Reproduction Failure: High Loss Throughout Training

[helloworld751]

Hi there,

I'm impressed by your excellent work. However, I am unable to reproduce the performance reported in your paper using your source code. The loss remains high throughout the training phase, resulting in extremely low accuracy. Could you provide any insights or guidance on this issue?

Thank you in advance.

My Colab notebook for reproduction is attached: https://colab.research.google.com/drive/1O5iAqSefg09TaexTA5XHFGSykeEU8KbM?usp=sharing

[jiangxinke]

Hello, I noticed you output the same accuracy. I suggest you check your training procedure. I will give you my log these days. Thanks for your notice.

[helloworld751]

Thank you for your quick response. The Recall and NDCG metrics look good, but the loss and accuracy are unusual. My main concern is that the node classification accuracy remains low (e.g., 15%~30%) with extremely high loss, regardless of the missing rate variation (from 0.9 to 0.1).

[jiangxinke]

Hello, this is my training log, you can refer to the namespace args parameters for adjusting:

DSAT_code/DSAT_run_node_cls.py Namespace(data='cora', type='struct', rate=0.1, attack_rate=0, nhid=16, dropout=0.4, ncomp=5, lr=0.001, wd=0.01, epoch=4000, patience=20000, verbose='store_true', predict_turns_num=1, discriminator=True, structure_feature='one-hot', cuda=True, hidden=40, enc_name='GCN', r_type='flip', alpha=0.2, loss_alpha_missing=0, FP=20, lambda_gan=1, gan_alpha_missing=10, seed=10242, not_change_adj=True, beta_2=200, use_lcc=False, position_aware_c=10, split_train=True, common_z=True, use_fix=True, profile=False, filter='SGC', dataset='cora') 0%| | 0/1 [00:00<?, ?it/s]epoch: 1 train loss: 1.91768 train acc: 0.18571 val loss: 1.99232 val acc: 0.12800 epoch: 2 train loss: 1.89879 train acc: 0.20000 val loss: 1.98371 val acc: 0.10800 epoch: 3 train loss: 1.89934 train acc: 0.21429 val loss: 1.98908 val acc: 0.11200 epoch: 4 train loss: 1.88194 train acc: 0.19286 val loss: 1.98508 val acc: 0.11400 epoch: 5 train loss: 1.88196 train acc: 0.21429 val loss: 1.98484 val acc: 0.13000 epoch: 6 train loss: 1.86551 train acc: 0.22143 val loss: 1.97582 val acc: 0.13400 epoch: 7 train loss: 1.86487 train acc: 0.22143 val loss: 1.97349 val acc: 0.13400 epoch: 8 train loss: 1.84910 train acc: 0.27143 val loss: 1.96361 val acc: 0.14800 epoch: 9 train loss: 1.84966 train acc: 0.25714 val loss: 1.96154 val acc: 0.14800 epoch: 10 train loss: 1.83431 train acc: 0.25714 val loss: 1.95037 val acc: 0.15800 epoch: 11 train loss: 1.83442 train acc: 0.27143 val loss: 1.94766 val acc: 0.16600 epoch: 12 train loss: 1.81964 train acc: 0.27857 val loss: 1.93760 val acc: 0.18400 epoch: 13 train loss: 1.82101 train acc: 0.30714 val loss: 1.93608 val acc: 0.18000 epoch: 14 train loss: 1.80660 train acc: 0.34286 val loss: 1.92644 val acc: 0.19600 epoch: 15 train loss: 1.80588 train acc: 0.35000 val loss: 1.92472 val acc: 0.20200 epoch: 16 train loss: 1.79185 train acc: 0.38571 val loss: 1.91450 val acc: 0.20800 epoch: 17 train loss: 1.79349 train acc: 0.37857 val loss: 1.91283 val acc: 0.21400 epoch: 18 train loss: 1.77972 train acc: 0.41429 val loss: 1.90248 val acc: 0.22600 epoch: 19 train loss: 1.77974 train acc: 0.37857 val loss: 1.90066 val acc: 0.22800 epoch: 20 train loss: 1.76614 train acc: 0.43571 val loss: 1.89047 val acc: 0.24400 epoch: 21 train loss: 1.76841 train acc: 0.43571 val loss: 1.88969 val acc: 0.24000 epoch: 22 train loss: 1.75505 train acc: 0.47143 val loss: 1.87971 val acc: 0.25400 epoch: 23 train loss: 1.75742 train acc: 0.46429 val loss: 1.87787 val acc: 0.25200 epoch: 24 train loss: 1.74428 train acc: 0.49286 val loss: 1.86819 val acc: 0.26200 epoch: 25 train loss: 1.74370 train acc: 0.46429 val loss: 1.86539 val acc: 0.27000 epoch: 26 train loss: 1.73066 train acc: 0.50000 val loss: 1.85599 val acc: 0.28200 epoch: 27 train loss: 1.73052 train acc: 0.50714 val loss: 1.85355 val acc: 0.29200 epoch: 28 train loss: 1.71766 train acc: 0.54286 val loss: 1.84461 val acc: 0.32400 epoch: 29 train loss: 1.71774 train acc: 0.52857 val loss: 1.84265 val acc: 0.32000 epoch: 30 train loss: 1.70494 train acc: 0.55000 val loss: 1.83471 val acc: 0.34400 epoch: 31 train loss: 1.70403 train acc: 0.55714 val loss: 1.83188 val acc: 0.36000 epoch: 32 train loss: 1.69108 train acc: 0.62143 val loss: 1.82420 val acc: 0.38800 epoch: 33 train loss: 1.69497 train acc: 0.60714 val loss: 1.82334 val acc: 0.38800 epoch: 34 train loss: 1.68219 train acc: 0.65000 val loss: 1.81628 val acc: 0.41000 epoch: 35 train loss: 1.68384 train acc: 0.67857 val loss: 1.81259 val acc: 0.42400 epoch: 36 train loss: 1.67118 train acc: 0.68571 val loss: 1.80608 val acc: 0.44200 epoch: 37 train loss: 1.67222 train acc: 0.68571 val loss: 1.80701 val acc: 