoch 57, train 99/100, loss=1.1477 acc=0.5867
epoch 57, train 100/100, loss=1.7570 acc=0.6000
best epoch 50, best val acc=0.7228
epoch 57, val, loss=0.9436 acc=0.7158
ETA:3.7h/6.4h
epoch 58, train 1/100, loss=0.5177 acc=0.8000
epoch 58, train 2/100, loss=0.1170 acc=0.9467
epoch 58, train 3/100, loss=0.5064 acc=0.7600
epoch 58, train 4/100, loss=0.6030 acc=0.8400
epoch 58, train 5/100, loss=0.4234 acc=0.8533
epoch 58, train 6/100, loss=0.5539 acc=0.7600
epoch 58, train 7/100, loss=0.5448 acc=0.8267
epoch 58, train 8/100, loss=0.9683 acc=0.6667
epoch 58, train 9/100, loss=0.6510 acc=0.8000
epoch 58, train 10/100, loss=0.8698 acc=0.6933
epoch 58, train 11/100, loss=0.9367 acc=0.7467
epoch 58, train 12/100, loss=0.4935 acc=0.8533
epoch 58, train 13/100, loss=0.5677 acc=0.8000
epoch 58, train 14/100, loss=0.7950 acc=0.7200
epoch 58, train 15/100, loss=0.0687 acc=0.9733
epoch 58, train 16/100, loss=0.8904 acc=0.7067
epoch 58, train 17/100, loss=0.5111 acc=0.8133
epoch 58, train 18/100, loss=0.2133 acc=0.9200
epoch 58, train 19/100, loss=0.8456 acc=0.6933
epoch 58, train 20/100, loss=0.3330 acc=0.8533
epoch 58, train 21/100, loss=0.2640 acc=0.9067
epoch 58, train 22/100, loss=1.3314 acc=0.6133
epoch 58, train 23/100, loss=0.1512 acc=0.9467
epoch 58, train 24/100, loss=0.2402 acc=0.9067
epoch 58, train 25/100, loss=0.3085 acc=0.8933
epoch 58, train 26/100, loss=0.5821 acc=0.8400
epoch 58, train 27/100, loss=0.1506 acc=0.9333
epoch 58, train 28/100, loss=0.6306 acc=0.7600
epoch 58, train 29/100, loss=0.2685 acc=0.9333
epoch 58, train 30/100, loss=0.3221 acc=0.8800
epoch 58, train 31/100, loss=1.0798 acc=0.6000
epoch 58, train 32/100, loss=1.2701 acc=0.6667
epoch 58, train 33/100, loss=1.2239 acc=0.5333
epoch 58, train 34/100, loss=0.4854 acc=0.8267
epoch 58, train 35/100, loss=0.0752 acc=0.9600
epoch 58, train 36/100, loss=1.0067 acc=0.6933
epoch 58, train 37/100, loss=0.9089 acc=0.6800
epoch 58, train 38/100, loss=0.3321 acc=0.8933
epoch 58, train 39/100, loss=0.5560 acc=0.7600
epoch 58, train 40/100, loss=0.0697 acc=0.9733
epoch 58, train 41/100, loss=0.5244 acc=0.8400
epoch 58, train 42/100, loss=0.2901 acc=0.9333
epoch 58, train 43/100, loss=0.6681 acc=0.8000
epoch 58, train 44/100, loss=0.6775 acc=0.7600
epoch 58, train 45/100, loss=0.4023 acc=0.8667
epoch 58, train 46/100, loss=0.3143 acc=0.8533
epoch 58, train 47/100, loss=0.8915 acc=0.6133
epoch 58, train 48/100, loss=0.7282 acc=0.6800
epoch 58, train 49/100, loss=0.5165 acc=0.8000
epoch 58, train 50/100, loss=0.6580 acc=0.6933
epoch 58, train 51/100, loss=0.8696 acc=0.7333
epoch 58, train 52/100, loss=0.3408 acc=0.8800
epoch 58, train 53/100, loss=0.2353 acc=0.9200
epoch 58, train 54/100, loss=0.4021 acc=0.9200
epoch 58, train 55/100, loss=1.4619 acc=0.6400
epoch 58, train 56/100, loss=0.7545 acc=0.7600
epoch 58, train 57/100, loss=0.4000 acc=0.8267
epoch 58, train 58/100, loss=0.7311 acc=0.7600
epoch 58, train 59/100, loss=0.4929 acc=0.8533
epoch 58, train 60/100, loss=1.2601 acc=0.6000
epoch 58, train 61/100, loss=0.9439 acc=0.7333
epoch 58, train 62/100, loss=0.4632 acc=0.8267
epoch 58, train 63/100, loss=0.4635 acc=0.8667
epoch 58, train 64/100, loss=0.3501 acc=0.9067
epoch 58, train 65/100, loss=0.7807 acc=0.6933
epoch 58, train 66/100, loss=0.5334 acc=0.8000
epoch 58, train 67/100, loss=0.4340 acc=0.8133
epoch 58, train 68/100, loss=0.6485 acc=0.7733
epoch 58, train 69/100, loss=0.2182 acc=0.8933
epoch 58, train 70/100, loss=0.7841 acc=0.8267
epoch 58, train 71/100, loss=0.2156 acc=0.8933
epoch 58, train 72/100, loss=0.5825 acc=0.7867
epoch 58, train 73/100, loss=0.9102 acc=0.6667
epoch 58, train 74/100, loss=0.3545 acc=0.8667
epoch 58, train 75/100, loss=0.5916 acc=0.7867
epoch 58, train 76/100, loss=0.5348 acc=0.8000
epoch 58, train 77/100, loss=0.2005 acc=0.9333
epoch 58, train 78/100, loss=0.3119 acc=0.8800
epoch 58, train 79/100, loss=0.3184 acc=0.8933
epoch 58, train 80/100, loss=0.9168 acc=0.7600
epoch 58, train 81/100, loss=0.2196 acc=0.8667
epoch 58, train 82/100, loss=0.5549 acc=0.7600
epoch 58, train 83/100, loss=0.6466 acc=0.7600
epoch 58, train 84/100, loss=0.5145 acc=0.7733
epoch 58, train 85/100, loss=0.7807 acc=0.7600
epoch 58, train 86/100, loss=0.2415 acc=0.9333
epoch 58, train 87/100, loss=1.3022 acc=0.6400
epoch 58, train 88/100, loss=0.8182 acc=0.7333
epoch 58, train 89/100, loss=0.6630 acc=0.7600
epoch 58, train 90/100, loss=0.1858 acc=0.9467
epoch 58, train 91/100, loss=0.5307 acc=0.7867
epoch 58, train 92/100, loss=0.5996 acc=0.8267
epoch 58, train 93/100, loss=0.2597 acc=0.8667
epoch 58, train 94/100, loss=0.6582 acc=0.7733
epoch 58, train 95/100, loss=0.1806 acc=0.9333
epoch 58, train 96/100, loss=0.5478 acc=0.7867
epoch 58, train 97/100, loss=0.1997 acc=0.9467
epoch 58, train 98/100, loss=0.6314 acc=0.7867
epoch 58, train 99/100, loss=0.2676 acc=0.8933
epoch 58, train 100/100, loss=0.2082 acc=0.8933
best epoch 50, best val acc=0.7228
epoch 58, val, loss=0.9273 acc=0.7127
ETA:3.7h/6.4h
epoch 59, train 1/100, loss=0.5895 acc=0.8133
epoch 59, train 2/100, loss=0.5709 acc=0.7867
epoch 59, train 3/100, loss=0.7652 acc=0.7733
epoch 59, train 4/100, loss=0.6941 acc=0.8000
epoch 59, train 5/100, loss=1.0560 acc=0.6533
epoch 59, train 6/100, loss=1.1845 acc=0.6400
epoch 59, train 7/100, loss=0.2653 acc=0.9200
epoch 59, train 8/100, loss=1.3578 acc=0.6800
epoch 59, train 9/100, loss=0.3785 acc=0.8400
epoch 59, train 10/100, loss=0.4793 acc=0.8800
epoch 59, train 11/100, loss=0.2899 acc=0.8933
epoch 59, train 12/100, loss=0.6948 acc=0.7733
epoch 59, train 13/100, loss=0.1595 acc=0.9067
epoch 59, train 14/100, loss=0.4072 acc=0.8800
epoch 59, train 15/100, loss=0.3361 acc=0.8667
epoch 59, train 16/100, loss=0.7651 acc=0.6933
epoch 59, train 17/100, loss=0.1095 acc=0.9600
epoch 59, train 18/100, loss=0.4955 acc=0.8133
epoch 59, train 19/100, loss=0.5371 acc=0.8667
epoch 59, train 20/100, loss=0.4247 acc=0.7867
epoch 59, train 21/100, loss=0.2583 acc=0.8800
epoch 59, train 22/100, loss=0.4085 acc=0.8533
epoch 59, train 23/100, loss=0.4633 acc=0.8400
epoch 59, train 24/100, loss=0.2264 acc=0.9067
epoch 59, train 25/100, loss=0.8078 acc=0.6933
epoch 59, train 26/100, loss=0.7089 acc=0.7067
epoch 59, train 27/100, loss=0.6241 acc=0.7733
epoch 59, train 28/100, loss=0.8997 acc=0.7333
epoch 59, train 29/100, loss=0.3945 acc=0.8667
epoch 59, train 30/100, loss=0.3741 acc=0.8933
epoch 59, train 31/100, loss=0.5554 acc=0.8400
epoch 59, train 32/100, loss=1.1972 acc=0.6267
epoch 59, train 33/100, loss=1.3764 acc=0.7733
epoch 59, train 34/100, loss=0.2803 acc=0.8933
epoch 59, train 35/100, loss=0.0568 acc=0.9733
epoch 59, train 36/100, loss=0.5775 acc=0.7467
epoch 59, train 37/100, loss=0.0899 acc=0.9600
epoch 59, train 38/100, loss=0.3399 acc=0.8933
epoch 59, train 39/100, loss=0.7674 acc=0.7467
epoch 59, train 40/100, loss=0.5375 acc=0.8267
epoch 59, train 41/100, loss=0.5180 acc=0.8267
epoch 59, train 42/100, loss=0.4968 acc=0.7867
epoch 59, train 43/100, loss=0.6448 acc=0.7733
epoch 59, train 44/100, loss=0.6691 acc=0.7600
epoch 59, train 45/100, loss=0.3984 acc=0.8933
epoch 59, train 46/100, loss=0.5377 acc=0.8933
epoch 59, train 47/100, loss=0.7959 acc=0.7333
epoch 59, train 48/100, loss=0.5644 acc=0.7867
epoch 59, train 49/100, loss=0.4183 acc=0.8933
epoch 59, train 50/100, loss=0.3356 acc=0.9067
epoch 59, train 51/100, loss=0.5500 acc=0.8400
epoch 59, train 52/100, loss=0.1572 acc=0.9467
epoch 59, train 53/100, loss=0.7051 acc=0.8000
epoch 59, train 54/100, loss=0.1301 acc=0.9600
epoch 59, train 55/100, loss=0.3534 acc=0.9067
epoch 59, train 56/100, loss=1.0718 acc=0.7200
epoch 59, train 57/100, loss=0.0893 acc=0.9733
epoch 59, train 58/100, loss=0.4423 acc=0.8933
epoch 59, train 59/100, loss=0.4988 acc=0.8400
epoch 59, train 60/100, loss=0.9311 acc=0.7067
epoch 59, train 61/100, loss=0.7158 acc=0.7467
epoch 59, train 62/100, loss=0.4004 acc=0.9333
epoch 59, train 63/100, loss=0.4032 acc=0.8267
epoch 59, train 64/100, loss=0.2949 acc=0.9333
epoch 59, train 65/100, loss=0.3516 acc=0.8933
epoch 59, train 66/100, loss=0.5012 acc=0.8133
epoch 59, train 67/100, loss=0.4472 acc=0.8267
epoch 59, train 68/100, loss=0.4174 acc=0.8267
epoch 59, train 69/100, loss=0.2190 acc=0.9200
epoch 59, train 70/100, loss=0.4684 acc=0.8267
epoch 59, train 71/100, loss=0.3246 acc=0.8533
epoch 59, train 72/100, loss=0.2753 acc=0.8933
epoch 59, train 73/100, loss=0.4730 acc=0.8000
epoch 59, train 74/100, loss=0.5292 acc=0.8133
epoch 59, train 75/100, loss=0.2749 acc=0.8800
epoch 59, train 76/100, loss=0.3273 acc=0.8800
epoch 59, train 77/100, loss=0.1657 acc=0.9600
epoch 59, train 78/100, loss=0.2622 acc=0.8667
epoch 59, train 79/100, loss=0.7750 acc=0.7467
epoch 59, train 80/100, loss=0.1959 acc=0.8933
epoch 59, train 81/100, loss=0.6948 acc=0.8133
epoch 59, train 82/100, loss=0.6104 acc=0.7600
epoch 59, train 83/100, loss=0.2015 acc=0.9200
epoch 59, train 84/100, loss=0.9899 acc=0.6800
epoch 59, train 85/100, loss=0.1466 acc=0.9600
epoch 59, train 86/100, loss=0.8485 acc=0.7200
epoch 59, train 87/100, loss=0.3938 acc=0.8933
epoch 59, train 88/100, loss=0.2720 acc=0.8933
epoch 59, train 89/100, loss=0.5225 acc=0.8133
epoch 59, train 90/100, loss=0.6318 acc=0.8000
epoch 59, train 91/100, loss=0.4376 acc=0.8133
epoch 59, train 92/100, loss=0.2594 acc=0.9067
epoch 59, train 93/100, loss=0.3761 acc=0.8533
epoch 59, train 94/100, loss=0.8292 acc=0.7067
epoch 59, train 95/100, loss=0.4700 acc=0.8400
epoch 59, train 96/100, loss=0.4872 acc=0.9067
epoch 59, train 97/100, loss=0.2855 acc=0.8800
epoch 59, train 98/100, loss=0.3149 acc=0.9333
epoch 59, train 99/100, loss=0.5978 acc=0.8000
epoch 59, train 100/100, loss=0.7523 acc=0.7333
best epoch 50, best val acc=0.7228
epoch 59, val, loss=0.9484 acc=0.7149
ETA:3.8h/6.4h
epoch 60, train 1/100, loss=0.0816 acc=0.9733
epoch 60, train 2/100, loss=0.2495 acc=0.8667
epoch 60, train 3/100, loss=0.3978 acc=0.8800
epoch 60, train 4/100, loss=0.7916 acc=0.7867
epoch 60, train 5/100, loss=0.2578 acc=0.8933
epoch 60, train 6/100, loss=0.5412 acc=0.7467
epoch 60, train 7/100, loss=0.3669 acc=0.8667
epoch 60, train 8/100, loss=1.0854 acc=0.7067
epoch 60, train 9/100, loss=0.2445 acc=0.9067
epoch 60, train 10/100, loss=0.7991 acc=0.6933
epoch 60, train 11/100, loss=0.8863 acc=0.6667
epoch 60, train 12/100, loss=0.7310 acc=0.6933
epoch 60, train 13/100, loss=0.2794 acc=0.8800
epoch 60, train 14/100, loss=0.4096 acc=0.8133
epoch 60, train 15/100, loss=0.6862 acc=0.7733
epoch 60, train 16/100, loss=0.4409 acc=0.8267
epoch 60, train 17/100, loss=0.2707 acc=0.9067
epoch 60, train 18/100, loss=0.4121 acc=0.8667
epoch 60, train 19/100, loss=0.4879 acc=0.8400
epoch 60, train 20/100, loss=0.1702 acc=0.9467
epoch 60, train 21/100, loss=0.5506 acc=0.8267
epoch 60, train 22/100, loss=0.3305 acc=0.8800
epoch 60, train 23/100, loss=1.1354 acc=0.6133
epoch 60, train 24/100, loss=0.3471 acc=0.8667
epoch 60, train 25/100, loss=0.2035 acc=0.9333
epoch 60, train 26/100, loss=0.7715 acc=0.6800
epoch 60, train 27/100, loss=0.3770 acc=0.8533
epoch 60, train 28/100, loss=0.4517 acc=0.8533
epoch 60, train 29/100, loss=0.2892 acc=0.8933
epoch 60, train 30/100, loss=0.4929 acc=0.8667
epoch 60, train 31/100, loss=0.3846 acc=0.9067
epoch 60, train 32/100, loss=0.5745 acc=0.7733
epoch 60, train 33/100, loss=1.1791 acc=0.6533
epoch 60, train 34/100, loss=0.0738 acc=0.9867
epoch 60, train 35/100, loss=0.1433 acc=0.9600
epoch 60, train 36/100, loss=0.0717 acc=0.9600
epoch 60, train 37/100, loss=0.3402 acc=0.9067
epoch 60, train 38/100, loss=0.5724 acc=0.8933
epoch 60, train 39/100, loss=0.5309 acc=0.7467
epoch 60, train 40/100, loss=0.9188 acc=0.6400
epoch 60, train 41/100, loss=0.3742 acc=0.8800
epoch 60, train 42/100, loss=0.2777 acc=0.8933
epoch 60, train 43/100, loss=0.5522 acc=0.8400
epoch 60, train 44/100, loss=0.8312 acc=0.7867
epoch 60, train 45/100, loss=0.5146 acc=0.8400
epoch 60, train 46/100, loss=0.7818 acc=0.7733
epoch 60, train 47/100, loss=0.7115 acc=0.7200
epoch 60, train 48/100, loss=0.6604 acc=0.7600
epoch 60, train 49/100, loss=0.5630 acc=0.7867
epoch 60, train 50/100, loss=0.2013 acc=0.9067
epoch 60, train 51/100, loss=0.7977 acc=0.7333
epoch 60, train 52/100, loss=1.0359 acc=0.7467
epoch 60, train 53/100, loss=1.4461 acc=0.5733
epoch 60, train 54/100, loss=0.5136 acc=0.8533
epoch 60, train 55/100, loss=0.7371 acc=0.7867
epoch 60, train 56/100, loss=0.3961 acc=0.8933
epoch 60, train 57/100, loss=0.6539 acc=0.8000
epoch 60, train 58/100, loss=0.2816 acc=0.8400
epoch 60, train 59/100, loss=1.3563 acc=0.6133
epoch 60, train 60/100, loss=0.3863 acc=0.8667
epoch 60, train 61/100, loss=0.8214 acc=0.7333
epoch 60, train 62/100, loss=0.5741 acc=0.8267
epoch 60, train 63/100, loss=0.3203 acc=0.8400
epoch 60, train 64/100, loss=0.3823 acc=0.8800
epoch 60, train 65/100, loss=0.3584 acc=0.8400
epoch 60, train 66/100, loss=0.3870 acc=0.8667
epoch 60, train 67/100, loss=0.8804 acc=0.6800
epoch 60, train 68/100, loss=0.1348 acc=0.9467
epoch 60, train 69/100, loss=0.0488 acc=0.9867
epoch 60, train 70/100, loss=0.2801 acc=0.9200
epoch 60, train 71/100, loss=0.9922 acc=0.6933
epoch 60, train 72/100, loss=0.4508 acc=0.8133
epoch 60, train 73/100, loss=0.2966 acc=0.8800
epoch 60, train 74/100, loss=0.5447 acc=0.8267
epoch 60, train 75/100, loss=0.5406 acc=0.8267
epoch 60, train 76/100, loss=0.9886 acc=0.6667
epoch 60, train 77/100, loss=0.4286 acc=0.8400
epoch 60, train 78/100, loss=0.7571 acc=0.7467
epoch 60, train 79/100, loss=0.2784 acc=0.9200
epoch 60, train 80/100, loss=0.9051 acc=0.6933
epoch 60, train 81/100, loss=0.5693 acc=0.7733
epoch 60, train 82/100, loss=1.1880 acc=0.6933
epoch 60, train 83/100, loss=0.1930 acc=0.8933
epoch 60, train 84/100, loss=0.8828 acc=0.7600
epoch 60, train 85/100, loss=0.4915 acc=0.8667
epoch 60, train 86/100, loss=0.7102 acc=0.6933
epoch 60, train 87/100, loss=0.5382 acc=0.8267
epoch 60, train 88/100, loss=0.2153 acc=0.9467
epoch 60, train 89/100, loss=0.4518 acc=0.8267
epoch 60, train 90/100, loss=0.4202 acc=0.8400
epoch 60, train 91/100, loss=0.6298 acc=0.7467
epoch 60, train 92/100, loss=0.4453 acc=0.8533
epoch 60, train 93/100, loss=0.3684 acc=0.8933
epoch 60, train 94/100, loss=1.1981 acc=0.5333
epoch 60, train 95/100, loss=0.6340 acc=0.8267
epoch 60, train 96/100, loss=0.6075 acc=0.8133
epoch 60, train 97/100, loss=1.2229 acc=0.7200
epoch 60, train 98/100, loss=1.7232 acc=0.5733
epoch 60, train 99/100, loss=1.0156 acc=0.6667
epoch 60, train 100/100, loss=0.7199 acc=0.7600
best epoch 50, best val acc=0.7228
epoch 60, val, loss=0.9643 acc=0.7092
ETA:3.8h/6.4h
epoch 61, train 1/100, loss=1.5602 acc=0.5600
epoch 61, train 2/100, loss=0.4032 acc=0.8400
epoch 61, train 3/100, loss=0.0851 acc=0.9733
epoch 61, train 4/100, loss=0.5292 acc=0.8400
epoch 61, train 5/100, loss=0.2625 acc=0.8933
epoch 61, train 6/100, loss=0.4421 acc=0.8400
epoch 61, train 7/100, loss=0.3407 acc=0.8800
epoch 61, train 8/100, loss=0.4113 acc=0.9067
epoch 61, train 9/100, loss=0.7190 acc=0.7333
epoch 61, train 10/100, loss=0.6771 acc=0.7333
epoch 61, train 11/100, loss=0.2727 acc=0.9467
epoch 61, train 12/100, loss=0.6619 acc=0.8400
epoch 61, train 13/100, loss=1.2621 acc=0.6133
epoch 61, train 14/100, loss=0.0882 acc=0.9733
epoch 61, train 15/100, loss=0.3827 acc=0.8667
epoch 61, train 16/100, loss=1.2893 acc=0.6267
epoch 61, train 17/100, loss=0.2211 acc=0.9467
epoch 61, train 18/100, loss=0.1502 acc=0.9067
epoch 61, train 19/100, loss=0.5142 acc=0.7867
epoch 61, train 20/100, loss=0.9136 acc=0.7333
epoch 61, train 21/100, loss=0.5009 acc=0.8000
epoch 61, train 22/100, loss=0.4639 acc=0.8933
epoch 61, train 23/100, loss=0.2704 acc=0.8800
epoch 61, train 24/100, loss=0.0516 acc=1.0000
epoch 61, train 25/100, loss=0.2758 acc=0.8667
epoch 61, train 26/100, loss=0.2598 acc=0.9333
epoch 61, train 27/100, loss=0.6054 acc=0.8133
epoch 61, train 28/100, loss=0.2957 acc=0.8933
epoch 61, train 29/100, loss=0.5687 acc=0.8267
epoch 61, train 30/100, loss=1.2598 acc=0.6400
epoch 61, train 31/100, loss=0.7864 acc=0.7467
epoch 61, train 32/100, loss=0.6431 acc=0.8267
epoch 61, train 33/100, loss=0.8734 acc=0.7467
epoch 61, train 34/100, loss=0.1556 acc=0.9333
epoch 61, train 35/100, loss=0.6298 acc=0.7867
epoch 61, train 36/100, loss=0.3105 acc=0.8533
epoch 61, train 37/100, loss=1.0362 acc=0.6133
epoch 61, train 38/100, loss=1.4488 acc=0.5600
epoch 61, train 39/100, loss=0.5039 acc=0.8667
epoch 61, train 40/100, loss=0.9934 acc=0.6933
epoch 61, train 41/100, loss=0.3753 acc=0.8533
epoch 61, train 42/100, loss=0.7121 acc=0.7733
epoch 61, train 43/100, loss=0.3960 acc=0.8667
epoch 61, train 44/100, loss=0.9316 acc=0.7467
epoch 61, train 45/100, loss=0.3197 acc=0.8800
epoch 61, train 46/100, loss=0.6749 acc=0.7600
epoch 61, train 47/100, loss=0.1397 acc=0.9333
epoch 61, train 48/100, loss=0.0657 acc=0.9867
epoch 61, train 49/100, loss=0.9231 acc=0.7600
epoch 61, train 50/100, loss=0.4910 acc=0.7733
epoch 61, train 51/100, loss=0.6899 acc=0.7600
epoch 61, train 52/100, loss=0.1623 acc=0.9600
epoch 61, train 53/100, loss=0.7042 acc=0.6933
epoch 61, train 54/100, loss=0.1259 acc=0.9467
epoch 61, train 55/100, loss=0.2060 acc=0.9200
epoch 61, train 56/100, loss=0.6785 acc=0.7467
epoch 61, train 57/100, loss=0.2098 acc=0.8933
epoch 61, train 58/100, loss=0.2359 acc=0.9600
epoch 61, train 59/100, loss=1.3316 acc=0.7200
epoch 61, train 60/100, loss=0.3392 acc=0.8933
epoch 61, train 61/100, loss=0.3238 acc=0.9333
epoch 61, train 62/100, loss=0.5302 acc=0.8533
epoch 61, train 63/100, loss=0.6813 acc=0.7600
epoch 61, train 64/100, loss=0.3444 acc=0.8400
epoch 61, train 65/100, loss=0.0928 acc=0.9600
epoch 61, train 66/100, loss=1.1889 acc=0.6533
epoch 61, train 67/100, loss=1.1386 acc=0.7467
epoch 61, train 68/100, loss=0.1743 acc=0.9333
epoch 61, train 69/100, loss=0.3076 acc=0.8800
epoch 61, train 70/100, loss=0.4991 acc=0.8400
epoch 61, train 71/100, loss=0.9008 acc=0.7467
epoch 61, train 72/100, loss=0.2024 acc=0.9333
epoch 61, train 73/100, loss=0.8137 acc=0.7067
epoch 61, train 74/100, loss=0.3601 acc=0.8933
epoch 61, train 75/100, loss=0.7206 acc=0.7600
epoch 61, train 76/100, loss=1.0411 acc=0.6533
epoch 61, train 77/100, loss=0.8425 acc=0.6267
epoch 61, train 78/100, loss=0.2679 acc=0.9333
epoch 61, train 79/100, loss=0.6947 acc=0.7467
epoch 61, train 80/100, loss=1.6338 acc=0.5467
epoch 61, train 81/100, loss=0.8408 acc=0.7467
epoch 61, train 82/100, loss=0.4402 acc=0.8667
epoch 61, train 83/100, loss=0.7872 acc=0.7333
epoch 61, train 84/100, loss=1.4403 acc=0.5467
epoch 61, train 85/100, loss=0.5833 acc=0.7733
epoch 61, train 86/100, loss=0.2896 acc=0.9067
epoch 61, train 87/100, loss=0.6491 acc=0.7733
epoch 61, train 88/100, loss=0.6763 acc=0.7600
epoch 61, train 89/100, loss=1.6750 acc=0.5600
epoch 61, train 90/100, loss=0.7780 acc=0.7467
epoch 61, train 91/100, loss=0.4038 acc=0.8800
epoch 61, train 92/100, loss=0.9681 acc=0.7467
epoch 61, train 93/100, loss=0.4712 acc=0.8133
epoch 61, train 94/100, loss=0.3601 acc=0.8933
epoch 61, train 95/100, loss=0.4929 acc=0.8400
epoch 61, train 96/100, loss=0.7447 acc=0.7333
epoch 61, train 97/100, loss=0.3478 acc=0.8800
epoch 61, train 98/100, loss=0.4842 acc=0.8000
epoch 61, train 99/100, loss=0.2883 acc=0.8933
epoch 61, train 100/100, loss=0.5808 acc=0.8533
best epoch 50, best val acc=0.7228
epoch 61, val, loss=0.9285 acc=0.7129
ETA:3.9h/6.4h
epoch 62, train 1/100, loss=0.7993 acc=0.7200
epoch 62, train 2/100, loss=0.5931 acc=0.8000
epoch 62, train 3/100, loss=0.2875 acc=0.8800
epoch 62, train 4/100, loss=1.3131 acc=0.6400
epoch 62, train 5/100, loss=0.7095 acc=0.7200
epoch 62, train 6/100, loss=0.6241 acc=0.7867
epoch 62, train 7/100, loss=0.2885 acc=0.8800
epoch 62, train 8/100, loss=0.9749 acc=0.6533
epoch 62, train 9/100, loss=0.4265 acc=0.8400
epoch 62, train 10/100, loss=2.0979 acc=0.5200
epoch 62, train 11/100, loss=0.6818 acc=0.8533
epoch 62, train 12/100, loss=0.5522 acc=0.8000
epoch 62, train 13/100, loss=0.7572 acc=0.7867
epoch 62, train 14/100, loss=0.7195 acc=0.7467
epoch 62, train 15/100, loss=0.6303 acc=0.7867
epoch 62, train 16/100, loss=0.7647 acc=0.7067
epoch 62, train 17/100, loss=0.3724 acc=0.8400
epoch 62, train 18/100, loss=0.6787 acc=0.7867
epoch 62, train 19/100, loss=0.3629 acc=0.8533
epoch 62, train 20/100, loss=0.1302 acc=0.9600
epoch 62, train 21/100, loss=0.2778 acc=0.8933
epoch 62, train 22/100, loss=0.2384 acc=0.9067
epoch 62, train 23/100, loss=0.6554 acc=0.7867
epoch 62, train 24/100, loss=0.3446 acc=0.8400
epoch 62, train 25/100, loss=0.1547 acc=0.9733
epoch 62, train 26/100, loss=0.5262 acc=0.7867
epoch 62, train 27/100, loss=0.1703 acc=0.9067
epoch 62, train 28/100, loss=0.1766 acc=0.9333
epoch 62, train 29/100, loss=0.3720 acc=0.8533
epoch 62, train 30/100, loss=0.4377 acc=0.8533
epoch 62, train 31/100, loss=0.8450 acc=0.6667
epoch 62, train 32/100, loss=0.9449 acc=0.7467
epoch 62, train 33/100, loss=0.3675 acc=0.8533
epoch 62, train 34/100, loss=0.6593 acc=0.7867
epoch 62, train 35/100, loss=0.3043 acc=0.8933
epoch 62, train 36/100, loss=0.4574 acc=0.8267
epoch 62, train 37/100, loss=0.6401 acc=0.8000
epoch 62, train 38/100, loss=0.5332 acc=0.8667
epoch 62, train 39/100, loss=0.3734 acc=0.8400
epoch 62, train 40/100, loss=0.0992 acc=0.9733
epoch 62, train 41/100, loss=0.7602 acc=0.7733
epoch 62, train 42/100, loss=0.6206 acc=0.8133
epoch 62, train 43/100, loss=0.1360 acc=0.9467
epoch 62, train 44/100, loss=0.2360 acc=0.9200
epoch 62, train 45/100, loss=0.2816 acc=0.8933
epoch 62, train 46/100, loss=0.7783 acc=0.7733
epoch 62, train 47/100, loss=1.1634 acc=0.6133
epoch 62, train 48/100, loss=0.5242 acc=0.8267
epoch 62, train 49/100, loss=0.1180 acc=0.9467
epoch 62, train 50/100, loss=1.4737 acc=0.6000
epoch 62, train 51/100, loss=0.4916 acc=0.8267
epoch 62, train 52/100, loss=0.4977 acc=0.7733
epoch 62, train 53/100, loss=0.2844 acc=0.8933
epoch 62, train 54/100, loss=0.3538 acc=0.8800
epoch 62, train 55/100, loss=0.5368 acc=0.8267
epoch 62, train 56/100, loss=0.5421 acc=0.8133
epoch 62, train 57/100, loss=0.2748 acc=0.9200
epoch 62, train 58/100, loss=0.5677 acc=0.7867
epoch 62, train 59/100, loss=1.0510 acc=0.7333
epoch 62, train 60/100, loss=0.1219 acc=0.9333
epoch 62, train 61/100, loss=0.0996 acc=0.9467
epoch 62, train 62/100, loss=0.1688 acc=0.9467
epoch 62, train 63/100, loss=0.6255 acc=0.8133
epoch 62, train 64/100, loss=0.2139 acc=0.9333
epoch 62, train 65/100, loss=0.3513 acc=0.8933
epoch 62, train 66/100, loss=0.1113 acc=0.9467
epoch 62, train 67/100, loss=0.5001 acc=0.8667
epoch 62, train 68/100, loss=0.9792 acc=0.6667
epoch 62, train 69/100, loss=0.8735 acc=0.7600
epoch 62, train 70/100, loss=0.7322 acc=0.7467
epoch 62, train 71/100, loss=0.1074 acc=0.9600
epoch 62, train 72/100, loss=0.8484 acc=0.7867
epoch 62, train 73/100, loss=0.2679 acc=0.9067
epoch 62, train 74/100, loss=1.4054 acc=0.5467
epoch 62, train 75/100, loss=0.9272 acc=0.6933
epoch 62, train 76/100, loss=0.1281 acc=0.9600
epoch 62, train 77/100, loss=1.4133 acc=0.5067
epoch 62, train 78/100, loss=0.8540 acc=0.7200
epoch 62, train 79/100, loss=0.2947 acc=0.8933
epoch 62, train 80/100, loss=0.7048 acc=0.7467
epoch 62, train 81/100, loss=0.4639 acc=0.8133
epoch 62, train 82/100, loss=0.3470 acc=0.9067
epoch 62, train 83/100, loss=0.6959 acc=0.7600
epoch 62, train 84/100, loss=0.6727 acc=0.7467
epoch 62, train 85/100, loss=0.0746 acc=0.9867
epoch 62, train 86/100, loss=0.6410 acc=0.8000
epoch 62, train 87/100, loss=0.5083 acc=0.8400
epoch 62, train 88/100, loss=0.8346 acc=0.7200
epoch 62, train 89/100, loss=0.2312 acc=0.8933
epoch 62, train 90/100, loss=0.4550 acc=0.8800
epoch 62, train 91/100, loss=0.2992 acc=0.8667
epoch 62, train 92/100, loss=0.4892 acc=0.8667
epoch 62, train 93/100, loss=0.4178 acc=0.8667
epoch 62, train 94/100, loss=0.9368 acc=0.7333
epoch 62, train 95/100, loss=0.1430 acc=0.9467
epoch 62, train 96/100, loss=0.5243 acc=0.8400
epoch 62, train 97/100, loss=0.6636 acc=0.7733
epoch 62, train 98/100, loss=0.4630 acc=0.8133
epoch 62, train 99/100, loss=0.2705 acc=0.8800
epoch 62, train 100/100, loss=0.4443 acc=0.8400
best epoch 50, best val acc=0.7228
epoch 62, val, loss=0.9118 acc=0.7173
ETA:4.0h/6.4h
epoch 63, train 1/100, loss=0.0898 acc=0.9733
epoch 63, train 2/100, loss=0.1618 acc=0.9333
epoch 63, train 3/100, loss=0.7168 acc=0.7600
epoch 63, train 4/100, loss=0.5755 acc=0.8400
epoch 63, train 5/100, loss=1.2622 acc=0.6267
epoch 63, train 6/100, loss=0.3428 acc=0.8800
epoch 63, train 7/100, loss=0.6695 acc=0.7867
epoch 63, train 8/100, loss=0.7930 acc=0.8267
epoch 63, train 9/100, loss=0.3333 acc=0.8800
epoch 63, train 10/100, loss=0.6452 acc=0.8000
epoch 63, train 11/100, loss=0.4572 acc=0.8400
epoch 63, train 12/100, loss=0.7443 acc=0.7867
epoch 63, train 13/100, loss=0.7835 acc=0.7600
epoch 63, train 14/100, loss=0.3327 acc=0.8800
epoch 63, train 15/100, loss=1.0984 acc=0.6400
epoch 63, train 16/100, loss=0.6111 acc=0.7867
epoch 63, train 17/100, loss=0.3686 acc=0.9067
epoch 63, train 18/100, loss=0.6338 acc=0.7067
epoch 63, train 19/100, loss=0.2262 acc=0.8800
epoch 63, train 20/100, loss=0.3302 acc=0.8933
epoch 63, train 21/100, loss=1.2818 acc=0.5867
epoch 63, train 22/100, loss=0.5642 acc=0.8400
epoch 63, train 23/100, loss=0.1402 acc=0.9467
epoch 63, train 24/100, loss=0.3954 acc=0.9067
epoch 63, train 25/100, loss=0.5617 acc=0.8267
epoch 63, train 26/100, loss=0.3267 acc=0.8667
epoch 63, train 27/100, loss=1.1923 acc=0.6933
epoch 63, train 28/100, loss=1.0951 acc=0.6000
epoch 63, train 29/100, loss=0.5319 acc=0.8000
epoch 63, train 30/100, loss=0.7702 acc=0.7333
epoch 63, train 31/100, loss=1.2844 acc=0.6667
epoch 63, train 32/100, loss=0.8227 acc=0.6933
epoch 63, train 33/100, loss=0.7643 acc=0.8533
epoch 63, train 34/100, loss=0.1667 acc=0.9333
epoch 63, train 35/100, loss=0.1814 acc=0.9200
