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cifar10_log.txt
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cifar10_log.txt
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==> Resuming from checkpoint..
/usr/local/lib/python3.5/dist-packages/sklearn/utils/linear_assignment_.py:21: DeprecationWarning: The linear_assignment_ module is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.
DeprecationWarning)
Epoch: [0][0/390] Train loss: -0.0416 (-0.0416) MI loss: -0.4341 (-0.4341) CE loss: 0.3925 (0.3925)
Epoch: [0][80/390] Train loss: -0.3049 (-0.2004) MI loss: -1.0518 (-0.7683) CE loss: 0.7469 (0.5679)
Epoch: [0][160/390] Train loss: -0.2634 (-0.2458) MI loss: -1.0720 (-0.9368) CE loss: 0.8086 (0.6911)
Epoch: [0][240/390] Train loss: -0.5141 (-0.2922) MI loss: -1.2707 (-1.0305) CE loss: 0.7566 (0.7383)
Epoch: [0][320/390] Train loss: -0.4839 (-0.3263) MI loss: -1.3093 (-1.0877) CE loss: 0.8254 (0.7613)
accuracy: [55.24, 50.99, 49.096000000000004, 64.866, 56.733999999999995]
state saving...
best loss: -0.36
best accuracy: [55.24, 50.99, 49.096000000000004, 64.866, 56.733999999999995]
Epoch: [1][0/390] Train loss: -0.3906 (-0.3906) MI loss: -1.3101 (-1.3101) CE loss: 0.9195 (0.9195)
Epoch: [1][80/390] Train loss: -0.5831 (-0.5140) MI loss: -1.3870 (-1.3337) CE loss: 0.8039 (0.8196)
Epoch: [1][160/390] Train loss: -0.6433 (-0.5163) MI loss: -1.3605 (-1.3363) CE loss: 0.7172 (0.8200)
Epoch: [1][240/390] Train loss: -0.4555 (-0.5219) MI loss: -1.3106 (-1.3390) CE loss: 0.8550 (0.8171)
Epoch: [1][320/390] Train loss: -0.3119 (-0.5273) MI loss: -1.2547 (-1.3468) CE loss: 0.9428 (0.8195)
accuracy: [56.916, 55.245999999999995, 53.508, 67.986, 58.745999999999995]
state saving...
best loss: -0.53
best accuracy: [56.916, 55.245999999999995, 53.508, 67.986, 58.745999999999995]
Epoch: [2][0/390] Train loss: -0.6060 (-0.6060) MI loss: -1.4388 (-1.4388) CE loss: 0.8329 (0.8329)
Epoch: [2][80/390] Train loss: -0.8020 (-0.5613) MI loss: -1.4885 (-1.3722) CE loss: 0.6865 (0.8109)
Epoch: [2][160/390] Train loss: -0.5416 (-0.5648) MI loss: -1.4208 (-1.3746) CE loss: 0.8792 (0.8098)
Epoch: [2][240/390] Train loss: -0.6818 (-0.5672) MI loss: -1.4826 (-1.3748) CE loss: 0.8007 (0.8076)
Epoch: [2][320/390] Train loss: -0.5001 (-0.5755) MI loss: -1.3281 (-1.3789) CE loss: 0.8281 (0.8033)
accuracy: [57.562000000000005, 56.22200000000001, 53.418, 69.112, 62.66199999999999]
state saving...
best loss: -0.58
best accuracy: [57.562000000000005, 56.22200000000001, 53.418, 69.112, 62.66199999999999]
Epoch: [3][0/390] Train loss: -0.6681 (-0.6681) MI loss: -1.4075 (-1.4075) CE loss: 0.7394 (0.7394)
Epoch: [3][80/390] Train loss: -0.4084 (-0.6299) MI loss: -1.2663 (-1.4098) CE loss: 0.8579 (0.7799)
Epoch: [3][160/390] Train loss: -0.3075 (-0.6180) MI loss: -1.2987 (-1.4067) CE loss: 0.9913 (0.7887)
Epoch: [3][240/390] Train loss: -0.6658 (-0.6191) MI loss: -1.4310 (-1.4079) CE loss: 0.7652 (0.7888)
Epoch: [3][320/390] Train loss: -0.6912 (-0.6180) MI loss: -1.4423 (-1.4062) CE loss: 0.7511 (0.7882)
accuracy: [57.312, 59.114, 51.495999999999995, 70.384, 62.442]
state saving...
best loss: -0.61
best accuracy: [57.312, 59.114, 51.495999999999995, 70.384, 62.442]
Epoch: [4][0/390] Train loss: -0.9242 (-0.9242) MI loss: -1.5577 (-1.5577) CE loss: 0.6335 (0.6335)
Epoch: [4][80/390] Train loss: -0.5830 (-0.6660) MI loss: -1.3598 (-1.4350) CE loss: 0.7769 (0.7690)
Epoch: [4][160/390] Train loss: -0.5240 (-0.6455) MI loss: -1.3889 (-1.4297) CE loss: 0.8649 (0.7842)
Epoch: [4][240/390] Train loss: -0.7190 (-0.6418) MI loss: -1.4666 (-1.4222) CE loss: 0.7476 (0.7804)
Epoch: [4][320/390] Train loss: -0.5660 (-0.6406) MI loss: -1.4081 (-1.4209) CE loss: 0.8422 (0.7802)
accuracy: [57.355999999999995, 60.72, 53.217999999999996, 69.94399999999999, 65.596]
state saving...
best loss: -0.63
best accuracy: [57.355999999999995, 60.72, 53.217999999999996, 69.94399999999999, 65.596]
Epoch: [5][0/390] Train loss: -0.7814 (-0.7814) MI loss: -1.4919 (-1.4919) CE loss: 0.7105 (0.7105)
Epoch: [5][80/390] Train loss: -0.6712 (-0.6703) MI loss: -1.4619 (-1.4420) CE loss: 0.7907 (0.7717)
Epoch: [5][160/390] Train loss: -0.7292 (-0.6783) MI loss: -1.4703 (-1.4411) CE loss: 0.7410 (0.7629)
Epoch: [5][240/390] Train loss: -0.5264 (-0.6691) MI loss: -1.3844 (-1.4399) CE loss: 0.8580 (0.7708)
Epoch: [5][320/390] Train loss: -0.7079 (-0.6615) MI loss: -1.4586 (-1.4380) CE loss: 0.7507 (0.7764)
accuracy: [56.908, 61.25000000000001, 53.454, 70.882, 65.404]
state saving...
best loss: -0.65
best accuracy: [56.908, 61.25000000000001, 53.454, 70.882, 65.404]
Epoch: [6][0/390] Train loss: -0.7311 (-0.7311) MI loss: -1.4914 (-1.4914) CE loss: 0.7603 (0.7603)
Epoch: [6][80/390] Train loss: -0.6156 (-0.6424) MI loss: -1.4180 (-1.4369) CE loss: 0.8023 (0.7944)
Epoch: [6][160/390] Train loss: -0.7000 (-0.6529) MI loss: -1.4309 (-1.4401) CE loss: 0.7308 (0.7872)
Epoch: [6][240/390] Train loss: -0.7075 (-0.6584) MI loss: -1.4306 (-1.4436) CE loss: 0.7231 (0.7852)
Epoch: [6][320/390] Train loss: -0.5949 (-0.6544) MI loss: -1.4064 (-1.4394) CE loss: 0.8115 (0.7850)
accuracy: [56.84, 60.922, 53.212, 71.146, 66.178]
state saving...
best loss: -0.66
best accuracy: [56.84, 60.922, 53.212, 71.146, 66.178]
Epoch: [7][0/390] Train loss: -0.6984 (-0.6984) MI loss: -1.4884 (-1.4884) CE loss: 0.7899 (0.7899)
Epoch: [7][80/390] Train loss: -0.6191 (-0.6664) MI loss: -1.3910 (-1.4554) CE loss: 0.7719 (0.7890)
Epoch: [7][160/390] Train loss: -0.6643 (-0.6699) MI loss: -1.4265 (-1.4538) CE loss: 0.7622 (0.7839)
Epoch: [7][240/390] Train loss: -0.4405 (-0.6688) MI loss: -1.4076 (-1.4496) CE loss: 0.9671 (0.7807)
Epoch: [7][320/390] Train loss: -0.5374 (-0.6735) MI loss: -1.3533 (-1.4519) CE loss: 0.8159 (0.7784)
accuracy: [58.17, 62.18, 54.416, 71.616, 66.94600000000001]
state saving...
best loss: -0.68
best accuracy: [58.17, 62.18, 54.416, 71.616, 66.94600000000001]
Epoch: [8][0/390] Train loss: -0.4280 (-0.4280) MI loss: -1.3529 (-1.3529) CE loss: 0.9249 (0.9249)
Epoch: [8][80/390] Train loss: -1.0554 (-0.6906) MI loss: -1.6534 (-1.4609) CE loss: 0.5981 (0.7703)
Epoch: [8][160/390] Train loss: -0.4035 (-0.6755) MI loss: -1.4373 (-1.4594) CE loss: 1.0337 (0.7839)
Epoch: [8][240/390] Train loss: -0.7253 (-0.6751) MI loss: -1.5161 (-1.4572) CE loss: 0.7908 (0.7821)
Epoch: [8][320/390] Train loss: -0.6391 (-0.6727) MI loss: -1.4246 (-1.4541) CE loss: 0.7855 (0.7814)
accuracy: [58.17, 63.578, 54.282, 72.63, 70.116]
best loss: -0.68
best accuracy: [58.17, 62.18, 54.416, 71.616, 66.94600000000001]
Epoch: [9][0/390] Train loss: -0.5132 (-0.5132) MI loss: -1.4083 (-1.4083) CE loss: 0.8951 (0.8951)
Epoch: [9][80/390] Train loss: -0.7370 (-0.7162) MI loss: -1.4455 (-1.4814) CE loss: 0.7085 (0.7652)
Epoch: [9][160/390] Train loss: -0.9184 (-0.7111) MI loss: -1.5599 (-1.4781) CE loss: 0.6415 (0.7670)
Epoch: [9][240/390] Train loss: -0.7163 (-0.7027) MI loss: -1.5045 (-1.4737) CE loss: 0.7882 (0.7710)
Epoch: [9][320/390] Train loss: -0.5925 (-0.6937) MI loss: -1.4256 (-1.4691) CE loss: 0.8330 (0.7755)
accuracy: [58.41, 62.83800000000001, 53.656000000000006, 73.10600000000001, 69.78]
state saving...
best loss: -0.69
best accuracy: [58.41, 62.83800000000001, 53.656000000000006, 73.10600000000001, 69.78]
Epoch: [10][0/390] Train loss: -0.6436 (-0.6436) MI loss: -1.4728 (-1.4728) CE loss: 0.8292 (0.8292)
Epoch: [10][80/390] Train loss: -0.7911 (-0.6998) MI loss: -1.5025 (-1.4713) CE loss: 0.7114 (0.7715)
Epoch: [10][160/390] Train loss: -0.4023 (-0.6940) MI loss: -1.3451 (-1.4700) CE loss: 0.9429 (0.7760)
Epoch: [10][240/390] Train loss: -0.4477 (-0.6980) MI loss: -1.3258 (-1.4705) CE loss: 0.8780 (0.7726)
Epoch: [10][320/390] Train loss: -0.9389 (-0.6941) MI loss: -1.5552 (-1.4691) CE loss: 0.6163 (0.7750)
accuracy: [57.982, 63.017999999999994, 53.49, 72.756, 70.832]
state saving...