0.43200 epoch: 38 train loss: 1.65984 train acc: 0.70000 val loss: 1.80107 val acc: 0.44600 epoch: 39 train loss: 1.66116 train acc: 0.70714 val loss: 1.79681 val acc: 0.46400 epoch: 40 train loss: 1.64900 train acc: 0.71429 val loss: 1.79103 val acc: 0.47800 epoch: 41 train loss: 1.65305 train acc: 0.72857 val loss: 1.79159 val acc: 0.48600 epoch: 42 train loss: 1.64102 train acc: 0.73571 val loss: 1.78607 val acc: 0.48400 epoch: 43 train loss: 1.63874 train acc: 0.76429 val loss: 1.78247 val acc: 0.49400 epoch: 44 train loss: 1.62683 train acc: 0.75714 val loss: 1.77712 val acc: 0.49800 epoch: 45 train loss: 1.63118 train acc: 0.76429 val loss: 1.77429 val acc: 0.50400 epoch: 46 train loss: 1.61944 train acc: 0.77857 val loss: 1.76926 val acc: 0.51200 epoch: 47 train loss: 1.61893 train acc: 0.76429 val loss: 1.77147 val acc: 0.51000 epoch: 48 train loss: 1.60739 train acc: 0.77143 val loss: 1.76648 val acc: 0.51600 epoch: 49 train loss: 1.60745 train acc: 0.78571 val loss: 1.76603 val acc: 0.52200 epoch: 50 train loss: 1.59606 train acc: 0.78571 val loss: 1.76101 val acc: 0.52800 epoch: 51 train loss: 1.59768 train acc: 0.80714 val loss: 1.75909 val acc: 0.53800 epoch: 52 train loss: 1.58645 train acc: 0.79286 val loss: 1.75367 val acc: 0.54400 epoch: 53 train loss: 1.58860 train acc: 0.79286 val loss: 1.75271 val acc: 0.52400 epoch: 54 train loss: 1.57748 train acc: 0.78571 val loss: 1.74713 val acc: 0.53400 epoch: 55 train loss: 1.57699 train acc: 0.78571 val loss: 1.74757 val acc: 0.54600 epoch: 56 train loss: 1.56604 train acc: 0.80000 val loss: 1.74177 val acc: 0.56000 epoch: 57 train loss: 1.56610 train acc: 0.80714 val loss: 1.74115 val acc: 0.56000 epoch: 58 train loss: 1.55521 train acc: 0.80714 val loss: 1.73508 val acc: 0.58200 epoch: 59 train loss: 1.54946 train acc: 0.82857 val loss: 1.73126 val acc: 0.59200 epoch: 60 train loss: 1.53862 train acc: 0.82857 val loss: 1.72485 val acc: 0.60200 epoch: 61 train loss: 1.53995 train acc: 0.84286 val loss: 1.72518 val acc: 0.60800 epoch: 62 train loss: 1.52923 train acc: 0.85000 val loss: 1.71847 val acc: 0.61600 epoch: 63 train loss: 1.53201 train acc: 0.86429 val loss: 1.71615 val acc: 0.62800 epoch: 64 train loss: 1.52148 train acc: 0.86429 val loss: 1.70925 val acc: 0.63400 epoch: 65 train loss: 1.52263 train acc: 0.85714 val loss: 1.70854 val acc: 0.62400 epoch: 66 train loss: 1.51209 train acc: 0.85714 val loss: 1.70157 val acc: 0.62600 epoch: 67 train loss: 1.51423 train acc: 0.85000 val loss: 1.70329 val acc: 0.64000 epoch: 68 train loss: 1.50379 train acc: 0.86429 val loss: 1.69631 val acc: 0.63800 epoch: 69 train loss: 1.50671 train acc: 0.87143 val loss: 1.69468 val acc: 0.65200 epoch: 70 train loss: 1.49634 train acc: 0.87143 val loss: 1.68745 val acc: 0.65800 epoch: 71 train loss: 1.49791 train acc: 0.87857 val loss: 1.68464 val acc: 0.65400 epoch: 72 train loss: 1.48754 train acc: 0.88571 val loss: 1.67746 val acc: 0.66400 epoch: 73 train loss: 1.48595 train acc: 0.88571 val loss: 1.67727 val acc: 0.67200 epoch: 74 train loss: 1.47573 train acc: 0.90000 val loss: 1.67010 val acc: 0.68000 epoch: 75 train loss: 1.47629 train acc: 0.88571 val loss: 1.67018 val acc: 0.67800 epoch: 76 train loss: 1.46612 train acc: 0.90000 val loss: 1.66296 val acc: 0.69000 epoch: 77 train loss: 1.46566 train acc: 0.90000 val loss: 1.66083 val acc: 0.68800 epoch: 78 train loss: 1.45565 train acc: 0.90000 val loss: 1.65368 val acc: 0.69400 epoch: 79 train loss: 1.45762 train acc: 0.89286 val loss: 1.65213 val acc: 0.69800 epoch: 80 train loss: 1.44764 train acc: 0.90000 val loss: 1.64542 val acc: 0.70200 epoch: 81 train loss: 1.44958 train acc: 0.90000 val loss: 1.64537 val acc: 0.69800 epoch: 82 train loss: 1.43969 train acc: 0.90000 val loss: 1.63898 val acc: 0.70200 epoch: 83 train loss: 1.44191 train acc: 0.90000 val loss: 1.63553 val acc: 0.70200 epoch: 84 train loss: 1.43196 train acc: 0.89286 val loss: 1.62932 val acc: 0.70800 epoch: 85 train loss: 1.43358 train acc: 0.89286 val loss: 1.62932 val acc: 0.70600 epoch: 86 train loss: 1.42380 train acc: 0.89286 val loss: 1.62320 val acc: 0.71000 epoch: 87 train loss: 1.42643 train acc: 0.90000 val loss: 1.62359 val acc: 0.70800 epoch: 88 train loss: 1.41667 train acc: 0.89286 val loss: 1.61751 val acc: 0.71400 epoch: 89 train loss: 1.41385 train acc: 0.89286 val loss: 1.61597 val acc: 0.71000 epoch: 90 train loss: 1.40418 train acc: 0.89286 val loss: 1.61011 val acc: 0.71400 epoch: 91 train loss: 1.40269 train acc: 0.90000 val loss: 1.60833 val acc: 0.71600 epoch: 92 train loss: 1.39317 train acc: 0.90000 val loss: 1.60263 val acc: 0.72400 epoch: 93 train loss: 1.39529 train acc: 0.88571 val loss: 1.60235 val acc: 0.72200 epoch: 94 train loss: 1.38577 train acc: 0.89286 val loss: 1.59640 