epoch 63, train 36/100, loss=0.8627 acc=0.7200
epoch 63, train 37/100, loss=1.0883 acc=0.7067
epoch 63, train 38/100, loss=0.6683 acc=0.8400
epoch 63, train 39/100, loss=0.5198 acc=0.8267
epoch 63, train 40/100, loss=1.7945 acc=0.5067
epoch 63, train 41/100, loss=0.6008 acc=0.7867
epoch 63, train 42/100, loss=0.6725 acc=0.7733
epoch 63, train 43/100, loss=0.4359 acc=0.8133
epoch 63, train 44/100, loss=1.0707 acc=0.6800
epoch 63, train 45/100, loss=0.4841 acc=0.8400
epoch 63, train 46/100, loss=0.2137 acc=0.9067
epoch 63, train 47/100, loss=0.3254 acc=0.9200
epoch 63, train 48/100, loss=0.5340 acc=0.8800
epoch 63, train 49/100, loss=0.7149 acc=0.7600
epoch 63, train 50/100, loss=0.5300 acc=0.8133
epoch 63, train 51/100, loss=1.0143 acc=0.7333
epoch 63, train 52/100, loss=0.2841 acc=0.8400
epoch 63, train 53/100, loss=0.2447 acc=0.8933
epoch 63, train 54/100, loss=0.1213 acc=0.9733
epoch 63, train 55/100, loss=0.3808 acc=0.8800
epoch 63, train 56/100, loss=0.4712 acc=0.8267
epoch 63, train 57/100, loss=0.5438 acc=0.7867
epoch 63, train 58/100, loss=0.2132 acc=0.9333
epoch 63, train 59/100, loss=0.8301 acc=0.7333
epoch 63, train 60/100, loss=0.5545 acc=0.8267
epoch 63, train 61/100, loss=0.4356 acc=0.8533
epoch 63, train 62/100, loss=0.4437 acc=0.8800
epoch 63, train 63/100, loss=0.1975 acc=0.9200
epoch 63, train 64/100, loss=0.6612 acc=0.8133
epoch 63, train 65/100, loss=0.4132 acc=0.8933
epoch 63, train 66/100, loss=0.2215 acc=0.9067
epoch 63, train 67/100, loss=0.2378 acc=0.9200
epoch 63, train 68/100, loss=0.3992 acc=0.8800
epoch 63, train 69/100, loss=0.5436 acc=0.8000
epoch 63, train 70/100, loss=0.2497 acc=0.9200
epoch 63, train 71/100, loss=0.9558 acc=0.7333
epoch 63, train 72/100, loss=0.0445 acc=0.9867
epoch 63, train 73/100, loss=0.3734 acc=0.8533
epoch 63, train 74/100, loss=0.3790 acc=0.8667
epoch 63, train 75/100, loss=0.1230 acc=0.9467
epoch 63, train 76/100, loss=0.6252 acc=0.7733
epoch 63, train 77/100, loss=0.5617 acc=0.8400
epoch 63, train 78/100, loss=0.3490 acc=0.8667
epoch 63, train 79/100, loss=0.4605 acc=0.8667
epoch 63, train 80/100, loss=0.2496 acc=0.9067
epoch 63, train 81/100, loss=0.9524 acc=0.6533
epoch 63, train 82/100, loss=0.5601 acc=0.8000
epoch 63, train 83/100, loss=0.2484 acc=0.9333
epoch 63, train 84/100, loss=0.6689 acc=0.8400
epoch 63, train 85/100, loss=0.9224 acc=0.7733
epoch 63, train 86/100, loss=1.6810 acc=0.6000
epoch 63, train 87/100, loss=0.2610 acc=0.9067
epoch 63, train 88/100, loss=0.3157 acc=0.9200
epoch 63, train 89/100, loss=0.0225 acc=1.0000
epoch 63, train 90/100, loss=0.3967 acc=0.8667
epoch 63, train 91/100, loss=0.1205 acc=0.9467
epoch 63, train 92/100, loss=0.2218 acc=0.9200
epoch 63, train 93/100, loss=0.3084 acc=0.8933
epoch 63, train 94/100, loss=0.5882 acc=0.8267
epoch 63, train 95/100, loss=1.6942 acc=0.5467
epoch 63, train 96/100, loss=0.2482 acc=0.9200
epoch 63, train 97/100, loss=0.4391 acc=0.8267
epoch 63, train 98/100, loss=0.9151 acc=0.7467
epoch 63, train 99/100, loss=1.2629 acc=0.6133
epoch 63, train 100/100, loss=0.6397 acc=0.7733
best epoch 50, best val acc=0.7228
epoch 63, val, loss=0.9171 acc=0.7167
ETA:4.0h/6.4h
epoch 64, train 1/100, loss=0.2593 acc=0.9200
epoch 64, train 2/100, loss=1.4221 acc=0.5733
epoch 64, train 3/100, loss=0.8511 acc=0.7467
epoch 64, train 4/100, loss=0.3387 acc=0.8667
epoch 64, train 5/100, loss=0.4581 acc=0.8800
epoch 64, train 6/100, loss=1.2555 acc=0.6533
epoch 64, train 7/100, loss=0.1304 acc=0.9733
epoch 64, train 8/100, loss=0.6222 acc=0.7600
epoch 64, train 9/100, loss=0.2484 acc=0.8933
epoch 64, train 10/100, loss=0.4389 acc=0.8800
epoch 64, train 11/100, loss=0.7486 acc=0.8400
epoch 64, train 12/100, loss=0.6151 acc=0.7733
epoch 64, train 13/100, loss=0.2586 acc=0.8933
epoch 64, train 14/100, loss=0.7033 acc=0.7467
epoch 64, train 15/100, loss=0.1083 acc=0.9733
epoch 64, train 16/100, loss=0.6070 acc=0.7733
epoch 64, train 17/100, loss=1.3092 acc=0.6933
epoch 64, train 18/100, loss=0.1678 acc=0.9200
epoch 64, train 19/100, loss=0.3648 acc=0.8533
epoch 64, train 20/100, loss=0.2317 acc=0.9200
epoch 64, train 21/100, loss=0.5735 acc=0.8800
epoch 64, train 22/100, loss=0.8218 acc=0.7467
epoch 64, train 23/100, loss=1.0718 acc=0.6533
epoch 64, train 24/100, loss=0.7920 acc=0.7333
epoch 64, train 25/100, loss=0.6814 acc=0.7600
epoch 64, train 26/100, loss=0.7362 acc=0.7867
epoch 64, train 27/100, loss=0.4837 acc=0.8267
epoch 64, train 28/100, loss=1.1930 acc=0.5867
epoch 64, train 29/100, loss=0.0498 acc=0.9733
epoch 64, train 30/100, loss=0.1806 acc=0.9467
epoch 64, train 31/100, loss=0.0946 acc=0.9600
epoch 64, train 32/100, loss=0.2390 acc=0.9200
epoch 64, train 33/100, loss=0.6387 acc=0.7467
epoch 64, train 34/100, loss=0.3309 acc=0.8533
epoch 64, train 35/100, loss=0.6544 acc=0.7600
epoch 64, train 36/100, loss=0.7571 acc=0.7467
epoch 64, train 37/100, loss=0.4533 acc=0.8667
epoch 64, train 38/100, loss=0.3835 acc=0.8933
epoch 64, train 39/100, loss=0.1475 acc=0.9600
epoch 64, train 40/100, loss=0.5749 acc=0.7733
epoch 64, train 41/100, loss=0.2350 acc=0.9467
epoch 64, train 42/100, loss=0.4730 acc=0.8000
epoch 64, train 43/100, loss=0.1815 acc=0.9067
epoch 64, train 44/100, loss=0.4420 acc=0.8800
epoch 64, train 45/100, loss=1.0956 acc=0.6800
epoch 64, train 46/100, loss=0.4299 acc=0.8400
epoch 64, train 47/100, loss=0.0755 acc=0.9600
epoch 64, train 48/100, loss=0.1472 acc=0.9600
epoch 64, train 49/100, loss=1.3452 acc=0.6133
epoch 64, train 50/100, loss=0.3467 acc=0.8800
epoch 64, train 51/100, loss=0.7784 acc=0.7200
epoch 64, train 52/100, loss=0.0650 acc=1.0000
epoch 64, train 53/100, loss=0.3736 acc=0.8533
epoch 64, train 54/100, loss=0.4544 acc=0.8533
epoch 64, train 55/100, loss=0.3077 acc=0.9067
epoch 64, train 56/100, loss=0.3979 acc=0.8933
epoch 64, train 57/100, loss=0.7746 acc=0.7467
epoch 64, train 58/100, loss=0.4328 acc=0.8533
epoch 64, train 59/100, loss=0.3040 acc=0.9200
epoch 64, train 60/100, loss=0.4395 acc=0.8667
epoch 64, train 61/100, loss=0.6202 acc=0.7867
epoch 64, train 62/100, loss=1.9804 acc=0.6000
epoch 64, train 63/100, loss=0.5519 acc=0.7733
epoch 64, train 64/100, loss=1.2323 acc=0.6667
epoch 64, train 65/100, loss=0.2452 acc=0.8933
epoch 64, train 66/100, loss=0.4682 acc=0.8667
epoch 64, train 67/100, loss=0.5235 acc=0.7867
epoch 64, train 68/100, loss=0.6146 acc=0.7733
epoch 64, train 69/100, loss=0.6514 acc=0.7333
epoch 64, train 70/100, loss=0.0358 acc=0.9867
epoch 64, train 71/100, loss=0.3543 acc=0.8400
epoch 64, train 72/100, loss=1.0264 acc=0.6400
epoch 64, train 73/100, loss=0.2435 acc=0.9200
epoch 64, train 74/100, loss=0.5214 acc=0.8133
epoch 64, train 75/100, loss=0.4886 acc=0.8533
epoch 64, train 76/100, loss=0.1206 acc=0.9467
epoch 64, train 77/100, loss=0.8408 acc=0.7333
epoch 64, train 78/100, loss=0.2283 acc=0.8933
epoch 64, train 79/100, loss=0.3020 acc=0.8933
epoch 64, train 80/100, loss=0.1452 acc=0.9333
epoch 64, train 81/100, loss=0.7517 acc=0.6800
epoch 64, train 82/100, loss=0.0597 acc=0.9867
epoch 64, train 83/100, loss=0.6702 acc=0.6933
epoch 64, train 84/100, loss=0.1004 acc=0.9600
epoch 64, train 85/100, loss=0.8424 acc=0.7333
epoch 64, train 86/100, loss=0.5149 acc=0.8133
epoch 64, train 87/100, loss=0.4959 acc=0.8533
epoch 64, train 88/100, loss=0.4999 acc=0.7867
epoch 64, train 89/100, loss=0.4820 acc=0.8000
epoch 64, train 90/100, loss=0.3490 acc=0.9067
epoch 64, train 91/100, loss=0.2418 acc=0.8933
epoch 64, train 92/100, loss=1.0561 acc=0.7333
epoch 64, train 93/100, loss=0.5746 acc=0.8133
epoch 64, train 94/100, loss=0.6861 acc=0.7333
epoch 64, train 95/100, loss=0.3630 acc=0.8533
epoch 64, train 96/100, loss=0.5746 acc=0.7867
epoch 64, train 97/100, loss=1.1307 acc=0.6133
epoch 64, train 98/100, loss=0.2375 acc=0.9200
epoch 64, train 99/100, loss=0.3446 acc=0.8800
epoch 64, train 100/100, loss=0.2353 acc=0.8933
best epoch 50, best val acc=0.7228
epoch 64, val, loss=0.9269 acc=0.7093
ETA:4.1h/6.4h
epoch 65, train 1/100, loss=0.5228 acc=0.8000
epoch 65, train 2/100, loss=0.6912 acc=0.7333
epoch 65, train 3/100, loss=0.6194 acc=0.7733
epoch 65, train 4/100, loss=0.1608 acc=0.9467
epoch 65, train 5/100, loss=0.5648 acc=0.7733
epoch 65, train 6/100, loss=0.4421 acc=0.8667
epoch 65, train 7/100, loss=0.6660 acc=0.8533
epoch 65, train 8/100, loss=0.3747 acc=0.8667
epoch 65, train 9/100, loss=0.2700 acc=0.8933
epoch 65, train 10/100, loss=0.2573 acc=0.9067
epoch 65, train 11/100, loss=0.6435 acc=0.7200
epoch 65, train 12/100, loss=0.5052 acc=0.8400
epoch 65, train 13/100, loss=0.6418 acc=0.7467
epoch 65, train 14/100, loss=0.4347 acc=0.8667
epoch 65, train 15/100, loss=0.9689 acc=0.6400
epoch 65, train 16/100, loss=0.1156 acc=0.9333
epoch 65, train 17/100, loss=0.5366 acc=0.8267
epoch 65, train 18/100, loss=0.1914 acc=0.9467
epoch 65, train 19/100, loss=0.9180 acc=0.6267
epoch 65, train 20/100, loss=0.7986 acc=0.7200
epoch 65, train 21/100, loss=0.6712 acc=0.7600
epoch 65, train 22/100, loss=0.6723 acc=0.7333
epoch 65, train 23/100, loss=0.1632 acc=0.9333
epoch 65, train 24/100, loss=0.4940 acc=0.8667
epoch 65, train 25/100, loss=0.2247 acc=0.9200
epoch 65, train 26/100, loss=0.6295 acc=0.7867
epoch 65, train 27/100, loss=0.2530 acc=0.8667
epoch 65, train 28/100, loss=0.0321 acc=1.0000
epoch 65, train 29/100, loss=0.8558 acc=0.7467
epoch 65, train 30/100, loss=0.6327 acc=0.7733
epoch 65, train 31/100, loss=0.5304 acc=0.7867
epoch 65, train 32/100, loss=0.1910 acc=0.9333
epoch 65, train 33/100, loss=0.5186 acc=0.7733
epoch 65, train 34/100, loss=0.3606 acc=0.8933
epoch 65, train 35/100, loss=0.1996 acc=0.9067
epoch 65, train 36/100, loss=1.1164 acc=0.6133
epoch 65, train 37/100, loss=0.7461 acc=0.7733
epoch 65, train 38/100, loss=1.0755 acc=0.6667
epoch 65, train 39/100, loss=0.9347 acc=0.7867
epoch 65, train 40/100, loss=0.2425 acc=0.8933
epoch 65, train 41/100, loss=0.1403 acc=0.9467
epoch 65, train 42/100, loss=1.5290 acc=0.6000
epoch 65, train 43/100, loss=0.1629 acc=0.9333
epoch 65, train 44/100, loss=1.3125 acc=0.5733
epoch 65, train 45/100, loss=0.2898 acc=0.8667
epoch 65, train 46/100, loss=0.2567 acc=0.9200
epoch 65, train 47/100, loss=1.1139 acc=0.6667
epoch 65, train 48/100, loss=0.5203 acc=0.8533
epoch 65, train 49/100, loss=0.5145 acc=0.8133
epoch 65, train 50/100, loss=0.2478 acc=0.8933
epoch 65, train 51/100, loss=0.5445 acc=0.7467
epoch 65, train 52/100, loss=1.0361 acc=0.6933
epoch 65, train 53/100, loss=0.6065 acc=0.7067
epoch 65, train 54/100, loss=0.1079 acc=0.9467
epoch 65, train 55/100, loss=0.9012 acc=0.6533
epoch 65, train 56/100, loss=0.2500 acc=0.9067
epoch 65, train 57/100, loss=0.4730 acc=0.8267
epoch 65, train 58/100, loss=0.2445 acc=0.9467
epoch 65, train 59/100, loss=0.5614 acc=0.8533
epoch 65, train 60/100, loss=0.3449 acc=0.8400
epoch 65, train 61/100, loss=0.2996 acc=0.9067
epoch 65, train 62/100, loss=0.6043 acc=0.7600
epoch 65, train 63/100, loss=0.5859 acc=0.8400
epoch 65, train 64/100, loss=0.4872 acc=0.8933
epoch 65, train 65/100, loss=0.3501 acc=0.9200
epoch 65, train 66/100, loss=0.2110 acc=0.9467
epoch 65, train 67/100, loss=0.2129 acc=0.9200
epoch 65, train 68/100, loss=0.8227 acc=0.7333
epoch 65, train 69/100, loss=0.3550 acc=0.8800
epoch 65, train 70/100, loss=0.5922 acc=0.8267
epoch 65, train 71/100, loss=0.2670 acc=0.8933
epoch 65, train 72/100, loss=0.1088 acc=0.9600
epoch 65, train 73/100, loss=1.4138 acc=0.5333
epoch 65, train 74/100, loss=0.8049 acc=0.7333
epoch 65, train 75/100, loss=0.5002 acc=0.8400
epoch 65, train 76/100, loss=0.2833 acc=0.9067
epoch 65, train 77/100, loss=0.9335 acc=0.6533
epoch 65, train 78/100, loss=0.5247 acc=0.8133
epoch 65, train 79/100, loss=0.3581 acc=0.9067
epoch 65, train 80/100, loss=0.4953 acc=0.8133
epoch 65, train 81/100, loss=0.2101 acc=0.9467
epoch 65, train 82/100, loss=0.8237 acc=0.7067
epoch 65, train 83/100, loss=0.2238 acc=0.9467
epoch 65, train 84/100, loss=0.4517 acc=0.8000
epoch 65, train 85/100, loss=0.6548 acc=0.8133
epoch 65, train 86/100, loss=0.5981 acc=0.8000
epoch 65, train 87/100, loss=0.2400 acc=0.8800
epoch 65, train 88/100, loss=0.4440 acc=0.8933
epoch 65, train 89/100, loss=0.5420 acc=0.8400
epoch 65, train 90/100, loss=0.3631 acc=0.8667
epoch 65, train 91/100, loss=0.1657 acc=0.9467
epoch 65, train 92/100, loss=0.1928 acc=0.9200
epoch 65, train 93/100, loss=0.6686 acc=0.8667
epoch 65, train 94/100, loss=0.7936 acc=0.7467
epoch 65, train 95/100, loss=0.1617 acc=0.9733
epoch 65, train 96/100, loss=0.4581 acc=0.8933
epoch 65, train 97/100, loss=0.7377 acc=0.8267
epoch 65, train 98/100, loss=0.8220 acc=0.7067
epoch 65, train 99/100, loss=0.2794 acc=0.9200
epoch 65, train 100/100, loss=0.8444 acc=0.6933
best epoch 50, best val acc=0.7228
epoch 65, val, loss=0.9186 acc=0.7094
ETA:4.2h/6.4h
epoch 66, train 1/100, loss=0.1612 acc=0.9467
epoch 66, train 2/100, loss=0.9913 acc=0.6933
epoch 66, train 3/100, loss=0.6669 acc=0.7867
epoch 66, train 4/100, loss=0.7794 acc=0.7333
epoch 66, train 5/100, loss=0.7554 acc=0.7467
epoch 66, train 6/100, loss=0.0746 acc=0.9600
epoch 66, train 7/100, loss=0.7341 acc=0.7600
epoch 66, train 8/100, loss=1.0977 acc=0.7200
epoch 66, train 9/100, loss=0.7026 acc=0.7600
epoch 66, train 10/100, loss=0.5926 acc=0.7733
epoch 66, train 11/100, loss=0.8572 acc=0.7200
epoch 66, train 12/100, loss=0.8046 acc=0.8000
epoch 66, train 13/100, loss=0.2167 acc=0.9200
epoch 66, train 14/100, loss=0.7674 acc=0.7733
epoch 66, train 15/100, loss=0.6192 acc=0.7733
epoch 66, train 16/100, loss=0.5082 acc=0.8267
epoch 66, train 17/100, loss=0.3008 acc=0.9333
epoch 66, train 18/100, loss=0.3588 acc=0.8400
epoch 66, train 19/100, loss=0.4111 acc=0.8533
epoch 66, train 20/100, loss=0.8292 acc=0.7067
epoch 66, train 21/100, loss=0.2762 acc=0.8800
epoch 66, train 22/100, loss=0.6411 acc=0.7600
epoch 66, train 23/100, loss=0.6405 acc=0.7467
epoch 66, train 24/100, loss=0.7053 acc=0.7600
epoch 66, train 25/100, loss=0.6579 acc=0.8533
epoch 66, train 26/100, loss=0.7463 acc=0.7733
epoch 66, train 27/100, loss=0.6420 acc=0.8000
epoch 66, train 28/100, loss=0.3232 acc=0.8533
epoch 66, train 29/100, loss=0.2785 acc=0.9067
epoch 66, train 30/100, loss=0.1300 acc=0.9600
epoch 66, train 31/100, loss=0.4353 acc=0.8667
epoch 66, train 32/100, loss=0.4303 acc=0.8667
epoch 66, train 33/100, loss=0.3783 acc=0.8533
epoch 66, train 34/100, loss=0.3429 acc=0.8800
epoch 66, train 35/100, loss=1.0808 acc=0.6933
epoch 66, train 36/100, loss=0.2109 acc=0.9200
epoch 66, train 37/100, loss=1.2394 acc=0.6800
epoch 66, train 38/100, loss=0.5464 acc=0.7600
epoch 66, train 39/100, loss=0.2496 acc=0.8933
epoch 66, train 40/100, loss=1.0543 acc=0.6933
epoch 66, train 41/100, loss=0.2521 acc=0.8667
epoch 66, train 42/100, loss=0.6311 acc=0.8133
epoch 66, train 43/100, loss=0.5150 acc=0.8533
epoch 66, train 44/100, loss=0.0994 acc=0.9867
epoch 66, train 45/100, loss=0.1578 acc=0.9467
epoch 66, train 46/100, loss=0.6716 acc=0.7733
epoch 66, train 47/100, loss=0.6416 acc=0.8000
epoch 66, train 48/100, loss=0.1019 acc=0.9733
epoch 66, train 49/100, loss=0.6540 acc=0.7600
epoch 66, train 50/100, loss=0.1410 acc=0.9600
epoch 66, train 51/100, loss=0.1939 acc=0.9467
epoch 66, train 52/100, loss=0.5760 acc=0.8133
epoch 66, train 53/100, loss=0.2472 acc=0.9200
epoch 66, train 54/100, loss=0.7850 acc=0.8400
epoch 66, train 55/100, loss=0.4205 acc=0.8533
epoch 66, train 56/100, loss=0.3357 acc=0.9200
epoch 66, train 57/100, loss=0.6418 acc=0.7467
epoch 66, train 58/100, loss=0.3935 acc=0.8133
epoch 66, train 59/100, loss=0.3598 acc=0.8800
epoch 66, train 60/100, loss=0.8466 acc=0.7333
epoch 66, train 61/100, loss=0.6107 acc=0.8533
epoch 66, train 62/100, loss=1.0718 acc=0.6133
epoch 66, train 63/100, loss=0.4772 acc=0.8533
epoch 66, train 64/100, loss=0.5304 acc=0.7733
epoch 66, train 65/100, loss=0.8227 acc=0.7600
epoch 66, train 66/100, loss=0.3731 acc=0.8533
epoch 66, train 67/100, loss=0.4628 acc=0.8267
epoch 66, train 68/100, loss=0.2757 acc=0.8800
epoch 66, train 69/100, loss=0.2432 acc=0.9200
epoch 66, train 70/100, loss=0.1909 acc=0.9600
epoch 66, train 71/100, loss=0.6596 acc=0.7600
epoch 66, train 72/100, loss=0.8038 acc=0.7067
epoch 66, train 73/100, loss=1.1936 acc=0.6000
epoch 66, train 74/100, loss=0.2838 acc=0.8933
epoch 66, train 75/100, loss=0.5559 acc=0.8800
epoch 66, train 76/100, loss=0.9263 acc=0.6400
epoch 66, train 77/100, loss=0.1510 acc=0.9467
epoch 66, train 78/100, loss=0.2582 acc=0.8800
epoch 66, train 79/100, loss=0.2638 acc=0.9067
epoch 66, train 80/100, loss=0.2549 acc=0.9333
epoch 66, train 81/100, loss=0.3841 acc=0.8800
epoch 66, train 82/100, loss=0.8238 acc=0.7200
epoch 66, train 83/100, loss=0.6159 acc=0.8133
epoch 66, train 84/100, loss=0.1156 acc=0.9600
epoch 66, train 85/100, loss=0.7509 acc=0.7600
epoch 66, train 86/100, loss=0.3053 acc=0.8933
epoch 66, train 87/100, loss=1.0816 acc=0.6133
epoch 66, train 88/100, loss=0.1252 acc=0.9600
epoch 66, train 89/100, loss=0.3676 acc=0.8667
epoch 66, train 90/100, loss=0.1646 acc=0.9467
epoch 66, train 91/100, loss=0.2989 acc=0.9200
epoch 66, train 92/100, loss=0.0492 acc=0.9867
epoch 66, train 93/100, loss=0.1820 acc=0.9467
epoch 66, train 94/100, loss=0.4255 acc=0.8400
epoch 66, train 95/100, loss=0.6688 acc=0.7333
epoch 66, train 96/100, loss=0.6965 acc=0.7333
epoch 66, train 97/100, loss=1.0273 acc=0.7067
epoch 66, train 98/100, loss=0.6706 acc=0.7867
epoch 66, train 99/100, loss=1.2129 acc=0.6133
epoch 66, train 100/100, loss=0.3705 acc=0.8667
best epoch 50, best val acc=0.7228
epoch 66, val, loss=0.8841 acc=0.7275
ETA:4.2h/6.4h
epoch 67, train 1/100, loss=0.7981 acc=0.6933
epoch 67, train 2/100, loss=0.0962 acc=0.9867
epoch 67, train 3/100, loss=0.2190 acc=0.9067
epoch 67, train 4/100, loss=0.3109 acc=0.9067
epoch 67, train 5/100, loss=1.0952 acc=0.5867
epoch 67, train 6/100, loss=0.6783 acc=0.7333
epoch 67, train 7/100, loss=0.3089 acc=0.8933
epoch 67, train 8/100, loss=0.6047 acc=0.8000
epoch 67, train 9/100, loss=0.1209 acc=0.9733
epoch 67, train 10/100, loss=0.1773 acc=0.9467
epoch 67, train 11/100, loss=0.6834 acc=0.7333
epoch 67, train 12/100, loss=0.3490 acc=0.8800
epoch 67, train 13/100, loss=0.1588 acc=0.9200
epoch 67, train 14/100, loss=1.1395 acc=0.6267
epoch 67, train 15/100, loss=0.8374 acc=0.6933
epoch 67, train 16/100, loss=0.6303 acc=0.7467
epoch 67, train 17/100, loss=0.9208 acc=0.6267
epoch 67, train 18/100, loss=0.4103 acc=0.8800
epoch 67, train 19/100, loss=0.5159 acc=0.8133
epoch 67, train 20/100, loss=0.2209 acc=0.9467
epoch 67, train 21/100, loss=0.5707 acc=0.8267
epoch 67, train 22/100, loss=0.4657 acc=0.8533
epoch 67, train 23/100, loss=1.0439 acc=0.6667
epoch 67, train 24/100, loss=0.2927 acc=0.9333
epoch 67, train 25/100, loss=0.3402 acc=0.8800
epoch 67, train 26/100, loss=0.2805 acc=0.8933
epoch 67, train 27/100, loss=0.2122 acc=0.9467
epoch 67, train 28/100, loss=0.6604 acc=0.8133
epoch 67, train 29/100, loss=0.1373 acc=0.9467
epoch 67, train 30/100, loss=0.5704 acc=0.8000
epoch 67, train 31/100, loss=0.2806 acc=0.9067
epoch 67, train 32/100, loss=0.2702 acc=0.8933
epoch 67, train 33/100, loss=0.3113 acc=0.8667
epoch 67, train 34/100, loss=0.3124 acc=0.8533
epoch 67, train 35/100, loss=1.4397 acc=0.4533
epoch 67, train 36/100, loss=0.4972 acc=0.8400
epoch 67, train 37/100, loss=0.3608 acc=0.8800
epoch 67, train 38/100, loss=0.1861 acc=0.9200
epoch 67, train 39/100, loss=0.2554 acc=0.9333
epoch 67, train 40/100, loss=0.7186 acc=0.7733
epoch 67, train 41/100, loss=1.1817 acc=0.6667
epoch 67, train 42/100, loss=0.5459 acc=0.7867
epoch 67, train 43/100, loss=1.0893 acc=0.7200
epoch 67, train 44/100, loss=0.9529 acc=0.6400
epoch 67, train 45/100, loss=1.4208 acc=0.5733
epoch 67, train 46/100, loss=0.4415 acc=0.8933
epoch 67, train 47/100, loss=0.7979 acc=0.7333
epoch 67, train 48/100, loss=0.9499 acc=0.6533
epoch 67, train 49/100, loss=0.1496 acc=0.9467
epoch 67, train 50/100, loss=0.5311 acc=0.8133
epoch 67, train 51/100, loss=0.5212 acc=0.8533
epoch 67, train 52/100, loss=0.4177 acc=0.8933
epoch 67, train 53/100, loss=0.5009 acc=0.7733
epoch 67, train 54/100, loss=0.2059 acc=0.9200
epoch 67, train 55/100, loss=0.7002 acc=0.8133
epoch 67, train 56/100, loss=0.4571 acc=0.7867
epoch 67, train 57/100, loss=0.1745 acc=0.9200
epoch 67, train 58/100, loss=0.1114 acc=0.9867
epoch 67, train 59/100, loss=0.3502 acc=0.8800
epoch 67, train 60/100, loss=0.4864 acc=0.8400
epoch 67, train 61/100, loss=1.3036 acc=0.5867
epoch 67, train 62/100, loss=1.7324 acc=0.6667
epoch 67, train 63/100, loss=0.5761 acc=0.8267
epoch 67, train 64/100, loss=0.6244 acc=0.6933
epoch 67, train 65/100, loss=0.1666 acc=0.9467
epoch 67, train 66/100, loss=1.0173 acc=0.7200
epoch 67, train 67/100, loss=1.6500 acc=0.5867
epoch 67, train 68/100, loss=0.2199 acc=0.9333
epoch 67, train 69/100, loss=0.4365 acc=0.8800
epoch 67, train 70/100, loss=0.2353 acc=0.9333
epoch 67, train 71/100, loss=0.3409 acc=0.8533
epoch 67, train 72/100, loss=0.6151 acc=0.7200
epoch 67, train 73/100, loss=0.6972 acc=0.7333
epoch 67, train 74/100, loss=0.2085 acc=0.9467
epoch 67, train 75/100, loss=0.5440 acc=0.7467
epoch 67, train 76/100, loss=0.5959 acc=0.7867
epoch 67, train 77/100, loss=0.8056 acc=0.7067
epoch 67, train 78/100, loss=0.3116 acc=0.8800
epoch 67, train 79/100, loss=0.3298 acc=0.8800
epoch 67, train 80/100, loss=0.1211 acc=0.9200
epoch 67, train 81/100, loss=1.1751 acc=0.6000
epoch 67, train 82/100, loss=0.5276 acc=0.8000
epoch 67, train 83/100, loss=0.6632 acc=0.8267
epoch 67, train 84/100, loss=0.2247 acc=0.8933
epoch 67, train 85/100, loss=0.2664 acc=0.9067
epoch 67, train 86/100, loss=0.2246 acc=0.9200
epoch 67, train 87/100, loss=0.2672 acc=0.9200
epoch 67, train 88/100, loss=0.7333 acc=0.7733
epoch 67, train 89/100, loss=0.4402 acc=0.8133
epoch 67, train 90/100, loss=0.5271 acc=0.8267
epoch 67, train 91/100, loss=0.4763 acc=0.8133
epoch 67, train 92/100, loss=0.2094 acc=0.9333
epoch 67, train 93/100, loss=0.6990 acc=0.7867
epoch 67, train 94/100, loss=0.6432 acc=0.7067
epoch 67, train 95/100, loss=0.3156 acc=0.8533
epoch 67, train 96/100, loss=0.8552 acc=0.6667
epoch 67, train 97/100, loss=0.1376 acc=0.9333
epoch 67, train 98/100, loss=0.2687 acc=0.9067
epoch 67, train 99/100, loss=0.2027 acc=0.9333
epoch 67, train 100/100, loss=0.3022 acc=0.8667
best epoch 66, best val acc=0.7275
epoch 67, val, loss=0.9650 acc=0.7027
ETA:4.3h/6.4h
epoch 68, train 1/100, loss=0.5783 acc=0.7600
epoch 68, train 2/100, loss=0.1213 acc=0.9600
epoch 68, train 3/100, loss=0.3719 acc=0.9200
epoch 68, train 4/100, loss=0.9100 acc=0.7200
epoch 68, train 5/100, loss=0.3256 acc=0.8933
epoch 68, train 6/100, loss=0.9742 acc=0.6933
epoch 68, train 7/100, loss=0.3835 acc=0.8533
epoch 68, train 8/100, loss=0.3332 acc=0.9067
epoch 68, train 9/100, loss=0.6809 acc=0.7867
epoch 68, train 10/100, loss=0.6357 acc=0.7867
epoch 68, train 11/100, loss=0.1814 acc=0.9333
epoch 68, train 12/100, loss=0.1160 acc=0.9733
epoch 68, train 13/100, loss=0.0283 acc=1.0000
epoch 68, train 14/100, loss=0.3198 acc=0.8800
epoch 68, train 15/100, loss=0.1713 acc=0.9467
epoch 68, train 16/100, loss=0.6291 acc=0.7200
epoch 68, train 17/100, loss=0.5449 acc=0.7600
epoch 68, train 18/100, loss=0.2062 acc=0.9333
epoch 68, train 19/100, loss=0.9574 acc=0.6667
epoch 68, train 20/100, loss=0.7042 acc=0.7200
epoch 68, train 21/100, loss=0.5948 acc=0.8000
epoch 68, train 22/100, loss=0.5223 acc=0.8133
epoch 68, train 23/100, loss=0.2586 acc=0.9200
epoch 68, train 24/100, loss=0.2301 acc=0.9200
epoch 68, train 25/100, loss=0.1316 acc=0.9600
epoch 68, train 26/100, loss=0.2659 acc=0.8800
epoch 68, train 27/100, loss=0.1624 acc=0.9467
epoch 68, train 28/100, loss=0.3323 acc=0.8933
epoch 68, train 29/100, loss=0.3215 acc=0.8800
epoch 68, train 30/100, loss=0.3900 acc=0.8667
epoch 68, train 31/100, loss=0.3524 acc=0.8667
epoch 68, train 32/100, loss=0.4509 acc=0.8400
epoch 68, train 33/100, loss=0.4642 acc=0.8533
epoch 68, train 34/100, loss=0.3581 acc=0.8267
epoch 68, train 35/100, loss=0.3916 acc=0.8267
epoch 68, train 36/100, loss=0.1783 acc=0.9333
epoch 68, train 37/100, loss=0.4594 acc=0.8533
epoch 68, train 38/100, loss=1.1950 acc=0.6800
epoch 68, train 39/100, loss=0.3097 acc=0.8933
epoch 68, train 40/100, loss=0.1566 acc=0.9600
epoch 68, train 41/100, loss=0.2366 acc=0.9067
epoch 68, train 42/100, loss=0.2909 acc=0.8667
epoch 68, train 43/100, loss=1.7356 acc=0.4800
epoch 68, train 44/100, loss=0.9553 acc=0.7200
epoch 68, train 45/100, loss=0.4044 acc=0.8667
epoch 68, train 46/100, loss=0.8118 acc=0.7600
epoch 68, train 47/100, loss=0.1916 acc=0.9467
epoch 68, train 48/100, loss=0.5483 acc=0.8133
epoch 68, train 49/100, loss=0.6684 acc=0.7867
epoch 68, train 50/100, loss=0.3289 acc=0.8667
epoch 68, train 51/100, loss=0.5186 acc=0.8400
epoch 68, train 52/100, loss=0.4055 acc=0.8533
epoch 68, train 53/100, loss=0.7291 acc=0.7733
epoch 68, train 54/100, loss=0.2919 acc=0.8933
epoch 68, train 55/100, loss=0.2470 acc=0.8933
epoch 68, train 56/100, loss=0.5278 acc=0.8133
epoch 68, train 57/100, loss=1.0498 acc=0.6400
epoch 68, train 58/100, loss=0.5503 acc=0.8400
epoch 68, train 59/100, loss=0.6936 acc=0.7200
epoch 68, train 60/100, loss=0.4575 acc=0.8133
epoch 68, train 61/100, loss=1.1668 acc=0.6533
epoch 68, train 62/100, loss=0.1042 acc=0.9600
epoch 68, train 63/100, loss=0.1986 acc=0.9600
epoch 68, train 64/100, loss=0.2393 acc=0.9200
epoch 68, train 65/100, loss=0.2001 acc=0.9467
epoch 68, train 66/100, loss=0.6672 acc=0.7200
epoch 68, train 67/100, loss=0.4081 acc=0.8800
epoch 68, train 68/100, loss=0.1475 acc=0.9200
epoch 68, train 69/100, loss=0.4358 acc=0.8267
epoch 68, train 70/100, loss=0.4502 acc=0.8400
epoch 68, train 71/100, loss=0.5104 acc=0.7733
epoch 68, train 72/100, loss=0.6557 acc=0.8133
epoch 68, train 73/100, loss=0.7125 acc=0.8533
epoch 68, train 74/100, loss=0.7570 acc=0.6667