best loss: -0.70
best accuracy: [57.982, 63.017999999999994, 53.49, 72.756, 70.832]
Epoch: [11][0/390] Train loss: -0.5017 (-0.5017) MI loss: -1.4195 (-1.4195) CE loss: 0.9177 (0.9177)
Epoch: [11][80/390] Train loss: -0.5529 (-0.7059) MI loss: -1.4284 (-1.4788) CE loss: 0.8754 (0.7729)
Epoch: [11][160/390] Train loss: -0.6979 (-0.6996) MI loss: -1.4490 (-1.4752) CE loss: 0.7512 (0.7757)
Epoch: [11][240/390] Train loss: -0.6116 (-0.7038) MI loss: -1.4212 (-1.4763) CE loss: 0.8096 (0.7724)
Epoch: [11][320/390] Train loss: -0.7384 (-0.7166) MI loss: -1.5135 (-1.4811) CE loss: 0.7750 (0.7645)
accuracy: [58.426, 63.254, 54.056000000000004, 73.048, 71.14399999999999]
state saving...
best loss: -0.72
best accuracy: [58.426, 63.254, 54.056000000000004, 73.048, 71.14399999999999]
Epoch: [12][0/390] Train loss: -0.9542 (-0.9542) MI loss: -1.6498 (-1.6498) CE loss: 0.6956 (0.6956)
Epoch: [12][80/390] Train loss: -0.7119 (-0.7096) MI loss: -1.4364 (-1.4852) CE loss: 0.7245 (0.7756)
Epoch: [12][160/390] Train loss: -0.9173 (-0.7134) MI loss: -1.6101 (-1.4829) CE loss: 0.6928 (0.7695)
Epoch: [12][240/390] Train loss: -0.9359 (-0.7106) MI loss: -1.5675 (-1.4829) CE loss: 0.6316 (0.7723)
Epoch: [12][320/390] Train loss: -0.7258 (-0.7111) MI loss: -1.4218 (-1.4817) CE loss: 0.6961 (0.7707)
accuracy: [59.099999999999994, 62.12, 54.37, 73.482, 70.964]
best loss: -0.72
best accuracy: [58.426, 63.254, 54.056000000000004, 73.048, 71.14399999999999]
Epoch: [13][0/390] Train loss: -0.7142 (-0.7142) MI loss: -1.4189 (-1.4189) CE loss: 0.7048 (0.7048)
Epoch: [13][80/390] Train loss: -0.8861 (-0.6787) MI loss: -1.5944 (-1.4591) CE loss: 0.7084 (0.7804)
Epoch: [13][160/390] Train loss: -0.7998 (-0.6987) MI loss: -1.5473 (-1.4751) CE loss: 0.7474 (0.7764)
Epoch: [13][240/390] Train loss: -0.7457 (-0.7150) MI loss: -1.4968 (-1.4846) CE loss: 0.7511 (0.7696)
Epoch: [13][320/390] Train loss: -0.5507 (-0.7199) MI loss: -1.3938 (-1.4871) CE loss: 0.8431 (0.7672)
accuracy: [58.792, 64.60000000000001, 54.856, 73.682, 71.664]
best loss: -0.72
best accuracy: [58.426, 63.254, 54.056000000000004, 73.048, 71.14399999999999]
Epoch: [14][0/390] Train loss: -0.9437 (-0.9437) MI loss: -1.5243 (-1.5243) CE loss: 0.5806 (0.5806)
Epoch: [14][80/390] Train loss: -0.6648 (-0.7355) MI loss: -1.4802 (-1.4926) CE loss: 0.8154 (0.7571)
Epoch: [14][160/390] Train loss: -0.6804 (-0.7395) MI loss: -1.4557 (-1.4969) CE loss: 0.7753 (0.7574)
Epoch: [14][240/390] Train loss: -0.6952 (-0.7385) MI loss: -1.4248 (-1.4973) CE loss: 0.7295 (0.7589)
Epoch: [14][320/390] Train loss: -0.7554 (-0.7364) MI loss: -1.5588 (-1.4963) CE loss: 0.8034 (0.7599)
accuracy: [58.678, 64.378, 54.75, 74.31400000000001, 73.532]
state saving...
best loss: -0.73
best accuracy: [58.678, 64.378, 54.75, 74.31400000000001, 73.532]
Epoch: [15][0/390] Train loss: -0.3687 (-0.3687) MI loss: -1.3090 (-1.3090) CE loss: 0.9404 (0.9404)
Epoch: [15][80/390] Train loss: -0.6796 (-0.7643) MI loss: -1.3824 (-1.5086) CE loss: 0.7028 (0.7443)
Epoch: [15][160/390] Train loss: -0.5766 (-0.7468) MI loss: -1.4445 (-1.5037) CE loss: 0.8680 (0.7569)
Epoch: [15][240/390] Train loss: -0.7575 (-0.7312) MI loss: -1.5260 (-1.4952) CE loss: 0.7685 (0.7640)
Epoch: [15][320/390] Train loss: -0.7522 (-0.7311) MI loss: -1.5194 (-1.4947) CE loss: 0.7672 (0.7636)
accuracy: [57.538, 63.256, 53.227999999999994, 72.666, 71.698]
best loss: -0.73
best accuracy: [58.678, 64.378, 54.75, 74.31400000000001, 73.532]
Epoch: [16][0/390] Train loss: -0.9118 (-0.9118) MI loss: -1.5774 (-1.5774) CE loss: 0.6656 (0.6656)
Epoch: [16][80/390] Train loss: -0.8300 (-0.7605) MI loss: -1.4707 (-1.5017) CE loss: 0.6407 (0.7412)
Epoch: [16][160/390] Train loss: -0.8718 (-0.7548) MI loss: -1.5239 (-1.4985) CE loss: 0.6521 (0.7437)
Epoch: [16][240/390] Train loss: -0.6045 (-0.7518) MI loss: -1.4301 (-1.4975) CE loss: 0.8257 (0.7458)
Epoch: [16][320/390] Train loss: -0.8548 (-0.7474) MI loss: -1.5316 (-1.4985) CE loss: 0.6768 (0.7510)
accuracy: [57.934, 64.038, 54.26800000000001, 74.05199999999999, 73.76]
state saving...
best loss: -0.74
best accuracy: [57.934, 64.038, 54.26800000000001, 74.05199999999999, 73.76]
Epoch: [17][0/390] Train loss: -0.6482 (-0.6482) MI loss: -1.4544 (-1.4544) CE loss: 0.8063 (0.8063)
Epoch: [17][80/390] Train loss: -0.6844 (-0.7613) MI loss: -1.4743 (-1.5148) CE loss: 0.7899 (0.7535)
Epoch: [17][160/390] Train loss: -0.3352 (-0.7652) MI loss: -1.2823 (-1.5129) CE loss: 0.9471 (0.7476)
Epoch: [17][240/390] Train loss: -0.9958 (-0.7540) MI loss: -1.6559 (-1.5063) CE loss: 0.6602 (0.7523)
Epoch: [17][320/390] Train loss: -0.6895 (-0.7502) MI loss: -1.4309 (-1.5035) CE loss: 0.7414 (0.7533)
accuracy: [59.288, 64.362, 55.023999999999994, 73.202, 73.08200000000001]
best loss: -0.74
best accuracy: [57.934, 64.038, 54.26800000000001, 74.05199999999999, 73.76]
Epoch: [18][0/390] Train loss: -0.6522 (-0.6522) MI loss: -1.4406 (-1.4406) CE loss: 0.7884 (0.7884)
Epoch: [18][80/390] Train loss: -0.9919 (-0.7729) MI loss: -1.6329 (-1.5210) CE loss: 0.6411 (0.7482)
Epoch: [18][160/390] Train loss: -0.8493 (-0.7693) MI loss: -1.4955 (-1.5204) CE loss: 0.6462 (0.7510)
Epoch: [18][240/390] Train loss: -0.6626 (-0.7578) MI loss: -1.4197 (-1.5127) CE loss: 0.7572 (0.7549)
Epoch: [18][320/390] Train loss: -0.5448 (-0.7534) MI loss: -1.3268 (-1.5092) CE loss: 0.7820 (0.7557)
accuracy: [57.821999999999996, 63.546, 54.982, 72.61, 72.35000000000001]
state saving...
best loss: -0.75
best accuracy: [57.821999999999996, 63.546, 54.982, 72.61, 72.35000000000001]
Epoch: [19][0/390] Train loss: -0.8216 (-0.8216) MI loss: -1.5450 (-1.5450) CE loss: 0.7234 (0.7234)
Epoch: [19][80/390] Train loss: -0.6538 (-0.7504) MI loss: -1.4473 (-1.5146) CE loss: 0.7934 (0.7643)
Epoch: [19][160/390] Train loss: -0.5668 (-0.7487) MI loss: -1.4355 (-1.5084) CE loss: 0.8688 (0.7597)
Epoch: [19][240/390] Train loss: -0.5088 (-0.7521) MI loss: -1.3605 (-1.5097) CE loss: 0.8517 (0.7576)
Epoch: [19][320/390] Train loss: -0.9326 (-0.7467) MI loss: -1.6292 (-1.5068) CE loss: 0.6966 (0.7601)
accuracy: [58.955999999999996, 64.716, 55.482, 73.44000000000001, 73.158]
best loss: -0.75
best accuracy: [57.821999999999996, 63.546, 54.982, 72.61, 72.35000000000001]
Epoch: [20][0/390] Train loss: -0.8985 (-0.8985) MI loss: -1.5902 (-1.5902) CE loss: 0.6917 (0.6917)
Epoch: [20][80/390] Train loss: -0.6592 (-0.7477) MI loss: -1.4032 (-1.5009) CE loss: 0.7440 (0.7532)
Epoch: [20][160/390] Train loss: -0.7111 (-0.7633) MI loss: -1.4679 (-1.5110) CE loss: 0.7567 (0.7477)
Epoch: [20][240/390] Train loss: -0.4186 (-0.7620) MI loss: -1.3574 (-1.5126) CE loss: 0.9388 (0.7506)
Epoch: [20][320/390] Train loss: -0.5239 (-0.7637) MI loss: -1.3852 (-1.5129) CE loss: 0.8613 (0.7491)
accuracy: [60.065999999999995, 62.848000000000006, 55.088, 73.176, 73.614]
state saving...