val acc: 0.72800 epoch: 95 train loss: 1.38779 train acc: 0.88571 val loss: 1.59561 val acc: 0.72600 epoch: 96 train loss: 1.37836 train acc: 0.89286 val loss: 1.58959 val acc: 0.72600 epoch: 97 train loss: 1.37778 train acc: 0.90000 val loss: 1.58957 val acc: 0.73400 epoch: 98 train loss: 1.36844 train acc: 0.90000 val loss: 1.58359 val acc: 0.73000 epoch: 99 train loss: 1.36821 train acc: 0.90000 val loss: 1.58234 val acc: 0.73400 epoch: 100 train loss: 1.35899 train acc: 0.90000 val loss: 1.57641 val acc: 0.73200 epoch: 101 train loss: 1.36221 train acc: 0.90000 val loss: 1.57761 val acc: 0.73400 epoch: 102 train loss: 1.35308 train acc: 0.90000 val loss: 1.57198 val acc: 0.73800 epoch: 103 train loss: 1.35263 train acc: 0.90714 val loss: 1.57093 val acc: 0.74000 epoch: 104 train loss: 1.34355 train acc: 0.90000 val loss: 1.56513 val acc: 0.74400 epoch: 105 train loss: 1.34190 train acc: 0.90000 val loss: 1.56345 val acc: 0.74000 epoch: 106 train loss: 1.33291 train acc: 0.90000 val loss: 1.55761 val acc: 0.74200 epoch: 107 train loss: 1.33524 train acc: 0.90000 val loss: 1.55845 val acc: 0.74000 epoch: 108 train loss: 1.32629 train acc: 0.90000 val loss: 1.55246 val acc: 0.74000 epoch: 109 train loss: 1.32816 train acc: 0.90000 val loss: 1.55112 val acc: 0.74600 epoch: 110 train loss: 1.31924 train acc: 0.90000 val loss: 1.54509 val acc: 0.74600 epoch: 111 train loss: 1.31880 train acc: 0.90000 val loss: 1.54605 val acc: 0.74600 epoch: 112 train loss: 1.30998 train acc: 0.90000 val loss: 1.54002 val acc: 0.74800 epoch: 113 train loss: 1.30838 train acc: 0.90000 val loss: 1.53830 val acc: 0.74800 epoch: 114 train loss: 1.29964 train acc: 0.90000 val loss: 1.53234 val acc: 0.74400 epoch: 115 train loss: 1.29899 train acc: 0.90000 val loss: 1.53185 val acc: 0.74600 epoch: 116 train loss: 1.29026 train acc: 0.90000 val loss: 1.52575 val acc: 0.74600 epoch: 117 train loss: 1.29116 train acc: 0.90000 val loss: 1.52636 val acc: 0.74800 epoch: 118 train loss: 1.28255 train acc: 0.90000 val loss: 1.52019 val acc: 0.75000 epoch: 119 train loss: 1.28323 train acc: 0.90000 val loss: 1.52011 val acc: 0.75000 epoch: 120 train loss: 1.27469 train acc: 0.90000 val loss: 1.51399 val acc: 0.75000 epoch: 121 train loss: 1.27601 train acc: 0.90000 val loss: 1.51542 val acc: 0.74800 epoch: 122 train loss: 1.26751 train acc: 0.90000 val loss: 1.50932 val acc: 0.74800 epoch: 123 train loss: 1.26711 train acc: 0.90000 val loss: 1.50938 val acc: 0.74800 epoch: 124 train loss: 1.25873 train acc: 0.90000 val loss: 1.50343 val acc: 0.74600 epoch: 125 train loss: 1.25812 train acc: 0.90000 val loss: 1.50047 val acc: 0.74800 epoch: 126 train loss: 1.24975 train acc: 0.90000 val loss: 1.49455 val acc: 0.74800 epoch: 127 train loss: 1.24792 train acc: 0.90000 val loss: 1.49563 val acc: 0.74600 epoch: 128 train loss: 1.23963 train acc: 0.90000 val loss: 1.48981 val acc: 0.74600 epoch: 129 train loss: 1.24025 train acc: 0.90000 val loss: 1.48939 val acc: 0.74800 epoch: 130 train loss: 1.23211 train acc: 0.90000 val loss: 1.48343 val acc: 0.74800 epoch: 131 train loss: 1.23295 train acc: 0.90000 val loss: 1.48433 val acc: 0.74600 epoch: 132 train loss: 1.22479 train acc: 0.90000 val loss: 1.47817 val acc: 0.74600 epoch: 133 train loss: 1.22610 train acc: 0.90000 val loss: 1.47808 val acc: 0.74600 epoch: 134 train loss: 1.21803 train acc: 0.90000 val loss: 1.47198 val acc: 0.74600 epoch: 135 train loss: 1.21848 train acc: 0.90000 val loss: 1.47122 val acc: 0.74600 epoch: 136 train loss: 1.21043 train acc: 0.90000 val loss: 1.46506 val acc: 0.74600 epoch: 137 train loss: 1.20804 train acc: 0.90000 val loss: 1.46521 val acc: 0.75800 epoch: 138 train loss: 1.20007 train acc: 0.90000 val loss: 1.45884 val acc: 0.75800 epoch: 139 train loss: 1.20238 train acc: 0.90000 val loss: 1.45965 val acc: 0.75000 epoch: 140 train loss: 1.19450 train acc: 0.90000 val loss: 1.45322 val acc: 0.75000 epoch: 141 train loss: 1.19372 train acc: 0.90000 val loss: 1.45255 val acc: 0.74800 epoch: 142 train loss: 1.18587 train acc: 0.90000 val loss: 1.44626 val acc: 0.74800 epoch: 143 train loss: 1.18591 train acc: 0.90000 val loss: 1.44681 val acc: 0.74600 epoch: 144 train loss: 1.17813 train acc: 0.90000 val loss: 1.44066 val acc: 0.75000 epoch: 145 train loss: 1.17802 train acc: 0.90714 val loss: 1.44061 val acc: 0.75800 epoch: 146 train loss: 1.17035 train acc: 0.90000 val loss: 1.43444 val acc: 0.76000 epoch: 147 train loss: 1.17075 train acc: 0.90000 val loss: 1.43450 val acc: 0.75600 epoch: 148 train loss: 1.16307 train acc: 0.90000 val loss: 1.42835 val acc: 0.75800 epoch: 149 train loss: 1.16259 train acc: 0.90000 val loss: 1.42771 val acc: 0.75200 epoch: 150 train loss: 1.15500 train acc: 0.90000 val loss: 1.42142 val acc: 0.75400 epoch: 151 