epoch 68, train 75/100, loss=0.3512 acc=0.9067
epoch 68, train 76/100, loss=0.3512 acc=0.8667
epoch 68, train 77/100, loss=0.2794 acc=0.9200
epoch 68, train 78/100, loss=0.0941 acc=0.9733
epoch 68, train 79/100, loss=0.4864 acc=0.8533
epoch 68, train 80/100, loss=0.3097 acc=0.9333
epoch 68, train 81/100, loss=0.4584 acc=0.9067
epoch 68, train 82/100, loss=0.5584 acc=0.8267
epoch 68, train 83/100, loss=0.4206 acc=0.8400
epoch 68, train 84/100, loss=0.4240 acc=0.9067
epoch 68, train 85/100, loss=1.6130 acc=0.5600
epoch 68, train 86/100, loss=0.5342 acc=0.7867
epoch 68, train 87/100, loss=0.2617 acc=0.8800
epoch 68, train 88/100, loss=0.1582 acc=0.9467
epoch 68, train 89/100, loss=0.3867 acc=0.8933
epoch 68, train 90/100, loss=0.1724 acc=0.9467
epoch 68, train 91/100, loss=0.9293 acc=0.6800
epoch 68, train 92/100, loss=0.6139 acc=0.8267
epoch 68, train 93/100, loss=0.0922 acc=0.9867
epoch 68, train 94/100, loss=0.7348 acc=0.8000
epoch 68, train 95/100, loss=0.0949 acc=0.9867
epoch 68, train 96/100, loss=0.3180 acc=0.8667
epoch 68, train 97/100, loss=0.3432 acc=0.9200
epoch 68, train 98/100, loss=0.1612 acc=0.9733
epoch 68, train 99/100, loss=0.5118 acc=0.8533
epoch 68, train 100/100, loss=0.6131 acc=0.7867
best epoch 66, best val acc=0.7275
epoch 68, val, loss=0.9474 acc=0.7117
ETA:4.4h/6.4h
epoch 69, train 1/100, loss=0.4279 acc=0.8267
epoch 69, train 2/100, loss=0.0965 acc=0.9600
epoch 69, train 3/100, loss=1.0463 acc=0.6800
epoch 69, train 4/100, loss=1.4234 acc=0.5333
epoch 69, train 5/100, loss=0.3111 acc=0.8533
epoch 69, train 6/100, loss=0.4513 acc=0.8133
epoch 69, train 7/100, loss=0.1507 acc=0.9467
epoch 69, train 8/100, loss=0.2305 acc=0.9067
epoch 69, train 9/100, loss=0.3143 acc=0.9067
epoch 69, train 10/100, loss=0.4219 acc=0.8533
epoch 69, train 11/100, loss=0.7516 acc=0.7467
epoch 69, train 12/100, loss=1.2930 acc=0.6133
epoch 69, train 13/100, loss=0.5270 acc=0.8000
epoch 69, train 14/100, loss=0.1673 acc=0.9333
epoch 69, train 15/100, loss=0.4028 acc=0.8933
epoch 69, train 16/100, loss=0.1943 acc=0.9467
epoch 69, train 17/100, loss=0.6870 acc=0.7867
epoch 69, train 18/100, loss=0.5789 acc=0.8000
epoch 69, train 19/100, loss=0.5633 acc=0.8400
epoch 69, train 20/100, loss=0.5467 acc=0.8533
epoch 69, train 21/100, loss=0.6586 acc=0.7600
epoch 69, train 22/100, loss=0.8431 acc=0.7200
epoch 69, train 23/100, loss=0.4102 acc=0.8667
epoch 69, train 24/100, loss=0.9460 acc=0.6267
epoch 69, train 25/100, loss=0.3535 acc=0.8533
epoch 69, train 26/100, loss=0.8064 acc=0.6933
epoch 69, train 27/100, loss=0.3020 acc=0.8933
epoch 69, train 28/100, loss=0.2639 acc=0.8933
epoch 69, train 29/100, loss=0.5735 acc=0.8400
epoch 69, train 30/100, loss=0.5914 acc=0.7733
epoch 69, train 31/100, loss=0.1730 acc=0.9867
epoch 69, train 32/100, loss=1.4374 acc=0.6267
epoch 69, train 33/100, loss=0.8225 acc=0.7067
epoch 69, train 34/100, loss=0.2856 acc=0.8667
epoch 69, train 35/100, loss=0.3203 acc=0.8800
epoch 69, train 36/100, loss=0.4957 acc=0.8000
epoch 69, train 37/100, loss=0.1368 acc=0.9600
epoch 69, train 38/100, loss=0.1456 acc=0.9600
epoch 69, train 39/100, loss=0.4604 acc=0.8000
epoch 69, train 40/100, loss=0.5551 acc=0.8000
epoch 69, train 41/100, loss=0.7615 acc=0.8000
epoch 69, train 42/100, loss=0.3542 acc=0.8667
epoch 69, train 43/100, loss=0.7283 acc=0.7867
epoch 69, train 44/100, loss=0.7313 acc=0.7467
epoch 69, train 45/100, loss=0.5819 acc=0.8133
epoch 69, train 46/100, loss=0.0919 acc=0.9600
epoch 69, train 47/100, loss=0.7783 acc=0.7467
epoch 69, train 48/100, loss=0.0165 acc=1.0000
epoch 69, train 49/100, loss=0.3692 acc=0.8533
epoch 69, train 50/100, loss=0.7774 acc=0.7867
epoch 69, train 51/100, loss=1.0845 acc=0.7067
epoch 69, train 52/100, loss=0.5936 acc=0.8400
epoch 69, train 53/100, loss=1.1239 acc=0.7200
epoch 69, train 54/100, loss=0.2903 acc=0.8667
epoch 69, train 55/100, loss=1.3107 acc=0.5467
epoch 69, train 56/100, loss=0.6123 acc=0.8267
epoch 69, train 57/100, loss=0.9176 acc=0.6933
epoch 69, train 58/100, loss=0.6727 acc=0.7733
epoch 69, train 59/100, loss=0.5019 acc=0.7867
epoch 69, train 60/100, loss=0.6002 acc=0.8267
epoch 69, train 61/100, loss=0.7623 acc=0.7600
epoch 69, train 62/100, loss=0.5619 acc=0.8133
epoch 69, train 63/100, loss=0.3268 acc=0.9067
epoch 69, train 64/100, loss=0.3714 acc=0.8533
epoch 69, train 65/100, loss=0.1057 acc=0.9733
epoch 69, train 66/100, loss=0.3192 acc=0.8933
epoch 69, train 67/100, loss=0.7154 acc=0.7600
epoch 69, train 68/100, loss=0.4707 acc=0.8667
epoch 69, train 69/100, loss=0.7101 acc=0.7467
epoch 69, train 70/100, loss=0.7963 acc=0.7467
epoch 69, train 71/100, loss=0.1501 acc=0.9600
epoch 69, train 72/100, loss=1.0094 acc=0.6800
epoch 69, train 73/100, loss=0.2595 acc=0.9200
epoch 69, train 74/100, loss=0.3171 acc=0.8667
epoch 69, train 75/100, loss=1.7382 acc=0.5733
epoch 69, train 76/100, loss=0.5310 acc=0.8133
epoch 69, train 77/100, loss=0.7231 acc=0.7867
epoch 69, train 78/100, loss=0.4586 acc=0.8400
epoch 69, train 79/100, loss=0.1310 acc=0.9600
epoch 69, train 80/100, loss=0.6712 acc=0.7333
epoch 69, train 81/100, loss=0.3913 acc=0.8667
epoch 69, train 82/100, loss=0.3600 acc=0.8800
epoch 69, train 83/100, loss=0.5046 acc=0.8133
epoch 69, train 84/100, loss=0.1619 acc=0.9467
epoch 69, train 85/100, loss=0.1261 acc=0.9600
epoch 69, train 86/100, loss=0.6822 acc=0.7467
epoch 69, train 87/100, loss=0.0961 acc=0.9733
epoch 69, train 88/100, loss=0.6608 acc=0.7733
epoch 69, train 89/100, loss=1.1153 acc=0.6267
epoch 69, train 90/100, loss=0.3746 acc=0.8800
epoch 69, train 91/100, loss=0.2255 acc=0.9200
epoch 69, train 92/100, loss=0.6708 acc=0.7467
epoch 69, train 93/100, loss=0.1182 acc=0.9333
epoch 69, train 94/100, loss=0.5865 acc=0.7600
epoch 69, train 95/100, loss=0.1165 acc=0.9600
epoch 69, train 96/100, loss=0.6448 acc=0.8133
epoch 69, train 97/100, loss=0.3073 acc=0.8667
epoch 69, train 98/100, loss=0.3773 acc=0.8533
epoch 69, train 99/100, loss=0.7863 acc=0.7867
epoch 69, train 100/100, loss=0.3064 acc=0.8800
best epoch 66, best val acc=0.7275
epoch 69, val, loss=0.8778 acc=0.7236
ETA:4.4h/6.4h
epoch 70, train 1/100, loss=0.6424 acc=0.7600
epoch 70, train 2/100, loss=1.4629 acc=0.6133
epoch 70, train 3/100, loss=0.3138 acc=0.8800
epoch 70, train 4/100, loss=0.4044 acc=0.8133
epoch 70, train 5/100, loss=0.5303 acc=0.8400
epoch 70, train 6/100, loss=0.6319 acc=0.7867
epoch 70, train 7/100, loss=0.4533 acc=0.8667
epoch 70, train 8/100, loss=1.5001 acc=0.5467
epoch 70, train 9/100, loss=0.6542 acc=0.7733
epoch 70, train 10/100, loss=0.5907 acc=0.7867
epoch 70, train 11/100, loss=0.0700 acc=0.9600
epoch 70, train 12/100, loss=0.3568 acc=0.8533
epoch 70, train 13/100, loss=0.4179 acc=0.8533
epoch 70, train 14/100, loss=0.1748 acc=0.9467
epoch 70, train 15/100, loss=0.4171 acc=0.8533
epoch 70, train 16/100, loss=0.5159 acc=0.8000
epoch 70, train 17/100, loss=1.0181 acc=0.6800
epoch 70, train 18/100, loss=0.4802 acc=0.8000
epoch 70, train 19/100, loss=0.4002 acc=0.8800
epoch 70, train 20/100, loss=0.2166 acc=0.9200
epoch 70, train 21/100, loss=0.5821 acc=0.8400
epoch 70, train 22/100, loss=0.2716 acc=0.8933
epoch 70, train 23/100, loss=0.4433 acc=0.8133
epoch 70, train 24/100, loss=0.6456 acc=0.7600
epoch 70, train 25/100, loss=0.2094 acc=0.9200
epoch 70, train 26/100, loss=0.2106 acc=0.9600
epoch 70, train 27/100, loss=0.7976 acc=0.7200
epoch 70, train 28/100, loss=1.0114 acc=0.7200
epoch 70, train 29/100, loss=0.4417 acc=0.8000
epoch 70, train 30/100, loss=0.1207 acc=0.9600
epoch 70, train 31/100, loss=0.5283 acc=0.7867
epoch 70, train 32/100, loss=0.6078 acc=0.7733
epoch 70, train 33/100, loss=0.1504 acc=0.9467
epoch 70, train 34/100, loss=0.3045 acc=0.8800
epoch 70, train 35/100, loss=0.5144 acc=0.8000
epoch 70, train 36/100, loss=0.1723 acc=0.9200
epoch 70, train 37/100, loss=0.2198 acc=0.9200
epoch 70, train 38/100, loss=0.2754 acc=0.8933
epoch 70, train 39/100, loss=0.4503 acc=0.8800
epoch 70, train 40/100, loss=0.8789 acc=0.7067
epoch 70, train 41/100, loss=0.0549 acc=0.9867
epoch 70, train 42/100, loss=1.0314 acc=0.7067
epoch 70, train 43/100, loss=0.6463 acc=0.7733
epoch 70, train 44/100, loss=0.4018 acc=0.8667
epoch 70, train 45/100, loss=0.3600 acc=0.8667
epoch 70, train 46/100, loss=0.1428 acc=0.9467
epoch 70, train 47/100, loss=0.6027 acc=0.7867
epoch 70, train 48/100, loss=0.2549 acc=0.9067
epoch 70, train 49/100, loss=0.5677 acc=0.8400
epoch 70, train 50/100, loss=0.5168 acc=0.8133
epoch 70, train 51/100, loss=0.1872 acc=0.9067
epoch 70, train 52/100, loss=0.5333 acc=0.7733
epoch 70, train 53/100, loss=0.3638 acc=0.8800
epoch 70, train 54/100, loss=0.8247 acc=0.7867
epoch 70, train 55/100, loss=0.1392 acc=0.9733
epoch 70, train 56/100, loss=0.6333 acc=0.7867
epoch 70, train 57/100, loss=0.1623 acc=0.9333
epoch 70, train 58/100, loss=1.0911 acc=0.6667
epoch 70, train 59/100, loss=0.6043 acc=0.7467
epoch 70, train 60/100, loss=0.0248 acc=1.0000
epoch 70, train 61/100, loss=0.1853 acc=0.9467
epoch 70, train 62/100, loss=0.8565 acc=0.6533
epoch 70, train 63/100, loss=0.3889 acc=0.9067
epoch 70, train 64/100, loss=0.3204 acc=0.8933
epoch 70, train 65/100, loss=1.3603 acc=0.6400
epoch 70, train 66/100, loss=0.2797 acc=0.9067
epoch 70, train 67/100, loss=0.3379 acc=0.8667
epoch 70, train 68/100, loss=0.4554 acc=0.8133
epoch 70, train 69/100, loss=0.2381 acc=0.9467
epoch 70, train 70/100, loss=0.1571 acc=0.9467
epoch 70, train 71/100, loss=0.5821 acc=0.7467
epoch 70, train 72/100, loss=0.4950 acc=0.8000
epoch 70, train 73/100, loss=0.8320 acc=0.7200
epoch 70, train 74/100, loss=0.6657 acc=0.8000
epoch 70, train 75/100, loss=0.6979 acc=0.7600
epoch 70, train 76/100, loss=0.7375 acc=0.7333
epoch 70, train 77/100, loss=1.1214 acc=0.6400
epoch 70, train 78/100, loss=0.1800 acc=0.9333
epoch 70, train 79/100, loss=0.1779 acc=0.9067
epoch 70, train 80/100, loss=0.1923 acc=0.9467
epoch 70, train 81/100, loss=0.7936 acc=0.6800
epoch 70, train 82/100, loss=1.1698 acc=0.6400
epoch 70, train 83/100, loss=0.4737 acc=0.8400
epoch 70, train 84/100, loss=0.4195 acc=0.8400
epoch 70, train 85/100, loss=0.9172 acc=0.7200
epoch 70, train 86/100, loss=0.3876 acc=0.8933
epoch 70, train 87/100, loss=0.5195 acc=0.8000
epoch 70, train 88/100, loss=0.4934 acc=0.8267
epoch 70, train 89/100, loss=0.3452 acc=0.8533
epoch 70, train 90/100, loss=1.0864 acc=0.6400
epoch 70, train 91/100, loss=0.5389 acc=0.8133
epoch 70, train 92/100, loss=0.2108 acc=0.9200
epoch 70, train 93/100, loss=0.2051 acc=0.9467
epoch 70, train 94/100, loss=0.2124 acc=0.9200
epoch 70, train 95/100, loss=0.4792 acc=0.8533
epoch 70, train 96/100, loss=0.3403 acc=0.8800
epoch 70, train 97/100, loss=0.3549 acc=0.8533
epoch 70, train 98/100, loss=0.7929 acc=0.7067
epoch 70, train 99/100, loss=0.5123 acc=0.8267
epoch 70, train 100/100, loss=0.5760 acc=0.8267
best epoch 66, best val acc=0.7275
epoch 70, val, loss=0.9473 acc=0.7050
ETA:4.5h/6.4h
epoch 71, train 1/100, loss=0.5991 acc=0.7733
epoch 71, train 2/100, loss=0.3320 acc=0.8933
epoch 71, train 3/100, loss=0.1746 acc=0.9467
epoch 71, train 4/100, loss=0.3341 acc=0.9067
epoch 71, train 5/100, loss=0.5439 acc=0.7600
epoch 71, train 6/100, loss=0.6882 acc=0.7867
epoch 71, train 7/100, loss=0.8713 acc=0.7200
epoch 71, train 8/100, loss=0.9543 acc=0.7200
epoch 71, train 9/100, loss=1.0414 acc=0.7600
epoch 71, train 10/100, loss=0.2185 acc=0.9067
epoch 71, train 11/100, loss=1.0150 acc=0.7067
epoch 71, train 12/100, loss=0.6865 acc=0.7867
epoch 71, train 13/100, loss=0.1090 acc=0.9733
epoch 71, train 14/100, loss=0.9095 acc=0.6800
epoch 71, train 15/100, loss=0.4746 acc=0.8133
epoch 71, train 16/100, loss=0.0239 acc=0.9867
epoch 71, train 17/100, loss=0.1789 acc=0.9200
epoch 71, train 18/100, loss=0.3610 acc=0.9333
epoch 71, train 19/100, loss=0.8819 acc=0.7867
epoch 71, train 20/100, loss=1.2668 acc=0.6800
epoch 71, train 21/100, loss=0.3414 acc=0.9067
epoch 71, train 22/100, loss=0.5675 acc=0.7867
epoch 71, train 23/100, loss=0.2612 acc=0.9067
epoch 71, train 24/100, loss=0.2685 acc=0.8933
epoch 71, train 25/100, loss=0.4910 acc=0.8133
epoch 71, train 26/100, loss=0.1465 acc=0.9333
epoch 71, train 27/100, loss=0.5030 acc=0.8533
epoch 71, train 28/100, loss=0.5400 acc=0.7867
epoch 71, train 29/100, loss=0.3406 acc=0.9200
epoch 71, train 30/100, loss=0.8968 acc=0.6933
epoch 71, train 31/100, loss=0.9811 acc=0.6400
epoch 71, train 32/100, loss=0.8811 acc=0.6800
epoch 71, train 33/100, loss=0.3203 acc=0.8800
epoch 71, train 34/100, loss=0.8632 acc=0.6933
epoch 71, train 35/100, loss=0.5868 acc=0.8667
epoch 71, train 36/100, loss=0.2011 acc=0.9333
epoch 71, train 37/100, loss=0.5492 acc=0.8533
epoch 71, train 38/100, loss=0.1310 acc=0.9333
epoch 71, train 39/100, loss=1.3532 acc=0.6800
epoch 71, train 40/100, loss=1.0616 acc=0.6933
epoch 71, train 41/100, loss=0.3900 acc=0.8133
epoch 71, train 42/100, loss=0.5239 acc=0.8533
epoch 71, train 43/100, loss=0.6184 acc=0.8267
epoch 71, train 44/100, loss=0.2846 acc=0.8933
epoch 71, train 45/100, loss=0.4703 acc=0.8400
epoch 71, train 46/100, loss=0.3038 acc=0.8800
epoch 71, train 47/100, loss=0.8056 acc=0.7600
epoch 71, train 48/100, loss=0.6218 acc=0.7600
epoch 71, train 49/100, loss=0.2757 acc=0.9333
epoch 71, train 50/100, loss=0.6366 acc=0.7867
epoch 71, train 51/100, loss=0.5260 acc=0.8133
epoch 71, train 52/100, loss=0.9300 acc=0.5733
epoch 71, train 53/100, loss=0.6691 acc=0.8000
epoch 71, train 54/100, loss=0.4136 acc=0.8133
epoch 71, train 55/100, loss=0.5468 acc=0.8400
epoch 71, train 56/100, loss=0.6331 acc=0.7600
epoch 71, train 57/100, loss=0.2440 acc=0.9200
epoch 71, train 58/100, loss=0.6136 acc=0.8000
epoch 71, train 59/100, loss=1.3178 acc=0.6133
epoch 71, train 60/100, loss=1.3984 acc=0.5867
epoch 71, train 61/100, loss=0.6558 acc=0.7733
epoch 71, train 62/100, loss=0.3323 acc=0.8667
epoch 71, train 63/100, loss=0.3702 acc=0.8933
epoch 71, train 64/100, loss=0.3697 acc=0.8667
epoch 71, train 65/100, loss=0.9743 acc=0.7067
epoch 71, train 66/100, loss=0.8676 acc=0.7733
epoch 71, train 67/100, loss=0.4735 acc=0.8000
epoch 71, train 68/100, loss=0.5810 acc=0.8267
epoch 71, train 69/100, loss=0.4350 acc=0.8667
epoch 71, train 70/100, loss=0.2904 acc=0.9200
epoch 71, train 71/100, loss=0.2751 acc=0.9067
epoch 71, train 72/100, loss=0.4678 acc=0.8133
epoch 71, train 73/100, loss=1.0785 acc=0.6800
epoch 71, train 74/100, loss=0.4929 acc=0.8133
epoch 71, train 75/100, loss=0.2652 acc=0.9200
epoch 71, train 76/100, loss=0.2374 acc=0.9200
epoch 71, train 77/100, loss=0.4051 acc=0.8933
epoch 71, train 78/100, loss=0.3716 acc=0.8800
epoch 71, train 79/100, loss=0.7785 acc=0.7467
epoch 71, train 80/100, loss=0.2720 acc=0.9200
epoch 71, train 81/100, loss=0.6450 acc=0.8400
epoch 71, train 82/100, loss=0.7088 acc=0.8267
epoch 71, train 83/100, loss=0.7053 acc=0.7467
epoch 71, train 84/100, loss=1.0041 acc=0.6533
epoch 71, train 85/100, loss=0.1607 acc=0.9333
epoch 71, train 86/100, loss=0.7517 acc=0.7067
epoch 71, train 87/100, loss=0.2600 acc=0.9067
epoch 71, train 88/100, loss=0.3494 acc=0.9467
epoch 71, train 89/100, loss=0.4694 acc=0.8533
epoch 71, train 90/100, loss=0.1662 acc=0.9467
epoch 71, train 91/100, loss=0.1440 acc=0.9467
epoch 71, train 92/100, loss=1.1226 acc=0.6267
epoch 71, train 93/100, loss=0.1311 acc=0.9733
epoch 71, train 94/100, loss=0.5411 acc=0.8133
epoch 71, train 95/100, loss=0.5780 acc=0.8800
epoch 71, train 96/100, loss=1.0995 acc=0.7067
epoch 71, train 97/100, loss=0.4231 acc=0.8533
epoch 71, train 98/100, loss=2.0025 acc=0.5600
epoch 71, train 99/100, loss=0.6716 acc=0.7600
epoch 71, train 100/100, loss=0.1498 acc=0.9200
best epoch 66, best val acc=0.7275
epoch 71, val, loss=0.9319 acc=0.7141
ETA:4.6h/6.4h
epoch 72, train 1/100, loss=0.0709 acc=0.9733
epoch 72, train 2/100, loss=1.9890 acc=0.4667
epoch 72, train 3/100, loss=0.7244 acc=0.7200
epoch 72, train 4/100, loss=0.9011 acc=0.8000
epoch 72, train 5/100, loss=0.6239 acc=0.7867
epoch 72, train 6/100, loss=0.8619 acc=0.7333
epoch 72, train 7/100, loss=1.3718 acc=0.5867
epoch 72, train 8/100, loss=0.2326 acc=0.9333
epoch 72, train 9/100, loss=0.8325 acc=0.7733
epoch 72, train 10/100, loss=0.3638 acc=0.8533
epoch 72, train 11/100, loss=0.1972 acc=0.9467
epoch 72, train 12/100, loss=0.6391 acc=0.7867
epoch 72, train 13/100, loss=0.6628 acc=0.8133
epoch 72, train 14/100, loss=0.2061 acc=0.9467
epoch 72, train 15/100, loss=1.3243 acc=0.6667
epoch 72, train 16/100, loss=0.0999 acc=0.9733
epoch 72, train 17/100, loss=0.6335 acc=0.7733
epoch 72, train 18/100, loss=0.2512 acc=0.9067
epoch 72, train 19/100, loss=0.5664 acc=0.8133
epoch 72, train 20/100, loss=0.4435 acc=0.8400
epoch 72, train 21/100, loss=0.5280 acc=0.8533
epoch 72, train 22/100, loss=0.9269 acc=0.7333
epoch 72, train 23/100, loss=0.1016 acc=0.9600
epoch 72, train 24/100, loss=0.5565 acc=0.7867
epoch 72, train 25/100, loss=0.2834 acc=0.8800
epoch 72, train 26/100, loss=0.3951 acc=0.8800
epoch 72, train 27/100, loss=0.7123 acc=0.7467
epoch 72, train 28/100, loss=0.5982 acc=0.7867
epoch 72, train 29/100, loss=0.2748 acc=0.9200
epoch 72, train 30/100, loss=0.3419 acc=0.8800
epoch 72, train 31/100, loss=0.6820 acc=0.8000
epoch 72, train 32/100, loss=0.3103 acc=0.8667
epoch 72, train 33/100, loss=0.4738 acc=0.8267
epoch 72, train 34/100, loss=0.8483 acc=0.7733
epoch 72, train 35/100, loss=0.1175 acc=0.9600
epoch 72, train 36/100, loss=0.5205 acc=0.8800
epoch 72, train 37/100, loss=0.2708 acc=0.9200
epoch 72, train 38/100, loss=0.8739 acc=0.6800
epoch 72, train 39/100, loss=0.4944 acc=0.7867
epoch 72, train 40/100, loss=1.0627 acc=0.6667
epoch 72, train 41/100, loss=0.3475 acc=0.8800
epoch 72, train 42/100, loss=0.2630 acc=0.8800
epoch 72, train 43/100, loss=1.1425 acc=0.6400
epoch 72, train 44/100, loss=0.4106 acc=0.8667
epoch 72, train 45/100, loss=0.6594 acc=0.8267
epoch 72, train 46/100, loss=0.1740 acc=0.9200
epoch 72, train 47/100, loss=1.3412 acc=0.6533
epoch 72, train 48/100, loss=0.9778 acc=0.7200
epoch 72, train 49/100, loss=0.3581 acc=0.8267
epoch 72, train 50/100, loss=0.1940 acc=0.9467
epoch 72, train 51/100, loss=0.1300 acc=0.9200
epoch 72, train 52/100, loss=0.5681 acc=0.8000
epoch 72, train 53/100, loss=0.3224 acc=0.8933
epoch 72, train 54/100, loss=0.3982 acc=0.8533
epoch 72, train 55/100, loss=0.4874 acc=0.8133
epoch 72, train 56/100, loss=0.3196 acc=0.8933
epoch 72, train 57/100, loss=0.2535 acc=0.9333
epoch 72, train 58/100, loss=0.3422 acc=0.8800
epoch 72, train 59/100, loss=0.3703 acc=0.8800
epoch 72, train 60/100, loss=1.2026 acc=0.6000
epoch 72, train 61/100, loss=0.6069 acc=0.7733
epoch 72, train 62/100, loss=0.4697 acc=0.8533
epoch 72, train 63/100, loss=0.2707 acc=0.9067
epoch 72, train 64/100, loss=0.4294 acc=0.8000
epoch 72, train 65/100, loss=0.1121 acc=0.9467
epoch 72, train 66/100, loss=0.3746 acc=0.8533
epoch 72, train 67/100, loss=0.2875 acc=0.9067
epoch 72, train 68/100, loss=0.4957 acc=0.8267
epoch 72, train 69/100, loss=1.1720 acc=0.7333
epoch 72, train 70/100, loss=0.5943 acc=0.8533
epoch 72, train 71/100, loss=0.4470 acc=0.8533
epoch 72, train 72/100, loss=0.5225 acc=0.8133
epoch 72, train 73/100, loss=1.0662 acc=0.7467
epoch 72, train 74/100, loss=0.5469 acc=0.8133
epoch 72, train 75/100, loss=0.6666 acc=0.7467
epoch 72, train 76/100, loss=0.2780 acc=0.8800
epoch 72, train 77/100, loss=0.2839 acc=0.9200
epoch 72, train 78/100, loss=0.7411 acc=0.7867
epoch 72, train 79/100, loss=0.6622 acc=0.7067
epoch 72, train 80/100, loss=0.2099 acc=0.9467
epoch 72, train 81/100, loss=0.2670 acc=0.9333
epoch 72, train 82/100, loss=0.5588 acc=0.7733
epoch 72, train 83/100, loss=0.1816 acc=0.9467
epoch 72, train 84/100, loss=0.3018 acc=0.8800
epoch 72, train 85/100, loss=0.3145 acc=0.8667
epoch 72, train 86/100, loss=0.1130 acc=0.9600
epoch 72, train 87/100, loss=0.9198 acc=0.6667
epoch 72, train 88/100, loss=0.6196 acc=0.8000
epoch 72, train 89/100, loss=0.5926 acc=0.8000
epoch 72, train 90/100, loss=0.9556 acc=0.6533
epoch 72, train 91/100, loss=0.3902 acc=0.8400
epoch 72, train 92/100, loss=1.0008 acc=0.6000
epoch 72, train 93/100, loss=0.6728 acc=0.7867
epoch 72, train 94/100, loss=0.6993 acc=0.7600
epoch 72, train 95/100, loss=0.2463 acc=0.9333
epoch 72, train 96/100, loss=0.2593 acc=0.9467
epoch 72, train 97/100, loss=0.6163 acc=0.8133
epoch 72, train 98/100, loss=0.3102 acc=0.8933
epoch 72, train 99/100, loss=0.1681 acc=0.9467
epoch 72, train 100/100, loss=0.8154 acc=0.7733
best epoch 66, best val acc=0.7275
epoch 72, val, loss=0.9630 acc=0.7034
ETA:4.6h/6.4h
epoch 73, train 1/100, loss=0.5869 acc=0.7733
epoch 73, train 2/100, loss=0.7516 acc=0.6800
epoch 73, train 3/100, loss=0.5081 acc=0.8133
epoch 73, train 4/100, loss=0.9892 acc=0.6667
epoch 73, train 5/100, loss=0.4796 acc=0.8267
epoch 73, train 6/100, loss=1.0046 acc=0.6800
epoch 73, train 7/100, loss=0.4935 acc=0.8400
epoch 73, train 8/100, loss=0.2659 acc=0.9600
epoch 73, train 9/100, loss=0.3131 acc=0.9200
epoch 73, train 10/100, loss=0.4851 acc=0.8533
epoch 73, train 11/100, loss=0.7589 acc=0.7067
epoch 73, train 12/100, loss=0.5601 acc=0.7733
epoch 73, train 13/100, loss=0.3285 acc=0.8533
epoch 73, train 14/100, loss=0.6429 acc=0.7467
epoch 73, train 15/100, loss=0.3042 acc=0.8400
epoch 73, train 16/100, loss=0.1500 acc=0.9600
epoch 73, train 17/100, loss=0.2500 acc=0.9067
epoch 73, train 18/100, loss=0.5766 acc=0.8133
epoch 73, train 19/100, loss=0.4321 acc=0.8533
epoch 73, train 20/100, loss=0.6992 acc=0.7733
epoch 73, train 21/100, loss=0.2163 acc=0.9200
epoch 73, train 22/100, loss=0.1715 acc=0.9200
epoch 73, train 23/100, loss=0.5176 acc=0.8400
epoch 73, train 24/100, loss=0.9915 acc=0.6400
epoch 73, train 25/100, loss=0.7265 acc=0.7600
epoch 73, train 26/100, loss=0.6219 acc=0.7733
epoch 73, train 27/100, loss=1.0144 acc=0.8133
epoch 73, train 28/100, loss=1.4555 acc=0.4800
epoch 73, train 29/100, loss=0.8836 acc=0.7467
epoch 73, train 30/100, loss=0.3637 acc=0.8533
epoch 73, train 31/100, loss=0.9937 acc=0.6400
epoch 73, train 32/100, loss=0.3104 acc=0.8800
epoch 73, train 33/100, loss=0.1614 acc=0.9467
epoch 73, train 34/100, loss=0.7670 acc=0.7467
epoch 73, train 35/100, loss=0.6027 acc=0.8267
epoch 73, train 36/100, loss=0.7115 acc=0.7600
epoch 73, train 37/100, loss=0.2820 acc=0.8800
epoch 73, train 38/100, loss=0.0893 acc=0.9867
epoch 73, train 39/100, loss=0.1896 acc=0.9067
epoch 73, train 40/100, loss=0.3210 acc=0.8933
epoch 73, train 41/100, loss=0.1816 acc=0.9200
epoch 73, train 42/100, loss=0.4613 acc=0.8533
epoch 73, train 43/100, loss=0.3651 acc=0.8533
epoch 73, train 44/100, loss=0.3459 acc=0.8667
epoch 73, train 45/100, loss=1.2072 acc=0.6133
epoch 73, train 46/100, loss=0.3863 acc=0.8933
epoch 73, train 47/100, loss=0.2600 acc=0.8667
epoch 73, train 48/100, loss=0.6655 acc=0.7067
epoch 73, train 49/100, loss=0.4691 acc=0.7867
epoch 73, train 50/100, loss=0.4888 acc=0.7867
epoch 73, train 51/100, loss=0.2207 acc=0.8800
epoch 73, train 52/100, loss=0.2615 acc=0.9467
epoch 73, train 53/100, loss=0.6424 acc=0.7333
epoch 73, train 54/100, loss=0.3309 acc=0.8667
epoch 73, train 55/100, loss=0.9424 acc=0.7600
epoch 73, train 56/100, loss=0.4134 acc=0.8533
epoch 73, train 57/100, loss=0.2874 acc=0.8800
epoch 73, train 58/100, loss=0.8791 acc=0.7200
epoch 73, train 59/100, loss=0.4835 acc=0.8400
epoch 73, train 60/100, loss=0.7312 acc=0.7467
epoch 73, train 61/100, loss=0.4799 acc=0.8800
epoch 73, train 62/100, loss=0.2284 acc=0.9200
epoch 73, train 63/100, loss=0.9982 acc=0.6933
epoch 73, train 64/100, loss=0.3400 acc=0.8800
epoch 73, train 65/100, loss=0.4730 acc=0.8667
epoch 73, train 66/100, loss=0.2962 acc=0.8933
epoch 73, train 67/100, loss=0.5579 acc=0.8800
epoch 73, train 68/100, loss=0.0853 acc=0.9467
epoch 73, train 69/100, loss=0.4555 acc=0.8133
epoch 73, train 70/100, loss=0.5079 acc=0.8533
epoch 73, train 71/100, loss=0.1937 acc=0.9200
epoch 73, train 72/100, loss=0.8786 acc=0.7467
epoch 73, train 73/100, loss=0.5656 acc=0.7733
epoch 73, train 74/100, loss=0.8865 acc=0.6800
epoch 73, train 75/100, loss=0.3830 acc=0.8933
epoch 73, train 76/100, loss=0.2239 acc=0.9600
epoch 73, train 77/100, loss=0.7728 acc=0.7867
epoch 73, train 78/100, loss=0.6669 acc=0.7733
epoch 73, train 79/100, loss=0.1825 acc=0.9467
epoch 73, train 80/100, loss=0.2283 acc=0.9067
epoch 73, train 81/100, loss=0.5376 acc=0.8133
epoch 73, train 82/100, loss=0.1059 acc=0.9733
epoch 73, train 83/100, loss=0.5390 acc=0.7733
epoch 73, train 84/100, loss=0.3013 acc=0.8800
epoch 73, train 85/100, loss=1.0721 acc=0.6533
epoch 73, train 86/100, loss=0.4416 acc=0.8267
epoch 73, train 87/100, loss=0.1737 acc=0.9333
epoch 73, train 88/100, loss=0.2124 acc=0.9333
epoch 73, train 89/100, loss=0.9212 acc=0.6933
epoch 73, train 90/100, loss=0.1516 acc=0.9600
epoch 73, train 91/100, loss=1.1068 acc=0.6933
epoch 73, train 92/100, loss=0.3311 acc=0.9200
epoch 73, train 93/100, loss=0.6759 acc=0.8133
epoch 73, train 94/100, loss=0.5238 acc=0.8267
epoch 73, train 95/100, loss=0.4412 acc=0.9200
epoch 73, train 96/100, loss=0.2269 acc=0.8933
epoch 73, train 97/100, loss=0.3474 acc=0.8933
epoch 73, train 98/100, loss=0.3408 acc=0.8933
epoch 73, train 99/100, loss=1.3915 acc=0.5600
epoch 73, train 100/100, loss=1.6200 acc=0.6000
best epoch 66, best val acc=0.7275
epoch 73, val, loss=0.9338 acc=0.7095
ETA:4.7h/6.4h
epoch 74, train 1/100, loss=0.7445 acc=0.7600
epoch 74, train 2/100, loss=0.5351 acc=0.7600
epoch 74, train 3/100, loss=0.5254 acc=0.8133
epoch 74, train 4/100, loss=0.2612 acc=0.9067
epoch 74, train 5/100, loss=0.7969 acc=0.7467
epoch 74, train 6/100, loss=0.9080 acc=0.7200
epoch 74, train 7/100, loss=0.5131 acc=0.8533
epoch 74, train 8/100, loss=1.3848 acc=0.7067
epoch 74, train 9/100, loss=0.6715 acc=0.7067
epoch 74, train 10/100, loss=0.2121 acc=0.9333
epoch 74, train 11/100, loss=0.6357 acc=0.8000