best loss: -0.77
best accuracy: [60.065999999999995, 62.848000000000006, 55.088, 73.176, 73.614]
Epoch: [21][0/390] Train loss: -0.7886 (-0.7886) MI loss: -1.5429 (-1.5429) CE loss: 0.7543 (0.7543)
Epoch: [21][80/390] Train loss: -0.8378 (-0.7655) MI loss: -1.6445 (-1.5273) CE loss: 0.8067 (0.7618)
Epoch: [21][160/390] Train loss: -0.8154 (-0.7587) MI loss: -1.5404 (-1.5213) CE loss: 0.7250 (0.7626)
Epoch: [21][240/390] Train loss: -0.7179 (-0.7601) MI loss: -1.5474 (-1.5198) CE loss: 0.8295 (0.7597)
Epoch: [21][320/390] Train loss: -0.8558 (-0.7602) MI loss: -1.5496 (-1.5175) CE loss: 0.6938 (0.7573)
accuracy: [57.884, 65.25, 54.63, 74.68, 74.788]
best loss: -0.77
best accuracy: [60.065999999999995, 62.848000000000006, 55.088, 73.176, 73.614]
Epoch: [22][0/390] Train loss: -0.5661 (-0.5661) MI loss: -1.4201 (-1.4201) CE loss: 0.8540 (0.8540)
Epoch: [22][80/390] Train loss: -0.9784 (-0.7753) MI loss: -1.6035 (-1.5190) CE loss: 0.6251 (0.7437)
Epoch: [22][160/390] Train loss: -0.7617 (-0.7638) MI loss: -1.5177 (-1.5153) CE loss: 0.7560 (0.7515)
Epoch: [22][240/390] Train loss: -0.8603 (-0.7561) MI loss: -1.5483 (-1.5142) CE loss: 0.6880 (0.7581)
Epoch: [22][320/390] Train loss: -0.6139 (-0.7491) MI loss: -1.4645 (-1.5123) CE loss: 0.8506 (0.7632)
accuracy: [59.428000000000004, 64.282, 56.992, 73.738, 74.436]
best loss: -0.77
best accuracy: [60.065999999999995, 62.848000000000006, 55.088, 73.176, 73.614]
Epoch: [23][0/390] Train loss: -0.6810 (-0.6810) MI loss: -1.4968 (-1.4968) CE loss: 0.8158 (0.8158)
Epoch: [23][80/390] Train loss: -0.7209 (-0.7459) MI loss: -1.4704 (-1.5158) CE loss: 0.7495 (0.7698)
Epoch: [23][160/390] Train loss: -0.6985 (-0.7503) MI loss: -1.5365 (-1.5131) CE loss: 0.8381 (0.7627)
Epoch: [23][240/390] Train loss: -0.6638 (-0.7527) MI loss: -1.4538 (-1.5134) CE loss: 0.7900 (0.7607)
Epoch: [23][320/390] Train loss: -0.7804 (-0.7584) MI loss: -1.5386 (-1.5152) CE loss: 0.7582 (0.7567)
accuracy: [59.065999999999995, 65.2, 55.814, 73.312, 73.916]
best loss: -0.77
best accuracy: [60.065999999999995, 62.848000000000006, 55.088, 73.176, 73.614]
Epoch: [24][0/390] Train loss: -0.5345 (-0.5345) MI loss: -1.4344 (-1.4344) CE loss: 0.9000 (0.9000)
Epoch: [24][80/390] Train loss: -0.7262 (-0.7574) MI loss: -1.5098 (-1.5217) CE loss: 0.7836 (0.7644)
Epoch: [24][160/390] Train loss: -0.6737 (-0.7481) MI loss: -1.5058 (-1.5129) CE loss: 0.8321 (0.7648)
Epoch: [24][240/390] Train loss: -0.6468 (-0.7487) MI loss: -1.4111 (-1.5133) CE loss: 0.7643 (0.7647)
Epoch: [24][320/390] Train loss: -0.6782 (-0.7528) MI loss: -1.5503 (-1.5144) CE loss: 0.8721 (0.7615)
accuracy: [58.95399999999999, 66.308, 54.844, 74.72800000000001, 73.994]
best loss: -0.77
best accuracy: [60.065999999999995, 62.848000000000006, 55.088, 73.176, 73.614]
Epoch: [25][0/390] Train loss: -0.8005 (-0.8005) MI loss: -1.5085 (-1.5085) CE loss: 0.7080 (0.7080)
Epoch: [25][80/390] Train loss: -0.8133 (-0.7581) MI loss: -1.5121 (-1.5177) CE loss: 0.6987 (0.7596)
Epoch: [25][160/390] Train loss: -0.9155 (-0.7701) MI loss: -1.5235 (-1.5234) CE loss: 0.6080 (0.7533)
Epoch: [25][240/390] Train loss: -0.7592 (-0.7716) MI loss: -1.5611 (-1.5246) CE loss: 0.8019 (0.7530)
Epoch: [25][320/390] Train loss: -0.6817 (-0.7722) MI loss: -1.5452 (-1.5240) CE loss: 0.8636 (0.7517)
accuracy: [59.08, 65.648, 55.996, 74.892, 74.74199999999999]
state saving...
best loss: -0.77
best accuracy: [59.08, 65.648, 55.996, 74.892, 74.74199999999999]
Epoch: [26][0/390] Train loss: -0.6806 (-0.6806) MI loss: -1.5536 (-1.5536) CE loss: 0.8730 (0.8730)
Epoch: [26][80/390] Train loss: -0.5974 (-0.7901) MI loss: -1.3991 (-1.5296) CE loss: 0.8018 (0.7395)
Epoch: [26][160/390] Train loss: -0.9626 (-0.8010) MI loss: -1.5844 (-1.5420) CE loss: 0.6218 (0.7410)
Epoch: [26][240/390] Train loss: -0.5982 (-0.7862) MI loss: -1.4366 (-1.5335) CE loss: 0.8384 (0.7473)
Epoch: [26][320/390] Train loss: -0.6666 (-0.7828) MI loss: -1.4809 (-1.5324) CE loss: 0.8143 (0.7496)
accuracy: [60.087999999999994, 65.72, 55.226, 74.936, 74.548]
state saving...
best loss: -0.78
best accuracy: [60.087999999999994, 65.72, 55.226, 74.936, 74.548]
Epoch: [27][0/390] Train loss: -0.8826 (-0.8826) MI loss: -1.5641 (-1.5641) CE loss: 0.6815 (0.6815)
Epoch: [27][80/390] Train loss: -0.5982 (-0.8361) MI loss: -1.5057 (-1.5588) CE loss: 0.9075 (0.7227)
Epoch: [27][160/390] Train loss: -0.8406 (-0.8122) MI loss: -1.5860 (-1.5458) CE loss: 0.7454 (0.7336)
Epoch: [27][240/390] Train loss: -0.6896 (-0.7956) MI loss: -1.4100 (-1.5358) CE loss: 0.7203 (0.7401)
Epoch: [27][320/390] Train loss: -0.5279 (-0.7874) MI loss: -1.2798 (-1.5298) CE loss: 0.7519 (0.7424)
accuracy: [59.584, 66.444, 55.042, 75.71600000000001, 75.956]
state saving...
best loss: -0.79
best accuracy: [59.584, 66.444, 55.042, 75.71600000000001, 75.956]
Epoch: [28][0/390] Train loss: -0.8285 (-0.8285) MI loss: -1.5221 (-1.5221) CE loss: 0.6936 (0.6936)
Epoch: [28][80/390] Train loss: -0.8722 (-0.7860) MI loss: -1.5679 (-1.5325) CE loss: 0.6957 (0.7466)
Epoch: [28][160/390] Train loss: -0.8983 (-0.7673) MI loss: -1.6719 (-1.5291) CE loss: 0.7736 (0.7617)
Epoch: [28][240/390] Train loss: -0.6147 (-0.7631) MI loss: -1.4494 (-1.5252) CE loss: 0.8346 (0.7621)
Epoch: [28][320/390] Train loss: -0.7354 (-0.7605) MI loss: -1.4801 (-1.5261) CE loss: 0.7447 (0.7657)
accuracy: [60.06, 65.57, 55.688, 74.976, 75.27199999999999]
best loss: -0.79
best accuracy: [59.584, 66.444, 55.042, 75.71600000000001, 75.956]
Epoch: [29][0/390] Train loss: -1.0033 (-1.0033) MI loss: -1.6506 (-1.6506) CE loss: 0.6473 (0.6473)
Epoch: [29][80/390] Train loss: -0.6191 (-0.7916) MI loss: -1.4273 (-1.5369) CE loss: 0.8083 (0.7453)
Epoch: [29][160/390] Train loss: -0.5880 (-0.7943) MI loss: -1.4779 (-1.5370) CE loss: 0.8899 (0.7427)
Epoch: [29][240/390] Train loss: -0.6988 (-0.7891) MI loss: -1.5732 (-1.5362) CE loss: 0.8744 (0.7471)
Epoch: [29][320/390] Train loss: -1.0503 (-0.7773) MI loss: -1.5943 (-1.5287) CE loss: 0.5440 (0.7515)
accuracy: [59.448, 65.218, 55.976000000000006, 75.606, 74.35000000000001]
best loss: -0.79
best accuracy: [59.584, 66.444, 55.042, 75.71600000000001, 75.956]
Epoch: [30][0/390] Train loss: -0.7836 (-0.7836) MI loss: -1.5766 (-1.5766) CE loss: 0.7930 (0.7930)
Epoch: [30][80/390] Train loss: -0.5741 (-0.7853) MI loss: -1.4572 (-1.5361) CE loss: 0.8831 (0.7509)
Epoch: [30][160/390] Train loss: -0.8924 (-0.7821) MI loss: -1.4967 (-1.5354) CE loss: 0.6043 (0.7533)
Epoch: [30][240/390] Train loss: -0.7777 (-0.7796) MI loss: -1.5223 (-1.5332) CE loss: 0.7446 (0.7536)
Epoch: [30][320/390] Train loss: -0.8359 (-0.7783) MI loss: -1.5945 (-1.5328) CE loss: 0.7586 (0.7545)
accuracy: [59.672000000000004, 66.178, 55.254000000000005, 75.806, 74.57000000000001]
best loss: -0.79
best accuracy: [59.584, 66.444, 55.042, 75.71600000000001, 75.956]
Epoch: [31][0/390] Train loss: -0.7237 (-0.7237) MI loss: -1.4909 (-1.4909) CE loss: 0.7672 (0.7672)
Epoch: [31][80/390] Train loss: -0.7538 (-0.7922) MI loss: -1.5891 (-1.5417) CE loss: 0.8353 (0.7494)
Epoch: [31][160/390] Train loss: -0.8109 (-0.7795) MI loss: -1.5412 (-1.5337) CE loss: 0.7303 (0.7541)
Epoch: [31][240/390] Train loss: -0.9353 (-0.7824) MI loss: -1.6065 (-1.5355) CE loss: 0.6712 (0.7532)
Epoch: [31][320/390] Train loss: -0.9408 (-0.7822) MI loss: -1.6388 (-1.5330) CE loss: 0.6980 (0.7508)
accuracy: [60.23, 65.29, 55.623999999999995, 74.404, 74.936]
best loss: -0.79
best accuracy: [59.584, 66.444, 55.042, 75.71600000000001, 75.956]
Epoch: [32][0/390] Train loss: -0.6408 (-0.6408) MI loss: -1.5124 (-1.5124) CE loss: 0.8716 (0.8716)
Epoch: [32][80/390] Train loss: -0.9893 (-0.7985) MI loss: -1.5403 (-1.5381) CE loss: 0.5510 (0.7395)
Epoch: [32][160/390] Train loss: -0.8440 (-0.7957) MI loss: -1.5252 (-1.5403) CE loss: 0.6812 (0.7445)
Epoch: [32][240/390] Train loss: -0.9802 (-0.7998) MI loss: -1.6155 (-1.5422) CE loss: 0.6354 (0.7424)
Epoch: [32][320/390] Train loss: -0.7454 (-0.7949) MI loss: -1.4783 (-1.5401) CE loss: 0.7329 (0.7452)
accuracy: [59.37, 65.216, 56.989999999999995, 74.26, 74.03999999999999]
state saving...