train loss: 1.15448 train acc: 0.90000 val loss: 1.42063 val acc: 0.75600 epoch: 152 train loss: 1.14699 train acc: 0.90000 val loss: 1.41430 val acc: 0.75600 epoch: 153 train loss: 1.14766 train acc: 0.90000 val loss: 1.41373 val acc: 0.76200 epoch: 154 train loss: 1.14017 train acc: 0.90000 val loss: 1.40731 val acc: 0.76400 epoch: 155 train loss: 1.13972 train acc: 0.90000 val loss: 1.40753 val acc: 0.76600 epoch: 156 train loss: 1.13233 train acc: 0.90000 val loss: 1.40120 val acc: 0.76600 epoch: 157 train loss: 1.13221 train acc: 0.90000 val loss: 1.40156 val acc: 0.76800 epoch: 158 train loss: 1.12484 train acc: 0.90714 val loss: 1.39504 val acc: 0.76600 epoch: 159 train loss: 1.12439 train acc: 0.90000 val loss: 1.39461 val acc: 0.76600 epoch: 160 train loss: 1.11710 train acc: 0.90714 val loss: 1.38804 val acc: 0.76600 epoch: 161 train loss: 1.11711 train acc: 0.90000 val loss: 1.38864 val acc: 0.76600 epoch: 162 train loss: 1.10997 train acc: 0.91429 val loss: 1.38237 val acc: 0.76600 epoch: 163 train loss: 1.10954 train acc: 0.91429 val loss: 1.38065 val acc: 0.76600 epoch: 164 train loss: 1.10241 train acc: 0.91429 val loss: 1.37440 val acc: 0.76400 epoch: 165 train loss: 1.10314 train acc: 0.91429 val loss: 1.37488 val acc: 0.76400 epoch: 166 train loss: 1.09608 train acc: 0.91429 val loss: 1.36878 val acc: 0.76600 epoch: 167 train loss: 1.09708 train acc: 0.91429 val loss: 1.37062 val acc: 0.76600 epoch: 168 train loss: 1.09012 train acc: 0.91429 val loss: 1.36468 val acc: 0.76600 epoch: 169 train loss: 1.08922 train acc: 0.91429 val loss: 1.36283 val acc: 0.76600 epoch: 170 train loss: 1.08229 train acc: 0.91429 val loss: 1.35702 val acc: 0.76600 epoch: 171 train loss: 1.08244 train acc: 0.91429 val loss: 1.35676 val acc: 0.76600 epoch: 172 train loss: 1.07557 train acc: 0.91429 val loss: 1.35121 val acc: 0.76800 epoch: 173 train loss: 1.07577 train acc: 0.91429 val loss: 1.35112 val acc: 0.76600 epoch: 174 train loss: 1.06895 train acc: 0.91429 val loss: 1.34576 val acc: 0.76600 epoch: 175 train loss: 1.06965 train acc: 0.91429 val loss: 1.34704 val acc: 0.76800 epoch: 176 train loss: 1.06287 train acc: 0.91429 val loss: 1.34194 val acc: 0.76800 epoch: 177 train loss: 1.06276 train acc: 0.91429 val loss: 1.34204 val acc: 0.76800 epoch: 178 train loss: 1.05601 train acc: 0.91429 val loss: 1.33720 val acc: 0.76800 epoch: 179 train loss: 1.05535 train acc: 0.91429 val loss: 1.33677 val acc: 0.76800 epoch: 180 train loss: 1.04871 train acc: 0.91429 val loss: 1.33181 val acc: 0.76800 epoch: 181 train loss: 1.04893 train acc: 0.91429 val loss: 1.33243 val acc: 0.76800 epoch: 182 train loss: 1.04236 train acc: 0.91429 val loss: 1.32760 val acc: 0.76800 epoch: 183 train loss: 1.04330 train acc: 0.91429 val loss: 1.32736 val acc: 0.76800 epoch: 184 train loss: 1.03683 train acc: 0.91429 val loss: 1.32256 val acc: 0.76800 epoch: 185 train loss: 1.03609 train acc: 0.91429 val loss: 1.32276 val acc: 0.76800 epoch: 186 train loss: 1.02961 train acc: 0.91429 val loss: 1.31794 val acc: 0.76800 epoch: 187 train loss: 1.03073 train acc: 0.91429 val loss: 1.31730 val acc: 0.76800 epoch: 188 train loss: 1.02436 train acc: 0.91429 val loss: 1.31239 val acc: 0.76800 epoch: 189 train loss: 1.02346 train acc: 0.91429 val loss: 1.31279 val acc: 0.76800 epoch: 190 train loss: 1.01710 train acc: 0.91429 val loss: 1.30799 val acc: 0.76800 epoch: 191 train loss: 1.01723 train acc: 0.91429 val loss: 1.30868 val acc: 0.76800 epoch: 192 train loss: 1.01094 train acc: 0.91429 val loss: 1.30375 val acc: 0.76800 epoch: 193 train loss: 1.01092 train acc: 0.91429 val loss: 1.30324 val acc: 0.76800 epoch: 194 train loss: 1.00469 train acc: 0.91429 val loss: 1.29814 val acc: 0.77000 epoch: 195 train loss: 1.00480 train acc: 0.91429 val loss: 1.29771 val acc: 0.77000 epoch: 196 train loss: 0.99864 train acc: 0.91429 val loss: 1.29265 val acc: 0.77000 epoch: 197 train loss: 0.99859 train acc: 0.91429 val loss: 1.29338 val acc: 0.77000 epoch: 198 train loss: 0.99250 train acc: 0.91429 val loss: 1.28818 val acc: 0.77000 epoch: 199 train loss: 0.99278 train acc: 0.91429 val loss: 1.28744 val acc: 0.77000 epoch: 200 train loss: 0.98671 train acc: 0.91429 val loss: 1.28199 val acc: 0.77400 epoch: 201 train loss: 0.98667 train acc: 0.91429 val loss: 1.28211 val acc: 0.77400 epoch: 202 train loss: 0.98069 train acc: 0.91429 val loss: 1.27680 val acc: 0.77600 epoch: 203 train loss: 0.98085 train acc: 0.91429 val loss: 1.27711 val acc: 0.77400 epoch: 204 train loss: 0.97492 train acc: 0.91429 val loss: 1.27159 val acc: 0.77800 epoch: 205 train loss: 0.97429 train acc: 0.91429 val loss: 1.27076 val acc: 0.77800 epoch: 206 train loss: 0.96846 train acc: 0.91429 val 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loss: 0.85318 