epoch 74, train 12/100, loss=0.2160 acc=0.9333
epoch 74, train 13/100, loss=0.5055 acc=0.8400
epoch 74, train 14/100, loss=0.2099 acc=0.9467
epoch 74, train 15/100, loss=0.7672 acc=0.7867
epoch 74, train 16/100, loss=0.2355 acc=0.9600
epoch 74, train 17/100, loss=0.9314 acc=0.6400
epoch 74, train 18/100, loss=0.5721 acc=0.7467
epoch 74, train 19/100, loss=0.1629 acc=0.9333
epoch 74, train 20/100, loss=0.2764 acc=0.9067
epoch 74, train 21/100, loss=0.3876 acc=0.8667
epoch 74, train 22/100, loss=0.7075 acc=0.7467
epoch 74, train 23/100, loss=0.1425 acc=0.9600
epoch 74, train 24/100, loss=0.3702 acc=0.8933
epoch 74, train 25/100, loss=0.2404 acc=0.9600
epoch 74, train 26/100, loss=0.3226 acc=0.8800
epoch 74, train 27/100, loss=0.9106 acc=0.8000
epoch 74, train 28/100, loss=0.5701 acc=0.7733
epoch 74, train 29/100, loss=0.3116 acc=0.9067
epoch 74, train 30/100, loss=0.6248 acc=0.7600
epoch 74, train 31/100, loss=0.3776 acc=0.8800
epoch 74, train 32/100, loss=0.3307 acc=0.9200
epoch 74, train 33/100, loss=0.3328 acc=0.8533
epoch 74, train 34/100, loss=1.3006 acc=0.5467
epoch 74, train 35/100, loss=0.3359 acc=0.9067
epoch 74, train 36/100, loss=0.8236 acc=0.8000
epoch 74, train 37/100, loss=0.2036 acc=0.9067
epoch 74, train 38/100, loss=0.8488 acc=0.6667
epoch 74, train 39/100, loss=0.6059 acc=0.7467
epoch 74, train 40/100, loss=0.8429 acc=0.7867
epoch 74, train 41/100, loss=0.3379 acc=0.8667
epoch 74, train 42/100, loss=0.6290 acc=0.8000
epoch 74, train 43/100, loss=0.7020 acc=0.7867
epoch 74, train 44/100, loss=0.3285 acc=0.8933
epoch 74, train 45/100, loss=0.6588 acc=0.7867
epoch 74, train 46/100, loss=0.8328 acc=0.7600
epoch 74, train 47/100, loss=0.8635 acc=0.6800
epoch 74, train 48/100, loss=0.3993 acc=0.8800
epoch 74, train 49/100, loss=0.7848 acc=0.7867
epoch 74, train 50/100, loss=0.7814 acc=0.8000
epoch 74, train 51/100, loss=0.2978 acc=0.9200
epoch 74, train 52/100, loss=0.7478 acc=0.8000
epoch 74, train 53/100, loss=0.4141 acc=0.8667
epoch 74, train 54/100, loss=0.4504 acc=0.8267
epoch 74, train 55/100, loss=0.1232 acc=0.9600
epoch 74, train 56/100, loss=0.3468 acc=0.8800
epoch 74, train 57/100, loss=0.7877 acc=0.7600
epoch 74, train 58/100, loss=0.1239 acc=0.9467
epoch 74, train 59/100, loss=0.1427 acc=0.9600
epoch 74, train 60/100, loss=0.3796 acc=0.8667
epoch 74, train 61/100, loss=0.3322 acc=0.8800
epoch 74, train 62/100, loss=0.5824 acc=0.7867
epoch 74, train 63/100, loss=0.5420 acc=0.7600
epoch 74, train 64/100, loss=0.1781 acc=0.9333
epoch 74, train 65/100, loss=0.6818 acc=0.7867
epoch 74, train 66/100, loss=0.4552 acc=0.8400
epoch 74, train 67/100, loss=0.1639 acc=0.9467
epoch 74, train 68/100, loss=0.7990 acc=0.7733
epoch 74, train 69/100, loss=0.3146 acc=0.8667
epoch 74, train 70/100, loss=0.4157 acc=0.8267
epoch 74, train 71/100, loss=0.1236 acc=0.9467
epoch 74, train 72/100, loss=0.4061 acc=0.8800
epoch 74, train 73/100, loss=0.0419 acc=0.9867
epoch 74, train 74/100, loss=0.3888 acc=0.8667
epoch 74, train 75/100, loss=0.3124 acc=0.8933
epoch 74, train 76/100, loss=0.9065 acc=0.6800
epoch 74, train 77/100, loss=0.1126 acc=0.9733
epoch 74, train 78/100, loss=0.4753 acc=0.8667
epoch 74, train 79/100, loss=0.3596 acc=0.8800
epoch 74, train 80/100, loss=1.1166 acc=0.6933
epoch 74, train 81/100, loss=0.5651 acc=0.7200
epoch 74, train 82/100, loss=0.8032 acc=0.7333
epoch 74, train 83/100, loss=0.2573 acc=0.9200
epoch 74, train 84/100, loss=0.0990 acc=0.9467
epoch 74, train 85/100, loss=0.2653 acc=0.9333
epoch 74, train 86/100, loss=1.4703 acc=0.5600
epoch 74, train 87/100, loss=0.6038 acc=0.7733
epoch 74, train 88/100, loss=1.5606 acc=0.5067
epoch 74, train 89/100, loss=1.3483 acc=0.5733
epoch 74, train 90/100, loss=0.6566 acc=0.8000
epoch 74, train 91/100, loss=0.7973 acc=0.8000
epoch 74, train 92/100, loss=0.4413 acc=0.8800
epoch 74, train 93/100, loss=0.5521 acc=0.8000
epoch 74, train 94/100, loss=0.0637 acc=0.9867
epoch 74, train 95/100, loss=0.8445 acc=0.7200
epoch 74, train 96/100, loss=0.3503 acc=0.8667
epoch 74, train 97/100, loss=0.4352 acc=0.8400
epoch 74, train 98/100, loss=0.2312 acc=0.9467
epoch 74, train 99/100, loss=0.7231 acc=0.7200
epoch 74, train 100/100, loss=1.5770 acc=0.6400
best epoch 66, best val acc=0.7275
epoch 74, val, loss=0.9565 acc=0.7093
ETA:4.7h/6.4h
epoch 75, train 1/100, loss=0.1538 acc=0.9333
epoch 75, train 2/100, loss=0.4454 acc=0.8400
epoch 75, train 3/100, loss=2.0108 acc=0.5067
epoch 75, train 4/100, loss=0.7040 acc=0.8267
epoch 75, train 5/100, loss=0.2488 acc=0.8933
epoch 75, train 6/100, loss=1.6584 acc=0.5600
epoch 75, train 7/100, loss=0.1798 acc=0.9200
epoch 75, train 8/100, loss=0.9249 acc=0.6533
epoch 75, train 9/100, loss=0.4003 acc=0.8400
epoch 75, train 10/100, loss=0.5190 acc=0.7733
epoch 75, train 11/100, loss=0.3370 acc=0.8533
epoch 75, train 12/100, loss=0.4168 acc=0.8800
epoch 75, train 13/100, loss=0.5972 acc=0.7467
epoch 75, train 14/100, loss=0.5096 acc=0.8133
epoch 75, train 15/100, loss=0.7234 acc=0.8000
epoch 75, train 16/100, loss=0.3912 acc=0.8267
epoch 75, train 17/100, loss=0.4839 acc=0.8533
epoch 75, train 18/100, loss=0.1666 acc=0.9333
epoch 75, train 19/100, loss=0.3565 acc=0.8800
epoch 75, train 20/100, loss=0.0902 acc=0.9733
epoch 75, train 21/100, loss=0.4823 acc=0.7867
epoch 75, train 22/100, loss=0.3657 acc=0.8667
epoch 75, train 23/100, loss=1.2036 acc=0.5733
epoch 75, train 24/100, loss=0.3599 acc=0.8533
epoch 75, train 25/100, loss=1.9266 acc=0.5067
epoch 75, train 26/100, loss=0.3108 acc=0.8533
epoch 75, train 27/100, loss=0.3923 acc=0.8133
epoch 75, train 28/100, loss=0.4363 acc=0.8933
epoch 75, train 29/100, loss=0.6950 acc=0.8000
epoch 75, train 30/100, loss=1.0661 acc=0.6533
epoch 75, train 31/100, loss=0.7074 acc=0.7200
epoch 75, train 32/100, loss=0.2139 acc=0.9467
epoch 75, train 33/100, loss=1.0366 acc=0.6400
epoch 75, train 34/100, loss=0.7616 acc=0.6800
epoch 75, train 35/100, loss=0.7058 acc=0.7733
epoch 75, train 36/100, loss=0.4749 acc=0.8267
epoch 75, train 37/100, loss=0.1950 acc=0.9200
epoch 75, train 38/100, loss=0.9430 acc=0.6800
epoch 75, train 39/100, loss=0.2056 acc=0.9067
epoch 75, train 40/100, loss=1.0077 acc=0.7600
epoch 75, train 41/100, loss=0.4189 acc=0.8400
epoch 75, train 42/100, loss=1.5595 acc=0.6400
epoch 75, train 43/100, loss=0.2257 acc=0.9467
epoch 75, train 44/100, loss=0.4192 acc=0.8400
epoch 75, train 45/100, loss=0.2620 acc=0.8800
epoch 75, train 46/100, loss=0.5744 acc=0.7733
epoch 75, train 47/100, loss=0.2266 acc=0.9200
epoch 75, train 48/100, loss=0.5315 acc=0.8000
epoch 75, train 49/100, loss=0.4249 acc=0.8533
epoch 75, train 50/100, loss=0.4109 acc=0.8400
epoch 75, train 51/100, loss=0.7060 acc=0.7600
epoch 75, train 52/100, loss=1.7652 acc=0.4267
epoch 75, train 53/100, loss=0.6788 acc=0.7467
epoch 75, train 54/100, loss=0.1437 acc=0.9467
epoch 75, train 55/100, loss=0.1284 acc=0.9600
epoch 75, train 56/100, loss=0.6903 acc=0.7733
epoch 75, train 57/100, loss=0.6448 acc=0.8133
epoch 75, train 58/100, loss=0.8570 acc=0.7200
epoch 75, train 59/100, loss=0.2867 acc=0.9067
epoch 75, train 60/100, loss=0.2560 acc=0.9067
epoch 75, train 61/100, loss=0.3285 acc=0.8800
epoch 75, train 62/100, loss=0.3450 acc=0.8933
epoch 75, train 63/100, loss=0.5692 acc=0.8667
epoch 75, train 64/100, loss=1.4356 acc=0.5467
epoch 75, train 65/100, loss=1.5825 acc=0.5867
epoch 75, train 66/100, loss=0.4193 acc=0.8800
epoch 75, train 67/100, loss=0.2767 acc=0.9200
epoch 75, train 68/100, loss=0.3812 acc=0.8533
epoch 75, train 69/100, loss=0.5161 acc=0.8533
epoch 75, train 70/100, loss=0.5051 acc=0.8000
epoch 75, train 71/100, loss=0.5982 acc=0.8000
epoch 75, train 72/100, loss=1.0558 acc=0.6800
epoch 75, train 73/100, loss=0.7045 acc=0.7467
epoch 75, train 74/100, loss=0.2792 acc=0.8533
epoch 75, train 75/100, loss=0.0672 acc=0.9733
epoch 75, train 76/100, loss=0.7779 acc=0.6800
epoch 75, train 77/100, loss=0.3793 acc=0.8533
epoch 75, train 78/100, loss=0.1173 acc=0.9467
epoch 75, train 79/100, loss=0.4346 acc=0.8800
epoch 75, train 80/100, loss=0.3454 acc=0.8400
epoch 75, train 81/100, loss=0.3799 acc=0.8267
epoch 75, train 82/100, loss=0.7924 acc=0.7200
epoch 75, train 83/100, loss=0.3499 acc=0.8933
epoch 75, train 84/100, loss=0.4001 acc=0.8000
epoch 75, train 85/100, loss=0.3215 acc=0.8800
epoch 75, train 86/100, loss=1.3155 acc=0.5867
epoch 75, train 87/100, loss=0.2527 acc=0.8933
epoch 75, train 88/100, loss=1.1295 acc=0.6667
epoch 75, train 89/100, loss=0.3292 acc=0.8800
epoch 75, train 90/100, loss=0.4786 acc=0.8533
epoch 75, train 91/100, loss=0.4260 acc=0.8400
epoch 75, train 92/100, loss=0.1435 acc=0.9600
epoch 75, train 93/100, loss=0.6058 acc=0.7333
epoch 75, train 94/100, loss=0.4298 acc=0.8267
epoch 75, train 95/100, loss=0.2667 acc=0.8800
epoch 75, train 96/100, loss=0.4863 acc=0.8533
epoch 75, train 97/100, loss=0.6376 acc=0.7867
epoch 75, train 98/100, loss=0.3107 acc=0.8933
epoch 75, train 99/100, loss=0.3399 acc=0.8667
epoch 75, train 100/100, loss=2.0735 acc=0.3200
best epoch 66, best val acc=0.7275
epoch 75, val, loss=0.9584 acc=0.7110
ETA:4.8h/6.4h
epoch 76, train 1/100, loss=0.6468 acc=0.7867
epoch 76, train 2/100, loss=0.4706 acc=0.8533
epoch 76, train 3/100, loss=0.6407 acc=0.7733
epoch 76, train 4/100, loss=0.2186 acc=0.9333
epoch 76, train 5/100, loss=0.3329 acc=0.8667
epoch 76, train 6/100, loss=0.5870 acc=0.8133
epoch 76, train 7/100, loss=0.5835 acc=0.8000
epoch 76, train 8/100, loss=1.0651 acc=0.6400
epoch 76, train 9/100, loss=0.1786 acc=0.9467
epoch 76, train 10/100, loss=0.7632 acc=0.7467
epoch 76, train 11/100, loss=0.7356 acc=0.7600
epoch 76, train 12/100, loss=0.2373 acc=0.9067
epoch 76, train 13/100, loss=0.9537 acc=0.6933
epoch 76, train 14/100, loss=0.3509 acc=0.8400
epoch 76, train 15/100, loss=0.3536 acc=0.8933
epoch 76, train 16/100, loss=0.4112 acc=0.8533
epoch 76, train 17/100, loss=0.6356 acc=0.7733
epoch 76, train 18/100, loss=0.2839 acc=0.9467
epoch 76, train 19/100, loss=0.6172 acc=0.7733
epoch 76, train 20/100, loss=0.8889 acc=0.7067
epoch 76, train 21/100, loss=0.9306 acc=0.7067
epoch 76, train 22/100, loss=0.2733 acc=0.9333
epoch 76, train 23/100, loss=0.5237 acc=0.8000
epoch 76, train 24/100, loss=0.2699 acc=0.9067
epoch 76, train 25/100, loss=0.2282 acc=0.9200
epoch 76, train 26/100, loss=0.4067 acc=0.8400
epoch 76, train 27/100, loss=0.8701 acc=0.7200
epoch 76, train 28/100, loss=0.7389 acc=0.7333
epoch 76, train 29/100, loss=0.6641 acc=0.8267
epoch 76, train 30/100, loss=0.9919 acc=0.7600
epoch 76, train 31/100, loss=0.3620 acc=0.8533
epoch 76, train 32/100, loss=0.9770 acc=0.7067
epoch 76, train 33/100, loss=0.9493 acc=0.6933
epoch 76, train 34/100, loss=0.7382 acc=0.7467
epoch 76, train 35/100, loss=0.5241 acc=0.8133
epoch 76, train 36/100, loss=0.7155 acc=0.7600
epoch 76, train 37/100, loss=0.6860 acc=0.7733
epoch 76, train 38/100, loss=0.2308 acc=0.9600
epoch 76, train 39/100, loss=0.1813 acc=0.9467
epoch 76, train 40/100, loss=0.3158 acc=0.8800
epoch 76, train 41/100, loss=0.9384 acc=0.7067
epoch 76, train 42/100, loss=0.3714 acc=0.8933
epoch 76, train 43/100, loss=0.3383 acc=0.8800
epoch 76, train 44/100, loss=0.0821 acc=0.9600
epoch 76, train 45/100, loss=1.4478 acc=0.6000
epoch 76, train 46/100, loss=0.3838 acc=0.8800
epoch 76, train 47/100, loss=0.8747 acc=0.7467
epoch 76, train 48/100, loss=0.4459 acc=0.8933
epoch 76, train 49/100, loss=1.0957 acc=0.6800
epoch 76, train 50/100, loss=1.0892 acc=0.6000
epoch 76, train 51/100, loss=0.2766 acc=0.8800
epoch 76, train 52/100, loss=0.5107 acc=0.7867
epoch 76, train 53/100, loss=1.1614 acc=0.6533
epoch 76, train 54/100, loss=0.2620 acc=0.9200
epoch 76, train 55/100, loss=0.3550 acc=0.8800
epoch 76, train 56/100, loss=0.6054 acc=0.7333
epoch 76, train 57/100, loss=0.7056 acc=0.7733
epoch 76, train 58/100, loss=0.8760 acc=0.6933
epoch 76, train 59/100, loss=0.4266 acc=0.8000
epoch 76, train 60/100, loss=0.2430 acc=0.9467
epoch 76, train 61/100, loss=0.4328 acc=0.8000
epoch 76, train 62/100, loss=0.4368 acc=0.8800
epoch 76, train 63/100, loss=0.1382 acc=0.9067
epoch 76, train 64/100, loss=0.1772 acc=0.9067
epoch 76, train 65/100, loss=1.0280 acc=0.6533
epoch 76, train 66/100, loss=0.4555 acc=0.8267
epoch 76, train 67/100, loss=0.7235 acc=0.7467
epoch 76, train 68/100, loss=0.3229 acc=0.8667
epoch 76, train 69/100, loss=0.3445 acc=0.8933
epoch 76, train 70/100, loss=0.3753 acc=0.8667
epoch 76, train 71/100, loss=0.2758 acc=0.9467
epoch 76, train 72/100, loss=0.1213 acc=0.9333
epoch 76, train 73/100, loss=1.9505 acc=0.4133
epoch 76, train 74/100, loss=0.2058 acc=0.9067
epoch 76, train 75/100, loss=0.0934 acc=0.9733
epoch 76, train 76/100, loss=0.3133 acc=0.8800
epoch 76, train 77/100, loss=0.5076 acc=0.8800
epoch 76, train 78/100, loss=0.7479 acc=0.7067
epoch 76, train 79/100, loss=0.8890 acc=0.6933
epoch 76, train 80/100, loss=0.4655 acc=0.8000
epoch 76, train 81/100, loss=0.3819 acc=0.8533
epoch 76, train 82/100, loss=0.2576 acc=0.9067
epoch 76, train 83/100, loss=1.0828 acc=0.6533
epoch 76, train 84/100, loss=0.5762 acc=0.8000
epoch 76, train 85/100, loss=0.8629 acc=0.7333
epoch 76, train 86/100, loss=0.4886 acc=0.8133
epoch 76, train 87/100, loss=0.4054 acc=0.8267
epoch 76, train 88/100, loss=0.7576 acc=0.7200
epoch 76, train 89/100, loss=0.5952 acc=0.7867
epoch 76, train 90/100, loss=0.3210 acc=0.8933
epoch 76, train 91/100, loss=0.6673 acc=0.8267
epoch 76, train 92/100, loss=0.1569 acc=0.9200
epoch 76, train 93/100, loss=0.9109 acc=0.8000
epoch 76, train 94/100, loss=0.7061 acc=0.7867
epoch 76, train 95/100, loss=0.1958 acc=0.9333
epoch 76, train 96/100, loss=0.5423 acc=0.8400
epoch 76, train 97/100, loss=0.1903 acc=0.9467
epoch 76, train 98/100, loss=0.3427 acc=0.8400
epoch 76, train 99/100, loss=0.6516 acc=0.7467
epoch 76, train 100/100, loss=0.8020 acc=0.7333
best epoch 66, best val acc=0.7275
epoch 76, val, loss=0.9267 acc=0.7177
ETA:4.9h/6.4h
epoch 77, train 1/100, loss=0.3895 acc=0.8800
epoch 77, train 2/100, loss=0.7999 acc=0.7333
epoch 77, train 3/100, loss=0.1241 acc=0.9733
epoch 77, train 4/100, loss=0.4987 acc=0.8533
epoch 77, train 5/100, loss=0.5197 acc=0.8400
epoch 77, train 6/100, loss=0.1742 acc=0.9600
epoch 77, train 7/100, loss=0.2110 acc=0.9200
epoch 77, train 8/100, loss=0.3926 acc=0.8933
epoch 77, train 9/100, loss=1.1958 acc=0.6267
epoch 77, train 10/100, loss=0.2059 acc=0.9333
epoch 77, train 11/100, loss=0.5315 acc=0.8267
epoch 77, train 12/100, loss=1.1191 acc=0.6133
epoch 77, train 13/100, loss=1.0470 acc=0.6533
epoch 77, train 14/100, loss=0.4063 acc=0.8533
epoch 77, train 15/100, loss=0.0938 acc=0.9733
epoch 77, train 16/100, loss=0.8133 acc=0.7200
epoch 77, train 17/100, loss=0.4878 acc=0.8400
epoch 77, train 18/100, loss=0.2177 acc=0.9467
epoch 77, train 19/100, loss=0.5332 acc=0.8400
epoch 77, train 20/100, loss=0.3040 acc=0.9467
epoch 77, train 21/100, loss=0.1959 acc=0.9600
epoch 77, train 22/100, loss=0.0987 acc=0.9733
epoch 77, train 23/100, loss=1.1673 acc=0.6133
epoch 77, train 24/100, loss=0.6076 acc=0.8400
epoch 77, train 25/100, loss=0.3441 acc=0.9067
epoch 77, train 26/100, loss=1.0257 acc=0.6533
epoch 77, train 27/100, loss=0.6702 acc=0.8267
epoch 77, train 28/100, loss=0.0971 acc=0.9733
epoch 77, train 29/100, loss=0.2833 acc=0.8933
epoch 77, train 30/100, loss=0.6186 acc=0.7733
epoch 77, train 31/100, loss=0.2292 acc=0.9067
epoch 77, train 32/100, loss=0.9087 acc=0.7200
epoch 77, train 33/100, loss=0.3429 acc=0.9067
epoch 77, train 34/100, loss=0.3746 acc=0.8533
epoch 77, train 35/100, loss=0.3007 acc=0.8267
epoch 77, train 36/100, loss=0.3554 acc=0.8800
epoch 77, train 37/100, loss=0.2898 acc=0.9200
epoch 77, train 38/100, loss=0.3516 acc=0.8800
epoch 77, train 39/100, loss=0.4821 acc=0.8400
epoch 77, train 40/100, loss=0.3209 acc=0.9067
epoch 77, train 41/100, loss=0.2568 acc=0.9600
epoch 77, train 42/100, loss=1.3069 acc=0.6933
epoch 77, train 43/100, loss=0.6974 acc=0.7600
epoch 77, train 44/100, loss=0.8786 acc=0.7467
epoch 77, train 45/100, loss=1.3142 acc=0.5867
epoch 77, train 46/100, loss=0.2524 acc=0.9200
epoch 77, train 47/100, loss=1.4415 acc=0.4800
epoch 77, train 48/100, loss=0.6898 acc=0.8133
epoch 77, train 49/100, loss=0.5278 acc=0.7600
epoch 77, train 50/100, loss=0.4367 acc=0.8400
epoch 77, train 51/100, loss=1.2290 acc=0.6267
epoch 77, train 52/100, loss=0.5028 acc=0.8000
epoch 77, train 53/100, loss=0.4163 acc=0.8667
epoch 77, train 54/100, loss=1.4479 acc=0.6000
epoch 77, train 55/100, loss=0.1224 acc=0.9333
epoch 77, train 56/100, loss=0.8195 acc=0.7200
epoch 77, train 57/100, loss=0.6368 acc=0.7600
epoch 77, train 58/100, loss=0.2766 acc=0.8800
epoch 77, train 59/100, loss=0.0854 acc=0.9600
epoch 77, train 60/100, loss=0.4805 acc=0.8800
epoch 77, train 61/100, loss=0.4939 acc=0.8133
epoch 77, train 62/100, loss=0.3954 acc=0.8667
epoch 77, train 63/100, loss=1.6790 acc=0.4667
epoch 77, train 64/100, loss=0.3254 acc=0.8800
epoch 77, train 65/100, loss=0.4376 acc=0.8667
epoch 77, train 66/100, loss=0.8933 acc=0.8000
epoch 77, train 67/100, loss=1.6242 acc=0.5067
epoch 77, train 68/100, loss=0.2485 acc=0.9467
epoch 77, train 69/100, loss=0.8213 acc=0.6933
epoch 77, train 70/100, loss=0.5504 acc=0.8267
epoch 77, train 71/100, loss=0.0606 acc=0.9733
epoch 77, train 72/100, loss=0.5276 acc=0.8267
epoch 77, train 73/100, loss=1.0497 acc=0.7333
epoch 77, train 74/100, loss=0.0509 acc=0.9867
epoch 77, train 75/100, loss=0.1016 acc=0.9467
epoch 77, train 76/100, loss=0.6963 acc=0.8267
epoch 77, train 77/100, loss=0.9763 acc=0.7200
epoch 77, train 78/100, loss=0.6953 acc=0.7867
epoch 77, train 79/100, loss=0.4112 acc=0.8533
epoch 77, train 80/100, loss=0.8674 acc=0.7333
epoch 77, train 81/100, loss=0.2481 acc=0.9467
epoch 77, train 82/100, loss=0.4416 acc=0.8667
epoch 77, train 83/100, loss=0.5591 acc=0.8533
epoch 77, train 84/100, loss=0.1633 acc=0.9600
epoch 77, train 85/100, loss=0.0864 acc=0.9733
epoch 77, train 86/100, loss=0.1303 acc=0.9600
epoch 77, train 87/100, loss=0.4885 acc=0.8133
epoch 77, train 88/100, loss=0.8842 acc=0.6800
epoch 77, train 89/100, loss=0.2841 acc=0.9333
epoch 77, train 90/100, loss=0.7885 acc=0.7067
epoch 77, train 91/100, loss=0.2878 acc=0.8667
epoch 77, train 92/100, loss=1.0235 acc=0.6133
epoch 77, train 93/100, loss=0.6589 acc=0.8667
epoch 77, train 94/100, loss=1.5325 acc=0.5733
epoch 77, train 95/100, loss=0.5561 acc=0.8133
epoch 77, train 96/100, loss=0.4685 acc=0.8133
epoch 77, train 97/100, loss=0.1831 acc=0.9200
epoch 77, train 98/100, loss=0.1419 acc=0.9867
epoch 77, train 99/100, loss=0.6399 acc=0.8000
epoch 77, train 100/100, loss=1.0142 acc=0.6400
best epoch 66, best val acc=0.7275
epoch 77, val, loss=0.9175 acc=0.7163
ETA:4.9h/6.4h
epoch 78, train 1/100, loss=0.2286 acc=0.9200
epoch 78, train 2/100, loss=1.2268 acc=0.5733
epoch 78, train 3/100, loss=0.4761 acc=0.8267
epoch 78, train 4/100, loss=1.7406 acc=0.4400
epoch 78, train 5/100, loss=0.6480 acc=0.7600
epoch 78, train 6/100, loss=0.3549 acc=0.8267
epoch 78, train 7/100, loss=0.1506 acc=0.9333
epoch 78, train 8/100, loss=0.1530 acc=0.9467
epoch 78, train 9/100, loss=1.2429 acc=0.6533
epoch 78, train 10/100, loss=0.5320 acc=0.8267
epoch 78, train 11/100, loss=0.5335 acc=0.8133
epoch 78, train 12/100, loss=1.3770 acc=0.6000
epoch 78, train 13/100, loss=1.1321 acc=0.7067
epoch 78, train 14/100, loss=0.5730 acc=0.8267
epoch 78, train 15/100, loss=0.5768 acc=0.7600
epoch 78, train 16/100, loss=0.2841 acc=0.9200
epoch 78, train 17/100, loss=1.0498 acc=0.7200
epoch 78, train 18/100, loss=0.6164 acc=0.8000
epoch 78, train 19/100, loss=0.9912 acc=0.7067
epoch 78, train 20/100, loss=0.6833 acc=0.7867
epoch 78, train 21/100, loss=0.3975 acc=0.8667
epoch 78, train 22/100, loss=0.4588 acc=0.8533
epoch 78, train 23/100, loss=0.1756 acc=0.9467
epoch 78, train 24/100, loss=0.8816 acc=0.7467
epoch 78, train 25/100, loss=0.5664 acc=0.7467
epoch 78, train 26/100, loss=0.3104 acc=0.9067
epoch 78, train 27/100, loss=0.5908 acc=0.7733
epoch 78, train 28/100, loss=0.1728 acc=0.9333
epoch 78, train 29/100, loss=0.3346 acc=0.8933
epoch 78, train 30/100, loss=0.4733 acc=0.8800
epoch 78, train 31/100, loss=0.2462 acc=0.9200
epoch 78, train 32/100, loss=0.1011 acc=0.9467
epoch 78, train 33/100, loss=1.0809 acc=0.5600
epoch 78, train 34/100, loss=1.1141 acc=0.7333
epoch 78, train 35/100, loss=0.1700 acc=0.9067
epoch 78, train 36/100, loss=0.3861 acc=0.8800
epoch 78, train 37/100, loss=0.1943 acc=0.9467
epoch 78, train 38/100, loss=0.2206 acc=0.9200
epoch 78, train 39/100, loss=0.7552 acc=0.7600
epoch 78, train 40/100, loss=0.4935 acc=0.8400
epoch 78, train 41/100, loss=0.1965 acc=0.9467
epoch 78, train 42/100, loss=0.3761 acc=0.8400
epoch 78, train 43/100, loss=0.8363 acc=0.7733
epoch 78, train 44/100, loss=1.2725 acc=0.6933
epoch 78, train 45/100, loss=0.2773 acc=0.8933
epoch 78, train 46/100, loss=1.1248 acc=0.6000
epoch 78, train 47/100, loss=0.6669 acc=0.8133
epoch 78, train 48/100, loss=1.1510 acc=0.6133
epoch 78, train 49/100, loss=1.0108 acc=0.6533
epoch 78, train 50/100, loss=0.1418 acc=0.9467
epoch 78, train 51/100, loss=2.0922 acc=0.4667
epoch 78, train 52/100, loss=0.0854 acc=0.9467
epoch 78, train 53/100, loss=0.0524 acc=0.9867
epoch 78, train 54/100, loss=0.5066 acc=0.7867
epoch 78, train 55/100, loss=0.4730 acc=0.8267
epoch 78, train 56/100, loss=0.4118 acc=0.8400
epoch 78, train 57/100, loss=1.3117 acc=0.5467
epoch 78, train 58/100, loss=0.4578 acc=0.8933
epoch 78, train 59/100, loss=0.2191 acc=0.9200
epoch 78, train 60/100, loss=0.5609 acc=0.8400
epoch 78, train 61/100, loss=0.4648 acc=0.8667
epoch 78, train 62/100, loss=0.1943 acc=0.8933
epoch 78, train 63/100, loss=0.2009 acc=0.9067
epoch 78, train 64/100, loss=0.3692 acc=0.9067
epoch 78, train 65/100, loss=0.6585 acc=0.7867
epoch 78, train 66/100, loss=0.7419 acc=0.7200
epoch 78, train 67/100, loss=0.1592 acc=0.9200
epoch 78, train 68/100, loss=0.0831 acc=0.9867
epoch 78, train 69/100, loss=0.4329 acc=0.8400
epoch 78, train 70/100, loss=0.5043 acc=0.8800
epoch 78, train 71/100, loss=0.1209 acc=0.9467
epoch 78, train 72/100, loss=0.3600 acc=0.8400
epoch 78, train 73/100, loss=0.4147 acc=0.8800
epoch 78, train 74/100, loss=0.5022 acc=0.8667
epoch 78, train 75/100, loss=0.6906 acc=0.8000
epoch 78, train 76/100, loss=0.1766 acc=0.9733
epoch 78, train 77/100, loss=0.4417 acc=0.8533
epoch 78, train 78/100, loss=0.2978 acc=0.8800
epoch 78, train 79/100, loss=0.5550 acc=0.8133
epoch 78, train 80/100, loss=0.5437 acc=0.7867
epoch 78, train 81/100, loss=1.0643 acc=0.7467
epoch 78, train 82/100, loss=1.9761 acc=0.4267
epoch 78, train 83/100, loss=0.6922 acc=0.7867
epoch 78, train 84/100, loss=0.7686 acc=0.7333
epoch 78, train 85/100, loss=0.2738 acc=0.8933
epoch 78, train 86/100, loss=0.4417 acc=0.8533
epoch 78, train 87/100, loss=0.5094 acc=0.8400
epoch 78, train 88/100, loss=0.3894 acc=0.8533
epoch 78, train 89/100, loss=0.7208 acc=0.8267
epoch 78, train 90/100, loss=0.4569 acc=0.8133
epoch 78, train 91/100, loss=0.5904 acc=0.8133
epoch 78, train 92/100, loss=0.4528 acc=0.8667
epoch 78, train 93/100, loss=0.6993 acc=0.6800
epoch 78, train 94/100, loss=0.3892 acc=0.8267
epoch 78, train 95/100, loss=0.5762 acc=0.7200
epoch 78, train 96/100, loss=0.4383 acc=0.8667
epoch 78, train 97/100, loss=0.9611 acc=0.7200
epoch 78, train 98/100, loss=0.1626 acc=0.9333
epoch 78, train 99/100, loss=0.2003 acc=0.9333
epoch 78, train 100/100, loss=0.5670 acc=0.8133
best epoch 66, best val acc=0.7275
epoch 78, val, loss=0.9130 acc=0.7166
ETA:5.0h/6.4h
epoch 79, train 1/100, loss=1.2138 acc=0.5733
epoch 79, train 2/100, loss=0.9487 acc=0.7200
epoch 79, train 3/100, loss=0.2216 acc=0.8933
epoch 79, train 4/100, loss=0.2360 acc=0.9333
epoch 79, train 5/100, loss=0.3291 acc=0.8933
epoch 79, train 6/100, loss=0.7031 acc=0.7600
epoch 79, train 7/100, loss=0.4252 acc=0.8800
epoch 79, train 8/100, loss=0.0832 acc=0.9733
epoch 79, train 9/100, loss=0.2485 acc=0.9467
epoch 79, train 10/100, loss=0.6768 acc=0.7333
epoch 79, train 11/100, loss=0.9490 acc=0.6400
epoch 79, train 12/100, loss=0.8254 acc=0.7733
epoch 79, train 13/100, loss=0.1882 acc=0.9200
epoch 79, train 14/100, loss=0.8828 acc=0.6533
epoch 79, train 15/100, loss=0.8812 acc=0.6400
epoch 79, train 16/100, loss=1.6137 acc=0.5200
epoch 79, train 17/100, loss=0.2916 acc=0.8933
epoch 79, train 18/100, loss=0.2435 acc=0.9067
epoch 79, train 19/100, loss=0.5742 acc=0.8000
epoch 79, train 20/100, loss=0.3298 acc=0.9200
epoch 79, train 21/100, loss=0.3002 acc=0.8533
epoch 79, train 22/100, loss=0.3072 acc=0.9200
epoch 79, train 23/100, loss=0.3676 acc=0.9200
epoch 79, train 24/100, loss=0.5357 acc=0.8133
epoch 79, train 25/100, loss=0.7887 acc=0.7867
epoch 79, train 26/100, loss=0.8654 acc=0.7600
epoch 79, train 27/100, loss=0.0626 acc=0.9733
epoch 79, train 28/100, loss=0.4671 acc=0.8667
epoch 79, train 29/100, loss=0.3328 acc=0.8667
epoch 79, train 30/100, loss=0.8852 acc=0.6400
epoch 79, train 31/100, loss=0.3526 acc=0.8667
epoch 79, train 32/100, loss=0.0747 acc=0.9867
epoch 79, train 33/100, loss=0.7988 acc=0.7733
epoch 79, train 34/100, loss=0.9272 acc=0.7067
epoch 79, train 35/100, loss=0.1513 acc=0.9600
epoch 79, train 36/100, loss=0.5165 acc=0.7733
epoch 79, train 37/100, loss=0.2846 acc=0.8800
epoch 79, train 38/100, loss=0.2668 acc=0.9467
epoch 79, train 39/100, loss=0.6236 acc=0.8533
epoch 79, train 40/100, loss=0.1369 acc=0.9333
epoch 79, train 41/100, loss=0.6007 acc=0.7600
epoch 79, train 42/100, loss=0.5878 acc=0.8267
epoch 79, train 43/100, loss=0.1929 acc=0.9467
epoch 79, train 44/100, loss=0.1917 acc=0.8933
epoch 79, train 45/100, loss=0.6360 acc=0.8000
epoch 79, train 46/100, loss=0.7248 acc=0.7333
epoch 79, train 47/100, loss=0.9799 acc=0.6533
epoch 79, train 48/100, loss=0.3325 acc=0.8933
epoch 79, train 49/100, loss=0.7170 acc=0.7333
epoch 79, train 50/100, loss=0.5709 acc=0.8133