best loss: -0.79
best accuracy: [59.37, 65.216, 56.989999999999995, 74.26, 74.03999999999999]
Epoch: [33][0/390] Train loss: -0.7947 (-0.7947) MI loss: -1.5483 (-1.5483) CE loss: 0.7535 (0.7535)
Epoch: [33][80/390] Train loss: -0.5052 (-0.8010) MI loss: -1.3929 (-1.5430) CE loss: 0.8877 (0.7420)
Epoch: [33][160/390] Train loss: -0.8476 (-0.7937) MI loss: -1.6260 (-1.5367) CE loss: 0.7784 (0.7430)
Epoch: [33][240/390] Train loss: -0.7368 (-0.7923) MI loss: -1.5890 (-1.5375) CE loss: 0.8522 (0.7451)
Epoch: [33][320/390] Train loss: -0.6618 (-0.7920) MI loss: -1.4677 (-1.5367) CE loss: 0.8059 (0.7447)
accuracy: [61.529999999999994, 66.026, 56.926, 75.58, 75.30799999999999]
best loss: -0.79
best accuracy: [59.37, 65.216, 56.989999999999995, 74.26, 74.03999999999999]
Epoch: [34][0/390] Train loss: -0.5500 (-0.5500) MI loss: -1.3606 (-1.3606) CE loss: 0.8106 (0.8106)
Epoch: [34][80/390] Train loss: -0.9478 (-0.7911) MI loss: -1.7016 (-1.5426) CE loss: 0.7538 (0.7515)
Epoch: [34][160/390] Train loss: -0.6402 (-0.7759) MI loss: -1.5162 (-1.5340) CE loss: 0.8760 (0.7580)
Epoch: [34][240/390] Train loss: -0.6099 (-0.7793) MI loss: -1.4317 (-1.5351) CE loss: 0.8218 (0.7559)
Epoch: [34][320/390] Train loss: -0.6809 (-0.7779) MI loss: -1.3390 (-1.5326) CE loss: 0.6581 (0.7547)
accuracy: [60.346, 66.88, 57.264, 75.786, 75.31400000000001]
best loss: -0.79
best accuracy: [59.37, 65.216, 56.989999999999995, 74.26, 74.03999999999999]
Epoch: [35][0/390] Train loss: -0.5173 (-0.5173) MI loss: -1.3912 (-1.3912) CE loss: 0.8739 (0.8739)
Epoch: [35][80/390] Train loss: -0.9111 (-0.8106) MI loss: -1.6249 (-1.5415) CE loss: 0.7139 (0.7309)
Epoch: [35][160/390] Train loss: -0.5940 (-0.8018) MI loss: -1.4368 (-1.5454) CE loss: 0.8428 (0.7437)
Epoch: [35][240/390] Train loss: -0.7254 (-0.7871) MI loss: -1.4906 (-1.5412) CE loss: 0.7652 (0.7541)
Epoch: [35][320/390] Train loss: -0.5097 (-0.7869) MI loss: -1.3870 (-1.5394) CE loss: 0.8773 (0.7525)
accuracy: [60.529999999999994, 66.258, 56.554, 75.08, 74.984]
best loss: -0.79
best accuracy: [59.37, 65.216, 56.989999999999995, 74.26, 74.03999999999999]
Epoch: [36][0/390] Train loss: -0.8660 (-0.8660) MI loss: -1.6374 (-1.6374) CE loss: 0.7714 (0.7714)
Epoch: [36][80/390] Train loss: -1.0731 (-0.7922) MI loss: -1.6504 (-1.5481) CE loss: 0.5773 (0.7559)
Epoch: [36][160/390] Train loss: -0.6916 (-0.7970) MI loss: -1.5132 (-1.5463) CE loss: 0.8216 (0.7493)
Epoch: [36][240/390] Train loss: -0.7562 (-0.8030) MI loss: -1.4718 (-1.5491) CE loss: 0.7156 (0.7461)
Epoch: [36][320/390] Train loss: -0.5522 (-0.7931) MI loss: -1.4019 (-1.5430) CE loss: 0.8497 (0.7498)
accuracy: [60.292, 65.91, 56.072, 74.94800000000001, 74.002]
best loss: -0.79
best accuracy: [59.37, 65.216, 56.989999999999995, 74.26, 74.03999999999999]
Epoch: [37][0/390] Train loss: -0.4912 (-0.4912) MI loss: -1.4229 (-1.4229) CE loss: 0.9316 (0.9316)
Epoch: [37][80/390] Train loss: -0.7768 (-0.7985) MI loss: -1.4944 (-1.5446) CE loss: 0.7175 (0.7461)
Epoch: [37][160/390] Train loss: -0.8466 (-0.7817) MI loss: -1.5900 (-1.5419) CE loss: 0.7434 (0.7602)
Epoch: [37][240/390] Train loss: -0.5381 (-0.7791) MI loss: -1.3536 (-1.5389) CE loss: 0.8155 (0.7598)
Epoch: [37][320/390] Train loss: -0.6730 (-0.7900) MI loss: -1.4990 (-1.5419) CE loss: 0.8260 (0.7518)
accuracy: [59.724, 66.226, 56.696000000000005, 75.254, 74.458]
best loss: -0.79
best accuracy: [59.37, 65.216, 56.989999999999995, 74.26, 74.03999999999999]
Epoch: [38][0/390] Train loss: -1.0888 (-1.0888) MI loss: -1.6639 (-1.6639) CE loss: 0.5751 (0.5751)
Epoch: [38][80/390] Train loss: -0.8315 (-0.8020) MI loss: -1.5878 (-1.5525) CE loss: 0.7563 (0.7505)
Epoch: [38][160/390] Train loss: -0.5305 (-0.7916) MI loss: -1.5493 (-1.5460) CE loss: 1.0188 (0.7544)
Epoch: [38][240/390] Train loss: -0.8260 (-0.7873) MI loss: -1.5290 (-1.5399) CE loss: 0.7030 (0.7526)
Epoch: [38][320/390] Train loss: -1.1382 (-0.7837) MI loss: -1.7712 (-1.5394) CE loss: 0.6330 (0.7558)
accuracy: [60.478, 66.572, 57.826, 74.846, 74.362]
best loss: -0.79
best accuracy: [59.37, 65.216, 56.989999999999995, 74.26, 74.03999999999999]
Epoch: [39][0/390] Train loss: -0.9155 (-0.9155) MI loss: -1.5737 (-1.5737) CE loss: 0.6581 (0.6581)
Epoch: [39][80/390] Train loss: -1.0178 (-0.8019) MI loss: -1.6737 (-1.5495) CE loss: 0.6560 (0.7476)
Epoch: [39][160/390] Train loss: -0.7432 (-0.7954) MI loss: -1.5534 (-1.5472) CE loss: 0.8102 (0.7517)
Epoch: [39][240/390] Train loss: -0.9358 (-0.7928) MI loss: -1.6049 (-1.5426) CE loss: 0.6691 (0.7499)
Epoch: [39][320/390] Train loss: -0.6839 (-0.7921) MI loss: -1.4696 (-1.5435) CE loss: 0.7857 (0.7514)
accuracy: [58.692, 66.12599999999999, 56.42, 74.088, 74.17200000000001]
best loss: -0.79
best accuracy: [59.37, 65.216, 56.989999999999995, 74.26, 74.03999999999999]
Epoch: [40][0/390] Train loss: -1.0113 (-1.0113) MI loss: -1.6058 (-1.6058) CE loss: 0.5944 (0.5944)
Epoch: [40][80/390] Train loss: -0.7200 (-0.7942) MI loss: -1.5367 (-1.5462) CE loss: 0.8167 (0.7519)
Epoch: [40][160/390] Train loss: -0.7907 (-0.7837) MI loss: -1.4961 (-1.5394) CE loss: 0.7054 (0.7557)
Epoch: [40][240/390] Train loss: -0.8335 (-0.7824) MI loss: -1.5589 (-1.5385) CE loss: 0.7254 (0.7562)
Epoch: [40][320/390] Train loss: -0.8231 (-0.7871) MI loss: -1.5183 (-1.5397) CE loss: 0.6952 (0.7527)
accuracy: [59.376, 65.652, 55.606, 74.512, 74.566]
best loss: -0.79
best accuracy: [59.37, 65.216, 56.989999999999995, 74.26, 74.03999999999999]
Epoch: [41][0/390] Train loss: -0.8802 (-0.8802) MI loss: -1.6211 (-1.6211) CE loss: 0.7409 (0.7409)
Epoch: [41][80/390] Train loss: -0.8254 (-0.8326) MI loss: -1.6380 (-1.5640) CE loss: 0.8126 (0.7314)
Epoch: [41][160/390] Train loss: -0.6558 (-0.8276) MI loss: -1.3711 (-1.5576) CE loss: 0.7153 (0.7300)
Epoch: [41][240/390] Train loss: -1.1043 (-0.8116) MI loss: -1.7839 (-1.5523) CE loss: 0.6796 (0.7408)
Epoch: [41][320/390] Train loss: -0.7837 (-0.8023) MI loss: -1.5053 (-1.5464) CE loss: 0.7217 (0.7441)
accuracy: [60.136, 67.574, 55.928, 74.42999999999999, 74.684]
state saving...
best loss: -0.80
best accuracy: [60.136, 67.574, 55.928, 74.42999999999999, 74.684]
Epoch: [42][0/390] Train loss: -0.7747 (-0.7747) MI loss: -1.5380 (-1.5380) CE loss: 0.7634 (0.7634)
Epoch: [42][80/390] Train loss: -0.8974 (-0.7900) MI loss: -1.6220 (-1.5382) CE loss: 0.7246 (0.7482)
Epoch: [42][160/390] Train loss: -0.8488 (-0.7811) MI loss: -1.6145 (-1.5405) CE loss: 0.7657 (0.7594)
Epoch: [42][240/390] Train loss: -0.7174 (-0.7811) MI loss: -1.4743 (-1.5406) CE loss: 0.7569 (0.7595)
Epoch: [42][320/390] Train loss: -0.6213 (-0.7842) MI loss: -1.4959 (-1.5421) CE loss: 0.8746 (0.7579)
accuracy: [61.978, 66.73599999999999, 57.414, 75.936, 75.42]
best loss: -0.80
best accuracy: [60.136, 67.574, 55.928, 74.42999999999999, 74.684]
Epoch: [43][0/390] Train loss: -0.8498 (-0.8498) MI loss: -1.5417 (-1.5417) CE loss: 0.6918 (0.6918)
Epoch: [43][80/390] Train loss: -0.7712 (-0.7995) MI loss: -1.4507 (-1.5540) CE loss: 0.6795 (0.7546)
Epoch: [43][160/390] Train loss: -0.8990 (-0.7841) MI loss: -1.5286 (-1.5444) CE loss: 0.6296 (0.7603)
Epoch: [43][240/390] Train loss: -0.9431 (-0.7890) MI loss: -1.5549 (-1.5448) CE loss: 0.6119 (0.7558)
Epoch: [43][320/390] Train loss: -0.7851 (-0.7864) MI loss: -1.5380 (-1.5435) CE loss: 0.7529 (0.7571)
accuracy: [59.9, 66.828, 55.733999999999995, 75.422, 75.4]
best loss: -0.80
best accuracy: [60.136, 67.574, 55.928, 74.42999999999999, 74.684]
Epoch: [44][0/390] Train loss: -0.7378 (-0.7378) MI loss: -1.4992 (-1.4992) CE loss: 0.7614 (0.7614)
Epoch: [44][80/390] Train loss: -0.9289 (-0.7838) MI loss: -1.5899 (-1.5416) CE loss: 0.6611 (0.7578)
Epoch: [44][160/390] Train loss: -1.0435 (-0.7836) MI loss: -1.6684 (-1.5397) CE loss: 0.6249 (0.7562)
Epoch: [44][240/390] Train loss: -0.8544 (-0.7951) MI loss: -1.5452 (-1.5467) CE loss: 0.6907 (0.7516)
Epoch: [44][320/390] Train loss: -0.8332 (-0.7926) MI loss: -1.5997 (-1.5405) CE loss: 0.7665 (0.7479)
accuracy: [61.36200000000001, 67.57, 57.16, 75.998, 75.698]
best loss: -0.80
best accuracy: [60.136, 67.574, 55.928, 74.42999999999999, 74.684]
Epoch: [45][0/390] Train loss: -0.8867 (-0.8867) MI loss: -1.4931 (-1.4931) CE loss: 0.6063 (0.6063)
Epoch: [45][80/390] Train loss: -0.7297 (-0.8177) MI loss: -1.5287 (-1.5622) CE loss: 0.7990 (0.7445)
Epoch: [45][160/390] Train loss: -0.8810 (-0.8092) MI loss: -1.5702 (-1.5520) CE loss: 0.6892 (0.7428)