val acc: 0.79400 epoch: 677 train loss: 0.48548 train acc: 0.92143 val loss: 0.85310 val acc: 0.79400 epoch: 678 train loss: 0.48483 train acc: 0.92143 val loss: 0.85219 val acc: 0.79600 epoch: 679 train loss: 0.48476 train acc: 0.92143 val loss: 0.85241 val acc: 0.79600 epoch: 680 train loss: 0.48409 train acc: 0.92143 val loss: 0.85211 val acc: 0.79600 epoch: 681 train loss: 0.48404 train acc: 0.92143 val loss: 0.85162 val acc: 0.79600 epoch: 682 train loss: 0.48338 train acc: 0.91429 val loss: 0.85147 val acc: 0.79600 epoch: 683 train loss: 0.48317 train acc: 0.91429 val loss: 0.85169 val acc: 0.79400 epoch: 684 train loss: 0.48255 train acc: 0.92143 val loss: 0.85222 val acc: 0.79400 epoch: 685 train loss: 0.48244 train acc: 0.92143 val loss: 0.85263 val acc: 0.79400 epoch: 686 train loss: 0.48187 train acc: 0.92143 val loss: 0.85339 val acc: 0.79400 epoch: 687 train loss: 0.48187 train acc: 0.92143 val loss: 0.85261 val acc: 0.79400 epoch: 688 train loss: 0.48135 train acc: 0.92143 val loss: 0.85301 val acc: 0.79800 epoch: 689 train loss: 0.48144 train acc: 0.92143 val loss: 0.85327 val acc: 0.79400 epoch: 690 train loss: 0.48090 train acc: 0.92143 val loss: 0.85310 val acc: 0.79400 epoch: 691 train loss: 0.48126 train acc: 0.92143 val loss: 0.85325 val acc: 0.79400 epoch: 692 train loss: 0.48073 train acc: 0.92143 val loss: 0.85299 val acc: 0.79400 epoch: 693 train loss: 0.48057 train acc: 0.92143 val loss: 0.85324 val acc: 0.79600 epoch: 694 train loss: 0.48002 train acc: 0.92143 val loss: 0.85315 val acc: 0.79600 epoch: 695 train loss: 0.47976 train acc: 0.92143 val loss: 0.85181 val acc: 0.79600 epoch: 696 train loss: 0.47923 train acc: 0.92143 val loss: 0.85137 val acc: 0.79400 epoch: 697 train loss: 0.47954 train acc: 0.92143 val loss: 0.85251 val acc: 0.79400 epoch: 698 train loss: 0.47896 train acc: 0.92143 val loss: 0.85173 val acc: 0.79400 epoch: 699 train loss: 0.47880 train acc: 0.92143 val loss: 0.85156 val acc: 0.79400 epoch: 700 train loss: 0.47827 train acc: 0.92143 val loss: 0.85057 val acc: 0.79400 epoch: 701 train loss: 0.47843 train acc: 0.92143 val loss: 0.85113 val acc: 0.79400 epoch: 702 train loss: 0.47789 train acc: 0.92143 val loss: 0.85035 val acc: 0.79400 epoch: 703 train loss: 0.47756 train acc: 0.92143 val loss: 0.85081 val acc: 0.79400 epoch: 704 train loss: 0.47702 train acc: 0.91429 val loss: 0.85058 val acc: 0.79400 epoch: 705 train loss: 0.47726 train acc: 0.92143 val loss: 0.85009 val acc: 0.79400 epoch: 706 train loss: 0.47675 train acc: 0.91429 val loss: 0.85023 val acc: 0.79200 epoch: 707 train loss: 0.47622 train acc: 0.91429 val loss: 0.84960 val acc: 0.79400 epoch: 708 train loss: 0.47568 train acc: 0.91429 val loss: 0.84902 val acc: 0.79400 epoch: 709 train loss: 0.47604 train acc: 0.91429 val loss: 0.84990 val acc: 0.79400 epoch: 710 train loss: 0.47554 train acc: 0.91429 val loss: 0.84958 val acc: 0.79400 epoch: 711 train loss: 0.47559 train acc: 0.91429 val loss: 0.84963 val acc: 0.79400 epoch: 712 train loss: 0.47508 train acc: 0.91429 val loss: 0.84935 val acc: 0.79400 epoch: 713 train loss: 0.47533 train acc: 0.91429 val loss: 0.84918 val acc: 0.79400 epoch: 714 train loss: 0.47476 train acc: 0.91429 val loss: 0.84828 val acc: 0.79400 epoch: 715 train loss: 0.47430 train acc: 0.91429 val loss: 0.84886 val acc: 0.79600 epoch: 716 train loss: 0.47375 train acc: 0.91429 val loss: 0.84783 val acc: 0.79800 epoch: 717 train loss: 0.47376 train acc: 0.91429 val loss: 0.84697 val acc: 0.79600 epoch: 718 train loss: 0.47322 train acc: 0.91429 val loss: 0.84624 val acc: 0.79600 epoch: 719 train loss: 0.47338 train acc: 0.91429 val loss: 0.84686 val acc: 0.79600 epoch: 720 train loss: 0.47281 train acc: 0.91429 val loss: 0.84650 val acc: 0.79600 epoch: 721 train loss: 0.47261 train acc: 0.91429 val loss: 0.84633 val acc: 0.79600 epoch: 722 train loss: 0.47207 train acc: 0.91429 val loss: 0.84637 val acc: 0.79600 epoch: 723 train loss: 0.47233 train acc: 0.91429 val loss: 0.84616 val acc: 0.79600 epoch: 724 train loss: 0.47182 train acc: 0.92143 val loss: 0.84612 val acc: 0.79600 epoch: 725 train loss: 0.47113 train acc: 0.92143 val loss: 0.84558 val acc: 0.79800 epoch: 726 train loss: 0.47062 train acc: 0.92143 val loss: 0.84547 val acc: 0.79800 epoch: 727 train loss: 0.47135 train acc: 0.92143 val loss: 0.84512 val acc: 0.79800 epoch: 728 train loss: 0.47086 train acc: 0.92143 val loss: 0.84450 val acc: 0.79600 epoch: 729 train loss: 0.47048 train acc: 0.92143 val loss: 0.84530 val acc: 0.79800 epoch: 730 train loss: 0.47002 train acc: 0.92143 val loss: 0.84508 val acc: 0.79800 epoch: 731 train loss: 0.46994 train acc: 0.92143 val loss: 0.84469 val acc: 0.79800 epoch: 732 train loss: 0.46947 train acc: 0.92143 val loss: 0.84529 val acc: 0.79800