epoch 79, train 51/100, loss=0.4037 acc=0.8533
epoch 79, train 52/100, loss=0.9764 acc=0.7200
epoch 79, train 53/100, loss=0.8846 acc=0.7067
epoch 79, train 54/100, loss=1.4973 acc=0.6000
epoch 79, train 55/100, loss=0.0521 acc=0.9867
epoch 79, train 56/100, loss=0.4266 acc=0.8267
epoch 79, train 57/100, loss=1.2599 acc=0.6800
epoch 79, train 58/100, loss=0.2191 acc=0.9067
epoch 79, train 59/100, loss=0.1835 acc=0.8933
epoch 79, train 60/100, loss=0.7649 acc=0.7333
epoch 79, train 61/100, loss=0.8737 acc=0.6933
epoch 79, train 62/100, loss=0.7344 acc=0.7467
epoch 79, train 63/100, loss=0.3089 acc=0.8933
epoch 79, train 64/100, loss=0.3810 acc=0.8933
epoch 79, train 65/100, loss=0.4928 acc=0.8133
epoch 79, train 66/100, loss=0.3816 acc=0.9067
epoch 79, train 67/100, loss=0.6466 acc=0.8133
epoch 79, train 68/100, loss=0.0615 acc=0.9867
epoch 79, train 69/100, loss=0.0965 acc=0.9467
epoch 79, train 70/100, loss=0.4231 acc=0.8533
epoch 79, train 71/100, loss=1.0194 acc=0.6267
epoch 79, train 72/100, loss=1.5745 acc=0.5600
epoch 79, train 73/100, loss=0.2814 acc=0.9067
epoch 79, train 74/100, loss=0.2172 acc=0.8667
epoch 79, train 75/100, loss=1.0732 acc=0.6667
epoch 79, train 76/100, loss=0.7325 acc=0.7333
epoch 79, train 77/100, loss=0.6434 acc=0.7867
epoch 79, train 78/100, loss=0.2467 acc=0.9067
epoch 79, train 79/100, loss=0.5508 acc=0.8133
epoch 79, train 80/100, loss=0.4166 acc=0.8000
epoch 79, train 81/100, loss=0.2808 acc=0.9333
epoch 79, train 82/100, loss=0.2292 acc=0.9067
epoch 79, train 83/100, loss=0.2906 acc=0.9200
epoch 79, train 84/100, loss=0.1998 acc=0.9467
epoch 79, train 85/100, loss=0.6775 acc=0.8000
epoch 79, train 86/100, loss=0.5867 acc=0.8400
epoch 79, train 87/100, loss=0.3173 acc=0.8933
epoch 79, train 88/100, loss=0.6080 acc=0.8667
epoch 79, train 89/100, loss=0.8679 acc=0.7467
epoch 79, train 90/100, loss=0.4172 acc=0.8800
epoch 79, train 91/100, loss=0.3495 acc=0.8667
epoch 79, train 92/100, loss=0.5050 acc=0.7867
epoch 79, train 93/100, loss=0.6037 acc=0.8000
epoch 79, train 94/100, loss=0.3504 acc=0.8667
epoch 79, train 95/100, loss=0.3706 acc=0.8533
epoch 79, train 96/100, loss=0.5035 acc=0.8133
epoch 79, train 97/100, loss=0.8042 acc=0.7333
epoch 79, train 98/100, loss=0.4198 acc=0.8667
epoch 79, train 99/100, loss=0.5336 acc=0.8267
epoch 79, train 100/100, loss=1.6606 acc=0.4933
best epoch 66, best val acc=0.7275
epoch 79, val, loss=0.9246 acc=0.7181
ETA:5.1h/6.4h
epoch 80, train 1/100, loss=0.5812 acc=0.8133
epoch 80, train 2/100, loss=1.2303 acc=0.5867
epoch 80, train 3/100, loss=0.9200 acc=0.7200
epoch 80, train 4/100, loss=0.6330 acc=0.7867
epoch 80, train 5/100, loss=0.0746 acc=0.9867
epoch 80, train 6/100, loss=1.8304 acc=0.5600
epoch 80, train 7/100, loss=0.2304 acc=0.9467
epoch 80, train 8/100, loss=0.0841 acc=0.9467
epoch 80, train 9/100, loss=0.7225 acc=0.7600
epoch 80, train 10/100, loss=1.0208 acc=0.6133
epoch 80, train 11/100, loss=0.2238 acc=0.9600
epoch 80, train 12/100, loss=0.5572 acc=0.8933
epoch 80, train 13/100, loss=0.5951 acc=0.7067
epoch 80, train 14/100, loss=1.3487 acc=0.4800
epoch 80, train 15/100, loss=0.1877 acc=0.9067
epoch 80, train 16/100, loss=0.1214 acc=0.9467
epoch 80, train 17/100, loss=0.4016 acc=0.8533
epoch 80, train 18/100, loss=0.7283 acc=0.7333
epoch 80, train 19/100, loss=0.7454 acc=0.7733
epoch 80, train 20/100, loss=0.1010 acc=0.9733
epoch 80, train 21/100, loss=0.2504 acc=0.9067
epoch 80, train 22/100, loss=0.1793 acc=0.9333
epoch 80, train 23/100, loss=0.3667 acc=0.8933
epoch 80, train 24/100, loss=0.2838 acc=0.9200
epoch 80, train 25/100, loss=1.1412 acc=0.5867
epoch 80, train 26/100, loss=1.5315 acc=0.5600
epoch 80, train 27/100, loss=0.5337 acc=0.8267
epoch 80, train 28/100, loss=0.5842 acc=0.8400
epoch 80, train 29/100, loss=0.6232 acc=0.7467
epoch 80, train 30/100, loss=1.2023 acc=0.6400
epoch 80, train 31/100, loss=0.4834 acc=0.8533
epoch 80, train 32/100, loss=0.9374 acc=0.6933
epoch 80, train 33/100, loss=0.4654 acc=0.7600
epoch 80, train 34/100, loss=0.8745 acc=0.7467
epoch 80, train 35/100, loss=0.0897 acc=0.9467
epoch 80, train 36/100, loss=0.2352 acc=0.9333
epoch 80, train 37/100, loss=0.4705 acc=0.8267
epoch 80, train 38/100, loss=0.1072 acc=0.9733
epoch 80, train 39/100, loss=0.4537 acc=0.8267
epoch 80, train 40/100, loss=0.2971 acc=0.8800
epoch 80, train 41/100, loss=0.0766 acc=0.9600
epoch 80, train 42/100, loss=1.2606 acc=0.5733
epoch 80, train 43/100, loss=2.1512 acc=0.3867
epoch 80, train 44/100, loss=0.1584 acc=0.9600
epoch 80, train 45/100, loss=1.1221 acc=0.6667
epoch 80, train 46/100, loss=0.3003 acc=0.9200
epoch 80, train 47/100, loss=0.2183 acc=0.9067
epoch 80, train 48/100, loss=0.1368 acc=0.9600
epoch 80, train 49/100, loss=0.8554 acc=0.8000
epoch 80, train 50/100, loss=0.1887 acc=0.9467
epoch 80, train 51/100, loss=0.4475 acc=0.7600
epoch 80, train 52/100, loss=0.6710 acc=0.7600
epoch 80, train 53/100, loss=0.8265 acc=0.7733
epoch 80, train 54/100, loss=0.1421 acc=0.9600
epoch 80, train 55/100, loss=1.1529 acc=0.6400
epoch 80, train 56/100, loss=0.1727 acc=0.9333
epoch 80, train 57/100, loss=0.2993 acc=0.8667
epoch 80, train 58/100, loss=1.2045 acc=0.6933
epoch 80, train 59/100, loss=1.2704 acc=0.6800
epoch 80, train 60/100, loss=0.5364 acc=0.7867
epoch 80, train 61/100, loss=0.4105 acc=0.8667
epoch 80, train 62/100, loss=0.5133 acc=0.8267
epoch 80, train 63/100, loss=0.6229 acc=0.7733
epoch 80, train 64/100, loss=0.2703 acc=0.8800
epoch 80, train 65/100, loss=0.5335 acc=0.7867
epoch 80, train 66/100, loss=0.3461 acc=0.9067
epoch 80, train 67/100, loss=0.6035 acc=0.7867
epoch 80, train 68/100, loss=0.9463 acc=0.7733
epoch 80, train 69/100, loss=0.4439 acc=0.8667
epoch 80, train 70/100, loss=0.2427 acc=0.9067
epoch 80, train 71/100, loss=0.0549 acc=0.9733
epoch 80, train 72/100, loss=0.4381 acc=0.8400
epoch 80, train 73/100, loss=0.1729 acc=0.9200
epoch 80, train 74/100, loss=0.3742 acc=0.8400
epoch 80, train 75/100, loss=0.2483 acc=0.9333
epoch 80, train 76/100, loss=0.2941 acc=0.8800
epoch 80, train 77/100, loss=0.5567 acc=0.8400
epoch 80, train 78/100, loss=0.9207 acc=0.6933
epoch 80, train 79/100, loss=0.7645 acc=0.7333
epoch 80, train 80/100, loss=0.2305 acc=0.9067
epoch 80, train 81/100, loss=0.8579 acc=0.7333
epoch 80, train 82/100, loss=0.3580 acc=0.8667
epoch 80, train 83/100, loss=0.2294 acc=0.9200
epoch 80, train 84/100, loss=0.4176 acc=0.8933
epoch 80, train 85/100, loss=0.7919 acc=0.7867
epoch 80, train 86/100, loss=0.4067 acc=0.8400
epoch 80, train 87/100, loss=0.2583 acc=0.8800
epoch 80, train 88/100, loss=0.6780 acc=0.8133
epoch 80, train 89/100, loss=0.2260 acc=0.9467
epoch 80, train 90/100, loss=0.4727 acc=0.8533
epoch 80, train 91/100, loss=0.3561 acc=0.8800
epoch 80, train 92/100, loss=0.3227 acc=0.9200
epoch 80, train 93/100, loss=0.4447 acc=0.8400
epoch 80, train 94/100, loss=1.2135 acc=0.6400
epoch 80, train 95/100, loss=0.3760 acc=0.8533
epoch 80, train 96/100, loss=0.5418 acc=0.7867
epoch 80, train 97/100, loss=0.3339 acc=0.8667
epoch 80, train 98/100, loss=0.7091 acc=0.8133
epoch 80, train 99/100, loss=1.6322 acc=0.5467
epoch 80, train 100/100, loss=0.3281 acc=0.8533
best epoch 66, best val acc=0.7275
epoch 80, val, loss=0.9329 acc=0.7118
ETA:5.1h/6.4h
epoch 81, train 1/100, loss=1.0753 acc=0.6800
epoch 81, train 2/100, loss=0.4720 acc=0.8000
epoch 81, train 3/100, loss=0.6417 acc=0.7867
epoch 81, train 4/100, loss=1.7391 acc=0.4800
epoch 81, train 5/100, loss=1.1233 acc=0.5067
epoch 81, train 6/100, loss=0.6018 acc=0.7733
epoch 81, train 7/100, loss=0.4948 acc=0.8533
epoch 81, train 8/100, loss=0.8554 acc=0.6933
epoch 81, train 9/100, loss=0.3081 acc=0.8667
epoch 81, train 10/100, loss=0.6765 acc=0.8267
epoch 81, train 11/100, loss=0.4807 acc=0.8667
epoch 81, train 12/100, loss=0.9564 acc=0.6933
epoch 81, train 13/100, loss=0.2693 acc=0.9067
epoch 81, train 14/100, loss=0.3593 acc=0.8667
epoch 81, train 15/100, loss=0.3802 acc=0.8000
epoch 81, train 16/100, loss=0.4174 acc=0.8800
epoch 81, train 17/100, loss=0.8163 acc=0.7600
epoch 81, train 18/100, loss=1.2557 acc=0.6000
epoch 81, train 19/100, loss=0.2724 acc=0.8533
epoch 81, train 20/100, loss=0.9381 acc=0.6933
epoch 81, train 21/100, loss=0.2624 acc=0.8933
epoch 81, train 22/100, loss=0.8493 acc=0.7333
epoch 81, train 23/100, loss=0.0920 acc=0.9867
epoch 81, train 24/100, loss=0.1813 acc=0.9200
epoch 81, train 25/100, loss=0.8205 acc=0.7467
epoch 81, train 26/100, loss=0.2716 acc=0.9333
epoch 81, train 27/100, loss=0.3546 acc=0.9200
epoch 81, train 28/100, loss=0.7810 acc=0.7067
epoch 81, train 29/100, loss=0.4611 acc=0.7867
epoch 81, train 30/100, loss=0.9685 acc=0.6667
epoch 81, train 31/100, loss=0.4635 acc=0.8267
epoch 81, train 32/100, loss=0.3017 acc=0.8800
epoch 81, train 33/100, loss=0.5642 acc=0.7733
epoch 81, train 34/100, loss=0.9970 acc=0.7067
epoch 81, train 35/100, loss=0.4636 acc=0.8533
epoch 81, train 36/100, loss=0.4124 acc=0.8267
epoch 81, train 37/100, loss=0.6137 acc=0.8267
epoch 81, train 38/100, loss=1.0222 acc=0.6667
epoch 81, train 39/100, loss=1.1054 acc=0.6800
epoch 81, train 40/100, loss=0.3140 acc=0.8667
epoch 81, train 41/100, loss=0.4967 acc=0.8533
epoch 81, train 42/100, loss=0.3906 acc=0.8933
epoch 81, train 43/100, loss=0.3771 acc=0.8533
epoch 81, train 44/100, loss=0.1699 acc=0.9333
epoch 81, train 45/100, loss=0.6335 acc=0.7867
epoch 81, train 46/100, loss=1.4603 acc=0.5867
epoch 81, train 47/100, loss=0.2056 acc=0.9467
epoch 81, train 48/100, loss=1.0494 acc=0.6667
epoch 81, train 49/100, loss=0.6448 acc=0.8267
epoch 81, train 50/100, loss=0.9929 acc=0.7333
epoch 81, train 51/100, loss=0.5360 acc=0.8400
epoch 81, train 52/100, loss=0.1232 acc=0.9600
epoch 81, train 53/100, loss=0.1887 acc=0.9200
epoch 81, train 54/100, loss=0.7526 acc=0.7733
epoch 81, train 55/100, loss=0.2382 acc=0.8933
epoch 81, train 56/100, loss=1.5349 acc=0.5600
epoch 81, train 57/100, loss=0.5554 acc=0.7867
epoch 81, train 58/100, loss=0.4520 acc=0.8667
epoch 81, train 59/100, loss=0.4888 acc=0.8933
epoch 81, train 60/100, loss=0.1123 acc=0.9733
epoch 81, train 61/100, loss=0.5588 acc=0.8000
epoch 81, train 62/100, loss=1.0139 acc=0.7467
epoch 81, train 63/100, loss=1.0910 acc=0.6267
epoch 81, train 64/100, loss=0.3178 acc=0.9067
epoch 81, train 65/100, loss=1.3485 acc=0.6000
epoch 81, train 66/100, loss=0.8230 acc=0.7333
epoch 81, train 67/100, loss=0.7285 acc=0.7600
epoch 81, train 68/100, loss=0.2540 acc=0.8667
epoch 81, train 69/100, loss=0.3260 acc=0.8800
epoch 81, train 70/100, loss=0.7476 acc=0.7333
epoch 81, train 71/100, loss=0.5678 acc=0.8533
epoch 81, train 72/100, loss=0.2217 acc=0.9333
epoch 81, train 73/100, loss=0.3230 acc=0.9067
epoch 81, train 74/100, loss=0.4110 acc=0.9067
epoch 81, train 75/100, loss=0.5646 acc=0.8400
epoch 81, train 76/100, loss=0.3633 acc=0.8267
epoch 81, train 77/100, loss=1.4278 acc=0.4800
epoch 81, train 78/100, loss=0.5719 acc=0.7600
epoch 81, train 79/100, loss=0.4215 acc=0.8400
epoch 81, train 80/100, loss=0.3256 acc=0.9067
epoch 81, train 81/100, loss=0.5944 acc=0.8000
epoch 81, train 82/100, loss=0.6110 acc=0.7867
epoch 81, train 83/100, loss=0.6350 acc=0.8000
epoch 81, train 84/100, loss=0.6064 acc=0.7867
epoch 81, train 85/100, loss=0.3238 acc=0.8667
epoch 81, train 86/100, loss=0.3340 acc=0.8933
epoch 81, train 87/100, loss=0.3356 acc=0.9333
epoch 81, train 88/100, loss=0.2639 acc=0.8933
epoch 81, train 89/100, loss=0.5689 acc=0.8400
epoch 81, train 90/100, loss=0.4736 acc=0.8267
epoch 81, train 91/100, loss=0.1324 acc=0.9333
epoch 81, train 92/100, loss=0.3581 acc=0.9067
epoch 81, train 93/100, loss=0.4991 acc=0.7867
epoch 81, train 94/100, loss=0.4695 acc=0.8267
epoch 81, train 95/100, loss=0.1978 acc=0.9200
epoch 81, train 96/100, loss=0.7555 acc=0.7600
epoch 81, train 97/100, loss=0.2558 acc=0.9333
epoch 81, train 98/100, loss=0.5901 acc=0.7867
epoch 81, train 99/100, loss=0.4344 acc=0.8267
epoch 81, train 100/100, loss=0.5939 acc=0.7733
best epoch 66, best val acc=0.7275
epoch 81, val, loss=0.9400 acc=0.7095
ETA:5.2h/6.4h
epoch 82, train 1/100, loss=0.7312 acc=0.7333
epoch 82, train 2/100, loss=0.4624 acc=0.8800
epoch 82, train 3/100, loss=0.2464 acc=0.9467
epoch 82, train 4/100, loss=0.6553 acc=0.8000
epoch 82, train 5/100, loss=1.0727 acc=0.6267
epoch 82, train 6/100, loss=0.1814 acc=0.9333
epoch 82, train 7/100, loss=0.6945 acc=0.7467
epoch 82, train 8/100, loss=0.5929 acc=0.7733
epoch 82, train 9/100, loss=0.5660 acc=0.8267
epoch 82, train 10/100, loss=0.5024 acc=0.8267
epoch 82, train 11/100, loss=0.3781 acc=0.8400
epoch 82, train 12/100, loss=0.6476 acc=0.7600
epoch 82, train 13/100, loss=0.2793 acc=0.8933
epoch 82, train 14/100, loss=0.2560 acc=0.9467
epoch 82, train 15/100, loss=0.5023 acc=0.8267
epoch 82, train 16/100, loss=0.6992 acc=0.7333
epoch 82, train 17/100, loss=0.3176 acc=0.8667
epoch 82, train 18/100, loss=0.3522 acc=0.8533
epoch 82, train 19/100, loss=0.1056 acc=0.9467
epoch 82, train 20/100, loss=0.0687 acc=0.9867
epoch 82, train 21/100, loss=0.3507 acc=0.8667
epoch 82, train 22/100, loss=0.4461 acc=0.8400
epoch 82, train 23/100, loss=0.4933 acc=0.8400
epoch 82, train 24/100, loss=0.2236 acc=0.9067
epoch 82, train 25/100, loss=0.2547 acc=0.8933
epoch 82, train 26/100, loss=0.4727 acc=0.8667
epoch 82, train 27/100, loss=0.4904 acc=0.8000
epoch 82, train 28/100, loss=0.4207 acc=0.8267
epoch 82, train 29/100, loss=0.7873 acc=0.6933
epoch 82, train 30/100, loss=0.2049 acc=0.9067
epoch 82, train 31/100, loss=0.9096 acc=0.7467
epoch 82, train 32/100, loss=0.2827 acc=0.9067
epoch 82, train 33/100, loss=0.5118 acc=0.8533
epoch 82, train 34/100, loss=0.4430 acc=0.8667
epoch 82, train 35/100, loss=0.4598 acc=0.8800
epoch 82, train 36/100, loss=0.1845 acc=0.9333
epoch 82, train 37/100, loss=0.5627 acc=0.8133
epoch 82, train 38/100, loss=0.8564 acc=0.6933
epoch 82, train 39/100, loss=0.4408 acc=0.8400
epoch 82, train 40/100, loss=0.1290 acc=0.9600
epoch 82, train 41/100, loss=0.3499 acc=0.8933
epoch 82, train 42/100, loss=0.1511 acc=0.9333
epoch 82, train 43/100, loss=0.6356 acc=0.8133
epoch 82, train 44/100, loss=0.1248 acc=0.9067
epoch 82, train 45/100, loss=0.6717 acc=0.7733
epoch 82, train 46/100, loss=0.0930 acc=0.9600
epoch 82, train 47/100, loss=0.1752 acc=0.9333
epoch 82, train 48/100, loss=0.7969 acc=0.7733
epoch 82, train 49/100, loss=0.2681 acc=0.9200
epoch 82, train 50/100, loss=0.2420 acc=0.9200
epoch 82, train 51/100, loss=0.6770 acc=0.8267
epoch 82, train 52/100, loss=0.1175 acc=0.9333
epoch 82, train 53/100, loss=0.4989 acc=0.8800
epoch 82, train 54/100, loss=0.6343 acc=0.7333
epoch 82, train 55/100, loss=0.3840 acc=0.8533
epoch 82, train 56/100, loss=0.7478 acc=0.7467
epoch 82, train 57/100, loss=0.5358 acc=0.8133
epoch 82, train 58/100, loss=0.3784 acc=0.8267
epoch 82, train 59/100, loss=0.4680 acc=0.8533
epoch 82, train 60/100, loss=1.5334 acc=0.5600
epoch 82, train 61/100, loss=0.7672 acc=0.7867
epoch 82, train 62/100, loss=0.3050 acc=0.8800
epoch 82, train 63/100, loss=1.0011 acc=0.7200
epoch 82, train 64/100, loss=0.3502 acc=0.8267
epoch 82, train 65/100, loss=0.1793 acc=0.9600
epoch 82, train 66/100, loss=0.7135 acc=0.7867
epoch 82, train 67/100, loss=0.7061 acc=0.7733
epoch 82, train 68/100, loss=0.5765 acc=0.8533
epoch 82, train 69/100, loss=1.2447 acc=0.7067
epoch 82, train 70/100, loss=0.1916 acc=0.9467
epoch 82, train 71/100, loss=0.3144 acc=0.8933
epoch 82, train 72/100, loss=0.3557 acc=0.8933
epoch 82, train 73/100, loss=0.4982 acc=0.7600
epoch 82, train 74/100, loss=0.3310 acc=0.8800
epoch 82, train 75/100, loss=0.2749 acc=0.9067
epoch 82, train 76/100, loss=0.2670 acc=0.9200
epoch 82, train 77/100, loss=0.3342 acc=0.9200
epoch 82, train 78/100, loss=1.3880 acc=0.5867
epoch 82, train 79/100, loss=0.4695 acc=0.8133
epoch 82, train 80/100, loss=0.1870 acc=0.8800
epoch 82, train 81/100, loss=0.8463 acc=0.7600
epoch 82, train 82/100, loss=0.3651 acc=0.8800
epoch 82, train 83/100, loss=0.1748 acc=0.9200
epoch 82, train 84/100, loss=1.1450 acc=0.7200
epoch 82, train 85/100, loss=0.7471 acc=0.7733
epoch 82, train 86/100, loss=0.3605 acc=0.8933
epoch 82, train 87/100, loss=0.3740 acc=0.8133
epoch 82, train 88/100, loss=0.3404 acc=0.8800
epoch 82, train 89/100, loss=0.4053 acc=0.8400
epoch 82, train 90/100, loss=0.6458 acc=0.7467
epoch 82, train 91/100, loss=0.5025 acc=0.8000
epoch 82, train 92/100, loss=0.5469 acc=0.8267
epoch 82, train 93/100, loss=0.7088 acc=0.7467
epoch 82, train 94/100, loss=1.1059 acc=0.6533
epoch 82, train 95/100, loss=0.8264 acc=0.7333
epoch 82, train 96/100, loss=0.3092 acc=0.8933
epoch 82, train 97/100, loss=0.1752 acc=0.9200
epoch 82, train 98/100, loss=0.4086 acc=0.8933
epoch 82, train 99/100, loss=0.0133 acc=1.0000
epoch 82, train 100/100, loss=0.1844 acc=0.9200
best epoch 66, best val acc=0.7275
epoch 82, val, loss=0.9171 acc=0.7143
ETA:5.3h/6.4h
epoch 83, train 1/100, loss=0.1695 acc=0.9333
epoch 83, train 2/100, loss=0.4996 acc=0.8400
epoch 83, train 3/100, loss=0.6423 acc=0.7733
epoch 83, train 4/100, loss=0.4956 acc=0.8400
epoch 83, train 5/100, loss=0.3335 acc=0.8800
epoch 83, train 6/100, loss=1.0277 acc=0.6400
epoch 83, train 7/100, loss=0.3305 acc=0.8933
epoch 83, train 8/100, loss=0.4639 acc=0.8133
epoch 83, train 9/100, loss=0.3131 acc=0.8933
epoch 83, train 10/100, loss=0.2390 acc=0.9067
epoch 83, train 11/100, loss=0.6482 acc=0.7467
epoch 83, train 12/100, loss=0.6713 acc=0.7467
epoch 83, train 13/100, loss=0.7148 acc=0.7867
epoch 83, train 14/100, loss=0.5845 acc=0.8133
epoch 83, train 15/100, loss=0.2051 acc=0.9067
epoch 83, train 16/100, loss=0.3547 acc=0.8533
epoch 83, train 17/100, loss=0.1134 acc=0.9600
epoch 83, train 18/100, loss=0.6423 acc=0.8000
epoch 83, train 19/100, loss=0.8901 acc=0.7333
epoch 83, train 20/100, loss=0.2035 acc=0.9200
epoch 83, train 21/100, loss=0.1790 acc=0.9200
epoch 83, train 22/100, loss=0.3372 acc=0.9200
epoch 83, train 23/100, loss=0.0739 acc=0.9867
epoch 83, train 24/100, loss=0.6957 acc=0.7733
epoch 83, train 25/100, loss=1.8200 acc=0.4667
epoch 83, train 26/100, loss=0.4647 acc=0.8533
epoch 83, train 27/100, loss=0.4177 acc=0.8533
epoch 83, train 28/100, loss=1.0428 acc=0.6933
epoch 83, train 29/100, loss=0.5890 acc=0.8667
epoch 83, train 30/100, loss=0.5452 acc=0.8267
epoch 83, train 31/100, loss=0.5700 acc=0.8400
epoch 83, train 32/100, loss=0.4242 acc=0.8667
epoch 83, train 33/100, loss=0.3136 acc=0.8667
epoch 83, train 34/100, loss=0.1819 acc=0.9467
epoch 83, train 35/100, loss=0.6431 acc=0.7867
epoch 83, train 36/100, loss=0.5243 acc=0.8000
epoch 83, train 37/100, loss=0.3092 acc=0.8800
epoch 83, train 38/100, loss=0.3963 acc=0.8667
epoch 83, train 39/100, loss=0.7183 acc=0.7733
epoch 83, train 40/100, loss=0.4716 acc=0.8400
epoch 83, train 41/100, loss=0.1379 acc=0.9600
epoch 83, train 42/100, loss=0.8442 acc=0.6800
epoch 83, train 43/100, loss=0.7115 acc=0.7333
epoch 83, train 44/100, loss=0.2281 acc=0.9200
epoch 83, train 45/100, loss=0.2186 acc=0.9467
epoch 83, train 46/100, loss=0.6519 acc=0.8133
epoch 83, train 47/100, loss=0.4024 acc=0.8667
epoch 83, train 48/100, loss=0.9211 acc=0.6667
epoch 83, train 49/100, loss=0.6083 acc=0.7333
epoch 83, train 50/100, loss=1.3797 acc=0.5733
epoch 83, train 51/100, loss=2.6258 acc=0.4800
epoch 83, train 52/100, loss=1.1556 acc=0.6933
epoch 83, train 53/100, loss=0.2522 acc=0.9333
epoch 83, train 54/100, loss=0.9222 acc=0.6267
epoch 83, train 55/100, loss=0.3645 acc=0.8667
epoch 83, train 56/100, loss=0.6416 acc=0.8533
epoch 83, train 57/100, loss=0.5524 acc=0.8133
epoch 83, train 58/100, loss=0.2833 acc=0.9067
epoch 83, train 59/100, loss=0.4473 acc=0.8667
epoch 83, train 60/100, loss=0.8616 acc=0.7067
epoch 83, train 61/100, loss=0.1258 acc=0.9467
epoch 83, train 62/100, loss=1.0359 acc=0.6800
epoch 83, train 63/100, loss=0.2982 acc=0.8800
epoch 83, train 64/100, loss=0.4635 acc=0.8133
epoch 83, train 65/100, loss=0.6746 acc=0.8000
epoch 83, train 66/100, loss=0.1195 acc=0.9467
epoch 83, train 67/100, loss=0.9925 acc=0.7333
epoch 83, train 68/100, loss=1.7762 acc=0.5733
epoch 83, train 69/100, loss=1.0436 acc=0.6933
epoch 83, train 70/100, loss=0.5190 acc=0.8400
epoch 83, train 71/100, loss=0.5333 acc=0.8933
epoch 83, train 72/100, loss=0.6378 acc=0.8133
epoch 83, train 73/100, loss=0.8026 acc=0.7867
epoch 83, train 74/100, loss=0.2103 acc=0.9333
epoch 83, train 75/100, loss=0.4598 acc=0.8800
epoch 83, train 76/100, loss=0.4478 acc=0.8933
epoch 83, train 77/100, loss=0.0791 acc=0.9733
epoch 83, train 78/100, loss=0.1434 acc=0.9733
epoch 83, train 79/100, loss=0.3951 acc=0.8800
epoch 83, train 80/100, loss=0.5047 acc=0.8133
epoch 83, train 81/100, loss=0.2377 acc=0.8933
epoch 83, train 82/100, loss=0.5333 acc=0.8533
epoch 83, train 83/100, loss=0.2910 acc=0.9200
epoch 83, train 84/100, loss=0.3774 acc=0.8533
epoch 83, train 85/100, loss=0.2226 acc=0.9067
epoch 83, train 86/100, loss=0.7558 acc=0.7200
epoch 83, train 87/100, loss=1.1108 acc=0.6133
epoch 83, train 88/100, loss=0.4751 acc=0.8667
epoch 83, train 89/100, loss=0.3725 acc=0.8800
epoch 83, train 90/100, loss=1.5101 acc=0.6800
epoch 83, train 91/100, loss=0.5300 acc=0.8000
epoch 83, train 92/100, loss=0.2878 acc=0.8933
epoch 83, train 93/100, loss=0.8982 acc=0.6400
epoch 83, train 94/100, loss=0.3630 acc=0.8800
epoch 83, train 95/100, loss=0.9133 acc=0.6267
epoch 83, train 96/100, loss=1.0784 acc=0.7067
epoch 83, train 97/100, loss=1.0685 acc=0.6800
epoch 83, train 98/100, loss=0.1578 acc=0.9467
epoch 83, train 99/100, loss=0.3636 acc=0.9067
epoch 83, train 100/100, loss=0.4249 acc=0.8533
best epoch 66, best val acc=0.7275
epoch 83, val, loss=0.9544 acc=0.7075
ETA:5.3h/6.4h
epoch 84, train 1/100, loss=0.2413 acc=0.8933
epoch 84, train 2/100, loss=0.6274 acc=0.8133
epoch 84, train 3/100, loss=1.0913 acc=0.7333
epoch 84, train 4/100, loss=0.1851 acc=0.9467
epoch 84, train 5/100, loss=0.5006 acc=0.7867
epoch 84, train 6/100, loss=0.5477 acc=0.8400
epoch 84, train 7/100, loss=0.7207 acc=0.8133
epoch 84, train 8/100, loss=0.0533 acc=0.9867
epoch 84, train 9/100, loss=0.5560 acc=0.8000
epoch 84, train 10/100, loss=0.3930 acc=0.9067
epoch 84, train 11/100, loss=0.5239 acc=0.8000
epoch 84, train 12/100, loss=0.4989 acc=0.8267
epoch 84, train 13/100, loss=0.6433 acc=0.8267
epoch 84, train 14/100, loss=0.1634 acc=0.9467
epoch 84, train 15/100, loss=0.9183 acc=0.7733
epoch 84, train 16/100, loss=0.9617 acc=0.6800
epoch 84, train 17/100, loss=0.9008 acc=0.6667
epoch 84, train 18/100, loss=1.1223 acc=0.7200
epoch 84, train 19/100, loss=0.1547 acc=0.9467
epoch 84, train 20/100, loss=0.2709 acc=0.9067
epoch 84, train 21/100, loss=1.1678 acc=0.6667
epoch 84, train 22/100, loss=0.2786 acc=0.8933
epoch 84, train 23/100, loss=0.2844 acc=0.8667
epoch 84, train 24/100, loss=1.8016 acc=0.5467
epoch 84, train 25/100, loss=0.4985 acc=0.8400
epoch 84, train 26/100, loss=0.4261 acc=0.8133
epoch 84, train 27/100, loss=0.5975 acc=0.8133
epoch 84, train 28/100, loss=0.2745 acc=0.8800
epoch 84, train 29/100, loss=1.1363 acc=0.6267
epoch 84, train 30/100, loss=0.1792 acc=0.9467
epoch 84, train 31/100, loss=0.2798 acc=0.9333
epoch 84, train 32/100, loss=0.3101 acc=0.8667
epoch 84, train 33/100, loss=1.0179 acc=0.6533
epoch 84, train 34/100, loss=0.2288 acc=0.9333
epoch 84, train 35/100, loss=0.2093 acc=0.9067
epoch 84, train 36/100, loss=0.7069 acc=0.8133
epoch 84, train 37/100, loss=0.6703 acc=0.7600
epoch 84, train 38/100, loss=0.9885 acc=0.6267
epoch 84, train 39/100, loss=1.2513 acc=0.5333
epoch 84, train 40/100, loss=0.2552 acc=0.9067
epoch 84, train 41/100, loss=0.3863 acc=0.8667
epoch 84, train 42/100, loss=0.1222 acc=0.9600
epoch 84, train 43/100, loss=1.0102 acc=0.6933
epoch 84, train 44/100, loss=0.3282 acc=0.9067
epoch 84, train 45/100, loss=0.7852 acc=0.7600
epoch 84, train 46/100, loss=0.2713 acc=0.9333
epoch 84, train 47/100, loss=0.5630 acc=0.8133
epoch 84, train 48/100, loss=0.2441 acc=0.9333
epoch 84, train 49/100, loss=0.4612 acc=0.8400
epoch 84, train 50/100, loss=0.7763 acc=0.7200
epoch 84, train 51/100, loss=0.1907 acc=0.9200
epoch 84, train 52/100, loss=0.4910 acc=0.8533
epoch 84, train 53/100, loss=0.3160 acc=0.8800
epoch 84, train 54/100, loss=0.2518 acc=0.9333
epoch 84, train 55/100, loss=0.7087 acc=0.7467
epoch 84, train 56/100, loss=0.6678 acc=0.7467
epoch 84, train 57/100, loss=0.1518 acc=0.9333
epoch 84, train 58/100, loss=0.8638 acc=0.7333
epoch 84, train 59/100, loss=0.4256 acc=0.8400
epoch 84, train 60/100, loss=0.4984 acc=0.8133
epoch 84, train 61/100, loss=0.6767 acc=0.7600
epoch 84, train 62/100, loss=1.1170 acc=0.5733
epoch 84, train 63/100, loss=0.1667 acc=0.9333
epoch 84, train 64/100, loss=0.0496 acc=0.9867
epoch 84, train 65/100, loss=0.8277 acc=0.6533
epoch 84, train 66/100, loss=0.3162 acc=0.8667
epoch 84, train 67/100, loss=0.4637 acc=0.9067
epoch 84, train 68/100, loss=0.4329 acc=0.8400
epoch 84, train 69/100, loss=0.2236 acc=0.9467
epoch 84, train 70/100, loss=0.0984 acc=0.9600
epoch 84, train 71/100, loss=0.2053 acc=0.9333
epoch 84, train 72/100, loss=0.8132 acc=0.8000
epoch 84, train 73/100, loss=0.7135 acc=0.7333
epoch 84, train 74/100, loss=0.4886 acc=0.8400
epoch 84, train 75/100, loss=0.9657 acc=0.7467
epoch 84, train 76/100, loss=1.0354 acc=0.5600
epoch 84, train 77/100, loss=0.1277 acc=0.9600
epoch 84, train 78/100, loss=0.7859 acc=0.7200
epoch 84, train 79/100, loss=0.7866 acc=0.7467
epoch 84, train 80/100, loss=0.2051 acc=0.9467
epoch 84, train 81/100, loss=0.3826 acc=0.8400
epoch 84, train 82/100, loss=0.6721 acc=0.7067
epoch 84, train 83/100, loss=0.4520 acc=0.8400
epoch 84, train 84/100, loss=0.6088 acc=0.8000
epoch 84, train 85/100, loss=0.2099 acc=0.9333
epoch 84, train 86/100, loss=0.4920 acc=0.7867
epoch 84, train 87/100, loss=1.0454 acc=0.6933
epoch 84, train 88/100, loss=0.4328 acc=0.8133
epoch 84, train 89/100, loss=0.7290 acc=0.8533