Epoch: [45][240/390] Train loss: -0.7774 (-0.7983) MI loss: -1.5331 (-1.5490) CE loss: 0.7557 (0.7507)
Epoch: [45][320/390] Train loss: -0.7605 (-0.7947) MI loss: -1.5054 (-1.5456) CE loss: 0.7450 (0.7510)
accuracy: [60.328, 67.908, 56.42, 74.698, 75.246]
best loss: -0.80
best accuracy: [60.136, 67.574, 55.928, 74.42999999999999, 74.684]
Epoch: [46][0/390] Train loss: -0.8790 (-0.8790) MI loss: -1.5621 (-1.5621) CE loss: 0.6831 (0.6831)
Epoch: [46][80/390] Train loss: -0.6880 (-0.8131) MI loss: -1.4772 (-1.5668) CE loss: 0.7892 (0.7536)
Epoch: [46][160/390] Train loss: -0.9151 (-0.8087) MI loss: -1.6490 (-1.5572) CE loss: 0.7339 (0.7485)
Epoch: [46][240/390] Train loss: -1.0058 (-0.8052) MI loss: -1.6245 (-1.5548) CE loss: 0.6187 (0.7496)
Epoch: [46][320/390] Train loss: -0.6419 (-0.7930) MI loss: -1.5381 (-1.5462) CE loss: 0.8962 (0.7532)
accuracy: [59.870000000000005, 66.694, 55.66799999999999, 74.002, 73.54]
best loss: -0.80
best accuracy: [60.136, 67.574, 55.928, 74.42999999999999, 74.684]
Epoch: [47][0/390] Train loss: -0.8623 (-0.8623) MI loss: -1.6552 (-1.6552) CE loss: 0.7929 (0.7929)
Epoch: [47][80/390] Train loss: -0.6768 (-0.8194) MI loss: -1.4343 (-1.5574) CE loss: 0.7575 (0.7381)
Epoch: [47][160/390] Train loss: -0.7725 (-0.8089) MI loss: -1.6331 (-1.5523) CE loss: 0.8606 (0.7434)
Epoch: [47][240/390] Train loss: -0.6126 (-0.7996) MI loss: -1.4999 (-1.5490) CE loss: 0.8873 (0.7495)
Epoch: [47][320/390] Train loss: -0.8730 (-0.7944) MI loss: -1.5636 (-1.5458) CE loss: 0.6905 (0.7514)
accuracy: [60.272000000000006, 67.414, 57.230000000000004, 75.358, 74.536]
best loss: -0.80
best accuracy: [60.136, 67.574, 55.928, 74.42999999999999, 74.684]
Epoch: [48][0/390] Train loss: -0.7343 (-0.7343) MI loss: -1.5312 (-1.5312) CE loss: 0.7970 (0.7970)
Epoch: [48][80/390] Train loss: -1.0599 (-0.8179) MI loss: -1.6239 (-1.5654) CE loss: 0.5641 (0.7474)
Epoch: [48][160/390] Train loss: -0.8279 (-0.7859) MI loss: -1.5614 (-1.5475) CE loss: 0.7335 (0.7616)
Epoch: [48][240/390] Train loss: -1.0223 (-0.7869) MI loss: -1.6729 (-1.5456) CE loss: 0.6507 (0.7587)
Epoch: [48][320/390] Train loss: -0.7708 (-0.7836) MI loss: -1.5701 (-1.5427) CE loss: 0.7993 (0.7590)
accuracy: [59.254, 66.374, 56.272, 74.10799999999999, 73.66]
best loss: -0.80
best accuracy: [60.136, 67.574, 55.928, 74.42999999999999, 74.684]
Epoch: [49][0/390] Train loss: -0.9751 (-0.9751) MI loss: -1.6042 (-1.6042) CE loss: 0.6290 (0.6290)
Epoch: [49][80/390] Train loss: -0.6951 (-0.7900) MI loss: -1.4637 (-1.5441) CE loss: 0.7686 (0.7541)
Epoch: [49][160/390] Train loss: -0.7156 (-0.8037) MI loss: -1.4865 (-1.5517) CE loss: 0.7709 (0.7479)
Epoch: [49][240/390] Train loss: -1.0131 (-0.8094) MI loss: -1.6528 (-1.5554) CE loss: 0.6397 (0.7460)
Epoch: [49][320/390] Train loss: -0.6336 (-0.7994) MI loss: -1.4213 (-1.5516) CE loss: 0.7877 (0.7522)
accuracy: [61.11, 68.30000000000001, 56.45400000000001, 76.02, 75.324]
best loss: -0.80
best accuracy: [60.136, 67.574, 55.928, 74.42999999999999, 74.684]
Epoch: [50][0/390] Train loss: -0.6851 (-0.6851) MI loss: -1.4645 (-1.4645) CE loss: 0.7794 (0.7794)
Epoch: [50][80/390] Train loss: -0.9262 (-0.8120) MI loss: -1.6825 (-1.5640) CE loss: 0.7563 (0.7520)
Epoch: [50][160/390] Train loss: -1.0671 (-0.8142) MI loss: -1.7077 (-1.5643) CE loss: 0.6406 (0.7501)
Epoch: [50][240/390] Train loss: -0.8201 (-0.8006) MI loss: -1.5533 (-1.5578) CE loss: 0.7332 (0.7572)
Epoch: [50][320/390] Train loss: -0.6833 (-0.7993) MI loss: -1.4655 (-1.5521) CE loss: 0.7821 (0.7528)
accuracy: [59.952000000000005, 68.134, 56.276, 76.01599999999999, 75.564]
best loss: -0.80
best accuracy: [60.136, 67.574, 55.928, 74.42999999999999, 74.684]
Epoch: [51][0/390] Train loss: -0.8865 (-0.8865) MI loss: -1.5917 (-1.5917) CE loss: 0.7051 (0.7051)
Epoch: [51][80/390] Train loss: -0.8417 (-0.7842) MI loss: -1.5425 (-1.5384) CE loss: 0.7008 (0.7541)
Epoch: [51][160/390] Train loss: -0.6559 (-0.7963) MI loss: -1.4348 (-1.5491) CE loss: 0.7789 (0.7528)
Epoch: [51][240/390] Train loss: -0.8815 (-0.8062) MI loss: -1.5963 (-1.5539) CE loss: 0.7148 (0.7477)
Epoch: [51][320/390] Train loss: -0.9869 (-0.8019) MI loss: -1.5945 (-1.5532) CE loss: 0.6076 (0.7513)
accuracy: [62.402, 67.96600000000001, 56.477999999999994, 75.998, 75.18599999999999]
best loss: -0.80
best accuracy: [60.136, 67.574, 55.928, 74.42999999999999, 74.684]
Epoch: [52][0/390] Train loss: -0.8453 (-0.8453) MI loss: -1.5307 (-1.5307) CE loss: 0.6854 (0.6854)
Epoch: [52][80/390] Train loss: -0.6104 (-0.7905) MI loss: -1.4862 (-1.5509) CE loss: 0.8758 (0.7604)
Epoch: [52][160/390] Train loss: -0.8360 (-0.7864) MI loss: -1.7125 (-1.5495) CE loss: 0.8765 (0.7631)
Epoch: [52][240/390] Train loss: -0.8635 (-0.7953) MI loss: -1.5920 (-1.5520) CE loss: 0.7285 (0.7567)
Epoch: [52][320/390] Train loss: -0.5348 (-0.7909) MI loss: -1.4061 (-1.5467) CE loss: 0.8713 (0.7558)
accuracy: [60.736000000000004, 68.566, 56.842000000000006, 75.68, 75.132]
best loss: -0.80
best accuracy: [60.136, 67.574, 55.928, 74.42999999999999, 74.684]
Epoch: [53][0/390] Train loss: -0.8040 (-0.8040) MI loss: -1.5081 (-1.5081) CE loss: 0.7041 (0.7041)
Epoch: [53][80/390] Train loss: -0.8094 (-0.8349) MI loss: -1.5464 (-1.5638) CE loss: 0.7371 (0.7289)
Epoch: [53][160/390] Train loss: -0.7217 (-0.8212) MI loss: -1.5712 (-1.5613) CE loss: 0.8495 (0.7401)
Epoch: [53][240/390] Train loss: -0.7894 (-0.8060) MI loss: -1.4625 (-1.5536) CE loss: 0.6731 (0.7476)
Epoch: [53][320/390] Train loss: -0.6197 (-0.7886) MI loss: -1.4996 (-1.5458) CE loss: 0.8800 (0.7572)
accuracy: [61.385999999999996, 68.978, 57.264, 76.336, 74.786]
best loss: -0.80
best accuracy: [60.136, 67.574, 55.928, 74.42999999999999, 74.684]
Epoch: [54][0/390] Train loss: -0.8718 (-0.8718) MI loss: -1.6151 (-1.6151) CE loss: 0.7433 (0.7433)
Epoch: [54][80/390] Train loss: -0.9987 (-0.8152) MI loss: -1.6487 (-1.5645) CE loss: 0.6500 (0.7493)
Epoch: [54][160/390] Train loss: -0.9123 (-0.8028) MI loss: -1.5602 (-1.5576) CE loss: 0.6479 (0.7548)
Epoch: [54][240/390] Train loss: -0.8335 (-0.8068) MI loss: -1.5886 (-1.5570) CE loss: 0.7551 (0.7502)
Epoch: [54][320/390] Train loss: -0.8046 (-0.7995) MI loss: -1.5088 (-1.5543) CE loss: 0.7042 (0.7547)
accuracy: [61.717999999999996, 67.624, 55.972, 76.48, 75.642]
best loss: -0.80
best accuracy: [60.136, 67.574, 55.928, 74.42999999999999, 74.684]
Epoch: [55][0/390] Train loss: -1.1195 (-1.1195) MI loss: -1.7435 (-1.7435) CE loss: 0.6240 (0.6240)
Epoch: [55][80/390] Train loss: -0.9414 (-0.8118) MI loss: -1.6277 (-1.5646) CE loss: 0.6862 (0.7527)
Epoch: [55][160/390] Train loss: -0.8308 (-0.8223) MI loss: -1.5972 (-1.5717) CE loss: 0.7664 (0.7494)
Epoch: [55][240/390] Train loss: -0.8476 (-0.8099) MI loss: -1.5919 (-1.5622) CE loss: 0.7443 (0.7524)
Epoch: [55][320/390] Train loss: -0.7217 (-0.8071) MI loss: -1.5021 (-1.5572) CE loss: 0.7804 (0.7501)
accuracy: [60.852, 68.24, 56.108000000000004, 76.07000000000001, 74.618]
state saving...
best loss: -0.80
best accuracy: [60.852, 68.24, 56.108000000000004, 76.07000000000001, 74.618]
Epoch: [56][0/390] Train loss: -0.7552 (-0.7552) MI loss: -1.5549 (-1.5549) CE loss: 0.7997 (0.7997)
Epoch: [56][80/390] Train loss: -0.7370 (-0.8308) MI loss: -1.5699 (-1.5627) CE loss: 0.8329 (0.7319)
Epoch: [56][160/390] Train loss: -0.7956 (-0.8072) MI loss: -1.6010 (-1.5467) CE loss: 0.8054 (0.7394)
Epoch: [56][240/390] Train loss: -0.7888 (-0.8085) MI loss: -1.5285 (-1.5521) CE loss: 0.7397 (0.7436)
Epoch: [56][320/390] Train loss: -0.5445 (-0.7981) MI loss: -1.3481 (-1.5471) CE loss: 0.8036 (0.7490)
accuracy: [62.112, 66.60199999999999, 56.018, 74.802, 73.262]
best loss: -0.80
best accuracy: [60.852, 68.24, 56.108000000000004, 76.07000000000001, 74.618]
Epoch: [57][0/390] Train loss: -0.8289 (-0.8289) MI loss: -1.6021 (-1.6021) CE loss: 0.7732 (0.7732)
Epoch: [57][80/390] Train loss: -0.7160 (-0.8381) MI loss: -1.5546 (-1.5736) CE loss: 0.8386 (0.7354)
Epoch: [57][160/390] Train loss: -0.6734 (-0.8247) MI loss: -1.5608 (-1.5677) CE loss: 0.8874 (0.7431)
Epoch: [57][240/390] Train loss: -0.8611 (-0.8161) MI loss: -1.5571 (-1.5645) CE loss: 0.6959 (0.7483)
Epoch: [57][320/390] Train loss: -0.8928 (-0.8151) MI loss: -1.5619 (-1.5642) CE loss: 0.6692 (0.7491)
accuracy: [61.968, 67.622, 54.827999999999996, 76.118, 75.44]
state saving...