[jiangxinke]

And this is my training log for mr=0.9. You can refer to the namespace to avoid NaN problem.

Namespace(data='cora', type='struct', rate=0.9, attack_rate=0, nhid=16, dropout=0.4, ncomp=5, lr=0.001, wd=0.01, epoch=4000, patience=20000, verbose='store_true', predict_turns_num=1, discriminator=True, structure_feature='one-hot', cuda=True, hidden=40, enc_name='GCN', r_type='flip', alpha=0.2, loss_alpha_missing=0, FP=20, lambda_gan=1, gan_alpha_missing=10, seed=10242, not_change_adj=True, beta_2=200, use_lcc=False, position_aware_c=10, split_train=True, common_z=True, use_fix=True, profile=False, filter='SGC', dataset='cora') 0%| | 0/1 [00:00<?, ?it/s]epoch: 1 train loss: 2.25785 train acc: 0.17857 val loss: 2.51057 val acc: 0.13000 epoch: 2 train loss: 2.08813 train acc: 0.15000 val loss: 2.26111 val acc: 0.07800 epoch: 3 train loss: 2.14041 train acc: 0.15000 val loss: 2.33822 val acc: 0.07200 epoch: 4 train loss: 2.09670 train acc: 0.15000 val loss: 2.14330 val acc: 0.07200 epoch: 5 train loss: 2.10337 train acc: 0.15000 val loss: 2.14131 val acc: 0.07600 epoch: 6 train loss: 2.01823 train acc: 0.13571 val loss: 1.95448 val acc: 0.16000 epoch: 7 train loss: 1.99930 train acc: 0.14286 val loss: 1.95038 val acc: 0.15000 epoch: 8 train loss: 1.97232 train acc: 0.15714 val loss: 1.88634 val acc: 0.19000 epoch: 9 train loss: 1.97371 train acc: 0.14286 val loss: 1.88775 val acc: 0.18400 epoch: 10 train loss: 1.98965 train acc: 0.11429 val loss: 1.90858 val acc: 0.24000 epoch: 11 train loss: 1.98922 train acc: 0.16429 val loss: 1.91313 val acc: 0.21800 epoch: 12 train loss: 2.00478 train acc: 0.13571 val loss: 1.95405 val acc: 0.11400 epoch: 13 train loss: 2.00009 train acc: 0.14286 val loss: 1.94945 val acc: 0.12000 epoch: 14 train loss: 1.99998 train acc: 0.16429 val loss: 1.97070 val acc: 0.13800 epoch: 15 train loss: 1.99824 train acc: 0.12857 val loss: 1.97170 val acc: 0.10400 epoch: 16 train loss: 2.00151 train acc: 0.14286 val loss: 1.97662 val acc: 0.12400 epoch: 17 train loss: 1.99747 train acc: 0.14286 val loss: 1.97396 val acc: 0.12600 epoch: 18 train loss: 2.00028 train acc: 0.14286 val loss: 1.96625 val acc: 0.12600 epoch: 19 train loss: 1.99948 train acc: 0.14286 val loss: 1.96444 val acc: 0.12400 epoch: 20 train loss: 1.98990 train acc: 0.14286 val loss: 1.94432 val acc: 0.12800 epoch: 21 train loss: 1.98157 train acc: 0.14286 val loss: 1.94189 val acc: 0.13200 epoch: 22 train loss: 1.96707 train acc: 0.16429 val loss: 1.91653 val acc: 0.27200 epoch: 23 train loss: 1.96848 train acc: 0.14286 val loss: 1.92087 val acc: 0.26000 epoch: 24 train loss: 1.95882 train acc: 0.15000 val loss: 1.90560 val acc: 0.31000 epoch: 25 train loss: 1.95387 train acc: 0.15000 val loss: 1.90554 val acc: 0.31200 epoch: 26 train loss: 1.95019 train acc: 0.15000 val loss: 1.90101 val acc: 0.31200 epoch: 27 train loss: 1.94705 train acc: 0.15000 val loss: 1.90110 val acc: 0.31400 epoch: 28 train loss: 1.94709 train acc: 0.15000 val loss: 1.90491 val acc: 0.31200 epoch: 29 train loss: 1.94929 train acc: 0.14286 val loss: 1.90914 val acc: 0.31200 epoch: 30 train loss: 1.95016 train acc: 0.12857 val loss: 1.91840 val acc: 0.29200 epoch: 31 train loss: 1.94963 train acc: 0.12143 val loss: 1.92022 val acc: 0.29000 epoch: 32 train loss: 1.94988 train acc: 0.14286 val loss: 1.93184 val acc: 0.16200 epoch: 33 train loss: 1.94765 train acc: 0.15000 val loss: 1.93107 val acc: 0.15600 epoch: 34 train loss: 1.94723 train acc: 0.15000 val loss: 1.94376 val acc: 0.15800 epoch: 35 train loss: 1.94479 train acc: 0.14286 val loss: 1.94200 val acc: 0.15400 epoch: 36 train loss: 1.94392 train acc: 0.13571 val loss: 1.95470 val acc: 0.15000 epoch: 37 train loss: 1.94326 train acc: 0.15000 val loss: 1.95494 val acc: 0.15600 epoch: 38 train loss: 1.94244 train acc: 0.13571 val loss: 1.96705 val acc: 0.15200 epoch: 39 train loss: 1.94175 train acc: 0.13571 val loss: 1.96566 val acc: 0.15000 epoch: 40 train loss: 1.94137 train acc: 0.15714 val loss: 1.97648 val acc: 0.13800 epoch: 41 train loss: 1.94264 train acc: 0.15000 val loss: 1.97316 val acc: 0.15000 epoch: 42 train loss: 1.94267 train acc: 0.17143 val loss: 1.98237 val acc: 0.11400 epoch: 43 train loss: 1.94120 train acc: 0.19286 val loss: 1.98011 val acc: 0.14400 epoch: 44 train loss: 1.94156 train acc: 0.15000 val loss: 1.98764 val acc: 0.08000 epoch: 45 train loss: 1.94085 train acc: 0.16429 val loss: 1.98418 val acc: 0.08600 epoch: 46 train loss: 1.94143 train acc: 0.15000 val loss: 1.99052 val acc: 0.08000 epoch: 47 train loss: 1.93969 train acc: 0.15714 val loss: 1.98883 val acc: 0.07400 epoch: 48 train loss: 1.94025 train acc: 0.15000 val loss: 1.99330 val acc: 0.07600 epoch: 49 train loss: 1.94096 train acc: 0.18571 val loss: 1.99210 val acc: 0.07600 epoch: 50 train loss: 1.94122 train acc: 0.18571 val loss: 1.99467 val acc: 0.07600 epoch: 51 train loss: 1.94130 train acc: 0.15000 val loss: 1.99337 val acc: 0.07800 epoch: 52 train loss: 1.94119 train acc: 0.15000 val loss: 1.99397 val acc: 0.07800 epoch: 53 train loss: 1.93947 train acc: 0.15714 val loss: 1.99061 val acc: 0.08000 epoch: 54 train loss: 1.93901 train acc: 0.15714 val loss: 1.98982 val acc: 0.08000 epoch: 55 train loss: 1.93979 train acc: 0.15714 val loss: 1.98985 val acc: 0.07800 epoch: 56 train loss: 1.93909 train acc: 0.15714 val loss: 1.98797 val acc: 0.07800 epoch: 57 train loss: 1.93712 train acc: 0.15714 val loss: 1.98652 val acc: 0.07800 epoch: 58 train loss: 1.93621 train acc: 0.15000 val loss: 1.98361 val acc: 0.07800 epoch: 59 train loss: 1.93668 train acc: 0.14286 val loss: 1.98407 val acc: 0.07800 epoch: 60 train loss: 1.93568 train acc: 0.15714 val loss: 1.98010 val acc: 0.08200 epoch: 61 train loss: 1.93814 train acc: 0.15000 val loss: 1.98061 val acc: 0.08400 epoch: 62 train loss: 1.93709 train acc: 0.15000 val loss: 1.97631 val acc: 