epoch 84, train 90/100, loss=0.1099 acc=0.9467
epoch 84, train 91/100, loss=1.1889 acc=0.5867
epoch 84, train 92/100, loss=0.5603 acc=0.8000
epoch 84, train 93/100, loss=0.2382 acc=0.8933
epoch 84, train 94/100, loss=0.5821 acc=0.8133
epoch 84, train 95/100, loss=0.1847 acc=0.9600
epoch 84, train 96/100, loss=0.7595 acc=0.7333
epoch 84, train 97/100, loss=0.0952 acc=0.9600
epoch 84, train 98/100, loss=0.2926 acc=0.8800
epoch 84, train 99/100, loss=0.1604 acc=0.9333
epoch 84, train 100/100, loss=0.2837 acc=0.9200
best epoch 66, best val acc=0.7275
epoch 84, val, loss=0.9190 acc=0.7132
ETA:5.4h/6.4h
epoch 85, train 1/100, loss=0.4546 acc=0.8800
epoch 85, train 2/100, loss=0.8502 acc=0.6533
epoch 85, train 3/100, loss=0.4332 acc=0.8667
epoch 85, train 4/100, loss=0.9094 acc=0.7333
epoch 85, train 5/100, loss=0.1624 acc=0.9333
epoch 85, train 6/100, loss=0.6933 acc=0.7600
epoch 85, train 7/100, loss=0.8564 acc=0.6800
epoch 85, train 8/100, loss=0.2837 acc=0.9067
epoch 85, train 9/100, loss=0.3021 acc=0.9200
epoch 85, train 10/100, loss=0.6057 acc=0.7467
epoch 85, train 11/100, loss=0.4400 acc=0.8667
epoch 85, train 12/100, loss=1.3523 acc=0.6000
epoch 85, train 13/100, loss=0.3693 acc=0.8800
epoch 85, train 14/100, loss=0.4539 acc=0.8667
epoch 85, train 15/100, loss=1.2841 acc=0.4933
epoch 85, train 16/100, loss=0.6754 acc=0.8000
epoch 85, train 17/100, loss=0.3309 acc=0.8933
epoch 85, train 18/100, loss=1.0748 acc=0.6267
epoch 85, train 19/100, loss=0.6894 acc=0.7733
epoch 85, train 20/100, loss=0.5667 acc=0.8133
epoch 85, train 21/100, loss=0.8894 acc=0.7200
epoch 85, train 22/100, loss=0.2720 acc=0.8800
epoch 85, train 23/100, loss=0.4789 acc=0.8667
epoch 85, train 24/100, loss=0.9469 acc=0.6800
epoch 85, train 25/100, loss=0.4953 acc=0.8533
epoch 85, train 26/100, loss=0.2886 acc=0.9200
epoch 85, train 27/100, loss=0.5358 acc=0.8533
epoch 85, train 28/100, loss=0.3121 acc=0.8667
epoch 85, train 29/100, loss=1.2940 acc=0.6800
epoch 85, train 30/100, loss=0.4752 acc=0.8800
epoch 85, train 31/100, loss=0.5685 acc=0.8267
epoch 85, train 32/100, loss=0.1780 acc=0.9200
epoch 85, train 33/100, loss=0.2088 acc=0.9067
epoch 85, train 34/100, loss=0.0819 acc=0.9733
epoch 85, train 35/100, loss=0.3958 acc=0.8533
epoch 85, train 36/100, loss=0.6418 acc=0.8133
epoch 85, train 37/100, loss=0.3056 acc=0.8933
epoch 85, train 38/100, loss=0.4802 acc=0.8533
epoch 85, train 39/100, loss=0.1800 acc=0.9333
epoch 85, train 40/100, loss=0.8617 acc=0.6933
epoch 85, train 41/100, loss=0.3349 acc=0.9200
epoch 85, train 42/100, loss=0.2883 acc=0.9067
epoch 85, train 43/100, loss=0.5583 acc=0.8267
epoch 85, train 44/100, loss=0.3920 acc=0.8400
epoch 85, train 45/100, loss=0.3774 acc=0.8800
epoch 85, train 46/100, loss=0.7832 acc=0.7600
epoch 85, train 47/100, loss=0.1469 acc=0.9600
epoch 85, train 48/100, loss=0.5042 acc=0.8667
epoch 85, train 49/100, loss=1.2509 acc=0.6000
epoch 85, train 50/100, loss=0.0573 acc=0.9600
epoch 85, train 51/100, loss=0.5397 acc=0.8000
epoch 85, train 52/100, loss=0.7798 acc=0.7467
epoch 85, train 53/100, loss=0.3692 acc=0.8533
epoch 85, train 54/100, loss=1.1430 acc=0.6400
epoch 85, train 55/100, loss=1.2512 acc=0.5600
epoch 85, train 56/100, loss=0.3102 acc=0.8933
epoch 85, train 57/100, loss=0.7524 acc=0.7600
epoch 85, train 58/100, loss=0.1236 acc=0.9600
epoch 85, train 59/100, loss=0.2105 acc=0.9333
epoch 85, train 60/100, loss=0.2883 acc=0.8933
epoch 85, train 61/100, loss=0.5918 acc=0.7733
epoch 85, train 62/100, loss=0.7254 acc=0.8133
epoch 85, train 63/100, loss=0.1546 acc=0.9600
epoch 85, train 64/100, loss=0.1274 acc=0.9467
epoch 85, train 65/100, loss=0.6147 acc=0.7467
epoch 85, train 66/100, loss=0.1831 acc=0.9200
epoch 85, train 67/100, loss=0.3120 acc=0.8667
epoch 85, train 68/100, loss=0.1652 acc=0.9333
epoch 85, train 69/100, loss=0.3130 acc=0.8933
epoch 85, train 70/100, loss=0.5167 acc=0.8400
epoch 85, train 71/100, loss=0.4571 acc=0.8667
epoch 85, train 72/100, loss=0.4562 acc=0.8800
epoch 85, train 73/100, loss=0.7906 acc=0.6400
epoch 85, train 74/100, loss=0.6426 acc=0.7067
epoch 85, train 75/100, loss=0.3020 acc=0.9067
epoch 85, train 76/100, loss=0.3809 acc=0.8400
epoch 85, train 77/100, loss=0.5761 acc=0.8533
epoch 85, train 78/100, loss=0.3495 acc=0.8933
epoch 85, train 79/100, loss=0.7204 acc=0.7067
epoch 85, train 80/100, loss=0.6451 acc=0.7733
epoch 85, train 81/100, loss=0.2513 acc=0.9200
epoch 85, train 82/100, loss=0.3639 acc=0.8933
epoch 85, train 83/100, loss=0.6115 acc=0.7867
epoch 85, train 84/100, loss=0.2331 acc=0.8800
epoch 85, train 85/100, loss=0.6037 acc=0.8000
epoch 85, train 86/100, loss=0.0943 acc=0.9733
epoch 85, train 87/100, loss=0.7929 acc=0.7733
epoch 85, train 88/100, loss=1.0424 acc=0.7200
epoch 85, train 89/100, loss=0.6184 acc=0.7733
epoch 85, train 90/100, loss=0.5408 acc=0.8400
epoch 85, train 91/100, loss=0.1829 acc=0.9467
epoch 85, train 92/100, loss=0.4876 acc=0.8667
epoch 85, train 93/100, loss=0.4536 acc=0.8400
epoch 85, train 94/100, loss=0.6290 acc=0.7600
epoch 85, train 95/100, loss=0.3800 acc=0.8267
epoch 85, train 96/100, loss=1.1021 acc=0.6933
epoch 85, train 97/100, loss=0.5665 acc=0.8267
epoch 85, train 98/100, loss=0.5785 acc=0.8533
epoch 85, train 99/100, loss=0.5198 acc=0.8400
epoch 85, train 100/100, loss=0.5595 acc=0.7867
best epoch 66, best val acc=0.7275
epoch 85, val, loss=0.9320 acc=0.7127
ETA:5.4h/6.4h
epoch 86, train 1/100, loss=0.1540 acc=0.9467
epoch 86, train 2/100, loss=0.2291 acc=0.9200
epoch 86, train 3/100, loss=0.0889 acc=0.9600
epoch 86, train 4/100, loss=0.2045 acc=0.9067
epoch 86, train 5/100, loss=0.1930 acc=0.9200
epoch 86, train 6/100, loss=0.4046 acc=0.9067
epoch 86, train 7/100, loss=1.1410 acc=0.6667
epoch 86, train 8/100, loss=1.0592 acc=0.5467
epoch 86, train 9/100, loss=0.3201 acc=0.8933
epoch 86, train 10/100, loss=0.6183 acc=0.8133
epoch 86, train 11/100, loss=0.2819 acc=0.9067
epoch 86, train 12/100, loss=0.1887 acc=0.9600
epoch 86, train 13/100, loss=0.1633 acc=0.9467
epoch 86, train 14/100, loss=0.3617 acc=0.8667
epoch 86, train 15/100, loss=0.3269 acc=0.8933
epoch 86, train 16/100, loss=1.0983 acc=0.6133
epoch 86, train 17/100, loss=0.6160 acc=0.6933
epoch 86, train 18/100, loss=0.4846 acc=0.8133
epoch 86, train 19/100, loss=0.3855 acc=0.8800
epoch 86, train 20/100, loss=0.8984 acc=0.7333
epoch 86, train 21/100, loss=0.6057 acc=0.8133
epoch 86, train 22/100, loss=0.3672 acc=0.8933
epoch 86, train 23/100, loss=0.8255 acc=0.6933
epoch 86, train 24/100, loss=0.5811 acc=0.7867
epoch 86, train 25/100, loss=0.3395 acc=0.8800
epoch 86, train 26/100, loss=0.6549 acc=0.8000
epoch 86, train 27/100, loss=0.8443 acc=0.6933
epoch 86, train 28/100, loss=0.8096 acc=0.6933
epoch 86, train 29/100, loss=0.9751 acc=0.6933
epoch 86, train 30/100, loss=0.1479 acc=0.9600
epoch 86, train 31/100, loss=0.7660 acc=0.7733
epoch 86, train 32/100, loss=0.1918 acc=0.9600
epoch 86, train 33/100, loss=0.5265 acc=0.8400
epoch 86, train 34/100, loss=0.9478 acc=0.7067
epoch 86, train 35/100, loss=0.7125 acc=0.7733
epoch 86, train 36/100, loss=0.5783 acc=0.8267
epoch 86, train 37/100, loss=0.1870 acc=0.9200
epoch 86, train 38/100, loss=1.0150 acc=0.6933
epoch 86, train 39/100, loss=0.4287 acc=0.8400
epoch 86, train 40/100, loss=1.3212 acc=0.6533
epoch 86, train 41/100, loss=0.6843 acc=0.7333
epoch 86, train 42/100, loss=0.4946 acc=0.8133
epoch 86, train 43/100, loss=0.1126 acc=0.9600
epoch 86, train 44/100, loss=0.1854 acc=0.9200
epoch 86, train 45/100, loss=0.7250 acc=0.7333
epoch 86, train 46/100, loss=0.3361 acc=0.8667
epoch 86, train 47/100, loss=0.4457 acc=0.8400
epoch 86, train 48/100, loss=0.2787 acc=0.9333
epoch 86, train 49/100, loss=0.8399 acc=0.7867
epoch 86, train 50/100, loss=0.8301 acc=0.7600
epoch 86, train 51/100, loss=0.1004 acc=0.9467
epoch 86, train 52/100, loss=0.1875 acc=0.9200
epoch 86, train 53/100, loss=0.4440 acc=0.8667
epoch 86, train 54/100, loss=0.2578 acc=0.9200
epoch 86, train 55/100, loss=0.2188 acc=0.9200
epoch 86, train 56/100, loss=0.4350 acc=0.8667
epoch 86, train 57/100, loss=1.1029 acc=0.6667
epoch 86, train 58/100, loss=0.3972 acc=0.8933
epoch 86, train 59/100, loss=0.2938 acc=0.9067
epoch 86, train 60/100, loss=0.3026 acc=0.8933
epoch 86, train 61/100, loss=0.9657 acc=0.7333
epoch 86, train 62/100, loss=0.3922 acc=0.8800
epoch 86, train 63/100, loss=1.3862 acc=0.6133
epoch 86, train 64/100, loss=1.4455 acc=0.5733
epoch 86, train 65/100, loss=0.8468 acc=0.7467
epoch 86, train 66/100, loss=0.1491 acc=0.9600
epoch 86, train 67/100, loss=0.6958 acc=0.7467
epoch 86, train 68/100, loss=1.0677 acc=0.6667
epoch 86, train 69/100, loss=0.7300 acc=0.7733
epoch 86, train 70/100, loss=0.1369 acc=0.9600
epoch 86, train 71/100, loss=0.2943 acc=0.8933
epoch 86, train 72/100, loss=0.7694 acc=0.7467
epoch 86, train 73/100, loss=0.3681 acc=0.8933
epoch 86, train 74/100, loss=0.2435 acc=0.8933
epoch 86, train 75/100, loss=0.6952 acc=0.7867
epoch 86, train 76/100, loss=0.8029 acc=0.6667
epoch 86, train 77/100, loss=0.4831 acc=0.8267
epoch 86, train 78/100, loss=0.3157 acc=0.9067
epoch 86, train 79/100, loss=1.0850 acc=0.6400
epoch 86, train 80/100, loss=0.5306 acc=0.8000
epoch 86, train 81/100, loss=0.4019 acc=0.8667
epoch 86, train 82/100, loss=0.6119 acc=0.8267
epoch 86, train 83/100, loss=0.6797 acc=0.7733
epoch 86, train 84/100, loss=1.3005 acc=0.6000
epoch 86, train 85/100, loss=0.1535 acc=0.9333
epoch 86, train 86/100, loss=0.3007 acc=0.8800
epoch 86, train 87/100, loss=0.3191 acc=0.8800
epoch 86, train 88/100, loss=0.3207 acc=0.9067
epoch 86, train 89/100, loss=0.7512 acc=0.7733
epoch 86, train 90/100, loss=0.3154 acc=0.8533
epoch 86, train 91/100, loss=0.3098 acc=0.8800
epoch 86, train 92/100, loss=0.0974 acc=0.9600
epoch 86, train 93/100, loss=0.1691 acc=0.9333
epoch 86, train 94/100, loss=0.5308 acc=0.7600
epoch 86, train 95/100, loss=1.0890 acc=0.6267
epoch 86, train 96/100, loss=0.0939 acc=0.9600
epoch 86, train 97/100, loss=0.2648 acc=0.8933
epoch 86, train 98/100, loss=0.4249 acc=0.8267
epoch 86, train 99/100, loss=0.4559 acc=0.8133
epoch 86, train 100/100, loss=0.2712 acc=0.9200
best epoch 66, best val acc=0.7275
epoch 86, val, loss=0.9074 acc=0.7152
ETA:5.5h/6.4h
epoch 87, train 1/100, loss=0.4253 acc=0.8400
epoch 87, train 2/100, loss=1.0929 acc=0.7200
epoch 87, train 3/100, loss=0.4699 acc=0.8800
epoch 87, train 4/100, loss=0.4724 acc=0.8400
epoch 87, train 5/100, loss=0.5294 acc=0.8400
epoch 87, train 6/100, loss=0.8553 acc=0.7467
epoch 87, train 7/100, loss=0.6540 acc=0.7200
epoch 87, train 8/100, loss=0.7536 acc=0.7200
epoch 87, train 9/100, loss=0.7667 acc=0.7600
epoch 87, train 10/100, loss=0.6736 acc=0.7733
epoch 87, train 11/100, loss=0.4661 acc=0.8667
epoch 87, train 12/100, loss=0.6176 acc=0.8533
epoch 87, train 13/100, loss=0.5753 acc=0.8400
epoch 87, train 14/100, loss=0.9827 acc=0.6800
epoch 87, train 15/100, loss=0.2768 acc=0.9067
epoch 87, train 16/100, loss=0.1648 acc=0.9333
epoch 87, train 17/100, loss=0.2680 acc=0.8933
epoch 87, train 18/100, loss=0.4187 acc=0.8667
epoch 87, train 19/100, loss=0.9152 acc=0.7733
epoch 87, train 20/100, loss=0.2966 acc=0.8800
epoch 87, train 21/100, loss=0.2769 acc=0.8933
epoch 87, train 22/100, loss=0.4936 acc=0.8667
epoch 87, train 23/100, loss=0.3578 acc=0.8533
epoch 87, train 24/100, loss=0.7186 acc=0.8000
epoch 87, train 25/100, loss=0.3145 acc=0.9067
epoch 87, train 26/100, loss=0.6405 acc=0.8400
epoch 87, train 27/100, loss=0.1639 acc=0.9067
epoch 87, train 28/100, loss=0.3641 acc=0.8800
epoch 87, train 29/100, loss=0.9102 acc=0.7467
epoch 87, train 30/100, loss=0.1944 acc=0.9333
epoch 87, train 31/100, loss=1.2632 acc=0.5200
epoch 87, train 32/100, loss=0.2083 acc=0.9067
epoch 87, train 33/100, loss=0.3995 acc=0.8667
epoch 87, train 34/100, loss=0.3000 acc=0.8800
epoch 87, train 35/100, loss=0.9949 acc=0.6667
epoch 87, train 36/100, loss=0.5649 acc=0.8133
epoch 87, train 37/100, loss=0.5693 acc=0.8533
epoch 87, train 38/100, loss=0.3343 acc=0.8800
epoch 87, train 39/100, loss=0.4057 acc=0.8933
epoch 87, train 40/100, loss=0.6189 acc=0.8000
epoch 87, train 41/100, loss=0.1593 acc=0.9467
epoch 87, train 42/100, loss=0.1600 acc=0.9200
epoch 87, train 43/100, loss=0.2032 acc=0.9467
epoch 87, train 44/100, loss=0.6485 acc=0.7600
epoch 87, train 45/100, loss=0.7370 acc=0.7467
epoch 87, train 46/100, loss=0.8513 acc=0.6533
epoch 87, train 47/100, loss=0.3979 acc=0.8400
epoch 87, train 48/100, loss=0.2202 acc=0.9067
epoch 87, train 49/100, loss=0.5127 acc=0.7867
epoch 87, train 50/100, loss=0.4625 acc=0.8667
epoch 87, train 51/100, loss=1.2608 acc=0.6267
epoch 87, train 52/100, loss=0.4658 acc=0.8800
epoch 87, train 53/100, loss=0.7029 acc=0.7200
epoch 87, train 54/100, loss=0.2329 acc=0.9333
epoch 87, train 55/100, loss=0.2365 acc=0.9200
epoch 87, train 56/100, loss=0.6841 acc=0.7733
epoch 87, train 57/100, loss=0.4220 acc=0.8667
epoch 87, train 58/100, loss=0.8185 acc=0.7467
epoch 87, train 59/100, loss=0.3520 acc=0.8933
epoch 87, train 60/100, loss=0.3341 acc=0.8267
epoch 87, train 61/100, loss=0.3910 acc=0.8533
epoch 87, train 62/100, loss=1.3291 acc=0.6133
epoch 87, train 63/100, loss=0.5704 acc=0.8000
epoch 87, train 64/100, loss=0.2645 acc=0.8800
epoch 87, train 65/100, loss=0.4667 acc=0.8533
epoch 87, train 66/100, loss=0.8496 acc=0.7600
epoch 87, train 67/100, loss=0.4425 acc=0.8800
epoch 87, train 68/100, loss=0.6640 acc=0.7200
epoch 87, train 69/100, loss=0.3419 acc=0.9067
epoch 87, train 70/100, loss=1.0234 acc=0.5867
epoch 87, train 71/100, loss=0.0885 acc=0.9733
epoch 87, train 72/100, loss=0.3514 acc=0.8533
epoch 87, train 73/100, loss=0.4567 acc=0.8533
epoch 87, train 74/100, loss=0.6643 acc=0.7333
epoch 87, train 75/100, loss=0.8992 acc=0.7200
epoch 87, train 76/100, loss=0.2714 acc=0.9067
epoch 87, train 77/100, loss=0.1114 acc=0.9600
epoch 87, train 78/100, loss=0.2254 acc=0.9067
epoch 87, train 79/100, loss=0.6944 acc=0.8000
epoch 87, train 80/100, loss=0.4161 acc=0.8533
epoch 87, train 81/100, loss=0.6891 acc=0.8133
epoch 87, train 82/100, loss=0.0676 acc=0.9733
epoch 87, train 83/100, loss=0.3880 acc=0.8667
epoch 87, train 84/100, loss=0.7586 acc=0.7067
epoch 87, train 85/100, loss=0.2854 acc=0.9333
epoch 87, train 86/100, loss=0.0430 acc=0.9867
epoch 87, train 87/100, loss=0.8471 acc=0.7200
epoch 87, train 88/100, loss=0.0178 acc=0.9867
epoch 87, train 89/100, loss=1.3629 acc=0.5333
epoch 87, train 90/100, loss=0.8469 acc=0.6800
epoch 87, train 91/100, loss=0.2458 acc=0.9067
epoch 87, train 92/100, loss=0.5626 acc=0.7867
epoch 87, train 93/100, loss=0.6589 acc=0.8000
epoch 87, train 94/100, loss=0.9836 acc=0.7333
epoch 87, train 95/100, loss=0.1211 acc=0.9867
epoch 87, train 96/100, loss=0.3856 acc=0.8800
epoch 87, train 97/100, loss=0.6259 acc=0.8133
epoch 87, train 98/100, loss=0.5688 acc=0.8000
epoch 87, train 99/100, loss=0.1638 acc=0.9467
epoch 87, train 100/100, loss=0.3918 acc=0.8667
best epoch 66, best val acc=0.7275
epoch 87, val, loss=0.9492 acc=0.7077
ETA:5.6h/6.4h
epoch 88, train 1/100, loss=0.9374 acc=0.7067
epoch 88, train 2/100, loss=0.6264 acc=0.7733
epoch 88, train 3/100, loss=0.4881 acc=0.8267
epoch 88, train 4/100, loss=0.7502 acc=0.7467
epoch 88, train 5/100, loss=0.5072 acc=0.7600
epoch 88, train 6/100, loss=0.2605 acc=0.9200
epoch 88, train 7/100, loss=0.1810 acc=0.9333
epoch 88, train 8/100, loss=0.8927 acc=0.7467
epoch 88, train 9/100, loss=0.3925 acc=0.8400
epoch 88, train 10/100, loss=0.6068 acc=0.8000
epoch 88, train 11/100, loss=0.4577 acc=0.8933
epoch 88, train 12/100, loss=0.2747 acc=0.9200
epoch 88, train 13/100, loss=0.3617 acc=0.8533
epoch 88, train 14/100, loss=0.1166 acc=0.9333
epoch 88, train 15/100, loss=1.6719 acc=0.5600
epoch 88, train 16/100, loss=0.1969 acc=0.9467
epoch 88, train 17/100, loss=0.2321 acc=0.9067
epoch 88, train 18/100, loss=0.3352 acc=0.9200
epoch 88, train 19/100, loss=0.4950 acc=0.8133
epoch 88, train 20/100, loss=0.2665 acc=0.9467
epoch 88, train 21/100, loss=0.6734 acc=0.7733
epoch 88, train 22/100, loss=1.3955 acc=0.6133
epoch 88, train 23/100, loss=0.1640 acc=0.9600
epoch 88, train 24/100, loss=1.1573 acc=0.6667
epoch 88, train 25/100, loss=0.1653 acc=0.9733
epoch 88, train 26/100, loss=0.3144 acc=0.8667
epoch 88, train 27/100, loss=0.1361 acc=0.9067
epoch 88, train 28/100, loss=0.5442 acc=0.8000
epoch 88, train 29/100, loss=0.7332 acc=0.8000
epoch 88, train 30/100, loss=0.5395 acc=0.7333
epoch 88, train 31/100, loss=0.4077 acc=0.8933
epoch 88, train 32/100, loss=1.5020 acc=0.6533
epoch 88, train 33/100, loss=0.4293 acc=0.8667
epoch 88, train 34/100, loss=0.0196 acc=1.0000
epoch 88, train 35/100, loss=0.1425 acc=0.9200
epoch 88, train 36/100, loss=0.5182 acc=0.8000
epoch 88, train 37/100, loss=0.5189 acc=0.8400
epoch 88, train 38/100, loss=0.4318 acc=0.7733
epoch 88, train 39/100, loss=0.9988 acc=0.7067
epoch 88, train 40/100, loss=0.4366 acc=0.8800
epoch 88, train 41/100, loss=1.1116 acc=0.6400
epoch 88, train 42/100, loss=0.4025 acc=0.8400
epoch 88, train 43/100, loss=0.4028 acc=0.8800
epoch 88, train 44/100, loss=1.0622 acc=0.6267
epoch 88, train 45/100, loss=0.0636 acc=0.9867
epoch 88, train 46/100, loss=0.5601 acc=0.8133
epoch 88, train 47/100, loss=0.4955 acc=0.8400
epoch 88, train 48/100, loss=0.6326 acc=0.7333
epoch 88, train 49/100, loss=0.8930 acc=0.7867
epoch 88, train 50/100, loss=0.1863 acc=0.9333
epoch 88, train 51/100, loss=0.5529 acc=0.7867
epoch 88, train 52/100, loss=0.3980 acc=0.8533
epoch 88, train 53/100, loss=0.3099 acc=0.8933
epoch 88, train 54/100, loss=0.8210 acc=0.7600
epoch 88, train 55/100, loss=0.2233 acc=0.9200
epoch 88, train 56/100, loss=0.7283 acc=0.8533
epoch 88, train 57/100, loss=0.6893 acc=0.7867
epoch 88, train 58/100, loss=0.4351 acc=0.8400
epoch 88, train 59/100, loss=1.0941 acc=0.6533
epoch 88, train 60/100, loss=0.3204 acc=0.8667
epoch 88, train 61/100, loss=1.1261 acc=0.6000
epoch 88, train 62/100, loss=0.2384 acc=0.9067
epoch 88, train 63/100, loss=0.3740 acc=0.8667
epoch 88, train 64/100, loss=0.8942 acc=0.7200
epoch 88, train 65/100, loss=0.4382 acc=0.8400
epoch 88, train 66/100, loss=0.2118 acc=0.9467
epoch 88, train 67/100, loss=0.1665 acc=0.9200
epoch 88, train 68/100, loss=1.2362 acc=0.6400
epoch 88, train 69/100, loss=0.6626 acc=0.7600
epoch 88, train 70/100, loss=0.8279 acc=0.6933
epoch 88, train 71/100, loss=0.4504 acc=0.8267
epoch 88, train 72/100, loss=0.3139 acc=0.9067
epoch 88, train 73/100, loss=1.1808 acc=0.6133
epoch 88, train 74/100, loss=0.5839 acc=0.8267
epoch 88, train 75/100, loss=0.2533 acc=0.9067
epoch 88, train 76/100, loss=0.2169 acc=0.9200
epoch 88, train 77/100, loss=0.6613 acc=0.7733
epoch 88, train 78/100, loss=0.7167 acc=0.7067
epoch 88, train 79/100, loss=0.2286 acc=0.9067
epoch 88, train 80/100, loss=0.7196 acc=0.7733
epoch 88, train 81/100, loss=0.5987 acc=0.8000
epoch 88, train 82/100, loss=0.3662 acc=0.8667
epoch 88, train 83/100, loss=0.5974 acc=0.8133
epoch 88, train 84/100, loss=0.4609 acc=0.8267
epoch 88, train 85/100, loss=0.2453 acc=0.9200
epoch 88, train 86/100, loss=0.2994 acc=0.8667
epoch 88, train 87/100, loss=0.8729 acc=0.7600
epoch 88, train 88/100, loss=0.7141 acc=0.7600
epoch 88, train 89/100, loss=1.6429 acc=0.4800
epoch 88, train 90/100, loss=0.2803 acc=0.9067
epoch 88, train 91/100, loss=0.1694 acc=0.9467
epoch 88, train 92/100, loss=0.1044 acc=0.9600
epoch 88, train 93/100, loss=0.2983 acc=0.8933
epoch 88, train 94/100, loss=0.8126 acc=0.7200
epoch 88, train 95/100, loss=0.2577 acc=0.8800
epoch 88, train 96/100, loss=0.2764 acc=0.9067
epoch 88, train 97/100, loss=0.1706 acc=0.9333
epoch 88, train 98/100, loss=0.4421 acc=0.8800
epoch 88, train 99/100, loss=0.2634 acc=0.9200
epoch 88, train 100/100, loss=1.4462 acc=0.5333
best epoch 66, best val acc=0.7275
epoch 88, val, loss=0.9357 acc=0.7126
ETA:5.6h/6.4h
epoch 89, train 1/100, loss=0.0428 acc=1.0000
epoch 89, train 2/100, loss=0.3952 acc=0.8267
epoch 89, train 3/100, loss=0.1318 acc=0.9467
epoch 89, train 4/100, loss=0.5329 acc=0.8400
epoch 89, train 5/100, loss=0.9483 acc=0.7067
epoch 89, train 6/100, loss=0.5459 acc=0.8267
epoch 89, train 7/100, loss=0.0190 acc=0.9867
epoch 89, train 8/100, loss=0.7023 acc=0.7733
epoch 89, train 9/100, loss=0.6650 acc=0.8000
epoch 89, train 10/100, loss=0.1701 acc=0.9333
epoch 89, train 11/100, loss=0.9795 acc=0.6533
epoch 89, train 12/100, loss=0.8910 acc=0.7733
epoch 89, train 13/100, loss=0.4087 acc=0.8667
epoch 89, train 14/100, loss=1.0744 acc=0.6800
epoch 89, train 15/100, loss=0.1153 acc=0.9733
epoch 89, train 16/100, loss=0.1227 acc=0.9467
epoch 89, train 17/100, loss=0.1144 acc=0.9200
epoch 89, train 18/100, loss=0.5519 acc=0.7867
epoch 89, train 19/100, loss=0.3575 acc=0.8667
epoch 89, train 20/100, loss=0.6544 acc=0.8267
epoch 89, train 21/100, loss=0.5892 acc=0.7733
epoch 89, train 22/100, loss=0.7845 acc=0.7067
epoch 89, train 23/100, loss=0.5909 acc=0.8133
epoch 89, train 24/100, loss=0.0186 acc=1.0000
epoch 89, train 25/100, loss=0.1574 acc=0.9467
epoch 89, train 26/100, loss=0.5579 acc=0.8133
epoch 89, train 27/100, loss=0.2397 acc=0.9333
epoch 89, train 28/100, loss=0.8425 acc=0.7200
epoch 89, train 29/100, loss=1.1770 acc=0.7067
epoch 89, train 30/100, loss=0.2365 acc=0.8933
epoch 89, train 31/100, loss=0.6886 acc=0.7467
epoch 89, train 32/100, loss=0.3511 acc=0.8667
epoch 89, train 33/100, loss=0.8191 acc=0.6933
epoch 89, train 34/100, loss=0.4652 acc=0.8000
epoch 89, train 35/100, loss=0.7762 acc=0.7200
epoch 89, train 36/100, loss=0.1649 acc=0.9600
epoch 89, train 37/100, loss=0.2428 acc=0.9333
epoch 89, train 38/100, loss=0.3047 acc=0.8667
epoch 89, train 39/100, loss=0.2522 acc=0.9200
epoch 89, train 40/100, loss=0.5954 acc=0.8000
epoch 89, train 41/100, loss=0.6538 acc=0.8000
epoch 89, train 42/100, loss=0.2605 acc=0.8933
epoch 89, train 43/100, loss=0.8060 acc=0.6933
epoch 89, train 44/100, loss=1.1951 acc=0.6400
epoch 89, train 45/100, loss=0.6886 acc=0.7733
epoch 89, train 46/100, loss=0.1889 acc=0.9467
epoch 89, train 47/100, loss=0.4148 acc=0.9067
epoch 89, train 48/100, loss=0.5854 acc=0.7867
epoch 89, train 49/100, loss=0.4138 acc=0.8667
epoch 89, train 50/100, loss=0.4929 acc=0.8667
epoch 89, train 51/100, loss=0.7897 acc=0.7467
epoch 89, train 52/100, loss=1.2548 acc=0.6400
epoch 89, train 53/100, loss=0.2443 acc=0.9467
epoch 89, train 54/100, loss=0.0113 acc=1.0000
epoch 89, train 55/100, loss=0.1607 acc=0.9600
epoch 89, train 56/100, loss=0.6898 acc=0.7333
epoch 89, train 57/100, loss=0.5750 acc=0.8133
epoch 89, train 58/100, loss=0.4822 acc=0.8000
epoch 89, train 59/100, loss=0.2345 acc=0.8800
epoch 89, train 60/100, loss=1.2705 acc=0.6000
epoch 89, train 61/100, loss=0.1870 acc=0.9467
epoch 89, train 62/100, loss=1.6390 acc=0.7067
epoch 89, train 63/100, loss=0.5385 acc=0.8133
epoch 89, train 64/100, loss=0.8616 acc=0.7200
epoch 89, train 65/100, loss=1.0110 acc=0.6533
epoch 89, train 66/100, loss=0.1772 acc=0.9333
epoch 89, train 67/100, loss=0.4956 acc=0.8933
epoch 89, train 68/100, loss=0.3130 acc=0.9067
epoch 89, train 69/100, loss=0.5145 acc=0.8267
epoch 89, train 70/100, loss=0.6077 acc=0.7600
epoch 89, train 71/100, loss=1.1331 acc=0.6667
epoch 89, train 72/100, loss=1.2157 acc=0.6667
epoch 89, train 73/100, loss=0.2122 acc=0.8800
epoch 89, train 74/100, loss=0.3303 acc=0.9067
epoch 89, train 75/100, loss=0.5395 acc=0.7867
epoch 89, train 76/100, loss=0.6839 acc=0.8000
epoch 89, train 77/100, loss=0.1396 acc=0.9467
epoch 89, train 78/100, loss=0.7152 acc=0.7867
epoch 89, train 79/100, loss=1.0201 acc=0.6667
epoch 89, train 80/100, loss=1.6887 acc=0.5467
epoch 89, train 81/100, loss=0.5389 acc=0.8533
epoch 89, train 82/100, loss=0.3616 acc=0.8933
epoch 89, train 83/100, loss=0.6139 acc=0.7867
epoch 89, train 84/100, loss=0.1569 acc=0.9467
epoch 89, train 85/100, loss=0.3083 acc=0.9067
epoch 89, train 86/100, loss=0.5611 acc=0.8133
epoch 89, train 87/100, loss=0.0336 acc=0.9867
epoch 89, train 88/100, loss=0.4084 acc=0.8533
epoch 89, train 89/100, loss=1.0976 acc=0.7200
epoch 89, train 90/100, loss=0.3819 acc=0.8800
epoch 89, train 91/100, loss=0.9227 acc=0.7333
epoch 89, train 92/100, loss=0.7998 acc=0.6667
epoch 89, train 93/100, loss=0.5031 acc=0.8267
epoch 89, train 94/100, loss=0.4123 acc=0.8667
epoch 89, train 95/100, loss=0.3670 acc=0.8933
epoch 89, train 96/100, loss=0.9634 acc=0.6933
epoch 89, train 97/100, loss=0.0923 acc=0.9733
epoch 89, train 98/100, loss=0.8146 acc=0.6933
epoch 89, train 99/100, loss=0.0912 acc=0.9867
epoch 89, train 100/100, loss=0.1054 acc=0.9600
best epoch 66, best val acc=0.7275
epoch 89, val, loss=0.9278 acc=0.7125
ETA:5.7h/6.4h
epoch 90, train 1/100, loss=0.6482 acc=0.7600
epoch 90, train 2/100, loss=0.3683 acc=0.8667
epoch 90, train 3/100, loss=0.8797 acc=0.7333
epoch 90, train 4/100, loss=0.2697 acc=0.8800
epoch 90, train 5/100, loss=0.3100 acc=0.8667
epoch 90, train 6/100, loss=0.2242 acc=0.9467
epoch 90, train 7/100, loss=0.5857 acc=0.8533
epoch 90, train 8/100, loss=1.0380 acc=0.6933
epoch 90, train 9/100, loss=0.1784 acc=0.9067
epoch 90, train 10/100, loss=0.3057 acc=0.8800
epoch 90, train 11/100, loss=0.7502 acc=0.7733
epoch 90, train 12/100, loss=0.2147 acc=0.9467
epoch 90, train 13/100, loss=0.8282 acc=0.7067
epoch 90, train 14/100, loss=0.2790 acc=0.8800
epoch 90, train 15/100, loss=0.2680 acc=0.9200
epoch 90, train 16/100, loss=0.5068 acc=0.8800
epoch 90, train 17/100, loss=0.9586 acc=0.6933
epoch 90, train 18/100, loss=0.9540 acc=0.6400
epoch 90, train 19/100, loss=0.8403 acc=0.7467
epoch 90, train 20/100, loss=0.2589 acc=0.9333
epoch 90, train 21/100, loss=0.3530 acc=0.8800
epoch 90, train 22/100, loss=0.2051 acc=0.9333
epoch 90, train 23/100, loss=0.5527 acc=0.8133
epoch 90, train 24/100, loss=0.4576 acc=0.8533
epoch 90, train 25/100, loss=0.4254 acc=0.8533
epoch 90, train 26/100, loss=0.1990 acc=0.9067