best loss: -0.81
best accuracy: [61.968, 67.622, 54.827999999999996, 76.118, 75.44]
Epoch: [58][0/390] Train loss: -0.7276 (-0.7276) MI loss: -1.4864 (-1.4864) CE loss: 0.7588 (0.7588)
Epoch: [58][80/390] Train loss: -0.9822 (-0.8201) MI loss: -1.5810 (-1.5677) CE loss: 0.5988 (0.7475)
Epoch: [58][160/390] Train loss: -0.9080 (-0.7988) MI loss: -1.7072 (-1.5589) CE loss: 0.7992 (0.7601)
Epoch: [58][240/390] Train loss: -0.6253 (-0.8063) MI loss: -1.4244 (-1.5585) CE loss: 0.7992 (0.7523)
Epoch: [58][320/390] Train loss: -0.8295 (-0.8096) MI loss: -1.5469 (-1.5584) CE loss: 0.7174 (0.7488)
accuracy: [62.074, 68.978, 57.028, 76.812, 76.432]
best loss: -0.81
best accuracy: [61.968, 67.622, 54.827999999999996, 76.118, 75.44]
Epoch: [59][0/390] Train loss: -0.8499 (-0.8499) MI loss: -1.6283 (-1.6283) CE loss: 0.7785 (0.7785)
Epoch: [59][80/390] Train loss: -0.7835 (-0.8178) MI loss: -1.4917 (-1.5631) CE loss: 0.7082 (0.7453)
Epoch: [59][160/390] Train loss: -0.9080 (-0.8057) MI loss: -1.6145 (-1.5590) CE loss: 0.7065 (0.7534)
Epoch: [59][240/390] Train loss: -0.8039 (-0.8031) MI loss: -1.5163 (-1.5550) CE loss: 0.7124 (0.7519)
Epoch: [59][320/390] Train loss: -0.8250 (-0.8044) MI loss: -1.5302 (-1.5573) CE loss: 0.7052 (0.7529)
accuracy: [63.258, 68.848, 55.766000000000005, 76.73, 76.306]
best loss: -0.81
best accuracy: [61.968, 67.622, 54.827999999999996, 76.118, 75.44]
Epoch: [60][0/390] Train loss: -0.6481 (-0.6481) MI loss: -1.4104 (-1.4104) CE loss: 0.7623 (0.7623)
Epoch: [60][80/390] Train loss: -1.0155 (-0.8259) MI loss: -1.6729 (-1.5608) CE loss: 0.6574 (0.7349)
Epoch: [60][160/390] Train loss: -0.6370 (-0.8151) MI loss: -1.5164 (-1.5583) CE loss: 0.8793 (0.7432)
Epoch: [60][240/390] Train loss: -0.8380 (-0.8166) MI loss: -1.5490 (-1.5578) CE loss: 0.7110 (0.7412)
Epoch: [60][320/390] Train loss: -0.8475 (-0.8194) MI loss: -1.6090 (-1.5598) CE loss: 0.7615 (0.7404)
accuracy: [62.12, 69.406, 57.422, 76.726, 75.25]
state saving...
best loss: -0.82
best accuracy: [62.12, 69.406, 57.422, 76.726, 75.25]
Epoch: [61][0/390] Train loss: -0.8917 (-0.8917) MI loss: -1.6026 (-1.6026) CE loss: 0.7110 (0.7110)
Epoch: [61][80/390] Train loss: -0.9507 (-0.7928) MI loss: -1.7360 (-1.5619) CE loss: 0.7853 (0.7691)
Epoch: [61][160/390] Train loss: -0.6905 (-0.7943) MI loss: -1.4827 (-1.5600) CE loss: 0.7923 (0.7657)
Epoch: [61][240/390] Train loss: -0.8823 (-0.8052) MI loss: -1.5645 (-1.5606) CE loss: 0.6822 (0.7555)
Epoch: [61][320/390] Train loss: -0.6841 (-0.8059) MI loss: -1.4636 (-1.5606) CE loss: 0.7795 (0.7548)
accuracy: [62.092000000000006, 68.46799999999999, 56.532000000000004, 76.492, 75.672]
best loss: -0.82
best accuracy: [62.12, 69.406, 57.422, 76.726, 75.25]
Epoch: [62][0/390] Train loss: -0.9287 (-0.9287) MI loss: -1.6057 (-1.6057) CE loss: 0.6770 (0.6770)
Epoch: [62][80/390] Train loss: -0.9111 (-0.8041) MI loss: -1.6151 (-1.5557) CE loss: 0.7040 (0.7516)
Epoch: [62][160/390] Train loss: -1.0637 (-0.8145) MI loss: -1.7041 (-1.5583) CE loss: 0.6404 (0.7439)
Epoch: [62][240/390] Train loss: -0.6502 (-0.8036) MI loss: -1.5039 (-1.5558) CE loss: 0.8537 (0.7522)
Epoch: [62][320/390] Train loss: -0.6649 (-0.8012) MI loss: -1.5304 (-1.5526) CE loss: 0.8655 (0.7514)
accuracy: [61.17, 69.45400000000001, 57.33200000000001, 76.128, 75.77000000000001]
best loss: -0.82
best accuracy: [62.12, 69.406, 57.422, 76.726, 75.25]
Epoch: [63][0/390] Train loss: -0.6804 (-0.6804) MI loss: -1.5020 (-1.5020) CE loss: 0.8215 (0.8215)
Epoch: [63][80/390] Train loss: -1.0136 (-0.8106) MI loss: -1.6176 (-1.5593) CE loss: 0.6041 (0.7487)
Epoch: [63][160/390] Train loss: -0.6969 (-0.8101) MI loss: -1.5477 (-1.5607) CE loss: 0.8507 (0.7506)
Epoch: [63][240/390] Train loss: -0.7160 (-0.8111) MI loss: -1.5103 (-1.5598) CE loss: 0.7943 (0.7487)
Epoch: [63][320/390] Train loss: -0.8876 (-0.8099) MI loss: -1.6613 (-1.5594) CE loss: 0.7737 (0.7495)
accuracy: [63.044, 67.798, 56.838, 76.21, 75.738]
best loss: -0.82
best accuracy: [62.12, 69.406, 57.422, 76.726, 75.25]
Epoch: [64][0/390] Train loss: -0.7187 (-0.7187) MI loss: -1.4894 (-1.4894) CE loss: 0.7707 (0.7707)
Epoch: [64][80/390] Train loss: -0.9195 (-0.8002) MI loss: -1.5573 (-1.5519) CE loss: 0.6378 (0.7517)
Epoch: [64][160/390] Train loss: -0.6743 (-0.8190) MI loss: -1.5045 (-1.5666) CE loss: 0.8302 (0.7476)
Epoch: [64][240/390] Train loss: -0.5044 (-0.8013) MI loss: -1.4198 (-1.5567) CE loss: 0.9154 (0.7554)
Epoch: [64][320/390] Train loss: -0.7147 (-0.8074) MI loss: -1.4949 (-1.5582) CE loss: 0.7803 (0.7508)
accuracy: [61.62800000000001, 68.516, 57.138, 77.37, 76.964]
best loss: -0.82
best accuracy: [62.12, 69.406, 57.422, 76.726, 75.25]
Epoch: [65][0/390] Train loss: -0.7412 (-0.7412) MI loss: -1.4475 (-1.4475) CE loss: 0.7063 (0.7063)
Epoch: [65][80/390] Train loss: -0.8587 (-0.8371) MI loss: -1.6302 (-1.5799) CE loss: 0.7716 (0.7429)
Epoch: [65][160/390] Train loss: -0.9154 (-0.8361) MI loss: -1.6301 (-1.5755) CE loss: 0.7147 (0.7393)
Epoch: [65][240/390] Train loss: -0.7180 (-0.8302) MI loss: -1.4019 (-1.5678) CE loss: 0.6839 (0.7376)
Epoch: [65][320/390] Train loss: -0.8184 (-0.8267) MI loss: -1.5903 (-1.5686) CE loss: 0.7719 (0.7419)
accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
state saving...
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [66][0/390] Train loss: -0.8327 (-0.8327) MI loss: -1.6288 (-1.6288) CE loss: 0.7961 (0.7961)
Epoch: [66][80/390] Train loss: -0.9024 (-0.8238) MI loss: -1.5465 (-1.5734) CE loss: 0.6441 (0.7495)
Epoch: [66][160/390] Train loss: -1.0463 (-0.8139) MI loss: -1.7154 (-1.5650) CE loss: 0.6691 (0.7512)
Epoch: [66][240/390] Train loss: -1.0808 (-0.8090) MI loss: -1.6578 (-1.5611) CE loss: 0.5770 (0.7521)
Epoch: [66][320/390] Train loss: -0.6238 (-0.8075) MI loss: -1.5240 (-1.5603) CE loss: 0.9002 (0.7528)
accuracy: [61.388, 68.354, 56.34, 77.16, 76.27000000000001]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [67][0/390] Train loss: -0.7576 (-0.7576) MI loss: -1.5521 (-1.5521) CE loss: 0.7945 (0.7945)
Epoch: [67][80/390] Train loss: -0.8854 (-0.8275) MI loss: -1.5407 (-1.5682) CE loss: 0.6553 (0.7407)
Epoch: [67][160/390] Train loss: -0.8004 (-0.8065) MI loss: -1.5582 (-1.5601) CE loss: 0.7578 (0.7536)
Epoch: [67][240/390] Train loss: -0.6399 (-0.7884) MI loss: -1.4199 (-1.5505) CE loss: 0.7800 (0.7621)
Epoch: [67][320/390] Train loss: -0.7989 (-0.7884) MI loss: -1.4871 (-1.5513) CE loss: 0.6882 (0.7629)
accuracy: [62.11600000000001, 68.244, 56.424, 77.046, 75.31]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [68][0/390] Train loss: -0.9363 (-0.9363) MI loss: -1.6939 (-1.6939) CE loss: 0.7576 (0.7576)
Epoch: [68][80/390] Train loss: -0.9774 (-0.7927) MI loss: -1.6600 (-1.5611) CE loss: 0.6825 (0.7684)
Epoch: [68][160/390] Train loss: -1.1254 (-0.8013) MI loss: -1.7633 (-1.5661) CE loss: 0.6379 (0.7648)
Epoch: [68][240/390] Train loss: -0.8370 (-0.8057) MI loss: -1.5893 (-1.5669) CE loss: 0.7522 (0.7612)
Epoch: [68][320/390] Train loss: -0.8462 (-0.8020) MI loss: -1.6171 (-1.5633) CE loss: 0.7709 (0.7613)
accuracy: [61.646, 68.13, 56.052, 76.184, 74.936]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [69][0/390] Train loss: -0.5346 (-0.5346) MI loss: -1.3547 (-1.3547) CE loss: 0.8201 (0.8201)
Epoch: [69][80/390] Train loss: -0.7876 (-0.8137) MI loss: -1.5971 (-1.5613) CE loss: 0.8095 (0.7476)
Epoch: [69][160/390] Train loss: -0.8157 (-0.8057) MI loss: -1.5651 (-1.5615) CE loss: 0.7494 (0.7558)
Epoch: [69][240/390] Train loss: -0.5514 (-0.8062) MI loss: -1.3745 (-1.5610) CE loss: 0.8232 (0.7548)
Epoch: [69][320/390] Train loss: -0.9664 (-0.8092) MI loss: -1.7123 (-1.5615) CE loss: 0.7459 (0.7523)
accuracy: [60.47, 68.89999999999999, 56.934, 77.572, 75.834]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [70][0/390] Train loss: -0.6639 (-0.6639) MI loss: -1.4816 (-1.4816) CE loss: 0.8177 (0.8177)
Epoch: [70][80/390] Train loss: -0.9348 (-0.8104) MI loss: -1.6056 (-1.5696) CE loss: 0.6708 (0.7592)
Epoch: [70][160/390] Train loss: -0.9124 (-0.7829) MI loss: -1.6444 (-1.5512) CE loss: 0.7320 (0.7682)
Epoch: [70][240/390] Train loss: -0.7551 (-0.7875) MI loss: -1.5443 (-1.5542) CE loss: 0.7892 (0.7666)
Epoch: [70][320/390] Train loss: -0.6273 (-0.7873) MI loss: -1.3952 (-1.5533) CE loss: 0.7680 (0.7661)