0.09000 epoch: 63 train loss: 1.93628 train acc: 0.15714 val loss: 1.97393 val acc: 0.09200 epoch: 64 train loss: 1.93555 train acc: 0.18571 val loss: 1.96971 val acc: 0.12200 epoch: 65 train loss: 1.93630 train acc: 0.20714 val loss: 1.96920 val acc: 0.13200 epoch: 66 train loss: 1.93556 train acc: 0.20000 val loss: 1.96473 val acc: 0.17800 epoch: 67 train loss: 1.93435 train acc: 0.20714 val loss: 1.96335 val acc: 0.17400 epoch: 68 train loss: 1.93379 train acc: 0.17143 val loss: 1.95892 val acc: 0.15000 epoch: 69 train loss: 1.93234 train acc: 0.14286 val loss: 1.95709 val acc: 0.15000 epoch: 70 train loss: 1.93185 train acc: 0.14286 val loss: 1.95277 val acc: 0.14600 epoch: 71 train loss: 1.93421 train acc: 0.15000 val loss: 1.95202 val acc: 0.14800 epoch: 72 train loss: 1.93379 train acc: 0.15000 val loss: 1.94808 val acc: 0.14800 epoch: 73 train loss: 1.93356 train acc: 0.15000 val loss: 1.94776 val acc: 0.15000 epoch: 74 train loss: 1.93329 train acc: 0.14286 val loss: 1.94437 val acc: 0.15000 epoch: 75 train loss: 1.93290 train acc: 0.15000 val loss: 1.94417 val acc: 0.15200 epoch: 76 train loss: 1.93257 train acc: 0.15000 val loss: 1.94115 val acc: 0.15400 epoch: 77 train loss: 1.93224 train acc: 0.15000 val loss: 1.94132 val acc: 0.15400 epoch: 78 train loss: 1.93193 train acc: 0.15000 val loss: 1.93887 val acc: 0.15400 epoch: 79 train loss: 1.93273 train acc: 0.16429 val loss: 1.93904 val acc: 0.15800 epoch: 80 train loss: 1.93240 train acc: 0.16429 val loss: 1.93718 val acc: 0.15800 epoch: 81 train loss: 1.93134 train acc: 0.16429 val loss: 1.93692 val acc: 0.15600 epoch: 82 train loss: 1.93099 train acc: 0.15000 val loss: 1.93553 val acc: 0.15600 epoch: 83 train loss: 1.93166 train acc: 0.15000 val loss: 1.93624 val acc: 0.15600 epoch: 84 train loss: 1.93129 train acc: 0.16429 val loss: 1.93506 val acc: 0.16600 epoch: 85 train loss: 1.93111 train acc: 0.17857 val loss: 1.93524 val acc: 0.16600 epoch: 86 train loss: 1.93072 train acc: 0.18571 val loss: 1.93426 val acc: 0.18200 epoch: 87 train loss: 1.93103 train acc: 0.19286 val loss: 1.93482 val acc: 0.19000 epoch: 88 train loss: 1.93064 train acc: 0.19286 val loss: 1.93406 val acc: 0.20200 epoch: 89 train loss: 1.93054 train acc: 0.19286 val loss: 1.93466 val acc: 0.17800 epoch: 90 train loss: 1.93016 train acc: 0.20000 val loss: 1.93407 val acc: 0.18600 epoch: 91 train loss: 1.93093 train acc: 0.19286 val loss: 1.93435 val acc: 0.21200 epoch: 92 train loss: 1.93055 train acc: 0.22857 val loss: 1.93387 val acc: 0.24400 epoch: 93 train loss: 1.92997 train acc: 0.24286 val loss: 1.93390 val acc: 0.24600 epoch: 94 train loss: 1.92959 train acc: 0.25000 val loss: 1.93348 val acc: 0.25400 epoch: 95 train loss: 1.92950 train acc: 0.22143 val loss: 1.93395 val acc: 0.25200 epoch: 96 train loss: 1.92911 train acc: 0.22143 val loss: 1.93358 val acc: 0.24400 epoch: 97 train loss: 1.92886 train acc: 0.20714 val loss: 1.93405 val acc: 0.21200 epoch: 98 train loss: 1.92847 train acc: 0.19286 val loss: 1.93376 val acc: 0.20200 epoch: 99 train loss: 1.92906 train acc: 0.20000 val loss: 1.93406 val acc: 0.21600 epoch: 100 train loss: 1.92867 train acc: 0.20000 val loss: 1.93387 val acc: 0.21400 epoch: 101 train loss: 1.92850 train acc: 0.18571 val loss: 1.93448 val acc: 0.19600 epoch: 102 train loss: 1.92810 train acc: 0.18571 val loss: 1.93432 val acc: 0.19000 epoch: 103 train loss: 1.92772 train acc: 0.18571 val loss: 1.93478 val acc: 0.18200 epoch: 104 train loss: 1.92732 train acc: 0.17857 val loss: 1.93464 val acc: 0.18000 epoch: 105 train loss: 1.92741 train acc: 0.16429 val loss: 1.93447 val acc: 0.19400 epoch: 106 train loss: 1.92702 train acc: 0.16429 val loss: 1.93434 val acc: 0.19200 epoch: 107 train loss: 1.92717 train acc: 0.17857 val loss: 1.93462 val acc: 0.18200 epoch: 108 train loss: 1.92678 train acc: 0.17143 val loss: 1.93448 val acc: 0.17800 epoch: 109 train loss: 1.92692 train acc: 0.16429 val loss: 1.93462 val acc: 0.17800 epoch: 110 train loss: 1.92652 train acc: 0.16429 val loss: 1.93450 val acc: 0.17400 epoch: 111 train loss: 1.92601 train acc: 0.17143 val loss: 1.93461 val acc: 0.16200 epoch: 112 train loss: 1.92562 train acc: 0.17143 val loss: 1.93449 val acc: 0.15800 epoch: 113 train loss: 1.92600 train acc: 0.17143 val loss: 1.93461 val acc: 0.15200 epoch: 114 train loss: 1.92560 train acc: 0.17143 val loss: 1.93450 val acc: 0.15200 epoch: 115 train loss: 1.92551 train acc: 0.16429 val loss: 1.93485 val acc: 0.15200 epoch: 116 train loss: 1.92511 train acc: 0.16429 val loss: 1.93475 val acc: 0.15200 epoch: 117 train loss: 1.92510 train acc: 0.15000 val loss: 1.93478 val acc: 0.15600 epoch: 118 train loss: 1.92471 train acc: 0.14286 val loss: 1.93468 val acc: 0.15000 epoch: 119 train loss: 1.92478 train acc: 0.15714 val loss: 1.93483 val acc: 0.15200 epoch: 120 train loss: 1.92438 train acc: 0.14286 val loss: 1.93474 val acc: 0.15200 epoch: 121 train loss: 1.92451 train acc: 0.14286 val loss: 1.93495 val acc: 0.15000 epoch: 122 train loss: 1.92411 train acc: 0.14286 val loss: 1.93487 val acc: 0.15000 epoch: 123 train loss: 1.92400 train acc: 0.14286 val loss: 1.93514 val acc: 0.15000 epoch: 124 train loss: 1.92360 train acc: 0.13571 val loss: 1.93505 val acc: 0.14600 epoch: 125 train loss: 1.92360 train acc: 0.13571 val loss: 1.93498 val acc: 0.14400 epoch: 126 train loss: 1.92321 train acc: 0.13571 val loss: 1.93489 val acc: 0.14600 epoch: 127 train loss: 1.92330 train acc: 0.13571 val loss: 1.93489 val acc: 0.14600 epoch: 128 train loss: 1.92290 train acc: 0.13571 val loss: 1.93481 val acc: 0.14600 epoch: 129 train loss: 1.92271 train acc: 0.14286 val loss: 1.93469 val acc: 0.14600 epoch: 130 train loss: 1.92231 train acc: 0.14286 val loss: 1.93460 val acc: 0.14200 epoch: 131 train loss: 1.92231 train acc: 0.14286 val loss: 1.93472 val acc: 0.14400 epoch: 132 train loss: 