epoch 90, train 27/100, loss=0.5017 acc=0.8000
epoch 90, train 28/100, loss=0.7584 acc=0.7067
epoch 90, train 29/100, loss=0.3274 acc=0.8800
epoch 90, train 30/100, loss=0.7241 acc=0.8000
epoch 90, train 31/100, loss=0.8949 acc=0.7467
epoch 90, train 32/100, loss=0.4160 acc=0.8533
epoch 90, train 33/100, loss=0.4422 acc=0.8000
epoch 90, train 34/100, loss=0.7900 acc=0.7067
epoch 90, train 35/100, loss=1.0012 acc=0.6800
epoch 90, train 36/100, loss=0.3298 acc=0.8800
epoch 90, train 37/100, loss=0.6498 acc=0.8000
epoch 90, train 38/100, loss=1.5859 acc=0.5600
epoch 90, train 39/100, loss=0.1971 acc=0.9067
epoch 90, train 40/100, loss=0.7413 acc=0.8133
epoch 90, train 41/100, loss=0.3140 acc=0.8933
epoch 90, train 42/100, loss=0.3710 acc=0.9067
epoch 90, train 43/100, loss=0.2764 acc=0.9067
epoch 90, train 44/100, loss=0.2895 acc=0.8933
epoch 90, train 45/100, loss=0.6982 acc=0.7600
epoch 90, train 46/100, loss=0.3710 acc=0.8667
epoch 90, train 47/100, loss=0.2135 acc=0.9467
epoch 90, train 48/100, loss=0.4039 acc=0.8400
epoch 90, train 49/100, loss=0.8258 acc=0.6800
epoch 90, train 50/100, loss=0.3573 acc=0.8800
epoch 90, train 51/100, loss=0.5746 acc=0.8133
epoch 90, train 52/100, loss=0.4670 acc=0.8400
epoch 90, train 53/100, loss=0.0740 acc=0.9867
epoch 90, train 54/100, loss=0.9534 acc=0.6267
epoch 90, train 55/100, loss=0.6359 acc=0.8400
epoch 90, train 56/100, loss=0.4889 acc=0.8000
epoch 90, train 57/100, loss=0.2237 acc=0.9067
epoch 90, train 58/100, loss=0.8126 acc=0.7200
epoch 90, train 59/100, loss=0.6822 acc=0.8133
epoch 90, train 60/100, loss=0.3798 acc=0.8800
epoch 90, train 61/100, loss=0.4481 acc=0.8267
epoch 90, train 62/100, loss=0.2508 acc=0.8933
epoch 90, train 63/100, loss=1.6394 acc=0.5867
epoch 90, train 64/100, loss=0.1523 acc=0.9467
epoch 90, train 65/100, loss=0.5149 acc=0.8267
epoch 90, train 66/100, loss=0.2994 acc=0.9200
epoch 90, train 67/100, loss=0.7942 acc=0.7200
epoch 90, train 68/100, loss=0.3940 acc=0.8667
epoch 90, train 69/100, loss=0.2042 acc=0.9333
epoch 90, train 70/100, loss=0.1799 acc=0.9200
epoch 90, train 71/100, loss=0.8884 acc=0.7333
epoch 90, train 72/100, loss=0.5879 acc=0.8133
epoch 90, train 73/100, loss=0.3065 acc=0.8533
epoch 90, train 74/100, loss=0.2096 acc=0.9200
epoch 90, train 75/100, loss=0.4143 acc=0.8533
epoch 90, train 76/100, loss=0.5828 acc=0.7600
epoch 90, train 77/100, loss=0.1339 acc=0.9600
epoch 90, train 78/100, loss=0.4243 acc=0.8400
epoch 90, train 79/100, loss=1.2789 acc=0.6933
epoch 90, train 80/100, loss=0.2212 acc=0.9333
epoch 90, train 81/100, loss=0.4365 acc=0.8400
epoch 90, train 82/100, loss=0.2699 acc=0.9467
epoch 90, train 83/100, loss=0.4915 acc=0.8400
epoch 90, train 84/100, loss=0.9595 acc=0.6667
epoch 90, train 85/100, loss=0.4743 acc=0.8533
epoch 90, train 86/100, loss=0.3407 acc=0.9333
epoch 90, train 87/100, loss=0.7000 acc=0.8000
epoch 90, train 88/100, loss=0.4030 acc=0.8933
epoch 90, train 89/100, loss=0.5395 acc=0.8400
epoch 90, train 90/100, loss=0.3154 acc=0.8800
epoch 90, train 91/100, loss=0.2958 acc=0.9200
epoch 90, train 92/100, loss=1.0187 acc=0.5867
epoch 90, train 93/100, loss=0.4162 acc=0.8267
epoch 90, train 94/100, loss=0.6857 acc=0.7333
epoch 90, train 95/100, loss=0.1887 acc=0.9467
epoch 90, train 96/100, loss=1.0914 acc=0.7067
epoch 90, train 97/100, loss=0.5054 acc=0.8400
epoch 90, train 98/100, loss=0.4975 acc=0.8133
epoch 90, train 99/100, loss=0.8313 acc=0.6933
epoch 90, train 100/100, loss=0.0771 acc=0.9600
best epoch 66, best val acc=0.7275
epoch 90, val, loss=0.9143 acc=0.7100
ETA:5.8h/6.4h
epoch 91, train 1/100, loss=0.2083 acc=0.9200
epoch 91, train 2/100, loss=0.6046 acc=0.8267
epoch 91, train 3/100, loss=0.3259 acc=0.9067
epoch 91, train 4/100, loss=0.5593 acc=0.8533
epoch 91, train 5/100, loss=1.1213 acc=0.6533
epoch 91, train 6/100, loss=0.4718 acc=0.8000
epoch 91, train 7/100, loss=0.3564 acc=0.8667
epoch 91, train 8/100, loss=0.8603 acc=0.7067
epoch 91, train 9/100, loss=0.3254 acc=0.8933
epoch 91, train 10/100, loss=0.3107 acc=0.8933
epoch 91, train 11/100, loss=0.5830 acc=0.8000
epoch 91, train 12/100, loss=0.8688 acc=0.6800
epoch 91, train 13/100, loss=0.1536 acc=0.9467
epoch 91, train 14/100, loss=0.5052 acc=0.8133
epoch 91, train 15/100, loss=0.4398 acc=0.8267
epoch 91, train 16/100, loss=0.6859 acc=0.7867
epoch 91, train 17/100, loss=0.1622 acc=0.9467
epoch 91, train 18/100, loss=0.6917 acc=0.7200
epoch 91, train 19/100, loss=0.1658 acc=0.9467
epoch 91, train 20/100, loss=0.6866 acc=0.7867
epoch 91, train 21/100, loss=0.3438 acc=0.8800
epoch 91, train 22/100, loss=0.2630 acc=0.8800
epoch 91, train 23/100, loss=0.2822 acc=0.8933
epoch 91, train 24/100, loss=0.5913 acc=0.8267
epoch 91, train 25/100, loss=0.6537 acc=0.8000
epoch 91, train 26/100, loss=0.4810 acc=0.8400
epoch 91, train 27/100, loss=0.7542 acc=0.7200
epoch 91, train 28/100, loss=0.5584 acc=0.7867
epoch 91, train 29/100, loss=0.1341 acc=0.9467
epoch 91, train 30/100, loss=0.4026 acc=0.8400
epoch 91, train 31/100, loss=0.6328 acc=0.8667
epoch 91, train 32/100, loss=1.2585 acc=0.6400
epoch 91, train 33/100, loss=0.6250 acc=0.8133
epoch 91, train 34/100, loss=0.6904 acc=0.7333
epoch 91, train 35/100, loss=0.5270 acc=0.8000
epoch 91, train 36/100, loss=0.2929 acc=0.8933
epoch 91, train 37/100, loss=0.5139 acc=0.8533
epoch 91, train 38/100, loss=0.3299 acc=0.8933
epoch 91, train 39/100, loss=0.2882 acc=0.9067
epoch 91, train 40/100, loss=0.6354 acc=0.7467
epoch 91, train 41/100, loss=0.6536 acc=0.7333
epoch 91, train 42/100, loss=0.2137 acc=0.9200
epoch 91, train 43/100, loss=0.0905 acc=0.9467
epoch 91, train 44/100, loss=0.2427 acc=0.9333
epoch 91, train 45/100, loss=0.1316 acc=0.9600
epoch 91, train 46/100, loss=0.3424 acc=0.8667
epoch 91, train 47/100, loss=0.3728 acc=0.8933
epoch 91, train 48/100, loss=0.7265 acc=0.7733
epoch 91, train 49/100, loss=0.0677 acc=0.9867
epoch 91, train 50/100, loss=0.6229 acc=0.7600
epoch 91, train 51/100, loss=0.7794 acc=0.7600
epoch 91, train 52/100, loss=0.6070 acc=0.8000
epoch 91, train 53/100, loss=1.5279 acc=0.6000
epoch 91, train 54/100, loss=0.2544 acc=0.9333
epoch 91, train 55/100, loss=1.0383 acc=0.7067
epoch 91, train 56/100, loss=0.3069 acc=0.8667
epoch 91, train 57/100, loss=0.4226 acc=0.8533
epoch 91, train 58/100, loss=0.2442 acc=0.8533
epoch 91, train 59/100, loss=0.4150 acc=0.8933
epoch 91, train 60/100, loss=0.2535 acc=0.9200
epoch 91, train 61/100, loss=0.6805 acc=0.7600
epoch 91, train 62/100, loss=0.9692 acc=0.6533
epoch 91, train 63/100, loss=0.0445 acc=0.9867
epoch 91, train 64/100, loss=0.7254 acc=0.7733
epoch 91, train 65/100, loss=0.4582 acc=0.8000
epoch 91, train 66/100, loss=0.1189 acc=0.9467
epoch 91, train 67/100, loss=1.0403 acc=0.6400
epoch 91, train 68/100, loss=0.4696 acc=0.8667
epoch 91, train 69/100, loss=1.2020 acc=0.7467
epoch 91, train 70/100, loss=0.1820 acc=0.9333
epoch 91, train 71/100, loss=0.4152 acc=0.8533
epoch 91, train 72/100, loss=0.8484 acc=0.6800
epoch 91, train 73/100, loss=0.3783 acc=0.8267
epoch 91, train 74/100, loss=1.1721 acc=0.6133
epoch 91, train 75/100, loss=0.2752 acc=0.8933
epoch 91, train 76/100, loss=0.1979 acc=0.9333
epoch 91, train 77/100, loss=0.3858 acc=0.8800
epoch 91, train 78/100, loss=0.2289 acc=0.9067
epoch 91, train 79/100, loss=0.2447 acc=0.9467
epoch 91, train 80/100, loss=0.3961 acc=0.8533
epoch 91, train 81/100, loss=0.5561 acc=0.8133
epoch 91, train 82/100, loss=0.3899 acc=0.8667
epoch 91, train 83/100, loss=0.2695 acc=0.8933
epoch 91, train 84/100, loss=0.1403 acc=0.9467
epoch 91, train 85/100, loss=1.2358 acc=0.6533
epoch 91, train 86/100, loss=0.0849 acc=0.9600
epoch 91, train 87/100, loss=0.2005 acc=0.9467
epoch 91, train 88/100, loss=0.7603 acc=0.7200
epoch 91, train 89/100, loss=0.8996 acc=0.6800
epoch 91, train 90/100, loss=0.4532 acc=0.8267
epoch 91, train 91/100, loss=0.1483 acc=0.9333
epoch 91, train 92/100, loss=0.1906 acc=0.9333
epoch 91, train 93/100, loss=0.1785 acc=0.9333
epoch 91, train 94/100, loss=1.1067 acc=0.6267
epoch 91, train 95/100, loss=0.6410 acc=0.7733
epoch 91, train 96/100, loss=0.6555 acc=0.7733
epoch 91, train 97/100, loss=0.5496 acc=0.8000
epoch 91, train 98/100, loss=0.2919 acc=0.9333
epoch 91, train 99/100, loss=0.4187 acc=0.8667
epoch 91, train 100/100, loss=1.2458 acc=0.6000
best epoch 66, best val acc=0.7275
epoch 91, val, loss=0.9230 acc=0.7150
ETA:5.8h/6.4h
epoch 92, train 1/100, loss=0.5801 acc=0.8133
epoch 92, train 2/100, loss=0.2118 acc=0.9200
epoch 92, train 3/100, loss=0.1479 acc=0.9467
epoch 92, train 4/100, loss=0.4922 acc=0.8133
epoch 92, train 5/100, loss=0.3772 acc=0.8533
epoch 92, train 6/100, loss=0.1934 acc=0.9333
epoch 92, train 7/100, loss=0.4697 acc=0.8267
epoch 92, train 8/100, loss=0.7557 acc=0.7467
epoch 92, train 9/100, loss=0.4966 acc=0.8267
epoch 92, train 10/100, loss=0.1787 acc=0.9333
epoch 92, train 11/100, loss=0.2143 acc=0.9333
epoch 92, train 12/100, loss=0.3326 acc=0.9067
epoch 92, train 13/100, loss=0.4157 acc=0.8533
epoch 92, train 14/100, loss=0.2199 acc=0.9067
epoch 92, train 15/100, loss=0.2825 acc=0.9200
epoch 92, train 16/100, loss=0.5239 acc=0.8400
epoch 92, train 17/100, loss=0.3100 acc=0.9467
epoch 92, train 18/100, loss=0.5493 acc=0.7733
epoch 92, train 19/100, loss=0.2258 acc=0.9067
epoch 92, train 20/100, loss=0.9102 acc=0.7200
epoch 92, train 21/100, loss=0.3818 acc=0.8533
epoch 92, train 22/100, loss=0.6661 acc=0.7867
epoch 92, train 23/100, loss=1.1763 acc=0.6267
epoch 92, train 24/100, loss=0.2871 acc=0.9067
epoch 92, train 25/100, loss=0.8414 acc=0.8133
epoch 92, train 26/100, loss=0.9084 acc=0.6400
epoch 92, train 27/100, loss=0.2136 acc=0.9200
epoch 92, train 28/100, loss=0.1019 acc=0.9467
epoch 92, train 29/100, loss=0.1261 acc=0.9200
epoch 92, train 30/100, loss=0.0899 acc=0.9733
epoch 92, train 31/100, loss=0.7811 acc=0.7200
epoch 92, train 32/100, loss=0.1590 acc=0.9200
epoch 92, train 33/100, loss=0.2433 acc=0.9200
epoch 92, train 34/100, loss=0.6077 acc=0.8533
epoch 92, train 35/100, loss=1.0553 acc=0.6267
epoch 92, train 36/100, loss=1.1492 acc=0.6133
epoch 92, train 37/100, loss=0.5473 acc=0.8133
epoch 92, train 38/100, loss=0.5406 acc=0.8400
epoch 92, train 39/100, loss=1.9411 acc=0.5200
epoch 92, train 40/100, loss=0.3252 acc=0.8800
epoch 92, train 41/100, loss=0.7744 acc=0.7600
epoch 92, train 42/100, loss=0.1334 acc=0.9600
epoch 92, train 43/100, loss=1.6363 acc=0.4933
epoch 92, train 44/100, loss=0.1784 acc=0.9200
epoch 92, train 45/100, loss=0.3654 acc=0.9200
epoch 92, train 46/100, loss=0.1207 acc=0.9733
epoch 92, train 47/100, loss=0.2850 acc=0.8933
epoch 92, train 48/100, loss=0.7640 acc=0.7867
epoch 92, train 49/100, loss=0.4040 acc=0.8400
epoch 92, train 50/100, loss=0.3387 acc=0.9067
epoch 92, train 51/100, loss=0.8196 acc=0.7467
epoch 92, train 52/100, loss=0.5527 acc=0.7333
epoch 92, train 53/100, loss=0.0547 acc=0.9733
epoch 92, train 54/100, loss=0.2539 acc=0.9067
epoch 92, train 55/100, loss=0.2833 acc=0.9200
epoch 92, train 56/100, loss=0.6744 acc=0.8000
epoch 92, train 57/100, loss=0.2446 acc=0.8800
epoch 92, train 58/100, loss=0.7303 acc=0.7200
epoch 92, train 59/100, loss=0.4631 acc=0.8000
epoch 92, train 60/100, loss=0.6626 acc=0.7600
epoch 92, train 61/100, loss=0.4742 acc=0.8267
epoch 92, train 62/100, loss=0.4439 acc=0.8400
epoch 92, train 63/100, loss=0.4340 acc=0.8800
epoch 92, train 64/100, loss=0.1845 acc=0.9333
epoch 92, train 65/100, loss=0.3982 acc=0.8667
epoch 92, train 66/100, loss=0.2387 acc=0.8800
epoch 92, train 67/100, loss=0.3912 acc=0.8800
epoch 92, train 68/100, loss=1.1263 acc=0.5867
epoch 92, train 69/100, loss=1.6246 acc=0.5467
epoch 92, train 70/100, loss=1.4912 acc=0.5333
epoch 92, train 71/100, loss=0.8548 acc=0.7467
epoch 92, train 72/100, loss=0.5526 acc=0.8000
epoch 92, train 73/100, loss=0.3995 acc=0.8667
epoch 92, train 74/100, loss=0.1730 acc=0.9067
epoch 92, train 75/100, loss=1.0818 acc=0.6933
epoch 92, train 76/100, loss=0.2114 acc=0.8933
epoch 92, train 77/100, loss=0.1292 acc=0.9600
epoch 92, train 78/100, loss=0.3749 acc=0.8933
epoch 92, train 79/100, loss=0.4593 acc=0.8267
epoch 92, train 80/100, loss=0.3690 acc=0.8533
epoch 92, train 81/100, loss=0.3056 acc=0.8800
epoch 92, train 82/100, loss=0.1175 acc=0.9600
epoch 92, train 83/100, loss=0.4866 acc=0.8533
epoch 92, train 84/100, loss=0.1561 acc=0.9600
epoch 92, train 85/100, loss=0.6573 acc=0.7600
epoch 92, train 86/100, loss=0.3943 acc=0.9067
epoch 92, train 87/100, loss=0.3536 acc=0.8933
epoch 92, train 88/100, loss=0.1106 acc=0.9467
epoch 92, train 89/100, loss=0.3248 acc=0.9067
epoch 92, train 90/100, loss=0.3915 acc=0.8667
epoch 92, train 91/100, loss=0.1433 acc=0.9200
epoch 92, train 92/100, loss=0.7809 acc=0.7600
epoch 92, train 93/100, loss=0.3665 acc=0.9067
epoch 92, train 94/100, loss=0.6043 acc=0.7733
epoch 92, train 95/100, loss=0.5814 acc=0.8000
epoch 92, train 96/100, loss=0.8318 acc=0.6933
epoch 92, train 97/100, loss=0.0809 acc=0.9733
epoch 92, train 98/100, loss=0.6748 acc=0.7733
epoch 92, train 99/100, loss=1.5890 acc=0.4400
epoch 92, train 100/100, loss=0.3236 acc=0.8933
best epoch 66, best val acc=0.7275
epoch 92, val, loss=0.9409 acc=0.7060
ETA:5.9h/6.4h
epoch 93, train 1/100, loss=0.1912 acc=0.9333
epoch 93, train 2/100, loss=0.7160 acc=0.7467
epoch 93, train 3/100, loss=0.9071 acc=0.6800
epoch 93, train 4/100, loss=0.7092 acc=0.7467
epoch 93, train 5/100, loss=0.4830 acc=0.8667
epoch 93, train 6/100, loss=1.0713 acc=0.6267
epoch 93, train 7/100, loss=0.6106 acc=0.7867
epoch 93, train 8/100, loss=0.4143 acc=0.8800
epoch 93, train 9/100, loss=0.8261 acc=0.7067
epoch 93, train 10/100, loss=0.6880 acc=0.7600
epoch 93, train 11/100, loss=0.4653 acc=0.8533
epoch 93, train 12/100, loss=0.4428 acc=0.8400
epoch 93, train 13/100, loss=0.7301 acc=0.8267
epoch 93, train 14/100, loss=0.3201 acc=0.8933
epoch 93, train 15/100, loss=1.3308 acc=0.5733
epoch 93, train 16/100, loss=0.6544 acc=0.7600
epoch 93, train 17/100, loss=0.5219 acc=0.8400
epoch 93, train 18/100, loss=0.5134 acc=0.8533
epoch 93, train 19/100, loss=0.2954 acc=0.9200
epoch 93, train 20/100, loss=0.1845 acc=0.9333
epoch 93, train 21/100, loss=0.2647 acc=0.9200
epoch 93, train 22/100, loss=0.4913 acc=0.8667
epoch 93, train 23/100, loss=0.7076 acc=0.7200
epoch 93, train 24/100, loss=0.1493 acc=0.9600
epoch 93, train 25/100, loss=0.2400 acc=0.9467
epoch 93, train 26/100, loss=0.3970 acc=0.8667
epoch 93, train 27/100, loss=0.2666 acc=0.8933
epoch 93, train 28/100, loss=1.2126 acc=0.6400
epoch 93, train 29/100, loss=0.9321 acc=0.6800
epoch 93, train 30/100, loss=0.5633 acc=0.8133
epoch 93, train 31/100, loss=0.3225 acc=0.8667
epoch 93, train 32/100, loss=0.2368 acc=0.9467
epoch 93, train 33/100, loss=1.2947 acc=0.6933
epoch 93, train 34/100, loss=0.8613 acc=0.7600
epoch 93, train 35/100, loss=0.2214 acc=0.9333
epoch 93, train 36/100, loss=0.6391 acc=0.7867
epoch 93, train 37/100, loss=0.1544 acc=0.9467
epoch 93, train 38/100, loss=0.7700 acc=0.7067
epoch 93, train 39/100, loss=0.7290 acc=0.8267
epoch 93, train 40/100, loss=0.4759 acc=0.8400
epoch 93, train 41/100, loss=0.5779 acc=0.8000
epoch 93, train 42/100, loss=0.4340 acc=0.8533
epoch 93, train 43/100, loss=1.0386 acc=0.6533
epoch 93, train 44/100, loss=0.5716 acc=0.8267
epoch 93, train 45/100, loss=0.8897 acc=0.7200
epoch 93, train 46/100, loss=0.5643 acc=0.8133
epoch 93, train 47/100, loss=0.8704 acc=0.7200
epoch 93, train 48/100, loss=0.7686 acc=0.7067
epoch 93, train 49/100, loss=0.4489 acc=0.8533
epoch 93, train 50/100, loss=0.5533 acc=0.8667
epoch 93, train 51/100, loss=1.5589 acc=0.4933
epoch 93, train 52/100, loss=1.0239 acc=0.6267
epoch 93, train 53/100, loss=0.1532 acc=0.9600
epoch 93, train 54/100, loss=0.7301 acc=0.7600
epoch 93, train 55/100, loss=0.4852 acc=0.7867
epoch 93, train 56/100, loss=0.4278 acc=0.8533
epoch 93, train 57/100, loss=1.2211 acc=0.5600
epoch 93, train 58/100, loss=0.1764 acc=0.9467
epoch 93, train 59/100, loss=0.3078 acc=0.8667
epoch 93, train 60/100, loss=0.5487 acc=0.7200
epoch 93, train 61/100, loss=0.6630 acc=0.6800
epoch 93, train 62/100, loss=0.5701 acc=0.7733
epoch 93, train 63/100, loss=0.3927 acc=0.8667
epoch 93, train 64/100, loss=0.5537 acc=0.7867
epoch 93, train 65/100, loss=0.1009 acc=0.9600
epoch 93, train 66/100, loss=1.8421 acc=0.5733
epoch 93, train 67/100, loss=1.0274 acc=0.6533
epoch 93, train 68/100, loss=0.1373 acc=0.9733
epoch 93, train 69/100, loss=0.4163 acc=0.8933
epoch 93, train 70/100, loss=0.9578 acc=0.7200
epoch 93, train 71/100, loss=0.2072 acc=0.8933
epoch 93, train 72/100, loss=0.5274 acc=0.8533
epoch 93, train 73/100, loss=0.2527 acc=0.8800
epoch 93, train 74/100, loss=0.6085 acc=0.8267
epoch 93, train 75/100, loss=1.8160 acc=0.5333
epoch 93, train 76/100, loss=0.3626 acc=0.8800
epoch 93, train 77/100, loss=0.3138 acc=0.9067
epoch 93, train 78/100, loss=0.6720 acc=0.7333
epoch 93, train 79/100, loss=0.4685 acc=0.8400
epoch 93, train 80/100, loss=0.3769 acc=0.8933
epoch 93, train 81/100, loss=0.3776 acc=0.8667
epoch 93, train 82/100, loss=0.4282 acc=0.8533
epoch 93, train 83/100, loss=0.2996 acc=0.9333
epoch 93, train 84/100, loss=0.3814 acc=0.8667
epoch 93, train 85/100, loss=0.4234 acc=0.8800
epoch 93, train 86/100, loss=0.2667 acc=0.8800
epoch 93, train 87/100, loss=0.6469 acc=0.7333
epoch 93, train 88/100, loss=0.8538 acc=0.7067
epoch 93, train 89/100, loss=0.2006 acc=0.9067
epoch 93, train 90/100, loss=0.4753 acc=0.8667
epoch 93, train 91/100, loss=0.7748 acc=0.7733
epoch 93, train 92/100, loss=0.4449 acc=0.8533
epoch 93, train 93/100, loss=0.5398 acc=0.8400
epoch 93, train 94/100, loss=0.2921 acc=0.8933
epoch 93, train 95/100, loss=0.1109 acc=0.9733
epoch 93, train 96/100, loss=0.1806 acc=0.9333
epoch 93, train 97/100, loss=0.8741 acc=0.7600
epoch 93, train 98/100, loss=0.8460 acc=0.7467
epoch 93, train 99/100, loss=0.9373 acc=0.7067
epoch 93, train 100/100, loss=0.2324 acc=0.9200
best epoch 66, best val acc=0.7275
epoch 93, val, loss=0.9113 acc=0.7139
ETA:6.0h/6.4h
epoch 94, train 1/100, loss=0.6061 acc=0.7600
epoch 94, train 2/100, loss=0.4984 acc=0.8400
epoch 94, train 3/100, loss=0.1773 acc=0.9733
epoch 94, train 4/100, loss=1.2356 acc=0.6267
epoch 94, train 5/100, loss=0.2875 acc=0.8667
epoch 94, train 6/100, loss=0.5732 acc=0.8267
epoch 94, train 7/100, loss=0.4772 acc=0.8267
epoch 94, train 8/100, loss=0.5279 acc=0.7733
epoch 94, train 9/100, loss=0.3342 acc=0.8933
epoch 94, train 10/100, loss=0.6928 acc=0.8267
epoch 94, train 11/100, loss=0.0483 acc=0.9867
epoch 94, train 12/100, loss=0.4145 acc=0.9200
epoch 94, train 13/100, loss=0.4427 acc=0.8667
epoch 94, train 14/100, loss=0.0744 acc=0.9600
epoch 94, train 15/100, loss=0.1692 acc=0.9333
epoch 94, train 16/100, loss=0.7009 acc=0.7733
epoch 94, train 17/100, loss=0.6226 acc=0.7867
epoch 94, train 18/100, loss=0.1340 acc=0.9733
epoch 94, train 19/100, loss=0.4559 acc=0.8533
epoch 94, train 20/100, loss=0.2028 acc=0.9067
epoch 94, train 21/100, loss=0.4378 acc=0.9067
epoch 94, train 22/100, loss=0.8711 acc=0.7467
epoch 94, train 23/100, loss=0.7351 acc=0.7733
epoch 94, train 24/100, loss=0.3247 acc=0.8933
epoch 94, train 25/100, loss=0.3341 acc=0.8933
epoch 94, train 26/100, loss=0.4055 acc=0.8400
epoch 94, train 27/100, loss=0.5323 acc=0.8000
epoch 94, train 28/100, loss=0.5862 acc=0.8133
epoch 94, train 29/100, loss=0.0718 acc=0.9600
epoch 94, train 30/100, loss=0.6283 acc=0.8000
epoch 94, train 31/100, loss=0.3450 acc=0.8667
epoch 94, train 32/100, loss=0.6928 acc=0.8000
epoch 94, train 33/100, loss=0.7980 acc=0.6933
epoch 94, train 34/100, loss=0.6967 acc=0.7600
epoch 94, train 35/100, loss=0.3616 acc=0.8800
epoch 94, train 36/100, loss=1.1553 acc=0.6133
epoch 94, train 37/100, loss=0.0537 acc=0.9867
epoch 94, train 38/100, loss=0.5507 acc=0.7867
epoch 94, train 39/100, loss=0.2400 acc=0.9333
epoch 94, train 40/100, loss=0.9466 acc=0.6667
epoch 94, train 41/100, loss=0.4907 acc=0.8267
epoch 94, train 42/100, loss=1.1400 acc=0.6133
epoch 94, train 43/100, loss=0.4599 acc=0.8400
epoch 94, train 44/100, loss=0.1768 acc=0.9333
epoch 94, train 45/100, loss=0.5660 acc=0.8133
epoch 94, train 46/100, loss=0.9035 acc=0.6533
epoch 94, train 47/100, loss=0.2318 acc=0.9200
epoch 94, train 48/100, loss=0.5546 acc=0.7733
epoch 94, train 49/100, loss=0.3015 acc=0.8800
epoch 94, train 50/100, loss=0.9604 acc=0.6667
epoch 94, train 51/100, loss=1.0141 acc=0.6800
epoch 94, train 52/100, loss=0.9726 acc=0.6800
epoch 94, train 53/100, loss=0.3599 acc=0.9067
epoch 94, train 54/100, loss=0.5156 acc=0.8133
epoch 94, train 55/100, loss=0.7782 acc=0.7467
epoch 94, train 56/100, loss=1.1660 acc=0.6000
epoch 94, train 57/100, loss=0.3606 acc=0.8533
epoch 94, train 58/100, loss=0.3402 acc=0.9067
epoch 94, train 59/100, loss=1.0561 acc=0.7067
epoch 94, train 60/100, loss=0.3754 acc=0.8667
epoch 94, train 61/100, loss=0.9850 acc=0.7600
epoch 94, train 62/100, loss=0.3158 acc=0.9067
epoch 94, train 63/100, loss=0.3871 acc=0.8267
epoch 94, train 64/100, loss=0.1691 acc=0.9467
epoch 94, train 65/100, loss=0.2143 acc=0.9333
epoch 94, train 66/100, loss=0.1070 acc=0.9733
epoch 94, train 67/100, loss=0.9450 acc=0.7733
epoch 94, train 68/100, loss=1.3143 acc=0.4933
epoch 94, train 69/100, loss=0.2140 acc=0.9200
epoch 94, train 70/100, loss=0.3607 acc=0.8800
epoch 94, train 71/100, loss=0.5194 acc=0.8400
epoch 94, train 72/100, loss=0.9419 acc=0.7200
epoch 94, train 73/100, loss=0.5168 acc=0.9067
epoch 94, train 74/100, loss=0.4882 acc=0.8933
epoch 94, train 75/100, loss=1.2279 acc=0.7200
epoch 94, train 76/100, loss=0.4110 acc=0.8667
epoch 94, train 77/100, loss=0.2348 acc=0.8933
epoch 94, train 78/100, loss=0.3389 acc=0.8933
epoch 94, train 79/100, loss=0.9178 acc=0.6933
epoch 94, train 80/100, loss=0.9673 acc=0.6267
epoch 94, train 81/100, loss=0.9682 acc=0.7067
epoch 94, train 82/100, loss=0.7972 acc=0.7467
epoch 94, train 83/100, loss=2.4876 acc=0.4133
epoch 94, train 84/100, loss=0.3565 acc=0.8667
epoch 94, train 85/100, loss=1.2526 acc=0.6667
epoch 94, train 86/100, loss=0.7321 acc=0.7467
epoch 94, train 87/100, loss=0.7863 acc=0.7200
epoch 94, train 88/100, loss=1.5334 acc=0.4933
epoch 94, train 89/100, loss=0.3901 acc=0.9067
epoch 94, train 90/100, loss=0.3875 acc=0.8800
epoch 94, train 91/100, loss=0.6626 acc=0.7867
epoch 94, train 92/100, loss=0.1014 acc=0.9600
epoch 94, train 93/100, loss=0.1749 acc=0.9467
epoch 94, train 94/100, loss=1.0572 acc=0.6533
epoch 94, train 95/100, loss=0.4979 acc=0.8000
epoch 94, train 96/100, loss=0.2929 acc=0.9200
epoch 94, train 97/100, loss=0.5178 acc=0.8267
epoch 94, train 98/100, loss=0.5237 acc=0.7733
epoch 94, train 99/100, loss=0.8524 acc=0.7600
epoch 94, train 100/100, loss=0.1447 acc=0.9467
best epoch 66, best val acc=0.7275
epoch 94, val, loss=0.9409 acc=0.7136
ETA:6.0h/6.4h
epoch 95, train 1/100, loss=1.1360 acc=0.7200
epoch 95, train 2/100, loss=1.3057 acc=0.6933
epoch 95, train 3/100, loss=0.4052 acc=0.8400
epoch 95, train 4/100, loss=0.7496 acc=0.7333
epoch 95, train 5/100, loss=0.8555 acc=0.7200
epoch 95, train 6/100, loss=0.6809 acc=0.7467
epoch 95, train 7/100, loss=1.3712 acc=0.6533
epoch 95, train 8/100, loss=0.3044 acc=0.8667
epoch 95, train 9/100, loss=0.1740 acc=0.9467
epoch 95, train 10/100, loss=0.3693 acc=0.8933
epoch 95, train 11/100, loss=0.2483 acc=0.9067
epoch 95, train 12/100, loss=0.2026 acc=0.9600
epoch 95, train 13/100, loss=0.7041 acc=0.7867
epoch 95, train 14/100, loss=0.4216 acc=0.8533
epoch 95, train 15/100, loss=0.7692 acc=0.7600
epoch 95, train 16/100, loss=0.1031 acc=0.9200
epoch 95, train 17/100, loss=0.6591 acc=0.7467
epoch 95, train 18/100, loss=0.5162 acc=0.8000
epoch 95, train 19/100, loss=0.6616 acc=0.7467
epoch 95, train 20/100, loss=0.5363 acc=0.8133
epoch 95, train 21/100, loss=0.7507 acc=0.7733
epoch 95, train 22/100, loss=1.1389 acc=0.6800
epoch 95, train 23/100, loss=0.5885 acc=0.8000
epoch 95, train 24/100, loss=1.4857 acc=0.5600
epoch 95, train 25/100, loss=0.4535 acc=0.8533
epoch 95, train 26/100, loss=0.8865 acc=0.7867
epoch 95, train 27/100, loss=0.3348 acc=0.8800
epoch 95, train 28/100, loss=0.1200 acc=0.9467
epoch 95, train 29/100, loss=0.3074 acc=0.8667
epoch 95, train 30/100, loss=0.4228 acc=0.8267
epoch 95, train 31/100, loss=0.4580 acc=0.8533
epoch 95, train 32/100, loss=0.1811 acc=0.9200
epoch 95, train 33/100, loss=0.0184 acc=1.0000
epoch 95, train 34/100, loss=0.3652 acc=0.9067
epoch 95, train 35/100, loss=0.0872 acc=0.9733
epoch 95, train 36/100, loss=0.1437 acc=0.9467
epoch 95, train 37/100, loss=0.1929 acc=0.9333
epoch 95, train 38/100, loss=0.1988 acc=0.9333
epoch 95, train 39/100, loss=0.4228 acc=0.8800
epoch 95, train 40/100, loss=0.5403 acc=0.8000
epoch 95, train 41/100, loss=0.8285 acc=0.6533
epoch 95, train 42/100, loss=0.7945 acc=0.7333
epoch 95, train 43/100, loss=0.8561 acc=0.7333
epoch 95, train 44/100, loss=0.5614 acc=0.8533
epoch 95, train 45/100, loss=0.4510 acc=0.8267
epoch 95, train 46/100, loss=1.2287 acc=0.6933
epoch 95, train 47/100, loss=0.7560 acc=0.7600
epoch 95, train 48/100, loss=0.1665 acc=0.9467
epoch 95, train 49/100, loss=2.2692 acc=0.4933
epoch 95, train 50/100, loss=0.6883 acc=0.7200
epoch 95, train 51/100, loss=0.1855 acc=0.9333
epoch 95, train 52/100, loss=0.3329 acc=0.8800
epoch 95, train 53/100, loss=0.0735 acc=0.9733
epoch 95, train 54/100, loss=0.4450 acc=0.8533
epoch 95, train 55/100, loss=0.2743 acc=0.8933
epoch 95, train 56/100, loss=0.5683 acc=0.8267
epoch 95, train 57/100, loss=1.2585 acc=0.5600
epoch 95, train 58/100, loss=0.3716 acc=0.8667
epoch 95, train 59/100, loss=0.2419 acc=0.9200
epoch 95, train 60/100, loss=0.7528 acc=0.7200
epoch 95, train 61/100, loss=0.4324 acc=0.8400
epoch 95, train 62/100, loss=0.1525 acc=0.9467
epoch 95, train 63/100, loss=0.6505 acc=0.8133
epoch 95, train 64/100, loss=0.3228 acc=0.9067
epoch 95, train 65/100, loss=0.4936 acc=0.7867