accuracy: [61.224000000000004, 68.644, 56.406, 77.7, 74.886]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [71][0/390] Train loss: -0.8557 (-0.8557) MI loss: -1.6107 (-1.6107) CE loss: 0.7550 (0.7550)
Epoch: [71][80/390] Train loss: -0.9157 (-0.7920) MI loss: -1.5360 (-1.5588) CE loss: 0.6202 (0.7668)
Epoch: [71][160/390] Train loss: -0.8458 (-0.7953) MI loss: -1.5563 (-1.5587) CE loss: 0.7105 (0.7634)
Epoch: [71][240/390] Train loss: -0.9306 (-0.7988) MI loss: -1.6363 (-1.5603) CE loss: 0.7057 (0.7615)
Epoch: [71][320/390] Train loss: -0.8611 (-0.7933) MI loss: -1.5983 (-1.5566) CE loss: 0.7372 (0.7633)
accuracy: [61.882000000000005, 69.524, 56.886, 77.584, 76.238]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [72][0/390] Train loss: -0.9468 (-0.9468) MI loss: -1.5737 (-1.5737) CE loss: 0.6269 (0.6269)
Epoch: [72][80/390] Train loss: -0.8761 (-0.8084) MI loss: -1.6476 (-1.5563) CE loss: 0.7715 (0.7479)
Epoch: [72][160/390] Train loss: -0.8069 (-0.8065) MI loss: -1.5817 (-1.5605) CE loss: 0.7748 (0.7540)
Epoch: [72][240/390] Train loss: -0.7210 (-0.8029) MI loss: -1.5642 (-1.5584) CE loss: 0.8431 (0.7555)
Epoch: [72][320/390] Train loss: -0.7783 (-0.8044) MI loss: -1.5680 (-1.5588) CE loss: 0.7897 (0.7544)
accuracy: [63.404, 69.866, 57.56400000000001, 77.976, 76.664]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [73][0/390] Train loss: -1.0637 (-1.0637) MI loss: -1.6522 (-1.6522) CE loss: 0.5885 (0.5885)
Epoch: [73][80/390] Train loss: -0.5919 (-0.8109) MI loss: -1.4768 (-1.5671) CE loss: 0.8849 (0.7562)
Epoch: [73][160/390] Train loss: -0.7923 (-0.8213) MI loss: -1.5396 (-1.5717) CE loss: 0.7473 (0.7505)
Epoch: [73][240/390] Train loss: -0.6687 (-0.8151) MI loss: -1.5380 (-1.5673) CE loss: 0.8692 (0.7522)
Epoch: [73][320/390] Train loss: -0.7173 (-0.8123) MI loss: -1.6204 (-1.5633) CE loss: 0.9030 (0.7510)
accuracy: [62.694, 69.108, 56.16799999999999, 76.646, 75.888]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [74][0/390] Train loss: -0.8427 (-0.8427) MI loss: -1.5799 (-1.5799) CE loss: 0.7373 (0.7373)
Epoch: [74][80/390] Train loss: -0.8149 (-0.7815) MI loss: -1.5027 (-1.5543) CE loss: 0.6878 (0.7728)
Epoch: [74][160/390] Train loss: -0.8813 (-0.7977) MI loss: -1.5473 (-1.5572) CE loss: 0.6660 (0.7595)
Epoch: [74][240/390] Train loss: -0.5796 (-0.8061) MI loss: -1.4850 (-1.5636) CE loss: 0.9054 (0.7575)
Epoch: [74][320/390] Train loss: -0.8505 (-0.8101) MI loss: -1.5642 (-1.5652) CE loss: 0.7137 (0.7551)
accuracy: [62.660000000000004, 68.614, 56.736, 77.578, 75.89200000000001]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [75][0/390] Train loss: -0.9516 (-0.9516) MI loss: -1.5960 (-1.5960) CE loss: 0.6444 (0.6444)
Epoch: [75][80/390] Train loss: -0.8241 (-0.8266) MI loss: -1.6285 (-1.5758) CE loss: 0.8045 (0.7492)
Epoch: [75][160/390] Train loss: -1.1944 (-0.8182) MI loss: -1.7733 (-1.5689) CE loss: 0.5790 (0.7507)
Epoch: [75][240/390] Train loss: -0.6774 (-0.8133) MI loss: -1.5969 (-1.5683) CE loss: 0.9195 (0.7550)
Epoch: [75][320/390] Train loss: -0.7616 (-0.8109) MI loss: -1.5276 (-1.5680) CE loss: 0.7661 (0.7570)
accuracy: [62.842, 68.23599999999999, 57.001999999999995, 76.992, 76.22]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [76][0/390] Train loss: -0.9000 (-0.9000) MI loss: -1.5476 (-1.5476) CE loss: 0.6476 (0.6476)
Epoch: [76][80/390] Train loss: -0.6726 (-0.8175) MI loss: -1.5625 (-1.5827) CE loss: 0.8899 (0.7652)
Epoch: [76][160/390] Train loss: -0.7583 (-0.8108) MI loss: -1.4985 (-1.5718) CE loss: 0.7402 (0.7610)
Epoch: [76][240/390] Train loss: -0.8441 (-0.8096) MI loss: -1.6216 (-1.5677) CE loss: 0.7775 (0.7582)
Epoch: [76][320/390] Train loss: -0.9017 (-0.8038) MI loss: -1.5579 (-1.5656) CE loss: 0.6562 (0.7618)
accuracy: [61.428000000000004, 68.56, 57.154, 77.168, 76.46]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [77][0/390] Train loss: -0.8152 (-0.8152) MI loss: -1.5989 (-1.5989) CE loss: 0.7837 (0.7837)
Epoch: [77][80/390] Train loss: -0.8282 (-0.8291) MI loss: -1.5087 (-1.5775) CE loss: 0.6805 (0.7484)
Epoch: [77][160/390] Train loss: -0.7886 (-0.8166) MI loss: -1.6014 (-1.5733) CE loss: 0.8127 (0.7567)
Epoch: [77][240/390] Train loss: -0.8953 (-0.8193) MI loss: -1.6035 (-1.5706) CE loss: 0.7082 (0.7513)
Epoch: [77][320/390] Train loss: -1.1350 (-0.8123) MI loss: -1.7533 (-1.5683) CE loss: 0.6183 (0.7560)
accuracy: [62.96000000000001, 69.294, 58.656, 76.988, 76.16000000000001]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [78][0/390] Train loss: -0.8924 (-0.8924) MI loss: -1.6360 (-1.6360) CE loss: 0.7436 (0.7436)
Epoch: [78][80/390] Train loss: -0.6758 (-0.7996) MI loss: -1.5028 (-1.5496) CE loss: 0.8270 (0.7500)
Epoch: [78][160/390] Train loss: -0.9121 (-0.8142) MI loss: -1.5705 (-1.5607) CE loss: 0.6583 (0.7466)
Epoch: [78][240/390] Train loss: -0.8173 (-0.8124) MI loss: -1.5449 (-1.5636) CE loss: 0.7276 (0.7512)
Epoch: [78][320/390] Train loss: -0.9174 (-0.8064) MI loss: -1.6709 (-1.5635) CE loss: 0.7535 (0.7571)
accuracy: [63.914, 70.18799999999999, 57.782, 77.968, 77.24]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [79][0/390] Train loss: -1.0404 (-1.0404) MI loss: -1.6193 (-1.6193) CE loss: 0.5789 (0.5789)
Epoch: [79][80/390] Train loss: -0.6624 (-0.8341) MI loss: -1.4808 (-1.5794) CE loss: 0.8185 (0.7453)
Epoch: [79][160/390] Train loss: -0.8928 (-0.8230) MI loss: -1.5762 (-1.5742) CE loss: 0.6835 (0.7513)
Epoch: [79][240/390] Train loss: -0.7799 (-0.8140) MI loss: -1.5347 (-1.5675) CE loss: 0.7548 (0.7535)
Epoch: [79][320/390] Train loss: -0.9855 (-0.8156) MI loss: -1.6955 (-1.5683) CE loss: 0.7101 (0.7526)
accuracy: [61.39, 68.812, 57.879999999999995, 77.744, 75.6]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [80][0/390] Train loss: -0.9167 (-0.9167) MI loss: -1.5953 (-1.5953) CE loss: 0.6786 (0.6786)
Epoch: [80][80/390] Train loss: -0.6793 (-0.8196) MI loss: -1.5493 (-1.5748) CE loss: 0.8700 (0.7552)
Epoch: [80][160/390] Train loss: -0.5405 (-0.8029) MI loss: -1.3858 (-1.5624) CE loss: 0.8453 (0.7594)
Epoch: [80][240/390] Train loss: -0.6701 (-0.8034) MI loss: -1.4849 (-1.5629) CE loss: 0.8148 (0.7595)
Epoch: [80][320/390] Train loss: -0.7627 (-0.8079) MI loss: -1.5943 (-1.5642) CE loss: 0.8317 (0.7563)
accuracy: [63.066, 70.598, 58.245999999999995, 78.366, 76.42]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [81][0/390] Train loss: -1.0473 (-1.0473) MI loss: -1.6845 (-1.6845) CE loss: 0.6372 (0.6372)
Epoch: [81][80/390] Train loss: -0.9217 (-0.8341) MI loss: -1.6432 (-1.5731) CE loss: 0.7216 (0.7390)
Epoch: [81][160/390] Train loss: -0.6520 (-0.8222) MI loss: -1.5004 (-1.5729) CE loss: 0.8485 (0.7507)
Epoch: [81][240/390] Train loss: -0.6573 (-0.8165) MI loss: -1.3939 (-1.5674) CE loss: 0.7366 (0.7509)
Epoch: [81][320/390] Train loss: -0.5643 (-0.8127) MI loss: -1.4751 (-1.5641) CE loss: 0.9109 (0.7514)
accuracy: [62.488, 69.64, 57.752, 77.74, 76.824]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [82][0/390] Train loss: -0.8244 (-0.8244) MI loss: -1.6612 (-1.6612) CE loss: 0.8368 (0.8368)
Epoch: [82][80/390] Train loss: -0.8430 (-0.8245) MI loss: -1.5861 (-1.5734) CE loss: 0.7432 (0.7489)
Epoch: [82][160/390] Train loss: -0.8993 (-0.8104) MI loss: -1.5260 (-1.5666) CE loss: 0.6268 (0.7562)
Epoch: [82][240/390] Train loss: -0.8086 (-0.8053) MI loss: -1.5305 (-1.5636) CE loss: 0.7219 (0.7583)
Epoch: [82][320/390] Train loss: -0.6912 (-0.8011) MI loss: -1.5301 (-1.5615) CE loss: 0.8389 (0.7604)
accuracy: [62.626000000000005, 68.902, 57.967999999999996, 77.888, 77.564]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [83][0/390] Train loss: -0.8587 (-0.8587) MI loss: -1.6365 (-1.6365) CE loss: 0.7778 (0.7778)
Epoch: [83][80/390] Train loss: -0.5201 (-0.8161) MI loss: -1.4314 (-1.5788) CE loss: 0.9113 (0.7627)
Epoch: [83][160/390] Train loss: -0.6527 (-0.8033) MI loss: -1.5016 (-1.5688) CE loss: 0.8489 (0.7654)
Epoch: [83][240/390] Train loss: -0.9294 (-0.7956) MI loss: -1.5913 (-1.5632) CE loss: 0.6619 (0.7676)
Epoch: [83][320/390] Train loss: -0.5143 (-0.7869) MI loss: -1.4052 (-1.5583) CE loss: 0.8909 (0.7714)
accuracy: [61.78399999999999, 70.636, 58.156, 78.218, 77.334]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [84][0/390] Train loss: -0.9136 (-0.9136) MI loss: -1.5597 (-1.5597) CE loss: 0.6461 (0.6461)
Epoch: [84][80/390] Train loss: -0.6352 (-0.8051) MI loss: -1.4104 (-1.5710) CE loss: 0.7752 (0.7659)