1.92192 train acc: 0.14286 val loss: 1.93465 val acc: 0.14000 epoch: 133 train loss: 1.92210 train acc: 0.14286 val loss: 1.93468 val acc: 0.14000 epoch: 134 train loss: 1.92171 train acc: 0.14286 val loss: 1.93462 val acc: 0.13800 epoch: 135 train loss: 1.92164 train acc: 0.14286 val loss: 1.93472 val acc: 0.13800 epoch: 136 train loss: 1.92125 train acc: 0.14286 val 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loss: 1.88778 val acc: 0.31400 epoch: 842 train loss: 1.82916 train acc: 0.49286 val loss: 1.88767 val acc: 0.31400 epoch: 843 train loss: 1.82880 train acc: 0.49286 val loss: 1.88753 val acc: 0.31600 epoch: 844 train loss: 1.82859 train acc: 0.49286 val loss: 1.88743 val acc: 0.31600 epoch: 845 train loss: 1.82899 train acc: 0.49286 val loss: 1.88736 val acc: 0.31600 epoch: 846 train loss: 1.82880 train acc: 0.49286 val loss: 1.88727 val acc: 0.31600 epoch: 847 train loss: 1.82892 train acc: 0.49286 val loss: 1.88742 val acc: 0.31400 epoch: 848 train loss: 1.82872 train acc: 0.48571 val loss: 1.88732 val acc: 0.31400 epoch: 849 train loss: 1.82897 train acc: 0.49286 val loss: 1.88746 val acc: 0.31400 epoch: 850 train loss: 1.82877 train acc: 0.49286 val loss: 1.88738 val acc: 0.31400 epoch: 851 train loss: 1.82817 train acc: 0.49286 val loss: 1.88733 val acc: 0.31200 epoch: 852 train loss: 1.82797 train acc: 0.49286 val loss: 1.88722 val acc: 0.31200 epoch: 853 train loss: 1.82810 train acc: 0.49286 val loss: 1.88706 val acc: 0.31600 epoch: 854 train loss: 1.82789 train acc: 0.49286 val loss: 1.88692 val acc: 0.31600 epoch: 855 train loss: 1.82788 train acc: 0.49286 val loss: 1.88679 val acc: 0.31200 epoch: 856 train loss: 1.82768 train acc: 0.49286 val loss: 1.88666 val acc: 0.31600 epoch: 857 train loss: 1.82760 train acc: 0.49286 val loss: 1.88670 val acc: 0.31200 epoch: 858 train loss: 1.82739 train acc: 0.49286 val loss: 1.88658 val acc: 0.31400 epoch: 859 train loss: 1.82715 train acc: 0.49286 val loss: 1.88637 val acc: 0.31200 epoch: 860 train loss: 1.82693 train acc: 0.49286 val loss: 1.88626 val acc: 0.31200 epoch: 861 train loss: 1.82737 train acc: 0.49286 val loss: 1.88656 val acc: 0.31200 epoch: 862 train loss: 1.82716 train acc: 0.49286 val loss: 1.88648 val acc: 0.31200 epoch: 863 train loss: 1.82715 train acc: 0.49286 val loss: 1.88652 val acc: 0.31200 epoch: 864 train loss: 1.82695 train acc: 0.49286 val loss: 1.88644 val acc: 0.31200 epoch: 865 train loss: 1.82676 train acc: 0.49286 val loss: 1.88641 val acc: 0.31400 epoch: 866 train loss: 1.82656 train acc: 0.49286 val loss: 1.88629 val acc: 0.31400 epoch: 867 train loss: 1.82623 train acc: 0.49286 val loss: 1.88612 val acc: 0.31400 epoch: 868 train loss: 1.82603 train acc: 0.49286 val loss: 1.88602 val acc: 0.31400 epoch: 869 train loss: 1.82651 train acc: 0.49286 val loss: 1.88607 val acc: 0.31400 epoch: 870 train loss: 1.82631 train acc: 0.49286 val loss: 1.88597 val acc: 0.31600 epoch: 871 train loss: 1.82634 train acc: 0.49286 val loss: 1.88600 val acc: 0.31200 epoch: 872 train loss: 1.82613 train acc: 0.49286 val loss: 1.88592 val acc: 0.31200 epoch: 873 train loss: 1.82636 train acc: 0.49286 val loss: 1.88590 val acc: 0.31000 epoch: 874 train loss: 1.82614 train acc: 0.49286 val loss: 1.88581 val acc: 0.31200 epoch: 875 train loss: 1.82559 train acc: 0.48571 val loss: 1.88587 val acc: 0.31400 epoch: 876 train loss: 1.82538 train acc: 0.48571 val loss: 1.88577 val acc: 0.31400 epoch: 877 train loss: 1.82567 train acc: 0.49286 val loss: 1.88586 val acc: 0.31600 epoch: 878 train loss: 1.82545 train acc: 0.49286 val loss: 1.88578 val acc: 0.31600 epoch: 879 train loss: 1.82528 train acc: 0.49286 val loss: 1.88572 val acc: 0.31400 epoch: 880 train loss: 1.82506 train acc: 0.49286 val loss: 1.88567 val acc: 0.31400 epoch: 881 train loss: 1.82559 train acc: 0.48571 val loss: 1.88548 val acc: 0.31400 epoch: 882 train loss: 1.82538 train acc: 0.49286 val loss: 1.88540 val acc: 0.31400 epoch: 883 train loss: 1.82478 train acc: 0.49286 val loss: 1.88544 val acc: 0.31400 epoch: 884 train loss: 1.82458 train acc: 0.49286 val loss: 1.88537 val acc: 0.31400 epoch: 885 train loss: 1.82505 train acc: 0.49286 val loss: 1.88566 val acc: 0.31400 epoch: 886 train loss: 1.82483 train acc: 0.49286 val loss: 1.88558 val acc: 0.31400 epoch: 887 train loss: 1.82451 train acc: 0.49286 val loss: 1.88556 val acc: 0.31200 epoch: 888 train loss: 1.82429 train acc: 0.49286 val loss: 1.88548 val acc: 0.31200 epoch: 889 train loss: 1.82420 train acc: 0.49286 val loss: 1.88543 val acc: 0.31200 epoch: 890 train loss: 1.82398 train acc: 0.49286 val loss: 1.88534 val acc: 0.31200 epoch: 891 train loss: 1.82376 train acc: 0.49286 val loss: 1.88511 val acc: 0.31200 epoch: 892 train loss: 1.82353 train acc: 0.49286 val loss: 1.88503 val acc: 0.31200 epoch: 893 train loss: 1.82383 train acc: 0.49286 val loss: 1.88511 val acc: 0.31200 epoch: 894 train loss: 1.82361 train acc: 0.49286 val loss: 1.88502 val acc: 0.31400 epoch: 895 train loss: 1.82327 train acc: 0.49286 val loss: 1.88491 val acc: 0.31400 epoch: 896 train loss: 1.82305 train acc: 0.49286 val loss: 1.88484 val acc: 0.31600 epoch: 897 train loss: 1.82311 train acc: 0.48571 val loss: 1.88485 val acc: 0.31600 epoch: 898 train loss: 1.82290 train acc: 0.49286 val loss: 1.88476 val acc: 0.31600 epoch: 899 train loss: 1.82346 train acc: 0.49286 val loss: 1.88491 val acc: 0.31200 epoch: 900 train loss: 1.82324 train acc: 0.49286 val loss: 1.88480 val acc: 0.31200 epoch: 901 train loss: 1.82300 train acc: 0.49286 val loss: 1.88482 val acc: 0.31600 epoch: 902 train loss: 1.82279 train acc: 0.49286 val loss: 1.88469 val acc: 0.31600 epoch: 903 train loss: 1.82297 train acc: 0.49286 val loss: 1.88479 val acc: 0.31400 epoch: 904 train loss: 1.82276 train acc: 0.49286 val loss: 1.88467 val acc: 0.31400

[jiangxinke]

Hope my log could help you! Good Luck!