epoch 95, train 66/100, loss=0.3360 acc=0.8933
epoch 95, train 67/100, loss=0.3914 acc=0.9200
epoch 95, train 68/100, loss=0.8597 acc=0.6933
epoch 95, train 69/100, loss=0.1369 acc=0.9467
epoch 95, train 70/100, loss=0.1903 acc=0.9200
epoch 95, train 71/100, loss=0.8221 acc=0.7067
epoch 95, train 72/100, loss=0.4364 acc=0.8933
epoch 95, train 73/100, loss=1.2522 acc=0.7467
epoch 95, train 74/100, loss=0.2974 acc=0.9067
epoch 95, train 75/100, loss=0.0807 acc=0.9733
epoch 95, train 76/100, loss=1.1031 acc=0.7333
epoch 95, train 77/100, loss=1.1488 acc=0.6533
epoch 95, train 78/100, loss=0.4981 acc=0.8400
epoch 95, train 79/100, loss=0.6621 acc=0.7733
epoch 95, train 80/100, loss=0.4426 acc=0.8667
epoch 95, train 81/100, loss=1.1051 acc=0.6400
epoch 95, train 82/100, loss=0.1429 acc=0.9333
epoch 95, train 83/100, loss=0.4756 acc=0.8000
epoch 95, train 84/100, loss=0.6942 acc=0.7733
epoch 95, train 85/100, loss=0.2359 acc=0.9200
epoch 95, train 86/100, loss=0.5967 acc=0.7600
epoch 95, train 87/100, loss=0.6891 acc=0.7733
epoch 95, train 88/100, loss=0.2787 acc=0.8800
epoch 95, train 89/100, loss=0.5674 acc=0.8133
epoch 95, train 90/100, loss=0.4296 acc=0.8533
epoch 95, train 91/100, loss=0.9025 acc=0.7867
epoch 95, train 92/100, loss=0.3209 acc=0.8933
epoch 95, train 93/100, loss=0.7578 acc=0.7733
epoch 95, train 94/100, loss=0.6822 acc=0.7600
epoch 95, train 95/100, loss=0.1523 acc=0.9600
epoch 95, train 96/100, loss=0.2038 acc=0.9067
epoch 95, train 97/100, loss=0.4925 acc=0.8800
epoch 95, train 98/100, loss=0.6300 acc=0.7600
epoch 95, train 99/100, loss=0.8593 acc=0.6533
epoch 95, train 100/100, loss=0.0597 acc=0.9733
best epoch 66, best val acc=0.7275
epoch 95, val, loss=0.9163 acc=0.7126
ETA:6.1h/6.4h
epoch 96, train 1/100, loss=0.0308 acc=0.9867
epoch 96, train 2/100, loss=0.5893 acc=0.7467
epoch 96, train 3/100, loss=0.3900 acc=0.8800
epoch 96, train 4/100, loss=0.4380 acc=0.8933
epoch 96, train 5/100, loss=0.4631 acc=0.8533
epoch 96, train 6/100, loss=0.5665 acc=0.8400
epoch 96, train 7/100, loss=0.1321 acc=0.9467
epoch 96, train 8/100, loss=0.2160 acc=0.9200
epoch 96, train 9/100, loss=0.8452 acc=0.7600
epoch 96, train 10/100, loss=0.6764 acc=0.8267
epoch 96, train 11/100, loss=0.2329 acc=0.9333
epoch 96, train 12/100, loss=0.6664 acc=0.8000
epoch 96, train 13/100, loss=0.9253 acc=0.6933
epoch 96, train 14/100, loss=0.8820 acc=0.6800
epoch 96, train 15/100, loss=0.3080 acc=0.9067
epoch 96, train 16/100, loss=0.2708 acc=0.8933
epoch 96, train 17/100, loss=0.2689 acc=0.9333
epoch 96, train 18/100, loss=0.2676 acc=0.9333
epoch 96, train 19/100, loss=0.3339 acc=0.8933
epoch 96, train 20/100, loss=0.4558 acc=0.8800
epoch 96, train 21/100, loss=0.1992 acc=0.9467
epoch 96, train 22/100, loss=0.5165 acc=0.8133
epoch 96, train 23/100, loss=0.9082 acc=0.6667
epoch 96, train 24/100, loss=0.3402 acc=0.9067
epoch 96, train 25/100, loss=0.2310 acc=0.8933
epoch 96, train 26/100, loss=0.4541 acc=0.8933
epoch 96, train 27/100, loss=0.8536 acc=0.8133
epoch 96, train 28/100, loss=0.9742 acc=0.7333
epoch 96, train 29/100, loss=0.7017 acc=0.7067
epoch 96, train 30/100, loss=0.5253 acc=0.8133
epoch 96, train 31/100, loss=0.4687 acc=0.8533
epoch 96, train 32/100, loss=0.6194 acc=0.7600
epoch 96, train 33/100, loss=0.4237 acc=0.8933
epoch 96, train 34/100, loss=0.0301 acc=0.9867
epoch 96, train 35/100, loss=0.6719 acc=0.7867
epoch 96, train 36/100, loss=0.3274 acc=0.8533
epoch 96, train 37/100, loss=0.5472 acc=0.8400
epoch 96, train 38/100, loss=0.8552 acc=0.7600
epoch 96, train 39/100, loss=0.1910 acc=0.9600
epoch 96, train 40/100, loss=0.8868 acc=0.6133
epoch 96, train 41/100, loss=0.3352 acc=0.8933
epoch 96, train 42/100, loss=0.8965 acc=0.6667
epoch 96, train 43/100, loss=0.3459 acc=0.8933
epoch 96, train 44/100, loss=0.6565 acc=0.7867
epoch 96, train 45/100, loss=0.4430 acc=0.8800
epoch 96, train 46/100, loss=0.3543 acc=0.9333
epoch 96, train 47/100, loss=0.1273 acc=0.9467
epoch 96, train 48/100, loss=0.1583 acc=0.9467
epoch 96, train 49/100, loss=0.7663 acc=0.7467
epoch 96, train 50/100, loss=0.2893 acc=0.9067
epoch 96, train 51/100, loss=0.4769 acc=0.8000
epoch 96, train 52/100, loss=0.6270 acc=0.7867
epoch 96, train 53/100, loss=0.6494 acc=0.7867
epoch 96, train 54/100, loss=0.2484 acc=0.9467
epoch 96, train 55/100, loss=0.1991 acc=0.9067
epoch 96, train 56/100, loss=0.4857 acc=0.8267
epoch 96, train 57/100, loss=0.2948 acc=0.9067
epoch 96, train 58/100, loss=0.1759 acc=0.9467
epoch 96, train 59/100, loss=0.2533 acc=0.9067
epoch 96, train 60/100, loss=1.1853 acc=0.5867
epoch 96, train 61/100, loss=0.2906 acc=0.9067
epoch 96, train 62/100, loss=0.2483 acc=0.8800
epoch 96, train 63/100, loss=0.7328 acc=0.8000
epoch 96, train 64/100, loss=0.6036 acc=0.8000
epoch 96, train 65/100, loss=0.3168 acc=0.8933
epoch 96, train 66/100, loss=0.0760 acc=0.9600
epoch 96, train 67/100, loss=0.2177 acc=0.8933
epoch 96, train 68/100, loss=0.4483 acc=0.8400
epoch 96, train 69/100, loss=0.4020 acc=0.8800
epoch 96, train 70/100, loss=0.2540 acc=0.8933
epoch 96, train 71/100, loss=0.1772 acc=0.9467
epoch 96, train 72/100, loss=0.3516 acc=0.8667
epoch 96, train 73/100, loss=0.3024 acc=0.8400
epoch 96, train 74/100, loss=0.5092 acc=0.7733
epoch 96, train 75/100, loss=0.2215 acc=0.9200
epoch 96, train 76/100, loss=0.2490 acc=0.9067
epoch 96, train 77/100, loss=0.3111 acc=0.8933
epoch 96, train 78/100, loss=0.2942 acc=0.8933
epoch 96, train 79/100, loss=0.6886 acc=0.7733
epoch 96, train 80/100, loss=0.4295 acc=0.8533
epoch 96, train 81/100, loss=0.3051 acc=0.8400
epoch 96, train 82/100, loss=0.1712 acc=0.9067
epoch 96, train 83/100, loss=0.4827 acc=0.8533
epoch 96, train 84/100, loss=0.2409 acc=0.9333
epoch 96, train 85/100, loss=1.0488 acc=0.6933
epoch 96, train 86/100, loss=0.3449 acc=0.8667
epoch 96, train 87/100, loss=1.5438 acc=0.5467
epoch 96, train 88/100, loss=0.7827 acc=0.7067
epoch 96, train 89/100, loss=0.2016 acc=0.9600
epoch 96, train 90/100, loss=0.4138 acc=0.8800
epoch 96, train 91/100, loss=0.3644 acc=0.8800
epoch 96, train 92/100, loss=0.1150 acc=0.9733
epoch 96, train 93/100, loss=1.1261 acc=0.5467
epoch 96, train 94/100, loss=0.3425 acc=0.8800
epoch 96, train 95/100, loss=0.5092 acc=0.8400
epoch 96, train 96/100, loss=0.1392 acc=0.9600
epoch 96, train 97/100, loss=0.8020 acc=0.7733
epoch 96, train 98/100, loss=0.9110 acc=0.5733
epoch 96, train 99/100, loss=0.1763 acc=0.9067
epoch 96, train 100/100, loss=0.2655 acc=0.9333
best epoch 66, best val acc=0.7275
epoch 96, val, loss=0.9469 acc=0.7036
ETA:6.2h/6.4h
epoch 97, train 1/100, loss=0.2463 acc=0.9333
epoch 97, train 2/100, loss=0.6466 acc=0.8000
epoch 97, train 3/100, loss=0.6624 acc=0.7600
epoch 97, train 4/100, loss=0.2453 acc=0.8933
epoch 97, train 5/100, loss=0.3632 acc=0.9067
epoch 97, train 6/100, loss=0.4477 acc=0.8533
epoch 97, train 7/100, loss=1.3226 acc=0.5733
epoch 97, train 8/100, loss=0.6861 acc=0.7333
epoch 97, train 9/100, loss=0.1192 acc=0.9733
epoch 97, train 10/100, loss=1.4552 acc=0.6000
epoch 97, train 11/100, loss=0.2171 acc=0.9067
epoch 97, train 12/100, loss=0.4390 acc=0.8800
epoch 97, train 13/100, loss=0.2510 acc=0.9333
epoch 97, train 14/100, loss=0.3837 acc=0.8800
epoch 97, train 15/100, loss=0.3833 acc=0.8800
epoch 97, train 16/100, loss=0.9985 acc=0.6800
epoch 97, train 17/100, loss=0.2926 acc=0.8800
epoch 97, train 18/100, loss=0.6996 acc=0.8000
epoch 97, train 19/100, loss=0.9006 acc=0.7333
epoch 97, train 20/100, loss=0.5115 acc=0.8533
epoch 97, train 21/100, loss=0.8272 acc=0.7200
epoch 97, train 22/100, loss=0.2735 acc=0.9067
epoch 97, train 23/100, loss=0.2576 acc=0.8933
epoch 97, train 24/100, loss=0.2330 acc=0.8933
epoch 97, train 25/100, loss=0.1455 acc=0.9600
epoch 97, train 26/100, loss=0.7849 acc=0.7333
epoch 97, train 27/100, loss=0.3003 acc=0.8933
epoch 97, train 28/100, loss=0.2467 acc=0.9067
epoch 97, train 29/100, loss=0.9552 acc=0.6667
epoch 97, train 30/100, loss=0.7424 acc=0.7600
epoch 97, train 31/100, loss=1.7372 acc=0.5467
epoch 97, train 32/100, loss=0.6480 acc=0.7600
epoch 97, train 33/100, loss=0.3255 acc=0.8400
epoch 97, train 34/100, loss=0.6710 acc=0.7333
epoch 97, train 35/100, loss=1.6007 acc=0.5600
epoch 97, train 36/100, loss=0.8554 acc=0.7467
epoch 97, train 37/100, loss=0.6123 acc=0.7867
epoch 97, train 38/100, loss=0.6090 acc=0.8000
epoch 97, train 39/100, loss=0.2284 acc=0.9200
epoch 97, train 40/100, loss=0.1707 acc=0.9333
epoch 97, train 41/100, loss=0.5840 acc=0.8267
epoch 97, train 42/100, loss=0.5353 acc=0.7867
epoch 97, train 43/100, loss=0.5109 acc=0.8400
epoch 97, train 44/100, loss=0.7794 acc=0.7733
epoch 97, train 45/100, loss=0.4465 acc=0.8800
epoch 97, train 46/100, loss=0.5892 acc=0.8267
epoch 97, train 47/100, loss=1.1333 acc=0.7067
epoch 97, train 48/100, loss=0.9970 acc=0.7467
epoch 97, train 49/100, loss=0.5649 acc=0.7733
epoch 97, train 50/100, loss=1.2961 acc=0.5867
epoch 97, train 51/100, loss=0.8015 acc=0.8000
epoch 97, train 52/100, loss=0.5669 acc=0.8667
epoch 97, train 53/100, loss=0.9904 acc=0.7333
epoch 97, train 54/100, loss=0.7537 acc=0.7200
epoch 97, train 55/100, loss=0.7367 acc=0.6933
epoch 97, train 56/100, loss=0.8424 acc=0.6800
epoch 97, train 57/100, loss=0.3794 acc=0.8533
epoch 97, train 58/100, loss=0.6759 acc=0.7467
epoch 97, train 59/100, loss=0.4714 acc=0.8267
epoch 97, train 60/100, loss=0.8243 acc=0.7600
epoch 97, train 61/100, loss=0.6026 acc=0.7467
epoch 97, train 62/100, loss=0.2076 acc=0.9200
epoch 97, train 63/100, loss=0.3457 acc=0.9067
epoch 97, train 64/100, loss=0.5644 acc=0.7867
epoch 97, train 65/100, loss=0.3393 acc=0.8667
epoch 97, train 66/100, loss=0.1964 acc=0.9067
epoch 97, train 67/100, loss=0.4101 acc=0.8133
epoch 97, train 68/100, loss=0.3560 acc=0.8533
epoch 97, train 69/100, loss=1.1112 acc=0.7333
epoch 97, train 70/100, loss=0.5382 acc=0.7867
epoch 97, train 71/100, loss=0.2028 acc=0.9200
epoch 97, train 72/100, loss=0.6178 acc=0.8133
epoch 97, train 73/100, loss=0.3321 acc=0.8667
epoch 97, train 74/100, loss=0.2631 acc=0.9067
epoch 97, train 75/100, loss=0.0581 acc=0.9733
epoch 97, train 76/100, loss=1.2101 acc=0.6000
epoch 97, train 77/100, loss=1.3090 acc=0.5733
epoch 97, train 78/100, loss=0.3748 acc=0.8800
epoch 97, train 79/100, loss=0.3066 acc=0.8667
epoch 97, train 80/100, loss=0.2587 acc=0.9467
epoch 97, train 81/100, loss=0.2908 acc=0.8933
epoch 97, train 82/100, loss=0.5707 acc=0.8533
epoch 97, train 83/100, loss=0.1860 acc=0.9467
epoch 97, train 84/100, loss=0.4236 acc=0.8533
epoch 97, train 85/100, loss=0.3373 acc=0.8800
epoch 97, train 86/100, loss=0.2567 acc=0.8933
epoch 97, train 87/100, loss=0.7388 acc=0.7467
epoch 97, train 88/100, loss=0.5327 acc=0.8933
epoch 97, train 89/100, loss=0.4161 acc=0.8400
epoch 97, train 90/100, loss=0.5678 acc=0.7600
epoch 97, train 91/100, loss=0.5522 acc=0.8400
epoch 97, train 92/100, loss=0.8180 acc=0.7333
epoch 97, train 93/100, loss=0.1680 acc=0.9467
epoch 97, train 94/100, loss=0.4657 acc=0.8533
epoch 97, train 95/100, loss=0.7521 acc=0.7600
epoch 97, train 96/100, loss=0.1761 acc=0.9333
epoch 97, train 97/100, loss=0.3519 acc=0.8800
epoch 97, train 98/100, loss=0.8805 acc=0.6533
epoch 97, train 99/100, loss=0.6157 acc=0.7333
epoch 97, train 100/100, loss=0.7709 acc=0.7467
best epoch 66, best val acc=0.7275
epoch 97, val, loss=0.9275 acc=0.7165
ETA:6.2h/6.4h
epoch 98, train 1/100, loss=0.3918 acc=0.8800
epoch 98, train 2/100, loss=0.3217 acc=0.9067
epoch 98, train 3/100, loss=0.3125 acc=0.8800
epoch 98, train 4/100, loss=0.0651 acc=0.9733
epoch 98, train 5/100, loss=0.4075 acc=0.8667
epoch 98, train 6/100, loss=0.6861 acc=0.7600
epoch 98, train 7/100, loss=0.1510 acc=0.9067
epoch 98, train 8/100, loss=0.5736 acc=0.7867
epoch 98, train 9/100, loss=0.5257 acc=0.8267
epoch 98, train 10/100, loss=0.6326 acc=0.7867
epoch 98, train 11/100, loss=0.1551 acc=0.9600
epoch 98, train 12/100, loss=0.2119 acc=0.9333
epoch 98, train 13/100, loss=0.7090 acc=0.7333
epoch 98, train 14/100, loss=0.3582 acc=0.8667
epoch 98, train 15/100, loss=0.4848 acc=0.7867
epoch 98, train 16/100, loss=0.4053 acc=0.8667
epoch 98, train 17/100, loss=0.7373 acc=0.8000
epoch 98, train 18/100, loss=0.5110 acc=0.8400
epoch 98, train 19/100, loss=0.0945 acc=0.9600
epoch 98, train 20/100, loss=1.0995 acc=0.5867
epoch 98, train 21/100, loss=0.5812 acc=0.8133
epoch 98, train 22/100, loss=0.4202 acc=0.8800
epoch 98, train 23/100, loss=0.7101 acc=0.7600
epoch 98, train 24/100, loss=0.1574 acc=0.9067
epoch 98, train 25/100, loss=0.1243 acc=0.9467
epoch 98, train 26/100, loss=0.3973 acc=0.8400
epoch 98, train 27/100, loss=0.7153 acc=0.7333
epoch 98, train 28/100, loss=0.0640 acc=0.9733
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epoch 98, train 34/100, loss=1.2749 acc=0.6267
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epoch 98, train 36/100, loss=0.3172 acc=0.8667
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epoch 98, train 38/100, loss=0.3116 acc=0.9200
epoch 98, train 39/100, loss=0.3317 acc=0.8800
epoch 98, train 40/100, loss=0.7008 acc=0.7467
epoch 98, train 41/100, loss=1.0860 acc=0.6267
epoch 98, train 42/100, loss=0.9028 acc=0.7600
epoch 98, train 43/100, loss=0.3090 acc=0.9333
epoch 98, train 44/100, loss=0.4691 acc=0.8533
epoch 98, train 45/100, loss=1.3731 acc=0.5467
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epoch 98, train 48/100, loss=0.3576 acc=0.8533
epoch 98, train 49/100, loss=0.0170 acc=1.0000
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epoch 98, train 51/100, loss=0.1295 acc=0.9600
epoch 98, train 52/100, loss=0.7709 acc=0.6933
epoch 98, train 53/100, loss=0.3217 acc=0.9067
epoch 98, train 54/100, loss=1.0550 acc=0.7200
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epoch 98, train 57/100, loss=1.0341 acc=0.6800
epoch 98, train 58/100, loss=0.4197 acc=0.8133
epoch 98, train 59/100, loss=0.6400 acc=0.7733
epoch 98, train 60/100, loss=0.6338 acc=0.7600
epoch 98, train 61/100, loss=0.2675 acc=0.8933
epoch 98, train 62/100, loss=0.2008 acc=0.9067
epoch 98, train 63/100, loss=0.6473 acc=0.7733
epoch 98, train 64/100, loss=0.3935 acc=0.8800
epoch 98, train 65/100, loss=0.6287 acc=0.7600
epoch 98, train 66/100, loss=0.4660 acc=0.8000
epoch 98, train 67/100, loss=0.3729 acc=0.8400
epoch 98, train 68/100, loss=0.9286 acc=0.6800
epoch 98, train 69/100, loss=0.3525 acc=0.8933
epoch 98, train 70/100, loss=0.4737 acc=0.8133
epoch 98, train 71/100, loss=0.2677 acc=0.8800
epoch 98, train 72/100, loss=0.2695 acc=0.9067
epoch 98, train 73/100, loss=0.7515 acc=0.6667
epoch 98, train 74/100, loss=0.6821 acc=0.8133
epoch 98, train 75/100, loss=0.4876 acc=0.8267
epoch 98, train 76/100, loss=0.7894 acc=0.7467
epoch 98, train 77/100, loss=0.6474 acc=0.7200
epoch 98, train 78/100, loss=0.3676 acc=0.8800
epoch 98, train 79/100, loss=0.7847 acc=0.8000
epoch 98, train 80/100, loss=0.6946 acc=0.6933
epoch 98, train 81/100, loss=0.4633 acc=0.8533
epoch 98, train 82/100, loss=1.0606 acc=0.6667
epoch 98, train 83/100, loss=0.3244 acc=0.8933
epoch 98, train 84/100, loss=0.4898 acc=0.7333
epoch 98, train 85/100, loss=0.2172 acc=0.8933
epoch 98, train 86/100, loss=0.6929 acc=0.7333
epoch 98, train 87/100, loss=0.6017 acc=0.7600
epoch 98, train 88/100, loss=0.3693 acc=0.9067
epoch 98, train 89/100, loss=0.6233 acc=0.8400
epoch 98, train 90/100, loss=0.2988 acc=0.8800
epoch 98, train 91/100, loss=0.3320 acc=0.9067
epoch 98, train 92/100, loss=0.2423 acc=0.9600
epoch 98, train 93/100, loss=0.3427 acc=0.9200
epoch 98, train 94/100, loss=0.1937 acc=0.9467
epoch 98, train 95/100, loss=0.2631 acc=0.8667
epoch 98, train 96/100, loss=0.6910 acc=0.7867
epoch 98, train 97/100, loss=0.3394 acc=0.8800
epoch 98, train 98/100, loss=0.1161 acc=0.9733
epoch 98, train 99/100, loss=0.4786 acc=0.8533
epoch 98, train 100/100, loss=0.1599 acc=0.9333
best epoch 66, best val acc=0.7275
epoch 98, val, loss=0.9206 acc=0.7156
ETA:6.3h/6.4h
epoch 99, train 1/100, loss=0.3761 acc=0.8533
epoch 99, train 2/100, loss=1.3615 acc=0.5867
epoch 99, train 3/100, loss=1.2918 acc=0.6133
epoch 99, train 4/100, loss=0.9613 acc=0.6800
epoch 99, train 5/100, loss=0.5283 acc=0.8533
epoch 99, train 6/100, loss=0.5588 acc=0.7733
epoch 99, train 7/100, loss=0.9640 acc=0.6800
epoch 99, train 8/100, loss=0.3373 acc=0.8933
epoch 99, train 9/100, loss=0.8279 acc=0.8133
epoch 99, train 10/100, loss=0.4216 acc=0.8133
epoch 99, train 11/100, loss=0.2800 acc=0.9067
epoch 99, train 12/100, loss=0.3037 acc=0.9200
epoch 99, train 13/100, loss=0.5812 acc=0.7867
epoch 99, train 14/100, loss=0.7845 acc=0.7467
epoch 99, train 15/100, loss=0.8404 acc=0.6933
epoch 99, train 16/100, loss=0.0961 acc=0.9600
epoch 99, train 17/100, loss=0.3037 acc=0.9067
epoch 99, train 18/100, loss=0.3170 acc=0.8667
epoch 99, train 19/100, loss=0.3421 acc=0.9067
epoch 99, train 20/100, loss=0.5447 acc=0.7600
epoch 99, train 21/100, loss=0.6514 acc=0.7733
epoch 99, train 22/100, loss=0.2828 acc=0.8933
epoch 99, train 23/100, loss=0.5609 acc=0.7600
epoch 99, train 24/100, loss=0.5766 acc=0.8400
epoch 99, train 25/100, loss=0.2365 acc=0.9200
epoch 99, train 26/100, loss=0.3697 acc=0.8533
epoch 99, train 27/100, loss=0.4171 acc=0.8400
epoch 99, train 28/100, loss=0.5111 acc=0.8267
epoch 99, train 29/100, loss=0.4302 acc=0.8800
epoch 99, train 30/100, loss=0.5538 acc=0.7733
epoch 99, train 31/100, loss=0.5461 acc=0.8000
epoch 99, train 32/100, loss=0.4043 acc=0.8800
epoch 99, train 33/100, loss=0.7506 acc=0.6933
epoch 99, train 34/100, loss=0.7571 acc=0.7600
epoch 99, train 35/100, loss=0.3674 acc=0.9067
epoch 99, train 36/100, loss=0.3239 acc=0.9067
epoch 99, train 37/100, loss=0.4092 acc=0.8400
epoch 99, train 38/100, loss=0.8340 acc=0.6933
epoch 99, train 39/100, loss=0.8586 acc=0.7333
epoch 99, train 40/100, loss=0.2042 acc=0.9200
epoch 99, train 41/100, loss=0.2429 acc=0.9200
epoch 99, train 42/100, loss=0.8974 acc=0.7067
epoch 99, train 43/100, loss=0.1767 acc=0.9333
epoch 99, train 44/100, loss=0.5298 acc=0.8000
epoch 99, train 45/100, loss=0.5227 acc=0.7867
epoch 99, train 46/100, loss=0.2558 acc=0.9200
epoch 99, train 47/100, loss=0.8829 acc=0.6667
epoch 99, train 48/100, loss=0.5564 acc=0.7867
epoch 99, train 49/100, loss=0.3923 acc=0.8533
epoch 99, train 50/100, loss=0.5824 acc=0.8400
epoch 99, train 51/100, loss=0.6353 acc=0.6933
epoch 99, train 52/100, loss=0.1485 acc=0.9333
epoch 99, train 53/100, loss=0.3553 acc=0.8667
epoch 99, train 54/100, loss=0.5338 acc=0.8000
epoch 99, train 55/100, loss=0.1620 acc=0.9467
epoch 99, train 56/100, loss=0.6058 acc=0.7467
epoch 99, train 57/100, loss=0.5513 acc=0.8400
epoch 99, train 58/100, loss=0.4996 acc=0.8133
epoch 99, train 59/100, loss=0.6767 acc=0.7200
epoch 99, train 60/100, loss=0.8305 acc=0.8133
epoch 99, train 61/100, loss=0.3699 acc=0.8800
epoch 99, train 62/100, loss=0.4307 acc=0.8533
epoch 99, train 63/100, loss=0.7736 acc=0.7067
epoch 99, train 64/100, loss=1.0338 acc=0.6267
epoch 99, train 65/100, loss=0.5033 acc=0.8267
epoch 99, train 66/100, loss=0.2697 acc=0.8933
epoch 99, train 67/100, loss=0.8861 acc=0.7867
epoch 99, train 68/100, loss=0.3645 acc=0.8133
epoch 99, train 69/100, loss=0.3976 acc=0.8400
epoch 99, train 70/100, loss=1.1174 acc=0.6400
epoch 99, train 71/100, loss=0.8622 acc=0.7200
epoch 99, train 72/100, loss=0.5235 acc=0.8667
epoch 99, train 73/100, loss=0.5541 acc=0.8533
epoch 99, train 74/100, loss=0.1582 acc=0.9467
epoch 99, train 75/100, loss=0.2167 acc=0.9333
epoch 99, train 76/100, loss=0.1057 acc=0.9733
epoch 99, train 77/100, loss=0.5094 acc=0.8933
epoch 99, train 78/100, loss=0.6068 acc=0.8000
epoch 99, train 79/100, loss=0.4593 acc=0.8667
epoch 99, train 80/100, loss=0.3458 acc=0.9067
epoch 99, train 81/100, loss=0.6159 acc=0.7467
epoch 99, train 82/100, loss=0.0821 acc=0.9733
epoch 99, train 83/100, loss=0.3995 acc=0.8800
epoch 99, train 84/100, loss=0.1772 acc=0.9333
epoch 99, train 85/100, loss=0.5205 acc=0.7867
epoch 99, train 86/100, loss=0.5592 acc=0.8533
epoch 99, train 87/100, loss=0.4531 acc=0.8533
epoch 99, train 88/100, loss=0.1092 acc=0.9600
epoch 99, train 89/100, loss=0.1916 acc=0.9200
epoch 99, train 90/100, loss=0.4518 acc=0.8400
epoch 99, train 91/100, loss=0.3111 acc=0.8933
epoch 99, train 92/100, loss=0.3134 acc=0.9067
epoch 99, train 93/100, loss=0.4134 acc=0.8800
epoch 99, train 94/100, loss=0.0820 acc=0.9600
epoch 99, train 95/100, loss=0.3227 acc=0.9200
epoch 99, train 96/100, loss=0.8671 acc=0.7467
epoch 99, train 97/100, loss=0.8595 acc=0.7333
epoch 99, train 98/100, loss=0.3674 acc=0.8800
epoch 99, train 99/100, loss=0.6235 acc=0.7333
epoch 99, train 100/100, loss=0.2319 acc=0.9333
best epoch 66, best val acc=0.7275
epoch 99, val, loss=0.9100 acc=0.7180
ETA:6.3h/6.4h
epoch 100, train 1/100, loss=0.3683 acc=0.8933
epoch 100, train 2/100, loss=0.1817 acc=0.9200
epoch 100, train 3/100, loss=0.4282 acc=0.9067
epoch 100, train 4/100, loss=0.2228 acc=0.9200
epoch 100, train 5/100, loss=0.4096 acc=0.9067
epoch 100, train 6/100, loss=0.4633 acc=0.8533
epoch 100, train 7/100, loss=0.2416 acc=0.9067
epoch 100, train 8/100, loss=0.5142 acc=0.8400
epoch 100, train 9/100, loss=0.1863 acc=0.9467
epoch 100, train 10/100, loss=0.4118 acc=0.8533
epoch 100, train 11/100, loss=0.1026 acc=0.9467
epoch 100, train 12/100, loss=0.1582 acc=0.9333
epoch 100, train 13/100, loss=0.2855 acc=0.9333
epoch 100, train 14/100, loss=0.0899 acc=0.9600
epoch 100, train 15/100, loss=1.1735 acc=0.5600
epoch 100, train 16/100, loss=0.2305 acc=0.8933
epoch 100, train 17/100, loss=1.1967 acc=0.6000
epoch 100, train 18/100, loss=0.7534 acc=0.7867
epoch 100, train 19/100, loss=0.5580 acc=0.7600
epoch 100, train 20/100, loss=0.5184 acc=0.7733
epoch 100, train 21/100, loss=0.3241 acc=0.8400
epoch 100, train 22/100, loss=0.6482 acc=0.8267
epoch 100, train 23/100, loss=0.9205 acc=0.7333
epoch 100, train 24/100, loss=0.0325 acc=1.0000
epoch 100, train 25/100, loss=0.4455 acc=0.8133
epoch 100, train 26/100, loss=0.4041 acc=0.8800
epoch 100, train 27/100, loss=0.5646 acc=0.8133
epoch 100, train 28/100, loss=0.1867 acc=0.9600
epoch 100, train 29/100, loss=0.3231 acc=0.9200
epoch 100, train 30/100, loss=0.3573 acc=0.8933
epoch 100, train 31/100, loss=0.4865 acc=0.8267
epoch 100, train 32/100, loss=0.2501 acc=0.9067
epoch 100, train 33/100, loss=0.8418 acc=0.7333
epoch 100, train 34/100, loss=0.4078 acc=0.8400
epoch 100, train 35/100, loss=0.6362 acc=0.8267
epoch 100, train 36/100, loss=0.2654 acc=0.9333
epoch 100, train 37/100, loss=0.5648 acc=0.7733
epoch 100, train 38/100, loss=0.3321 acc=0.8933
epoch 100, train 39/100, loss=0.8670 acc=0.8000
epoch 100, train 40/100, loss=0.5274 acc=0.8000
epoch 100, train 41/100, loss=0.8428 acc=0.6400
epoch 100, train 42/100, loss=0.2463 acc=0.9200
epoch 100, train 43/100, loss=1.2397 acc=0.6000
epoch 100, train 44/100, loss=0.3953 acc=0.8400
epoch 100, train 45/100, loss=0.3493 acc=0.8933
epoch 100, train 46/100, loss=0.5377 acc=0.8667
epoch 100, train 47/100, loss=0.4085 acc=0.8133
epoch 100, train 48/100, loss=0.2048 acc=0.9467
epoch 100, train 49/100, loss=0.3953 acc=0.9067
epoch 100, train 50/100, loss=0.4051 acc=0.8533
epoch 100, train 51/100, loss=1.0848 acc=0.6533
epoch 100, train 52/100, loss=0.4534 acc=0.8400
epoch 100, train 53/100, loss=0.4800 acc=0.8533
epoch 100, train 54/100, loss=0.5894 acc=0.8267
epoch 100, train 55/100, loss=0.2349 acc=0.9333
epoch 100, train 56/100, loss=0.9697 acc=0.6533
epoch 100, train 57/100, loss=0.2566 acc=0.8800
epoch 100, train 58/100, loss=0.1145 acc=0.9600
epoch 100, train 59/100, loss=0.3236 acc=0.8667
epoch 100, train 60/100, loss=0.2456 acc=0.9333
epoch 100, train 61/100, loss=0.3334 acc=0.9067
epoch 100, train 62/100, loss=0.2518 acc=0.9200
epoch 100, train 63/100, loss=0.3317 acc=0.8667
epoch 100, train 64/100, loss=0.3027 acc=0.8800
epoch 100, train 65/100, loss=0.3427 acc=0.8667
epoch 100, train 66/100, loss=2.4489 acc=0.4533
epoch 100, train 67/100, loss=0.8156 acc=0.7733
epoch 100, train 68/100, loss=1.1867 acc=0.6267
epoch 100, train 69/100, loss=0.5190 acc=0.8267
epoch 100, train 70/100, loss=0.5036 acc=0.8267
epoch 100, train 71/100, loss=0.5990 acc=0.7600
epoch 100, train 72/100, loss=0.3058 acc=0.8667
epoch 100, train 73/100, loss=1.0029 acc=0.6933
epoch 100, train 74/100, loss=0.2664 acc=0.8533
epoch 100, train 75/100, loss=0.5654 acc=0.8267
epoch 100, train 76/100, loss=0.7847 acc=0.6933
epoch 100, train 77/100, loss=0.6680 acc=0.8400
epoch 100, train 78/100, loss=0.7753 acc=0.7333
epoch 100, train 79/100, loss=0.3534 acc=0.8800
epoch 100, train 80/100, loss=0.3864 acc=0.8800
epoch 100, train 81/100, loss=0.5532 acc=0.8000
epoch 100, train 82/100, loss=0.5613 acc=0.7733
epoch 100, train 83/100, loss=0.3524 acc=0.8933
epoch 100, train 84/100, loss=0.7861 acc=0.7867
epoch 100, train 85/100, loss=0.1404 acc=0.9600
epoch 100, train 86/100, loss=0.4511 acc=0.8133
epoch 100, train 87/100, loss=0.8543 acc=0.8000
epoch 100, train 88/100, loss=0.4826 acc=0.8267
epoch 100, train 89/100, loss=0.3442 acc=0.8400
epoch 100, train 90/100, loss=0.4843 acc=0.8533
epoch 100, train 91/100, loss=0.2062 acc=0.9200
epoch 100, train 92/100, loss=0.0089 acc=1.0000
epoch 100, train 93/100, loss=0.5406 acc=0.7067
epoch 100, train 94/100, loss=0.1206 acc=0.9600
epoch 100, train 95/100, loss=0.7208 acc=0.7067
epoch 100, train 96/100, loss=0.2825 acc=0.9333
epoch 100, train 97/100, loss=0.4477 acc=0.8800
epoch 100, train 98/100, loss=0.6123 acc=0.8000
epoch 100, train 99/100, loss=0.0972 acc=0.9600
epoch 100, train 100/100, loss=0.4700 acc=0.8267
best epoch 66, best val acc=0.7275
epoch 100, val, loss=0.9292 acc=0.7110
ETA:6.4h/6.4h
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batch 2: 67.33(70.67)
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batch 10000: 63.84(72.00)
Val Best Acc 0.7275, Test Acc 0.6384
Test Acc 0.6384 + 0.0021