Epoch: [84][160/390] Train loss: -0.7732 (-0.8136) MI loss: -1.5203 (-1.5732) CE loss: 0.7471 (0.7596)
Epoch: [84][240/390] Train loss: -0.4999 (-0.8171) MI loss: -1.4394 (-1.5679) CE loss: 0.9395 (0.7508)
Epoch: [84][320/390] Train loss: -0.7245 (-0.8173) MI loss: -1.5416 (-1.5687) CE loss: 0.8171 (0.7513)
accuracy: [64.19, 69.704, 58.262, 78.396, 77.532]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [85][0/390] Train loss: -1.1009 (-1.1009) MI loss: -1.7749 (-1.7749) CE loss: 0.6740 (0.6740)
Epoch: [85][80/390] Train loss: -0.8904 (-0.8048) MI loss: -1.6279 (-1.5758) CE loss: 0.7375 (0.7710)
Epoch: [85][160/390] Train loss: -0.8816 (-0.8069) MI loss: -1.5774 (-1.5714) CE loss: 0.6959 (0.7645)
Epoch: [85][240/390] Train loss: -0.5471 (-0.8062) MI loss: -1.4706 (-1.5679) CE loss: 0.9236 (0.7616)
Epoch: [85][320/390] Train loss: -0.7559 (-0.8025) MI loss: -1.4619 (-1.5655) CE loss: 0.7059 (0.7630)
accuracy: [61.36200000000001, 69.378, 57.864000000000004, 77.518, 75.90599999999999]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [86][0/390] Train loss: -0.8374 (-0.8374) MI loss: -1.5512 (-1.5512) CE loss: 0.7138 (0.7138)
Epoch: [86][80/390] Train loss: -1.0244 (-0.8164) MI loss: -1.6976 (-1.5750) CE loss: 0.6732 (0.7586)
Epoch: [86][160/390] Train loss: -0.9934 (-0.8037) MI loss: -1.6540 (-1.5662) CE loss: 0.6606 (0.7625)
Epoch: [86][240/390] Train loss: -0.7537 (-0.8099) MI loss: -1.5537 (-1.5675) CE loss: 0.8000 (0.7576)
Epoch: [86][320/390] Train loss: -0.9135 (-0.8055) MI loss: -1.5862 (-1.5649) CE loss: 0.6726 (0.7594)
accuracy: [62.464, 70.45400000000001, 59.616, 78.51, 76.302]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [87][0/390] Train loss: -0.9698 (-0.9698) MI loss: -1.7237 (-1.7237) CE loss: 0.7539 (0.7539)
Epoch: [87][80/390] Train loss: -0.8318 (-0.8125) MI loss: -1.6485 (-1.5693) CE loss: 0.8167 (0.7568)
Epoch: [87][160/390] Train loss: -0.6769 (-0.8149) MI loss: -1.4967 (-1.5736) CE loss: 0.8198 (0.7587)
Epoch: [87][240/390] Train loss: -0.7282 (-0.8174) MI loss: -1.6246 (-1.5708) CE loss: 0.8964 (0.7534)
Epoch: [87][320/390] Train loss: -0.7384 (-0.8141) MI loss: -1.5908 (-1.5680) CE loss: 0.8524 (0.7539)
accuracy: [62.602000000000004, 69.11, 57.354000000000006, 77.486, 76.19399999999999]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [88][0/390] Train loss: -0.9911 (-0.9911) MI loss: -1.5590 (-1.5590) CE loss: 0.5679 (0.5679)
Epoch: [88][80/390] Train loss: -0.8807 (-0.7924) MI loss: -1.6234 (-1.5567) CE loss: 0.7427 (0.7643)
Epoch: [88][160/390] Train loss: -0.6098 (-0.8108) MI loss: -1.4628 (-1.5645) CE loss: 0.8529 (0.7537)
Epoch: [88][240/390] Train loss: -0.7901 (-0.8180) MI loss: -1.5932 (-1.5704) CE loss: 0.8031 (0.7524)
Epoch: [88][320/390] Train loss: -0.7549 (-0.8136) MI loss: -1.5437 (-1.5702) CE loss: 0.7889 (0.7566)
accuracy: [62.254, 70.596, 59.233999999999995, 78.354, 76.228]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [89][0/390] Train loss: -0.8411 (-0.8411) MI loss: -1.5196 (-1.5196) CE loss: 0.6785 (0.6785)
Epoch: [89][80/390] Train loss: -0.8667 (-0.8119) MI loss: -1.5855 (-1.5661) CE loss: 0.7188 (0.7543)
Epoch: [89][160/390] Train loss: -0.8852 (-0.8076) MI loss: -1.6180 (-1.5638) CE loss: 0.7328 (0.7562)
Epoch: [89][240/390] Train loss: -0.9368 (-0.8089) MI loss: -1.6675 (-1.5677) CE loss: 0.7307 (0.7587)
Epoch: [89][320/390] Train loss: -0.6004 (-0.8106) MI loss: -1.4439 (-1.5663) CE loss: 0.8436 (0.7557)
accuracy: [63.198, 70.03, 57.92, 78.39, 77.11]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [90][0/390] Train loss: -0.8838 (-0.8838) MI loss: -1.5924 (-1.5924) CE loss: 0.7085 (0.7085)
Epoch: [90][80/390] Train loss: -0.6878 (-0.7928) MI loss: -1.5118 (-1.5581) CE loss: 0.8240 (0.7653)
Epoch: [90][160/390] Train loss: -0.9466 (-0.8011) MI loss: -1.5602 (-1.5644) CE loss: 0.6136 (0.7633)
Epoch: [90][240/390] Train loss: -0.5199 (-0.7990) MI loss: -1.4011 (-1.5639) CE loss: 0.8812 (0.7649)
Epoch: [90][320/390] Train loss: -1.0396 (-0.8028) MI loss: -1.6731 (-1.5637) CE loss: 0.6335 (0.7608)
accuracy: [63.324000000000005, 70.50999999999999, 59.184000000000005, 78.66199999999999, 76.926]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [91][0/390] Train loss: -0.4630 (-0.4630) MI loss: -1.4472 (-1.4472) CE loss: 0.9842 (0.9842)
Epoch: [91][80/390] Train loss: -0.7481 (-0.8222) MI loss: -1.5195 (-1.5842) CE loss: 0.7714 (0.7619)
Epoch: [91][160/390] Train loss: -0.9387 (-0.7902) MI loss: -1.6088 (-1.5692) CE loss: 0.6701 (0.7790)
Epoch: [91][240/390] Train loss: -0.7721 (-0.7962) MI loss: -1.6149 (-1.5710) CE loss: 0.8428 (0.7748)
Epoch: [91][320/390] Train loss: -0.7099 (-0.7971) MI loss: -1.5665 (-1.5712) CE loss: 0.8566 (0.7741)
accuracy: [61.794000000000004, 68.96, 57.955999999999996, 76.372, 75.024]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [92][0/390] Train loss: -0.9040 (-0.9040) MI loss: -1.6066 (-1.6066) CE loss: 0.7025 (0.7025)
Epoch: [92][80/390] Train loss: -0.4933 (-0.8415) MI loss: -1.4063 (-1.5842) CE loss: 0.9130 (0.7427)
Epoch: [92][160/390] Train loss: -0.6244 (-0.8179) MI loss: -1.4981 (-1.5741) CE loss: 0.8737 (0.7562)
Epoch: [92][240/390] Train loss: -0.6033 (-0.8066) MI loss: -1.4494 (-1.5681) CE loss: 0.8461 (0.7616)
Epoch: [92][320/390] Train loss: -0.7027 (-0.7995) MI loss: -1.4688 (-1.5636) CE loss: 0.7661 (0.7641)
accuracy: [62.8, 69.43, 58.886, 77.802, 76.42999999999999]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [93][0/390] Train loss: -0.8251 (-0.8251) MI loss: -1.5949 (-1.5949) CE loss: 0.7697 (0.7697)
Epoch: [93][80/390] Train loss: -0.9724 (-0.8407) MI loss: -1.5828 (-1.5853) CE loss: 0.6104 (0.7446)
Epoch: [93][160/390] Train loss: -0.8856 (-0.8224) MI loss: -1.6297 (-1.5762) CE loss: 0.7441 (0.7539)
Epoch: [93][240/390] Train loss: -0.8520 (-0.8260) MI loss: -1.4916 (-1.5770) CE loss: 0.6396 (0.7510)
Epoch: [93][320/390] Train loss: -0.8294 (-0.8138) MI loss: -1.5785 (-1.5688) CE loss: 0.7491 (0.7550)
accuracy: [61.866, 70.072, 58.486000000000004, 77.878, 75.616]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [94][0/390] Train loss: -0.8108 (-0.8108) MI loss: -1.6065 (-1.6065) CE loss: 0.7957 (0.7957)
Epoch: [94][80/390] Train loss: -0.8219 (-0.8300) MI loss: -1.6418 (-1.5812) CE loss: 0.8199 (0.7512)
Epoch: [94][160/390] Train loss: -0.9076 (-0.8037) MI loss: -1.6568 (-1.5681) CE loss: 0.7491 (0.7644)
Epoch: [94][240/390] Train loss: -0.8310 (-0.8038) MI loss: -1.6118 (-1.5641) CE loss: 0.7809 (0.7603)
Epoch: [94][320/390] Train loss: -1.0016 (-0.8064) MI loss: -1.6950 (-1.5655) CE loss: 0.6934 (0.7592)
accuracy: [62.641999999999996, 70.13000000000001, 58.553999999999995, 77.74600000000001, 76.488]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [95][0/390] Train loss: -0.7541 (-0.7541) MI loss: -1.5145 (-1.5145) CE loss: 0.7604 (0.7604)
Epoch: [95][80/390] Train loss: -0.9795 (-0.8200) MI loss: -1.6200 (-1.5681) CE loss: 0.6405 (0.7481)
Epoch: [95][160/390] Train loss: -0.8665 (-0.8246) MI loss: -1.6465 (-1.5716) CE loss: 0.7800 (0.7470)
Epoch: [95][240/390] Train loss: -0.6607 (-0.8223) MI loss: -1.5062 (-1.5711) CE loss: 0.8455 (0.7488)
Epoch: [95][320/390] Train loss: -0.9940 (-0.8217) MI loss: -1.5937 (-1.5720) CE loss: 0.5997 (0.7502)
accuracy: [62.612, 69.45, 57.443999999999996, 77.968, 77.11200000000001]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [96][0/390] Train loss: -0.7182 (-0.7182) MI loss: -1.5061 (-1.5061) CE loss: 0.7878 (0.7878)
Epoch: [96][80/390] Train loss: -0.7233 (-0.8137) MI loss: -1.5237 (-1.5725) CE loss: 0.8004 (0.7588)
Epoch: [96][160/390] Train loss: -0.9769 (-0.8021) MI loss: -1.6930 (-1.5703) CE loss: 0.7160 (0.7682)
Epoch: [96][240/390] Train loss: -0.8728 (-0.7992) MI loss: -1.6356 (-1.5672) CE loss: 0.7628 (0.7680)
Epoch: [96][320/390] Train loss: -0.8179 (-0.8025) MI loss: -1.5053 (-1.5680) CE loss: 0.6874 (0.7654)
accuracy: [61.844, 70.116, 58.664, 77.928, 76.142]
best loss: -0.82
best accuracy: [62.552, 68.238, 57.602, 76.55, 74.954]
Epoch: [97][0/390] Train loss: -0.8973 (-0.8973) MI loss: -1.6440 (-1.6440) CE loss: 0.7467 (0.7467)
Epoch: [97][80/390] Train loss: -0.8130 (-0.7876) MI loss: -1.5792 (-1.5624) CE loss: 0.7662 (0.7747)
Epoch: [97][160/390] Train loss: -0.8791 (-0.7895) MI loss: -1.6247 (-1.5618) CE loss: 0.7456 (0.7723)